From 575dec36b0a8b491862b51f13b74bc3ef6d8780f Mon Sep 17 00:00:00 2001
From: msyamkumar <msyamkumar@wisc.edu>
Date: Mon, 5 Dec 2022 10:32:07 -0600
Subject: [PATCH] Updating lecture files
---
.../lec_35_pandas3_data_transformation.ipynb | 31 +-
...pandas3_data_transformation_template.ipynb | 9 +
...ipynb => lec_36_plotting1_bar_plots.ipynb} | 209 +-
...lec_36_plotting1_bar_plots_template.ipynb} | 22 +-
.../lec_36_plotting2_scatter_plots.ipynb | 3003 -----------------
..._36_plotting2_scatter_plots_template.ipynb | 833 -----
...b => lec_37_plotting2_scatter_plots.ipynb} | 0
...37_plotting2_scatter_plots_template.ipynb} | 0
.../plotting1-checkpoint.ipynb | 2286 -------------
.../plotting1_template-checkpoint.ipynb | 654 ----
.../lec-38/lec_37_plotting3_line_plots.ipynb | 2642 ---------------
...lec_37_plotting3_line_plots_template.ipynb | 894 -----
...pynb => lec_38_plotting3_line_plots.ipynb} | 0
...ec_38_plotting3_line_plots_template.ipynb} | 0
.../demo_lec_38-checkpoint.ipynb | 2087 ------------
.../lec-39/lec_38_plotting4.ipynb | 2127 ------------
.../lec-39/lec_38_plotting4_template.ipynb | 898 -----
...heckpoint.ipynb => lec_39_plotting4.ipynb} | 0
....ipynb => lec_39_plotting4_template.ipynb} | 0
19 files changed, 139 insertions(+), 15556 deletions(-)
rename f22/meena_lec_notes/lec-36/{lec_35_plotting1_bar_plots.ipynb => lec_36_plotting1_bar_plots.ipynb} (91%)
rename f22/meena_lec_notes/lec-36/{lec_35_plotting1_bar_plots_template.ipynb => lec_36_plotting1_bar_plots_template.ipynb} (98%)
delete mode 100644 f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots.ipynb
delete mode 100644 f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots_template.ipynb
rename f22/meena_lec_notes/lec-37/{.ipynb_checkpoints/lec_36_plotting2_scatter_plots-checkpoint.ipynb => lec_37_plotting2_scatter_plots.ipynb} (100%)
rename f22/meena_lec_notes/lec-37/{.ipynb_checkpoints/lec_36_plotting2_scatter_plots_template-checkpoint.ipynb => lec_37_plotting2_scatter_plots_template.ipynb} (100%)
delete mode 100644 f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1_template-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots.ipynb
delete mode 100644 f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots_template.ipynb
rename f22/meena_lec_notes/lec-38/{.ipynb_checkpoints/lec_37_plotting3_line_plots-checkpoint.ipynb => lec_38_plotting3_line_plots.ipynb} (100%)
rename f22/meena_lec_notes/lec-38/{.ipynb_checkpoints/lec_37_plotting3_line_plots_template-checkpoint.ipynb => lec_38_plotting3_line_plots_template.ipynb} (100%)
delete mode 100644 f22/meena_lec_notes/lec-39/.ipynb_checkpoints/demo_lec_38-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-39/lec_38_plotting4.ipynb
delete mode 100644 f22/meena_lec_notes/lec-39/lec_38_plotting4_template.ipynb
rename f22/meena_lec_notes/lec-39/{.ipynb_checkpoints/lec_38_plotting4-checkpoint.ipynb => lec_39_plotting4.ipynb} (100%)
rename f22/meena_lec_notes/lec-39/{.ipynb_checkpoints/lec_38_plotting4_template-checkpoint.ipynb => lec_39_plotting4_template.ipynb} (100%)
diff --git a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
index 91ba588..257c333 100644
--- a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
+++ b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation.ipynb
@@ -3067,7 +3067,7 @@
{
"data": {
"text/plain": [
- "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fe4e6a87670>"
+ "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fbc472bad90>"
]
},
"execution_count": 19,
@@ -4150,21 +4150,21 @@
{
"data": {
"text/plain": [
- "['Pop',\n",
- " 'Trap Metal',\n",
- " 'hardstyle',\n",
- " 'trap',\n",
- " 'Rap',\n",
- " 'Emo',\n",
+ "['trance',\n",
+ " 'techno',\n",
" 'dnb',\n",
- " 'Hiphop',\n",
+ " 'Trap Metal',\n",
" 'RnB',\n",
- " 'trance',\n",
- " 'Dark Trap',\n",
- " 'Underground Rap',\n",
+ " 'Pop',\n",
" 'psytrance',\n",
" 'techhouse',\n",
- " 'techno']"
+ " 'trap',\n",
+ " 'Dark Trap',\n",
+ " 'Emo',\n",
+ " 'Underground Rap',\n",
+ " 'Rap',\n",
+ " 'Hiphop',\n",
+ " 'hardstyle']"
]
},
"execution_count": 32,
@@ -4508,10 +4508,13 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 37,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "# Close the database connection here\n",
+ "conn.close()"
+ ]
}
],
"metadata": {
diff --git a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
index 40be227..e589f30 100644
--- a/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
+++ b/f22/meena_lec_notes/lec-35/lec_35_pandas3_data_transformation_template.ipynb
@@ -756,6 +756,15 @@
"\n",
"\"\"\")"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Close the database connection here\n"
+ ]
}
],
"metadata": {
diff --git a/f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots.ipynb b/f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots.ipynb
similarity index 91%
rename from f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots.ipynb
rename to f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots.ipynb
index e9efdba..a187326 100644
--- a/f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots.ipynb
+++ b/f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots.ipynb
@@ -4,30 +4,6 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# this allows the full screen to be used\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
"outputs": [],
"source": [
"# import statements\n",
@@ -46,9 +22,20 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# establish connection to a new database\n",
"grades_conn = sqlite3.connect(\"student_grades.db\")\n",
@@ -97,7 +84,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -133,7 +120,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -204,7 +191,7 @@
"3 Seth BC 2.5 6"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -224,7 +211,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -233,13 +220,13 @@
"<AxesSubplot:xlabel='attendance', ylabel='gpa'>"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -263,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -312,7 +299,7 @@
"attendance 0.976831 1.000000"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -330,7 +317,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -352,7 +339,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -378,7 +365,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -412,7 +399,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -421,13 +408,13 @@
"<AxesSubplot:>"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -450,12 +437,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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SLVqay4F7Qnd9gm5/pXPntO9H9/nu8gzv5fky8MT5GkYbdh1Ft3WAZtM3gcfM1zC6ofaRdBuyafZ8FvijJC8G3gsU8Ik57femuxC9yzO8l+cNwMOSnALcf3TsTkkeA5wN7A/8+UC1acfeBjw2yYnA7UbHdktyb+A0upknbxmqOC3qeLphrdcDjwVOqqotAEn2AJ4MfG6w6qbI5fHLNJpK9pfAnnSzFbb9h7weeF5VvXeg0tRDklcD/5vu/qy7jZ4zeryyqk4csDwtYrTX0v2An1XVZXOO3wo4HPjmtkDflRneOyHJneh+xb4P3V/6C4EPVdWq+LWtdUkeCPw3tv/8Tqkq96jXzDO8taqMVlW+CDivqv556Hqk5XLMexmSHJhkwUU4SZ6QZP0US1JPVXUN3XDJbw1di3YsyY1JfrPExw1D1z0N7m2yPH9G95f/Ywu0vwj4Ad2sE82ei+j2n9HsO5mbridpDsN7eR7B4vfN+xTdjSs0m94O/EmSd1TVvw9djBZWVc8cuoZZZXgvzx3oFgss5Aq6neo0m35Ot6T6giR/R3eh8trxTlV18rQLk/oyvJfnauDui7Tfgy4gNJveO+frYxfoU3S/smsGJbk73eZh23bzvBg4o6ouGq6q6XK2yTIkOY1uPunBVXX5WNudgG8D51TVk4aoT4tLcmifflW1KhZ7tGa0uOo4YPexphuB11TV8dOvavoM72VI8gDgS8BPgTcC36A7U/sduouVtwUe4XxhaWUl+UO6nQO/QLfS+dujpoPodot8OHB0Vb1nmAqnx/BepiT/GXgPcHtuuhoe4Erg2VW10EwUScuU5Kt0q5h/t6puGGvbg26Tqj2r6kFD1DdNjnkvU1V9PMk6ug2O7kkX3BcAn6qqXw5anLaT5OmjL0+pqprzelFesJxJ9wVeOh7cAFV1Q5IPAidNv6zpM7x3wiikPzp0Hdqh99L9dvRBurO2ba+z8Ld4wXJGXU+37etCbjnqs8szvLUaPIru9li3ohvWOnzYcrQTvgI8J8m7xm8YneQOdOsrzhuksilzzLuHJJ+lOxN7zOhXs8/2+LaqqiMmXJp6SvIb4KiqOnX0ej+6hVavrqrvDlqcekvySOAzdFNx/5buZt/QXbB8Ft2Z9xFVde78P2HX4Zl3P3fjpi1Dt732X722jA+R7AU8lW7mguHdiKo6J8mT6PZkf9FY82XAM1ZDcIPh3UtVrV/staTpqaqPJfkE8CDgQLp/mC8CvlZVNw5a3BQZ3pKaMwrpr7CKb1dneEtqjsvjDe9ekrx7Gd9WVfXsFS9GO+Nxo+0LAPalu25x5GjF7LiqqjdPrTL1tsjy+NcncXm8bpJkOeNoVVXj/3NpIMv4DP38ZpDL429ieGtV6LsZ1VxuTDV7XB5/E4dNtCoYxLsMl8ePGN47Kcnt6aYrAVzinVmkiXJ5/Ig3IF6mJL+d5HN0d805b/S4IsnZSe4/bHXSLmvb8vib3anK5fHaoSQHA18E9gY+zvYXTZ5Ad0uth1fVd4apUNo1uTz+Job3MiT5CHAYcGhVfWus7WDgHOCsqnryAOVJu7QkT6BbHv9bY02XAcdU1cenX9X0Gd7LkORK4B1V9YoF2l8NPLeq1ky3Mml1SLIbLo/XMtyCxe8e/2+jPpImwOXxnnkvS5LvAJdV1WMXaP8ksK6qDppuZdLqkWRfutsQ3uymGlV12fQrmi5nmyzPycBjkpya5KAku48eByd5P/Bouru1SFpBSXZLclySH9FdtNwCXDLPY5fnmfcyJNkdOBU4km5/jG3jbLvRnQV8CPiD1TT+Jk1DktcDLwa+A5wFzLuuoqpeNc26hmB4L1GStXQ7mV1Jd7HkScB6brpo8tGq+vRgBUq7sCQ/Br5RVY8bupahecGyp9HV7bcDR3PTGNsXgSdW1dbBCpNWl9sCZwxdxCxwzLu/Y+hWb10OfAT4Ft0OZn8zZFHSKvMt4M5DFzELHDbpKclmYB/goVX189GxdwLPBNZW1dXDVSetDkkeT7ey8sFV9YOh6xmSwyb93Rs4YVtwj7wVeDZwL+DLg1Ql7cKSzHdjhUuB7yY5nW5myW/G2quqTpx4cQPzzLun0Wb+R1XV++ccW0O3MdURVXXWYMVJuyhvhLIwz7yXZvxfum2vb7ZIQNKKOHDHXVYnw3tp5t4DERa/D6L3QJR2UlVdOnQNs8phk568B6I0vCS3A/avqvMXaL8/8IOq+ul0K5s+z7z7O3zoAiTxeuCBo8d83kO3WdVzp1bRQAzvnrwHojQTDgfet0j7PwJHTamWQblIR1JL7kJ304WF/HDUZ5dneEtqyTXAAYu0HwD8akq1DMrwltSS84BnJLnleMPo2NNZJQvmDG9JLflzYH/gC0mekuQeSe6e5CnAF0Ztbxi0wilxqqCkpiR5DvCXwH+Yexi4HnhhVf31IIVNmeEtqTlJ7gr8PnAPuuC+APhwVf1o0MKmyPCW1KwkewAPAe4KfLeqvjNwSVPjmLekmZbksCRvGduagiTrga8C5wIfBM5P8u4BShyE4S1p1j0T+L2qunzs+MnAIXQXKt8MfJduJsozplveMBw2kTTTknwHOKuqjplz7D50YX1OVR02OrYP8HXgR1V1xBC1TpNn3pJm3Z2B748dO4xuR893bTtQVb8ETgXuP7XKBmR4S5p1ewG/HDv24NHz+J5DPwBuPfGKZoDhLWnWXQYcNHbsEcAV89zHcl/g6mkUNTTDW9KsOxd4epJDAJI8Ebgn8Ml5+h4CrIq53l6wlDTTkhwIfBvYG/h34PbAr4EHzZ3XnWR3umGTf6iqFwxR6zR55i1pplXVJcChwJl04f1J4LB5FuQcPmo/Y7oVDsMzb0lqkGfektQgw1uSGmR4S1KDDG9JatD/A0lY0uxTNUCUAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -489,22 +476,22 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
+ "Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -541,7 +528,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -553,7 +540,7 @@
"dtype: int64"
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -571,7 +558,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -587,7 +574,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -596,7 +583,7 @@
"Index(['Police', 'Fire', 'Schools'], dtype='object')"
]
},
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -619,7 +606,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -628,13 +615,13 @@
"[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -666,7 +653,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -675,13 +662,13 @@
"Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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HXJpJgUPIQVG7wjCz8/DArlz+OjCsD7v+PrASsE0f9hFC7gp55G4WScsAP8OvJpvcwPpjJU2SNOm1115revlC6InCHrklDQX2BpbHW8ZVsYqZ2cge7vZ04FngN42sbGbjgHEAw4cPtx7mFUJTFTK4JW0CXAbMCbxD31vGkbQrsDGwfun8PYQiK2RwA8cC04Bvmtmkvu5M0kD8aH0N8LKk5VLSkul5wbRsmplN72t+IbRCUc+5VwJ+m0VgJ3MDg4HNgCfLHrem9F3T670yyi+Epivqkfs14MMM9/cffF62SoOB3+HdYmcA3U4WEUK7KGpwn4t3VZ2cxc7SOfbFlcsldaU/nzazWdJDaGdFDe6zgQ0lXYFf/vks8EnlSmY2tXJZCP1FUYP7McDw7q/N66w3W18ySX3dlV1sIRRCUYP7KDy4Qwg1FDK4zezIvMsQQrsraldYCKEbhTxyA0gaAOyOT138hbT4GfxKrvFmNiOvsoXQDgoZ3JLmxkeTrY+fe7+UkjbFB6KMlrSpmb2fUxFDyF1Rq+WH4ZM1nAAMNrOlzGwpfOaU4/FbDx2aX/FCyF9Rg3sHYIKZHWhmb5YWmtl0M/sJPlHDTrmVLoQ2UNTgHsLMcd/VTEzrhNBvFTW4p+PXcdeyXFonhH6rqMF9A7BvtdsGSdoY+Dbwt5aXKoQ2UsjWcrxBbRRwjaT7gEfS8lXwmxFMAw7PqWwhtIVCBreZTZE0HJ+0YQtgrZT0DnAhcEhcNBL6u0IGN3x6xdcukoRfdy3gVTOLMechUODgLknB/Gre5Qih3RQiuNNMp59en1163Z2omof+rBDBDUwGZkiax8w+TK8bqX736XruEIqsKMFdun7744rXIYQaChHclddvx/XcIXSvqINYQgjdiOAOoUMVolouaQY9P8c2MyvE/xdCMxTlyz+eaEALoUcKEdxmNibvMoRQNHHOHUKHiuAOoUMVolou6ZlebGZmtmzmhQmhIAoR3MBUokEthB4pRHCb2Yhm7l/SivjkDmsBSwBz4D8o1wC/NrOX6mweQlsqRHC3wBDg88BlwPP4GPbVgLHAjpLWMLO4rDQUSgQ3YGY3ATdVLpd0Gz5N8hjguBYXK4Q+KURwS7oZP+ceZWYfp9fdMTMb2cesp6Tnhfq4nxBarhDBjd8LbAYz75X9BZrQwCZpLmA+YC7gi8CvUtI1WecVQrMVIrjNrKve6wztBZxS9noysKuZ3V5tZUlj8fNyhg5taHKYlug66OpebTf5l5tlXJKQp0IEdwtdDjyGH73XBLbEJ1+syszGAeMAhg8fHl11oa1EcJcxs+fx1nKAyyVdAtwtaW4zOzbHooXQY4UJbkln9nATM7M9+5KnmT2YbnqwLz5HegiFUZjgxrujjJmNat0xoE/BncwNLJzBfkJoqSIFN8D7wKXAWcADWe1U0uJm9nKV5RsCq1L/jqIhtKUiBfeaeGv2TsDOwH3AGcCFZja9j/s+XdLngZvxvu25gGHAjvgtin7Yx/2H0HKFueTTzB4ws/3xsd874Tf7OwV4UdIFkjbqw+4vBF4HdgNOAn4JrAP8AVjdzO7vS9lDyEORjtwApJsSTAAmSBoC7J4eO0iaCuxvZlf1cJ8T0j5D6BiFOXJXY2bPm9nPgY2AG4GlmXnHzxD6tcIduUskDQS2AvYARgIf4tXrS/MsVwjtonDBne7LvQd+3v05YBKwP3CBmb2VY9FCaCuFCW5JP8CD+ot4Y9rZwJlm9nCe5QqhXRUmuIHjgffwqvdfgY+AFSStUGsDM4sqeui3ihTc4KPFdsar5PUIH6EWt/AN/VaRgnuPvAsQQpEUJrjN7Jy8yxBCkRS6nzuEUFshgrteo1kD266YZVlCKIpCBDfwiKQzJa3a6AaS1pR0LhBdZaFfKso59xbACcADkh4ErgbuBp4G3sBbxxcGlgfWBTbB+8MfBTbPo8Ah5K0QwW1m10m6AdgenxXlEKrPflqayOFW/HLNS8xsRksKGUKbKURwA5jZJ/gAlgslLQZsgB+dB+OB/hpeBZ9oZtNyK2gIbaIwwV3OzF4hLtEMoa6iNKiFEHoogjuEDhXBHUKHiuAOoUNFcIfQoSK4Q+hQHRXckgZJWj7vcoTQDgoZ3JJGSxpXsexY4BXgMUl3SJo/n9KF0B4KGdzA3pQNwEmTJv4EuB34I35DgR/kU7QQ2kMhR6gBywF/KXu9HX4BycZm9qEkw8eh/yyPwoXQDop65F4QKJ/GeCRwY7obCfh0x0NbXqoQ2khRg/tl/PJOJA0G1sCr5CXzAZ+0vlghtI+iVstvBr4j6Q1gQ/yqsKvL0lcEXsijYCG0i6IeuQ8HXgKOwydmONbMJgNImh3YBpjY6M4krSDpKEn/lPSapHck3S/pUEnzNqH8ITRdIY/cZva8pFXw67nfMrOpZcnzAGOBB3qwy28B3wGuBM7Hb3iwIXAMsL2kdc3svUwKH0KLFC64Jc0HnAxca2Z/qUw3s7eBK3q424vxo395I93vJT0JHArsCZzayyKHkIvCVcvN7F18CqUFMtznpBo3Efxzem54YsYQ2kXhgjt5FOhqQT5D0vMrLcgrhEwVrlqeHAf8TtK5ZvZEMzKQNBvecPcxcEGNdcbi5/cMHdp/u9W7Drq6+5WqmPzLzTIuSShX1OBeCXgOeEjSVcCTwH8r1jEzO7oPefwWnyb5EDN7vNoKZjYOGAcwfPjwarOxhpCbogb3kWV/b1VjHQN6FdySjgb2A8aZ2bG92UcIeStqcC/TrB1LOhI4DDgL2KdZ+YTQbIUMbjOb0oz9SjoCOAIYD+xlZlHVDoVV1NbyzEk6HK/unwvsEXcqCUVXyCM3fDrM9JvAl4GFmPWHysxszwb39R388tCpwI3AzpLKV3nFzG7oa5lDaKVCBrekhYFb8MElwhvPStFoZcsaCm5g7fQ8FDinSvpEIII7FEpRq+XH4N1hewHL4sE8ClgZv5/Y3cAije7MzMaYmeo8RmT/L4TQXEUN7s2A8WZ2FvB2WvaJmT1uZrsC7wHRhRX6taIG9+L40Rl8BBnAXGXplwNbtrJAIbSbogb3G0DpOut38Es0lypL/whvZAuh3ypqcD+BX8tN6rK6DxgjaaCkeYDRwDM5li+E3BU1uK8HtpU0ML3+Dd4l9gbwKjAcODGnsoXQFgrZFQb8AjjezD4AMLMJkj4GdsUnRrzYzP5cbwchdLpCBncaFvpBxbJLgUvzKVEI7aeo1fIQQjcKceSWNLo325nZ+KzLEkJRFCK4gbP57BDTRhh+dVcI/VJRgnvDvAsQQtEUIrjNrOEbDIQQXDSohdChCnHklrR+b7Yzs9uyLksIRVGI4AZuxRvIGlW6nnu2ppQmhAIoSnDvkXcBQiiaQgS3mVWbHSWEUEc0qIXQoQpx5K5G0gBgd/ymBF9Ii5/Bx5ePj9lLQ39XyOCWNDdwDbA+3nD2UkraFJ+CabSkTc3s/ZyKGELuilotPwzYADgBGGxmS5nZUsAg4HhgBH5f7RD6raIG9w7ABDM70MzeLC00s+lm9hNgArBTbqULoQ0UNbiH4H3ftUxk5r21Q+iXihrc04Hl66Qvl9YJod8qanDfAOwraVRlgqSNgW8Df2t5qUJoI4VsLccb1EYB10i6D3gkLV8FWBOYBhyeU9lCaAuFDG4zmyJpOH5XkS2AtVLSO/jthA4xs6l5lS+EdlDI4AZIwbuL/Hacg/GLRV7t7T21JR2M/0gMA5YBpphZV0bFDaHlChvcJSmYX81gV7/A5z2/F/hcBvsLIVeFDG5J6+Ej0VYAFsBvBvg4cLWZ/aOXu13WzJ5J+38YmC+LsoaQl0IFt6QF8HPqr1N9ssSDJV0N7GJm7/Rk36XADqFTFCq4gYuBjYC/A2cAD+JH7QWA1fH7dW8O/BkfZx5Cv1WY4E592hsBJ5jZj6usch9wjqTjge9L+pqZ3dDkMo0FxgIMHTq0mVmF0GNFGsSyEzAFOLCb9Q4EpgI7N7tAZjbOzIab2fDBgwc3O7sQeqRIwT0MuLy7rq50Hffl+J0+Q+i3ihTcS+It4o14nLhwJPRzRQruBfARaI14h+jKCv1ckYJ7AD2b3rhI/1sImStMa3myqaTFG1hvWE93LGk3YOn0cjAwp6TD0uspZnZuT/cZQp6KFtw703greE/HmO+JT91U7uj0PBGI4A6FUqTgbuqdPs1sRDP3H0KrFSa4406fIfRMNDqF0KEiuEPoUBHcIXSoCO4QOlQEdwgdKoI7hA4VwR1Ch4rgDqFDRXCH0KEiuEPoUBHcIXSoCO4QOlQEdwgdKoI7hA4VwR1Ch4rgDqFDRXCH0KEiuEPoUBHcIXSoCO4QOlQEdwgdKoI7hA4VwR1Ch4rgDqFDRXCH0KEiuEPoUBHcIXSoCO5E0gBJ35f0mKT3JT0n6QRJ8+ZdthB6I4J7phOB3wCPAvsDfwG+C/xVUrxPoXAKc5fPZpK0Ch7Ql5rZNmXLnwVOBnYELsipeCH0ShyR3E6AgN9WLP8j8F9g11YXKIS+iuB2awMzgH+VLzSz94H7U3oIhSIzy7sMuZP0ELComS1WJW0CsB0w0Mw+rEgbC4xNL1cEHu9F9oOAab3YrreKkt/SZjY468L0J3HO7eYBPqiR9n7ZOp8JbjMbB4zrS8aSJpnZ8L7sI/IL1US13P0XGFgjba6ydUIojAhu9yIwSFK1AF8SmFZZJQ+h3UVwu7vx92Kd8oWS5gLWACY1Me8+Vesjv1BLNKgBklYDHgAuq+jn3h/v597NzM7Lq3wh9EYEdyLpFGA/4DLgGmBlfITaHcBXzWxGjsULocciuBNJswEH4F1bXXj3zZ+Bw83s3fxKFkLvRHCH0KGiQS2EDhXBHUKHihFqOZG0DDASWAw438wmS5oTWBx4Oet+9XRd+ldSfjea2StZ7r+bvAcBC5nZk63KM8SROxeSfgU8gfcBHwV8ISXNhV9Pvm/G+X0beAG4HhgPrJKWD04TU4ytt30P8hktaVzFsmOBV4DHJN0haf4s8grdi+BuMUl7Az8GTgM2xi81BcDM3gauBLbIML9tUl63AHtV5PcacB3wjYyy25uy2qCk4cBPgNvxy2fXAX6QUV6hGxHcrbcvPljmAOC+KukP4leYZeXHwC1mthVwRZX0ScCqGeW1HF7+ku2AN4CNzWwf4E/A9hnlFboRwd16KwA31El/Db9MMiur4QNzankJWDSjvBYE3ip7PRI/vy+1H0wChmaUV+hGBHfrvQ/Um3RxaWB6hvl9Qv3PeQngPxnl9TKwPPj5PD4u//ay9PlSeUILRHC33r+AraolpAtVdsOHvGblAWBUjfwG4FXnuzPK62bgO5J+BJwNGHB1WfqKeMNeaIEI7tb7NfAVSecCq6dli0saBdwKDAGOzzC/U4FNJB0NLJyWDZC0Ij7D6yr4xTFZOByv5h8HbAIca2aTASTNDmwDTMwor9CNGH6ag9T1dBIwJ956XfoQPgS+bWZnZ5zfMcAh+DxxA9Kz0uMIMzs6w7xmA74IvGVmU8uWLwBsCDxQCvjQXBHcOZG0OF4lXgkPsieBCWbWlGqrpLWAXSryO9fMmnmteshRBHcHS6PSfgjcZWZ/y7s8obXinLvFJC0jqeYgFUlbSOrKIi8z+w9eHV8qi/1VkjRD0ic9fHzcjLKEWcXY8tb7OR5sf62R/kPgObzVPAtP4+PVm2E8M9sLQpuJ4G69/6H+vGLXM3Mu9Cz8DjhQ0ulm9nqG+8XMxmS5v5CtCO7WWxQf7FHLq/iVW1l5Bx8C+rikc/CGtFmmaTaz8RnmGdpABHfrTQeWrZO+HB6QWTm77O/v11jH8Cp2JiQti1+MUrra7RngCjN7Oqs8QveitbzFJP0F7+9d1cxerkhbHHgYuM3Mts4ovw0aWc/MMhlckgbLHATMVpE0A/iFmR2eRT6hexHcLSZpDeCfwJvACfiNBg1YE29MWwj4nyL2P0v6Fn7l1534SLyHU9Iq+NVp6wF7mdlZ+ZSwf4ngzoGkzYGzgEWY2dosfMbVPc2sVkt6W5N0Dz7K7n/N7OOKtNnxi0jmNLNheZSvv4lz7hyY2VWShuIXdCyPB/bjwPVm9l5f9i1pdPrzXDOzstfdlSmLc+6VgYMrAzvt/2NJFwHHZpBPaEAEd05SEF/ehF2fjdcGLsKPoqXXqr1JZg1qH+KXddYyPxV3Sg3NE8Hdeb6KT2e0AF7N37CFed8N7C3pT5UTMEpaFO+/v6uF5enX4py7ySTdjB8ZR6Wq6c0NbGZmNrIPeX6C39/sgvR6PnzgzDFm9mhv99tAvusDN+FdeWfgkz2CN6jtgR+5R5rZ7dX3ELIUR+7m+wIzL7EsvW72L2plFXwgsCPekt204Daz2yRtjV9D/sOK5KnA7hHYrRPB3WRm1lXvdacxs79KuhoYBiyD/9A8DdwbN1NsrQjukLkUxHeT3fRNoRciuEPmYvhpe4jgbjJJZ/ZiMzOzPfuY9aZpOCvAPPh5/nZphFy1/E7sY35A3eGnx0mK4actFK3lTSapN+eZZmaVwdHMPPuUX1m+Mfy0jURwd6BGLxYpl8WFIzH8tL1EtbwDZXWFVy/E8NM2EsGdI0mL4N1FAM9mPVNKDmL4aRuJCRJzIOlLkibis67clR6vSrpV0ur1t25rpeGns8wkE8NPWy/OuVtM0qrAP/B7cV/FZxudtsCnQFrPzB7Jp4S9F8NP20sEd4tJuhQYAWxgZg9VpK0K3IbfcnebHIrXZ2na5lOZdTrlqcB+ZnZV60vVP0Vwt5ikacDpZvbTGunHAPuYWZa38W2pdIPBGH6as2hQa715qT/76UvUv8Vv24vhp+0hjtwtJukRYKqZbVIj/VpgqJmt0tqSZUvSPPg0UrNMElF+g8DQPNFa3nrjgVGSLpC0iqTZ0mNVSecDG/PZ6YgLQ9IASQdJegFvVJsMPFvlEVogjtwtlm5xewF+h0/Dr/UG/6EVMAHYuYjnp5KOA34EPALcAlTttzezn7WyXP1VBHcLSRqMXyk1DW9s2hroYmaj0+VmdmNuBewjSS8C95vZpnmXJUSDWkuk1uPfAXsx8xz0H8BWZvZabgXL3kLAFXkXIrg4526N/fDRWS8DlwIP4VdI/SHPQjXBQ8Dn8y5EcFEtbwFJk4C5gXXN7J207I/AGGCwmU3Pr3TZkbQZPjJtbTN7Lu/y9HdRLW+NFYGjSoGdnALsCawA/CuXUvWRpGoTL0wBHpV0Gd4y/klFupnZ0U0vXIgjdyukyRN2M7Pzy5YNwi8cGWlmt+RWuD7IYyKK0Lg4crdO5a9o+T3CimqZ7lcJeYngbp3yOc2g/rxmmc1p1kxmNiXvMoTaolreAnnNadZqkhYGhpjZgzXSVweeM7M3W1uy/imO3K3Ryvt15ek4YK30qOYs/GKSfVpWon4sgrsFcpzTrNU2BM6rk34lsFuLytLvxSCWkKUl8EkZank+rRNaIII7ZOk/wNJ10pcGPmhRWfq9CO6QpbuA3SXNX5mQlo2moAN2iiiCO2TpeGAIcKekbSUtJ2lZSdvidyEZgt+JJLRAdIWFTEnaGzgJmKN8MT5f+QFm9vtcCtYPRXCHzElaEtgeWA4P7MeBi83shVwL1s9EcIemSfcHWwdYEni0iHOxF1mcc4c+kTRC0skVQ2uR1AXcg9/87yLgwV7ezjj0UgR36KsxwDfMrHK65vHAanhD2on43Ud2l7R7a4vXf0W1PPRJmqr5FjPbr2zZSngw32ZmI9KyuYH7gBfMbGQeZe1v4sgd+urzwBMVy0bgV7z9qbTAzN7DZ30t8o0OCyWCO/TVQOC9imVrp+fKMfXPAQs2vUQBiOAOfTcVv4tnuf8BXq0yj9o8wPRWFCpEcIe+ux0YLWk1AElbAcsD11ZZdzUg+rpbJBrUQp9IWga/x/hc+B1GFgE+AoaV92unO608B1xiZvvnUdb+Jo7coU/M7FlgA+AaPLivBUZUGbCyYUqPmxa0SBy5Q+hQceQOoUNFcIfQoSK4Q+hQEdwhdKj/D3yl0TkN1o7pAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 108x288 with 1 Axes>"
]
@@ -710,7 +697,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -719,13 +706,13 @@
"Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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h+4HA45LOaW/dQRB0Hup+cyMNTR9LP5fDPb2DO21pFONTuHZO2lopnFBH3oqY2QQzu9DMdgOWBf4F/KYtw98gCDo3VRWfpC0rmJgsiA8LwT2+34w79D1Z0iI5+evpkY3BHSXvJ2mWvaCk+YBf473L2+rIOweSeqW8szCz6UBpAWexOuQPgqATUosB859xc5bb8VXXT4EVcA/qqwHDzGwsgKQjgYuAsZKGAZPw3uAOwP64h/maMbOvkpH0LcATkobgb4rsjq/SnllapW1L3goMBIZIGo73XqfhCyYHAo+Z2UutlA2CoAtRi+I7GldcmwC7AIviK6DPAOcAV5YymtlgSePxHtbhuBu4N3HbvtfrEdDM7pC0OXBCqnd+vBf2MzO7tN68OTyNu6gbAOwJ9MB7kGfiNoFVWXe5XowK84sg6PSEe8kG0r9/fwtnQ0HQHBpqxxcEQdDdCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhCMUXBEHhqGWTgqBGxk6eQsuxdzVbjKBJhH+QrkO37fFJGiDJJA1qtixBEHQuuqziyyi23A++B18QBMEcdIeh7nXA3Tnx9wLn4351gyAIZtEdFN8YM7u63sJpS/yFzGxaA2UKgqAT02WHutXIm+PLxkn6paTngenAMZk8u0t6SNJUSZ9KekzST5pwCEEQzCW6Q4+vp6Q+ZXEzqpQ5Evfd+zfgbdK2+JJOB44H7gFOBGYCOwE3STrUzC5qoNxBEDSJ7qD4Tk2fLDcAl7RS5lvAGmb2bilC0oa40jvLzI7L5L1A0q3AWZKGmdnUbEWSDgIOAuixyBJ1H0QQBB1Hd1B8Q4CbyuLeBsp7gVmGZZVeYk/cBeXQnB7k7bjDpe/iiyazMLMhSQYWWGbVcGASBF2A7qD4xpnZ/eWRkga0UublnLg1AQEvtlJuqTZJFgRBp6Q7KL56+DQnTniP74fAVxXKPTfXJAqCoMMoquLLYxywDfCamb3QbGGCIJh7dFtzljq4KoVnSupRnihpyQ6WJwiCuUT0+BJm9oSkk/EV4qck3QS8CSwD9AN+BMzfRBGDIGgQofgymNlpkkYDh+O2fgsB7wLPAkdUK7/ucr0YFTt0BEGnp8sqPjMbiS9I1JxerUzKcxcQe0sFQTcm5viCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcXfZd3c7I2MlTaDk2XvMNgkYxcS5t+tGle3zJVeSVZXETJY1sjkRBEHQFOkzxZXzaZj/TJI2WdETe5p9BEARzg2YMda8D7sa3h1oWGAScD6xNctPYTlbHfWcEQRDk0gzFN8bMri79kDQYeAE4UNKJZvZOeyo3s2rOxIMgKDhNn+Mzs0+AR/Ee4EqS5pX0W0nPS5ou6QNJt0hat5b6Ks3xSdpA0k2S3pE0Q9Lrkq6TtHJZvi0k3Svp49T+M5IOacSxBkHQOWj6qq4kAaukn+8D1wC7AfcBg4GlgV8Cj0r6vpk9WUcbPwaGA/8FLgVeSfVuDawDjE/5DgIuAf4DnJHybwkMlrSymf26zsMMgqAT0QzF11NSH7yHtwxwGPBtXNm04ErvRmAPMzMASTcAY4ALgO+3pTFJPYErgCnABmY2OZN8mqR5Ur5lUv3Xm9lPM3kulvQX4GhJl5jZ+LL6DyLNTfZYZIm2iBYEQZNoxlD3VOA93InP08D+wO3AjsBOKc8ZJaUHYGbPAHcCm0hqq3bZGugDnFem9Ep1z0xffwIsAFwmqU/2A9yBn6vNc8oPMbP+Zta/R89ebRQtCIJm0Iwe3xDgJnzl9b/Ay2b2IYCkFYGZ+GJHOc8COwAr4oqzVlZNYbUh8popvL+VPEu1od0gCDopzVB848ysknJp1QNanZTqrGbiUsq3D/BWhTwTGiJREARNpemLG2WMx4emawLPlKWtlcJX21jnSyncAF8wqcS4FL7fimIOgqAb0HRzljJuTeHv0movAJLWAbYHHjKztgxzAe7FV4t/lRYwvkamnRuBGcCpkhbMyddL0gJtbDsIgk5Ip+rxmdl9km4E9gAWk3Qns81ZpgOH11Hnp5IOAG4GnpVUMmdZAu9d/gm4zczekPRz3NzlBUlXAZNSvnXxxZe1gIntOsggCJpOp1J8iT1x05VBwHn4AsiDwIlmNraeCs3sdkmbAMcBBwALA+8ADwFjM/mukPQycAxwMLAo3lt8CTgReLu1dtZdrhej5tJuEkEQNA5lrEaCdtK/f38bNWpUs8UIgkIiabSZ9a8lb2eb4wuCIJjrhOILgqBwhOILgqBwhOILgqBwhOILgqBwxKpuA5E0ldlvigSNoQ9uUhQ0ju56TvuaWU2bmHRGO76uzEu1LqcHtSFpVJzTxhLnNIa6QRAUkFB8QRAUjlB8jWVIswXohsQ5bTyFP6exuBEEQeGIHl8QBIUjFF8QBIUjFF8QBIUjFF87kTSPpKMkvZgckL8u6TxJCzVbtq6IpNUknSbpP5LekzRV0lOSjo9z2jgk9ZT0qiST9Ndmy9PRhOJrP3/Gd3F+HvcRfBO+U/QdJZ+9QZvYHzgK979yGvBr/G2Y04FH8twCBHVxGv4GRyGJNzfagaS1cWX3dzPbJRP/Ku6cfA/g2iaJ11W5GTjLzKZk4i6RNA44Ht9Bu3A9lEYiaUPgSOA3+C7nhSN6JO3j/3C3lOeXxf8N+BTYq6MF6uqY2agypVfihhSu05HydDck9cDvz3uAvzdZnKYRPb72sRHuAP3xbKSZTZf0VEoPGsPyKXynqVJ0fY4C1gB2qZaxOxM9vvaxLO6Hd0ZO2mSgj6T5O1imbkfqpZwEfElMHdSNpBWBU4HTzGxik8VpKqH42kdP3BdvHtMzeYL2cT6wMXCSmcW2X/UzGHgVX4wrNDHUbR+fAktWSPtGJk9QJ5J+DxwKDDGzs5otT1dF0l7AVsCmZvZFs+VpNtHjax9v4sPZBXLSlsOHwZ93sEzdBkmnACcAVwCHNFearku6P/8E3A28LWkVSasAfVOWXilu0WbJ2NGE4msfT+Dn8H+ykZK+AawPhJPdOpF0MnAyMAw40GI3jfawILAEsC0wLvMZmdL3Sr8PbIZwzSCGuu3jBuA43Cbq35n4n+Fze9c0QaYuj6STgFOAq4D9zGxmcyXq8vwX2DUnfgngYty05TLgmY4UqpnEtlTtRNKF+BzULfhQYk38zY2HgR/EQ9s2JP0SN1B+DTgRNxfK8o6Z3dfhgnVDJLXgix0XmdmhTRanQ4keX/s5EpgIHIQPJd4HLsRXIEPptZ2S7eO3gKE56Q8CofiCdhE9viAICkcsbgRBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QUNQdKg5LhmQCZuQIob1DTBqiBpG0lfSlqjgXVOlDSyLG6kpIllcVdKsrK4U9I5a2mUPO1F0q2S/tlsORpJKL4CkFFApc9Xkj6S9KykoenhV7Pl7GgkzYvvWnKNmb2YiR+UOVfHVCi7fibPlR0kcrM4GRggaftmC9IoQvEVi+uAvYFBuOOeB4ABwD+Ae4u0LVFiV/zd6kobc04H9quQdgCzN5stZ3V877t6OB3fTWVSneUbjpk9je/kcmKTRWkYofiKxRgzu9rMrjKzi83sCGAl/MHfAleMnR5JCzeoql8Az6QHO49bgLUklW87tgDwUyo46zGzGfXuw2hmX5rZ9E64DddVQH9J/ZotSCMIxVdwzOwrM/sV8BCwjaRNsumSWiRdJekdSTMkjZd0pqS6ttRPDtiPl/QvSW9L+lzSa5IGS+qd07alea/dJY2W9Bm+CQSSVpB0uaRJSbZ3JT0iad8a5Fga2ATfUacSdwDvMWevbwdgcXyD1Ly655jjq5VKc3y1XodM+dVT+hsp/9OSfpTT3j6SHpf0saT/Spog6RpJS5RlLZ2nvO2tuhyxO0tQ4jJcEWyLK0Ek9cU9yPXC/TW8jA+Nfwf8r6TNzezLNrYzP+4kfDhwG75X3Eb40HETSf1yeks74lt9DQYuAT5J83P34TtdX5xk6wWsB3yf/J1dsmyWwsdbyfMFvqfifpKONrPPUvz+wJPAU1XaaAh1XoehuPx/xM/5kcCtklYrORqSb0c/FN9L8iTgM3xXnB/iLhXeK1VmZu+kxZkBc+EQO5xQfEGJ0iaUq2XiziTt3GtmpX/8iyX9ATgG2BdXmG1hBrBMRomAOwx/BLgUV3I3lpVZG1jPzF4oRUhaD59L+62ZndtGGQDWSuH4Kvkux5XGTsC1kpYHtgSOqKPNeqnnOrwPbFcaMksagSvPg3GFCbAzMBXfNzKrOCvN5Y0HvtPOY+kUxFA3KPFJChcBH5IC2wNPZh62EmfhG4Tu1NZGzPkstdFD0qKS+gAlc4m8B+uurNJLlJyOD5RUyeFTa5SGch9WkXcs7kKgNNzdF+9JdYiby3Zch79k5wnN7Alcya2ayTMF3yl82xpX9T8AvilpwTYcQqckFF9QYpEUlhTgEsA3gefKM5rZh8Bb+MJIm5G0m6TH8KHVR/iQakJKXiynyMs5MkwCzsBXT99K83/nSqrViXtJKdTywF8BbJ6GnIOA29I56AjqvQ4TcuI+BLLzqGfiq8e3Au9JGi7pwFYWj0rnqrMtvLSZUHxBifVSWPJbO1fs+iTtjPsqAR8ubocPHbdJcXn3ZK6LTjM7Ae/BHIkPww4EHpd0Tg2ilOavFq8h77X4EP1vwCr48LejqPc6fFWtPjMbhw/5t8Xn+vrix/iipJVzyi4OTDOzSmY8XYZQfEGJA1J4VwrfxYdGa5dnlLQYsAz5vYpq7I3bvw00s8FmdqeZ3U+ddmtmNsHMLjSz3YBlgX8Bv6lh+PtsCldtNZe38TFu2rIl8Dodu/X93LoOwCzTm7vN7Fdm1h9XgssCR+dkX4XZ561LE4qv4KR5tj+STDvM7GGA5C/kDmADSduUFTsWv3duqaPJr/Ch0qx7L80vndBGuXtJmi8bl3oipbnAvCFzlgdTuHGNTZ4NnAoc2pG+VObidSDNrZYzJoWLl+VdGu8RPjhHiS5IrOoWiw2TCQPAwviq6I74DX0vbpSb5Ti8l3OrpIuBV4BNgd3xnlU1k5E8bgZ2Af4paRgwX5KhrXaBA4Ehkobjw/NpQD98uPuYmb3UWmEzey/Z2v0QXxltFTN7hua5X5wb1wH8bZ0pqY7XgUXxOUzDDZazbJvCm+psq1MRiq9Y/F/6zMQVxRv4P/h1ZnZPeWYzmyTpO8BpuNPpRVOZs4DT67Dhw8yuT5PnR+E2Zh/hPZpj8VXDWnkaf3NiALAn0AN3SXkmcF6NdQwGbki2g6Pb0HaHMjeuQ2IwsBtu4rI4fv6fBA4zsxFlefcCRnXm89QWwstaUFgk9cAV6FNmtle1/EVF0vr4EHhHM7u9yeI0hFB8QaFJ82Z3Aevk2AoG+LZUQC8zG9hsWRpFKL4gCApHrOoGQVA4QvEFQVA4QvEFQVA4QvEFQVA4QvEFQVA4QvEFQVA4QvEFQVA4/j/NY7kHWQLkAwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 288x108 with 1 Axes>"
]
@@ -757,7 +744,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -766,13 +753,13 @@
"Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 288x108 with 1 Axes>"
]
@@ -792,7 +779,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -801,13 +788,13 @@
"Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 288x108 with 1 Axes>"
]
@@ -827,7 +814,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -836,13 +823,13 @@
"Text(0.5, 1.0, 'Annual City Spending')"
]
},
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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0ZnvrDoKg81D3mxtpaPpQ+rk87ukd3GlLo5iUwrVz0tZK4eQ68lbEzCab2XlmthvQF/gX8Ku2DH+DIOjcVFV8krasYGKyED4sBPf4fj3u0HeopEVz8tfTI5uAO0reT9Jse0FJCwC/xHuXN9WRdy4k9U55Z2NmM4DSAs7idcgfBEEnpBYD5j/j5iw346uunwAr4h7UVwdGmdlEAElHAucDEyWNAqbivcEdgP1xD/M1Y2ZfJiPpG4BHJA3H3xTZHV+lPb20StuWvBUYDAyXNBrvvU7HF0wOBB4ys+daKRsEQReiFsV3NK64NgF2ARbDV0CfAM4ERpQymtkwSZPwHtbhuBu413HbvlfqEdDMbpG0OXB8qrcn3gv7iZldVG/eHB7HXdQNAvYE5sd7kKfjNoFV6d93AOOGhs+NIOjsxLZUDWTgwIEWzoaCoDl06m2pgiAImk0oviAICkcoviAICkcoviAICkcoviAICkcoviAICkcoviAICkcoviAICkcoviAICkcoviAICkcoviAICke8q9tAFu4r639ws6UImkX4B2ku8a4uIGmQJJM0pNmyBEHQueiyii+j2HI/+B58QRAEc1HLfnydnauA23Pi7wTOwf3qBkEQzKY7KL4JZnZ5vYXTlvgLm9n0BsoUBEEnpssOdauRN8eXjZP0c0lPAzOAYzJ5dpd0n6Rpkj6R9JCkHzXhEIIgmEd0hx5fL0l9yuJmVilzJO6792/Am6Rt8SWdChwH3AGcAMwCdgKuk3SomZ3fQLmDIGgS3UHxnZw+Wa4BLmylzDeANc3s7VKEpA1xpXeGmR2byXuupBuBMySNMrNp2YokHQQcBNCzd93HEARBB9IdFN9w4LqyuDeB8l5gllFZpZfYE3dBOTKnB3kz7nDp2/iiyWzMbHiSgYX7Kgy5gqAL0B0U3wtmdnd5pKRBrZR5PieuPyDg2VbKLdMmyYIg6JR0B8VXD5/kxAnv8X0f+LJCuafmmURBEHQYRVV8ebwAbAO8bGbPNFuYIAjmHd3WnKUOLkvh6ZLmL0+UtHQHyxMEwTwienwJM3tE0lB8hfgxSdcBrwPLAQOAHwA9myhiEAQNIhRfBjM7RdJ44HDc1m9h4G3gSeCIauX79x3AuKHj5qmMQRC0ny6r+MxsLL4gUXN6tTIpz23Abe0WMAiCTkvM8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhkFrulN4qF+8r6H9xsKYKg+zBuaO36SdJ4MxtYS94u3eNLriJHlMVNkTS2ORIFQdAV6DDFl/Fpm/1MlzRe0hF5m38GQRDMC5qxLdVVwO349lB9gSHAOcDaJDeN7WQN3HdGEARBLs1QfBPM7PLSD0nDgGeAAyWdYGZvtadyM6vmTDwIgoLT9Dk+M/sYeBDvAa4sqYekX0t6WtIMSe9JukHSurXUV2mOT9IGkq6T9JakmZJekXSVpFXK8m0h6U5JH6b2n5B0SCOONQiCzkHTd2CWJGDV9PNd4ApgN+AuYBiwLPBz4EFJ3zWzR+to44fAaOC/wEXAi6nerYF1gEkp30HAhcB/gNNS/i2BYZJWMbNf1nmYQRB0Ipqh+HpJ6oP38JYDDgO+iSubFlzpXQvsYcnWRtI1wATgXOC7bWlMUi/gUuAjYAMzey2TfIqk+VK+5VL9V5vZjzN5LpD0F+BoSRea2aSy+g8izU327N0WyYIgaBbNGOqeDLyDO/F5HNgfuBnYEdgp5TnNMgaGZvYEcCuwiaSl2tje1kAf4OwypVeqe1b6+iNgQeBiSX2yH+AW/FxtnlN+uJkNNLOBPXq1UbIgCJpCM3p8w4Hr8JXX/wLPm9n7AJJWAmbhix3lPAnsAKyEK85aWS2F1YbI/VN4dyt5lmlDu0EQdFKaofheMLNKyqVVD2h1UqqzmolLKd8+wBsV8kxuiERBEDSVpi9ulDEJH5r2B54oS1srhS+1sc7nUrgBvmBSiRdS+G4rijkIgm5A081Zyrgxhb9Nq70ASFoH2B64z8zaMswFuBNfLf5FWsD4Cpl2rgVmAidLWignX29JC7ax7SAIOiGdqsdnZndJuhbYA1hc0q3MMWeZARxeR52fSDoAuB54UlLJnGUpvHf5J+AmM3tV0k9xc5dnJF0GTE351sUXX9YCprTrIIMgaDqdSvEl9sRNV4YAZ+MLIPcCJ5jZxHoqNLObJW0CHAscACwCvAXcB0zM5LtU0vPAMcDBwGJ4b/E54ATgzdba6d93AOOGjqtHxCAIOpDYlqqBDBw40MaNC8UXBM2gMNtSBUEQ1EMoviAICkcoviAICkcoviAICkcoviAICkes6jYQSdOY86ZI0Bj64CZFQePorue0n5nVtIlJZ7Tj68o8V+tyelAbksbFOW0scU5jqBsEQQEJxRcEQeEIxddYhjdbgG5InNPGU/hzGosbQRAUjujxBUFQOELxBUFQOELxBUFQOELxtRNJ80k6StKzyQH5K5LOlrRws2XrikhaXdIpkv4j6R1J0yQ9Jum4OKeNQ1IvSS9JMkl/bbY8HU0ovvbzZ3wX56dxH8HX4TtF31Ly2Ru0if2Bo3D/K6cAv8TfhjkVeCDPLUBQF6fgb3AUknhzox1IWhtXdn83s10y8S/hzsn3AK5sknhdleuBM8zso0zchZJeAI7Dd9AuXA+lkUjaEDgS+BW+y3nhiB5J+/g/3C3lOWXxfwM+AfbqaIG6OmY2rkzplbgmhet0pDzdDUnz4/fnHcDfmyxO04geX/vYCHeA/nA20sxmSHospQeNYYUUvtVUKbo+RwFrArtUy9idiR5f++iL++GdmZP2GtBHUs8OlqnbkXopJwJfEFMHdSNpJeBk4BQzm9JkcZpKKL720Qv3xZvHjEyeoH2cA2wMnGhmse1X/QwDXsIX4wpNDHXbxyfA0hXSvpbJE9SJpN8BhwLDzeyMZsvTVZG0F7AVsKmZfd5seZpN9Pjax+v4cHbBnLTl8WHwZx0sU7dB0knA8cClwCHNlabrku7PPwG3A29KWlXSqkC/lKV3ilusWTJ2NKH42scj+Dn8n2ykpK8B6wPhZLdOJA0FhgKjgAMtdtNoDwsBSwHbAi9kPmNT+l7p94HNEK4ZxFC3fVwDHIvbRP07E/8TfG7viibI1OWRdCJwEnAZsJ+ZzWquRF2e/wK75sQvBVyAm7ZcDDzRkUI1k9iWqp1IOg+fg7oBH0r0x9/cuB/4Xjy0bUPSz3ED5ZeBE3BzoSxvmdldHS5YN0RSC77Ycb6ZHdpkcTqU6PG1nyOBKcBB+FDiXeA8fAUylF7bKdk+fgMYmZN+LxCKL2gX0eMLgqBwxOJGEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASFIxRf0BAkDUmOawZl4galuCFNE6wKkraR9IWkNRtY5xRJY8vixkqaUhY3QpKVxZ2UzllLo+RpL5JulPTPZsvRSELxFYCMAip9vpT0gaQnJY1MD7+aLWdHI6kHvmvJFWb2bCZ+SOZcHVOh7PqZPCM6SORmMRQYJGn7ZgvSKELxFYurgL2BIbjjnnuAQcA/gDuLtC1RYlf83epKG3POAParkHYAczabLWcNfO+7ejgV301lap3lG46ZPY7v5HJCk0VpGKH4isUEM7vczC4zswvM7AhgZfzB3wJXjJ0eSYs0qKqfAU+kBzuPG4C1JJVvO7Yg8GMqOOsxs5n17sNoZl+Y2YxOuA3XZcBASQOaLUgjCMVXcMzsSzP7BXAfsI2kTbLpklokXSbpLUkzJU2SdLqkurbUTw7Yj5P0L0lvSvpM0suShklaMqdtS/Neu0saL+lTfBMIJK0o6RJJU5Nsb0t6QNK+NcixLLAJvqNOJW4B3mHuXt8OwBL4Bql5dc81x1crleb4ar0OmfJrpPRXU/7HJf0gp719JD0s6UNJ/5U0WdIVkpYqy1o6T3nbW3U5YneWoMTFuCLYFleCSOqHe5DrjftreB4fGv8W+F9Jm5vZF21spyfuJHw0cBO+V9xG+NBxE0kDcnpLO+JbfQ0DLgQ+TvNzd+E7XV+QZOsNrAd8l/ydXbJslsKHW8nzOb6n4n6SjjazT1P8/sCjwGNV2mgIdV6Hkbj8f8TP+ZHAjZJWLzkakm9HPxLfS/JE4FN8V5zv4y4V3ilVZmZvpcWZQfPgEDucUHxBidImlKtn4k4n7dxrZqV//Ask/QE4BtgXV5htYSawXEaJgDsMfwC4CFdy15aVWRtYz8yeKUVIWg+fS/u1mZ3VRhkA1krhpCr5LsGVxk7AlZJWALYEjqijzXqp5zq8C2xXGjJLGoMrz4NxhQmwMzAN3zcyqzgrzeVNAr7VzmPpFMRQNyjxcQoXBR+SAtsDj2YethJn4BuE7tTWRsz5NLUxv6TFJPUBSuYSeQ/WbVmllyg5HR8sqZLDp9YoDeXeryLvRNyFQGm4uy/ek+oQN5ftuA5/yc4TmtkjuJJbLZPnI3yn8G1rXNV/D/i6pIXacAidklB8QYlFU1hSgEsBXweeKs9oZu8Db+ALI21G0m6SHsKHVh/gQ6rJKXnxnCLP58gwFTgNXz19I83/nSWpVifuJaVQywN/KbB5GnIOAW5K56AjqPc6TM6Jex/IzqOejq8e3wi8I2m0pANbWTwqnavOtvDSZkLxBSXWS2HJb+08seuTtDPuqwR8uLgdPnTcJsXl3ZO5LjrN7Hi8B3MkPgw7EHhY0pk1iFKav1qihrxX4kP0vwGr4sPfjqLe6/BltfrM7AV8yL8tPtfXDz/GZyWtklN2CWC6mVUy4+kyhOILShyQwttS+DY+NFq7PKOkxYHlyO9VVGNv3P5tsJkNM7Nbzexu6rRbM7PJZnaeme0G9AX+BfyqhuHvkylcrdVc3saHuGnLlsArdOzW9/PqOgCzTW9uN7NfmNlAXAn2BY7Oyb4qc85blyYUX8FJ82x/JJl2mNn9AMlfyC3ABpK2KSv2G/zeuaGOJr/Eh0qz7700v3R8G+XuLWmBbFzqiZTmAvOGzFnuTeHGNTb5e+Bk4NCO9KUyD68DaW61nAkpXKIs77J4j/DeuUp0QWJVt1hsmEwYABbBV0V3xG/oO3Gj3CzH4r2cGyVdALwIbArsjvesqpmM5HE9sAvwT0mjgAWSDG21CxwMDJc0Gh+eTwcG4MPdh8zsudYKm9k7ydbu+/jKaKuY2RM0z/3ivLgO4G/rfJTqeAVYDJ/DNNxgOcu2KbyuzrY6FaH4isX/pc8sXFG8iv+DX2Vmd5RnNrOpkr4FnII7nV4slTkDOLUOGz7M7Oo0eX4UbmP2Ad6j+Q2+algrj+NvTgwC9gTmx11Sng6cXWMdw4Brku3g+Da03aHMi+uQGAbshpu4LIGf/0eBw8xsTFnevYBxnfk8tYXwshYUFknz4wr0MTPbq1r+oiJpfXwIvKOZ3dxkcRpCKL6g0KR5s9uAdXJsBQN8Wyqgt5kNbrYsjSIUXxAEhSNWdYMgKByh+IIgKByh+IIgKByh+IIgKByh+IIgKByh+IIgKByh+IIgKBz/HzBIymLSIc3YAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 288x108 with 1 Axes>"
]
@@ -870,12 +857,12 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 288x108 with 1 Axes>"
]
@@ -903,7 +890,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -924,7 +911,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -986,7 +973,7 @@
"1 CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"... "
]
},
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -1001,7 +988,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -1155,7 +1142,7 @@
"[3972 rows x 6 columns]"
]
},
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -1176,7 +1163,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -1549,7 +1536,7 @@
"55 25 24.19"
]
},
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -1573,7 +1560,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -1582,13 +1569,13 @@
"<AxesSubplot:>"
]
},
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1613,7 +1600,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -1680,7 +1667,7 @@
"4 3 2708.55"
]
},
- "execution_count": 29,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -1705,7 +1692,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -1714,13 +1701,13 @@
"<AxesSubplot:>"
]
},
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1748,7 +1735,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1815,7 +1802,7 @@
"4 3 2708.55"
]
},
- "execution_count": 31,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1834,7 +1821,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -1900,7 +1887,7 @@
"3 2708.55"
]
},
- "execution_count": 32,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -1919,7 +1906,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -1928,13 +1915,13 @@
"<AxesSubplot:xlabel='Route'>"
]
},
- "execution_count": 33,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1961,7 +1948,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -2027,7 +2014,7 @@
"3 2708.55"
]
},
- "execution_count": 34,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -2054,7 +2041,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -2063,13 +2050,13 @@
"<AxesSubplot:xlabel='Route'>"
]
},
- "execution_count": 35,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2096,7 +2083,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -2112,7 +2099,7 @@
"Name: daily, dtype: float64"
]
},
- "execution_count": 36,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -2126,7 +2113,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -2135,13 +2122,13 @@
"<AxesSubplot:xlabel='Route'>"
]
},
- "execution_count": 37,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2167,12 +2154,12 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAE2CAYAAACA+DK5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXZUlEQVR4nO3de5RlZX3m8e8DCghojIIiMNCGsUGD4qVRkGVEMVGXo44TZy3vIgJG1Cgxo6IOAkZRjFE0RO0wYsDBeElkJIuMiogSgYEG8YrKRUQhkVZUaLDxwm/+2LugKE9Xn6o6l65+v5+1au0+e7/n7N+BU0/t8+53vztVhSRp87bFtAuQJI2fYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIacLdhGiXZEzgaeCSwM3B34FrgLOBdVfXvA9q/E3g8sBVwKfCWqjpnmP3tsMMOtWLFiiHfgiQJ4JJLLvlJVe04aNtQYQ/sCjwA+DTwI+A3wEOBw4HnJHl4Vd0AkGQP4Py+zQnAL4DDgM8meWpVnb2xna1YsYI1a9YMWZokCSDJDza0baiwr6ovAF8Y8MJfBj4BHEwX7ADHA/cGHlVVl/XtTgW+BZyUZK/ysl1Jmqil9tnP/BX5fYAk2wHPAM6dCXqAqloHnAysBPZd4j4lSQu0oLBPsk2SHZLsmuRPgA/1m87qlw8DtgYuGPD0C/ulYS9JE7bQI/tDgbXAD4HP0nXXvKCqzuu379wvrxvw3Jl1uyxwn5KkJRr2BO2MM4DvANsDj6Drspl95nfbfnnbgOeun9PmLpIcTnfCl912222BZUmS5rOgsK+qH9GNxgE4I8k/ARcnuUdVHQ/c2m/besDTt+mXtw7YRlWtBlYDrFq1yhO4kjRCSzpBW1VfB74KHNGvur5fDuqqmVk3qItHkjRGo7iC9h7Affp/f4OuC2f/Ae3265cOoJekCRsq7JPstIH1TwD2ph9p0w+xPBM4MMk+s9ptT3dy9wrgoiXWLElaoGH77D+Q5AHAOXRj67cBHgU8B7gZeO2stkcBBwGfS/Ie4Ca6K2h3AZ7mBVWSlpskE93fOGJy2LD/GPBi4IV0o2+KLvQ/RDc3zrUzDavqyiQHAO8A3sCdc+M8ZZipEiRJozfsdAmfoJsWYShVdTnwzMUWJUkaLac4lqQGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBgwV9klWJjkuyYVJ1ia5OcllSd6UZLsB7fdMckaSnyW5Jcl5SZ44+vIlScMY9sj+EOBI4CrgOOB/AN8F/go4P8k9Zhom2QM4H9gfOKFvuz3w2SRPGl3pkqRh3W3Idp8Cjq+qX8xa98EkVwBvAl4K/G2//njg3sCjquoygCSnAt8CTkqyV1XVCGqXJA1pqCP7qlozJ+hnfLxf7g3Qd+k8Azh3Juj7568DTgZWAvsupWBJ0sIt9QTtrv3yx/3yYcDWwAUD2l7YLw17SZqwRYd9ki2Bo4HfAKf3q3ful9cNeMrMul028HqHJ1mTZM3atWsXW5YkaYClHNm/F9gPOLqqvtuv27Zf3jag/fo5be6iqlZX1aqqWrXjjjsuoSxJ0lyLCvskbwVeCayuquNnbbq1X2494GnbzGkjSZqQBYd9kmOANwOnAH82Z/P1/XJQV83MukFdPJKkMVpQ2Cd5C/AW4FTg0AFDKL9B14Wz/4Cn79cv1yy0SEnS0gwd9kmOBo4BTgNeUlW3z23TD7E8EzgwyT6znrs9cChwBXDREmuWJC3QUBdVJXkFcCxwLXA28Lwks5v8uKo+3//7KOAg4HNJ3gPcBBxG143zNC+okqTJG/YK2pmx8bsB/zBg+5eAzwNU1ZVJDgDeAbwB2Aq4FHhKVZ29tHIlSYsxVNhX1cHAwcO+aFVdDjxzcSVJkkbNKY4lqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBQ4V9kqOSfDLJ1UkqyTUbab9nkjOS/CzJLUnOS/LEkVQsSVqwuw3Z7u3AjcClwL3na5hkD+B84DfACcAvgMOAzyZ5alWdvehqJUmLMmzY71FVVwMk+Saw/Txtj6f7g/Coqrqsf86pwLeAk5LsVVW16IolSQs2VDfOTNBvTJLtgGcA584Eff/8dcDJwEpg34WXKUlaimGP7If1MGBr4IIB2y7sl/sCF414vwAkGcfLbpBfUCQtF6MejbNzv7xuwLaZdbsMemKSw5OsSbJm7dq1Iy5Lkto26rDftl/eNmDb+jlt7qKqVlfVqqpateOOO464LElq26jD/tZ+ufWAbdvMaSNJmpBRh/31/XJQV83MukFdPJKkMRp12H+Drgtn/wHb9uuXa0a8T0nSRow07PshlmcCBybZZ2Z9ku2BQ4ErGNNIHEnShg019DLJC4Hd+4c7AlsleXP/+AdVddqs5kcBBwGfS/Ie4Ca6K2h3AZ7mBVWSNHnDjrN/KfD4Oeve2i+/BNwR9lV1ZZIDgHcAbwC2optm4SlOlSBJ0zFU2FfVgQt50aq6HHjmYgqSJI2eUxxLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAXebdgEaXpKJ7q+qJro/SePjkb0kNcAje20y/OYijY9hL03IJP+Y+YdMc9mNI0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcC5cSQtmZPYbfo8spekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSA8YS9km2SHJkku8kWZ/kh0nenWS7cexPkjS/cR3Zvwf4G+DbwKuATwJ/DpyZxG8TkjRhI5/iOMkf0gX8P1fVn85a/33gfcBzgNNHvV9J0oaN4yj7uUCA985Z//fArcALxrBPSdI8xhH2+wK3AxfNXllV64HL+u2SpAkaR9jvDPykqm4bsO06YIckW41hv5KkDRjHbQm3BQYFPcD6WW1+NXtDksOBw/uH65J8dwy1bcgOwE8W+qRJ34ptCXx/AyyT97c5vzfw/Q20hPe3+4Y2jCPsbwXut4Ft28xqcxdVtRpYPYZ6NirJmqpaNY19T4Lvb/nanN8b+P4maRzdONfTddVsPWDbLnRdPL8asE2SNCbjCPuL+9d99OyVSbYBHg6sGcM+JUnzGEfYfxwo4DVz1h9G11f/v8ewz6WaSvfRBPn+lq/N+b2B729iUlWjf9Hk/cArgU8DZwEPpruC9ivAE6vq9pHvVJK0QeMK+y3pjuwPB1bQnY3+OHB0Va0b+Q4lSfMaS9hLkjYtTkomSQ0w7CVpiZJsn+SqJK+Zdi0bMo6LqqSJSbIn8PvADVV19bTrUZuqal2S+wKb7DnJ5sI+ySq6ydh2oRsKeivdnD1rquriada2FP1FbC8F9gZ+DJxeVVcMaPck4I1V9cQJl7hoSQ4AdqmqT8xa92Lg7cBOs9Z9D3hlVX1h8lWOxmb8+dwBOJjuD/NZVfWVfv3rgSOA+wAXAK+tqm9Mq84luhBYBZw87UIGqqomfoAnApcDv6WblXPuz2/77U+adq2LeG/bAl+d895uA143oO3zgd9Ou+YFvr9zgP815z3cDtwIfAQ4HvgocDPd/Ev7TbvmRbzHzfnzuRPwoznv5bl0w7HX04X8ZcBvgJ8Cu0675kW+z4f3n8mX0A9+2ZR+mhiNk+QJwGfppnI4me4v8HV0H7Rt6I6i9gcOBR4APLmqvjidahcuyRuBvwLeRjfE9T8BR9NdxfyhqjpiVtvnA6dW1ZbTqHUxktwAvK2qTuwff5fuj9kfVdXPZ7V7AF1wXF5VT51GrYvRwOfz3XTDsF8CXAt8EPg94OfAwdUfyff/Hf4v8IGqes1Uil2CJOfQTUS2gi70r+J35wGrqjpowqUBjQy9TPJlYDu6cLhlnnbbA+cBN1XV4ydV31Il+Srwnap67qx1WwAnAq+gOyo+rF+/HMP+l8DLq+ojSe4B3AIcUlUfGdD2DcBRVfV7Ey5z0Rr4fF4OfK6qXt0//hO6UD+2qo6d0/YfgH2r6iGTr3RpklxDN3vAvKrqgeOv5ne10mf/SOAv5/tFgjtOsqwG3jWZskZmD+ADs1dUd5Xyq5L8HHhTki2r6pBpFDcCPwJW9v/+NV03wPoNtL2N5TfKbHP/fO4GzO6H/1a//OqAtpcAzx57RWNQVSumXcN8ltsvxWL9CrjXkG3vxZy59peB9cDdB22oqv8JHAccnOQUluf/8/8DvDTJ/arqN3RHha9IcpeDlSTbAodw12BZDjb3z+evueuB5cwf6kEjV9bT3dZUI7Ycf/EX48vAkUkeOl+jfvuRwJcmUtXoXAXst6GNVXUMcCzwYrrgX27eRtf3eXGSI+juZ7wSuDzJcUmOSPJ24DvAQ4ATplfqomzun8/rgV1nPV4HvIru/9dcuwNrJ1HUuCR5YJJDk7wpyYp+3VZJdpvmXfpa6bNfSXfi7l7AF7nzBNhtwNbceQLsQOAXwAFVNck7ZS1JkuOAV9ONYrh5nnZHA8fQnSRaNn32AEl2o5sx9QC6ftGZo7+ZD3DoRuP8ZVX9/eQrXLwGPp8fBXaqqicN0fYCuntePH38lY1ekncCfwFsSffZ/OOqOifJvej+6L25qt47ldpaCHuAJH8AvAN4Ot0v0Fy3AWfSndy7apK1LVWSB9F1X3yyqi7dSNtXAKuq6iUTKW7EkvwR8FRgT+CewC/p+vQvAs6YPTpnOdnMP58r6Q5EztlIu/vTnXs6vao+NZHiRijJy+jqfx/wL8Dn6IbKntNvPx24v6NxJqTv130Y3Y3RZy5auR74xsZOkEnj5udz+UryNeDKqvrT/mratdw17N9Ad9HfrvO9zri0MhrnDlV1K93XZADS3dl3BbBzkqvKufY1RXM/n1pWVjJnVNwca+luQD4VrZygJcnxSW5M8sMkh/TrDqI7uXkl3cmiG/qvYpK0UOvprpfYkN3pLiSbiibCvp9H5fXAz+i+En8oyTOAz9CdRDkJ+BDdkLa/S/LMadUqzSfJC/orNbXpuQh41qAN/T24X0h3t76paCLs6S7V/n/AnlX1GOCvgVPp5hrZu6r+vJ9S4CHAD+iGt0mbot2BZXP1bGPeBeyf5DS68y4AOyV5MnAu3fDTv55Sbc2E/UrgY/0FOQCn0A1zO6mqfjnTqB/JcTLdhEaSNLSqOht4Od0VwGf3q0+juw/3PsBhVXXBlMpr5gTt1tx1QqKZgL9xQNufAvcYe0VSL8lC5uFfNnP+tKiqVif5DPDfgb3orv+4AvhEVV03zdpaCftrgMdw5zzTj+mXj6W7FH+2A+jmg5cmZQV3nk/amG3HW4qWqqr+A3j/tOuYq5Ww/0fg2CS/AP4deB3wPWCPJIcBn6K74u1g4Hl0X72kSfk+3fjsJ2+sYZI30019IS1IK2F/It1Vl3/RP/453V2dbgHOp5tfG7qvXDeyPOeP0fJ1CfCEIdu2dRXkMpNkf+CVwIOA+/K7k7pVVe0x8cJoJOyr6pb+MvvH0J2YvWjmsvokj6EbfbMz8G3gvVV17bRqVZO+Cjw7yYqqumYjbX9AN3GaNjFJXkQ3+OPXdD0Hm1SONDddgiSNQ38Htd/STZEwzPmXiWpl6KUkjdvudLdU3OSCHgx7SRqVHzF4xtJNgmEvSaPxQeD5STbJe0XYZy9Ji9AP+phtC+B4YCu6+ba+T9eHfxdVNZUT7Ia9JC1Cktv53aGws4daDto2tbvENTH0UpLGYFnd7c0je0lqgCdoJWkEkny4v0hzQ9sfneTDk6xpNsNekkbjYGC+qRAeCLx4MqX8LsNekiZjO7qpFKbCE7SStEhJdqObonrGXgOGZALch+7GJldOoq5BPEErSYuU5C3AW9j4bKQBbgdeUlVTmULdsJekRUqyD91tTAN8GFgNzL31YAHrgIur6ocTLXAWu3EkaZGq6mvA1wCS7A78U1V9c7pVDeaRvSQ1wNE4kjQiSbZLcmySrydZ1/98PckxSbabam0e2UvS0iW5D3Ae8GDgJ8B3+00rgR2By4HHVdWN06jPI3tJGo3jgL3o7kH7gKp6XFU9ju6Wp68A9gSOmVZxHtlL0ggkuRb416p62Qa2rwaeUlW7Tbayjkf2kjQa96e7efyGXNq3mQrDXpJG48fAI+bZ/oi+zVQY9pI0GmcCL03ysiR3ZGuSLZIcDhwCfGZaxdlnL0kjkOS+dFfP7gGs5c7ROHvSjca5EnhsVf10KvUZ9pI0GknuBbwe+K90UxoDXA2cAZxQVTdNpzLDXpKaYJ+9JDXAsJekBhj2ktQAw16blSQHJqk5P+uSXJrkyCRjn9Y7yYp+4quHj3tf0rCcz16bq48BZ9HdVGIn4EXA39BNUnX4mPe9gu7uRdcAl415X9JQDHttri6tqo/OPEjyd8B3gEOTvKmq1k6vNGny7MZRE6rqFuBCuiP9PWbWJ3lYkk8n+WmS9Um+neR1Sbac/fwk5ya5Zu7r9l02leSY/vHBwBf7zafM6ko6d9ZzkuTlSS5JcmuSm5N8MckTRvy2pTt4ZK+WzIT8jQBJVgFfAn4NnAT8B/B04J3APsDzF7GPLwNvB95Idz/S8/r1s+dEOQ14LvAp4BRg635fn0/y36pqapfUa/Nl2GtztW2SHbizz/7P6Caiuriqvte3OZEuaPevqq8DJPlb4OPA85J8uKq+sJCdVtXVST5PF/YXzO5K6l//WXTB/rKqWj1r/Yl03zxOTHJmebWjRsxuHG2ujqWbn+QG4OvAEcA/A88ASHI/4LHAZ2aCHqAP2bf3D581hrpeANwMnJFkh5kf4N50E2mtAB40hv2qcR7Za3O1GvgkcHfgoXTzlewKrO+3z8xb8q0Bz/02cDvwB2Oo68HAPZl/qtv7A9+bZ7u0YIa9NldXVNXZ/b//Ncm/Af8GfBB4Dl33zkJsqFtlob9DofvG8bx52nxzga8pbZRhryZU1flJTgNelOR9dNPNAvzhgOZ70XVxXj1r3Y3Aowa0HXT0P19/+xV0N6C+sKrWbbRwaUTss1dL3gr8Fjiuqm4AzgeenmTvmQZJAhzVP/z0rOd+D7hnkkfParsFcOSA/cyE+H0GbDuV7vfu+EEFJpnabeu0efPIXs2oqiuT/CPw/CSPA15NN/TyvCQzQy//C/Bk4PQ5I3FWA68FPt2PnPkV8GwG/w59m+4k7BFJbgV+DtxQVedU1aeSnAK8MskjgX8BfkJ3PmF/4D8znnMFapxH9mrN2+hOvh5XVWvoRuR8iW60zruB3elO5r5o9pOq6vt0N6RYS/cN4XXAV4AXz91BVf2S7rzATcB76aZuOHrW9kP617+d7lvE+/vXWced3yqkkfLmJZLUAI/sJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXg/wNYYuXwqa+/wQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2188,6 +2175,16 @@
"ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
"None"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Close the bus.db database connection here\n",
+ "conn.close()"
+ ]
}
],
"metadata": {
@@ -2206,7 +2203,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots_template.ipynb b/f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots_template.ipynb
similarity index 98%
rename from f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots_template.ipynb
rename to f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots_template.ipynb
index 2d9a33b..fa9affe 100644
--- a/f22/meena_lec_notes/lec-36/lec_35_plotting1_bar_plots_template.ipynb
+++ b/f22/meena_lec_notes/lec-36/lec_36_plotting1_bar_plots_template.ipynb
@@ -1,16 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# this allows the full screen to be used\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -728,6 +717,15 @@
"metadata": {},
"outputs": [],
"source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Close the bus.db database connection here\n"
+ ]
}
],
"metadata": {
@@ -746,7 +744,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots.ipynb b/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots.ipynb
deleted file mode 100644
index f207055..0000000
--- a/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots.ipynb
+++ /dev/null
@@ -1,3003 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "\n",
- "import sqlite3\n",
- "import os\n",
- "\n",
- "import matplotlib\n",
- "# new import statement\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "import requests\n",
- "matplotlib.rcParams[\"font.size\"] = 12"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Wrapping up bus dataset example"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### What are the top routes, and how many people ride them daily?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"bus.db\"\n",
- "# assert existence of path\n",
- "assert os.path.exists(path)\n",
- "\n",
- "# establish connection to bus.db\n",
- "conn = sqlite3.connect(path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>80</td>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6</td>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>10</td>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>3</td>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>4</td>\n",
- " <td>2656.99</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>15</td>\n",
- " <td>2179.98</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>38</td>\n",
- " <td>1955.85</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>28</td>\n",
- " <td>1868.31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>5</td>\n",
- " <td>1634.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>14</td>\n",
- " <td>1373.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>16</td>\n",
- " <td>1258.93</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>18</td>\n",
- " <td>1039.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>22</td>\n",
- " <td>995.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>19</td>\n",
- " <td>827.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>50</td>\n",
- " <td>748.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>67</td>\n",
- " <td>729.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>70</td>\n",
- " <td>710.80</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>30</td>\n",
- " <td>687.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>72</td>\n",
- " <td>636.95</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>13</td>\n",
- " <td>615.20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>40</td>\n",
- " <td>602.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>21</td>\n",
- " <td>590.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>20</td>\n",
- " <td>545.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>71</td>\n",
- " <td>497.09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>56</td>\n",
- " <td>477.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>57</td>\n",
- " <td>464.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>73</td>\n",
- " <td>448.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>75</td>\n",
- " <td>435.35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>44</td>\n",
- " <td>416.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>11</td>\n",
- " <td>392.43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>47</td>\n",
- " <td>379.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>81</td>\n",
- " <td>371.76</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>58</td>\n",
- " <td>362.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>12</td>\n",
- " <td>329.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>37</td>\n",
- " <td>319.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>27</td>\n",
- " <td>298.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>17</td>\n",
- " <td>294.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>82</td>\n",
- " <td>219.48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>33</td>\n",
- " <td>206.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1</td>\n",
- " <td>181.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>52</td>\n",
- " <td>176.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>39</td>\n",
- " <td>140.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>35</td>\n",
- " <td>140.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>31</td>\n",
- " <td>139.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>51</td>\n",
- " <td>137.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>55</td>\n",
- " <td>129.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>84</td>\n",
- " <td>114.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>29</td>\n",
- " <td>111.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>26</td>\n",
- " <td>107.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>32</td>\n",
- " <td>86.47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>34</td>\n",
- " <td>81.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>49</td>\n",
- " <td>61.83</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>36</td>\n",
- " <td>59.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>54</th>\n",
- " <td>48</td>\n",
- " <td>30.65</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>55</th>\n",
- " <td>25</td>\n",
- " <td>24.19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route daily\n",
- "0 80 10211.79\n",
- "1 2 4808.03\n",
- "2 6 4537.02\n",
- "3 10 4425.23\n",
- "4 3 2708.55\n",
- "5 4 2656.99\n",
- "6 15 2179.98\n",
- "7 38 1955.85\n",
- "8 28 1868.31\n",
- "9 5 1634.69\n",
- "10 14 1373.81\n",
- "11 16 1258.93\n",
- "12 18 1039.57\n",
- "13 22 995.21\n",
- "14 19 827.53\n",
- "15 50 748.75\n",
- "16 67 729.54\n",
- "17 70 710.80\n",
- "18 30 687.13\n",
- "19 72 636.95\n",
- "20 13 615.20\n",
- "21 40 602.92\n",
- "22 21 590.86\n",
- "23 20 545.91\n",
- "24 71 497.09\n",
- "25 56 477.44\n",
- "26 57 464.86\n",
- "27 73 448.87\n",
- "28 75 435.35\n",
- "29 44 416.90\n",
- "30 11 392.43\n",
- "31 47 379.89\n",
- "32 81 371.76\n",
- "33 58 362.59\n",
- "34 12 329.51\n",
- "35 37 319.82\n",
- "36 27 298.07\n",
- "37 17 294.55\n",
- "38 82 219.48\n",
- "39 33 206.53\n",
- "40 1 181.44\n",
- "41 52 176.24\n",
- "42 39 140.89\n",
- "43 35 140.42\n",
- "44 31 139.87\n",
- "45 51 137.57\n",
- "46 55 129.23\n",
- "47 84 114.21\n",
- "48 29 111.28\n",
- "49 26 107.10\n",
- "50 32 86.47\n",
- "51 34 81.97\n",
- "52 49 61.83\n",
- "53 36 59.13\n",
- "54 48 30.65\n",
- "55 25 24.19"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "\"\"\", conn)\n",
- "\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 10211.79\n",
- "1 4808.03\n",
- "2 4537.02\n",
- "3 4425.23\n",
- "4 2708.55\n",
- "5 2656.99\n",
- "6 2179.98\n",
- "7 1955.85\n",
- "8 1868.31\n",
- "9 1634.69\n",
- "10 1373.81\n",
- "11 1258.93\n",
- "12 1039.57\n",
- "13 995.21\n",
- "14 827.53\n",
- "15 748.75\n",
- "16 729.54\n",
- "17 710.80\n",
- "18 687.13\n",
- "19 636.95\n",
- "20 615.20\n",
- "21 602.92\n",
- "22 590.86\n",
- "23 545.91\n",
- "24 497.09\n",
- "25 477.44\n",
- "26 464.86\n",
- "27 448.87\n",
- "28 435.35\n",
- "29 416.90\n",
- "30 392.43\n",
- "31 379.89\n",
- "32 371.76\n",
- "33 362.59\n",
- "34 329.51\n",
- "35 319.82\n",
- "36 298.07\n",
- "37 294.55\n",
- "38 219.48\n",
- "39 206.53\n",
- "40 181.44\n",
- "41 176.24\n",
- "42 140.89\n",
- "43 140.42\n",
- "44 139.87\n",
- "45 137.57\n",
- "46 129.23\n",
- "47 114.21\n",
- "48 111.28\n",
- "49 107.10\n",
- "50 86.47\n",
- "51 81.97\n",
- "52 61.83\n",
- "53 59.13\n",
- "54 30.65\n",
- "55 24.19\n",
- "Name: daily, dtype: float64"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# let's extract daily column from df\n",
- "df[\"daily\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# let's create a bar plot from daily column Series\n",
- "df[\"daily\"].plot.bar()\n",
- "\n",
- "# Oops wrong x-axis labels!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>80</td>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6</td>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>10</td>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>3</td>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>4</td>\n",
- " <td>2656.99</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>15</td>\n",
- " <td>2179.98</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>38</td>\n",
- " <td>1955.85</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>28</td>\n",
- " <td>1868.31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>5</td>\n",
- " <td>1634.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>14</td>\n",
- " <td>1373.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>16</td>\n",
- " <td>1258.93</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>18</td>\n",
- " <td>1039.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>22</td>\n",
- " <td>995.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>19</td>\n",
- " <td>827.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>50</td>\n",
- " <td>748.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>67</td>\n",
- " <td>729.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>70</td>\n",
- " <td>710.80</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>30</td>\n",
- " <td>687.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>72</td>\n",
- " <td>636.95</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>13</td>\n",
- " <td>615.20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>40</td>\n",
- " <td>602.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>21</td>\n",
- " <td>590.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>20</td>\n",
- " <td>545.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>71</td>\n",
- " <td>497.09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>56</td>\n",
- " <td>477.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>57</td>\n",
- " <td>464.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>73</td>\n",
- " <td>448.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>75</td>\n",
- " <td>435.35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>44</td>\n",
- " <td>416.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>11</td>\n",
- " <td>392.43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>47</td>\n",
- " <td>379.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>81</td>\n",
- " <td>371.76</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>58</td>\n",
- " <td>362.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>12</td>\n",
- " <td>329.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>37</td>\n",
- " <td>319.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>27</td>\n",
- " <td>298.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>17</td>\n",
- " <td>294.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>82</td>\n",
- " <td>219.48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>33</td>\n",
- " <td>206.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1</td>\n",
- " <td>181.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>52</td>\n",
- " <td>176.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>39</td>\n",
- " <td>140.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>35</td>\n",
- " <td>140.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>31</td>\n",
- " <td>139.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>51</td>\n",
- " <td>137.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>55</td>\n",
- " <td>129.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>84</td>\n",
- " <td>114.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>29</td>\n",
- " <td>111.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>26</td>\n",
- " <td>107.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>32</td>\n",
- " <td>86.47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>34</td>\n",
- " <td>81.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>49</td>\n",
- " <td>61.83</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>36</td>\n",
- " <td>59.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>54</th>\n",
- " <td>48</td>\n",
- " <td>30.65</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>55</th>\n",
- " <td>25</td>\n",
- " <td>24.19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route daily\n",
- "0 80 10211.79\n",
- "1 2 4808.03\n",
- "2 6 4537.02\n",
- "3 10 4425.23\n",
- "4 3 2708.55\n",
- "5 4 2656.99\n",
- "6 15 2179.98\n",
- "7 38 1955.85\n",
- "8 28 1868.31\n",
- "9 5 1634.69\n",
- "10 14 1373.81\n",
- "11 16 1258.93\n",
- "12 18 1039.57\n",
- "13 22 995.21\n",
- "14 19 827.53\n",
- "15 50 748.75\n",
- "16 67 729.54\n",
- "17 70 710.80\n",
- "18 30 687.13\n",
- "19 72 636.95\n",
- "20 13 615.20\n",
- "21 40 602.92\n",
- "22 21 590.86\n",
- "23 20 545.91\n",
- "24 71 497.09\n",
- "25 56 477.44\n",
- "26 57 464.86\n",
- "27 73 448.87\n",
- "28 75 435.35\n",
- "29 44 416.90\n",
- "30 11 392.43\n",
- "31 47 379.89\n",
- "32 81 371.76\n",
- "33 58 362.59\n",
- "34 12 329.51\n",
- "35 37 319.82\n",
- "36 27 298.07\n",
- "37 17 294.55\n",
- "38 82 219.48\n",
- "39 33 206.53\n",
- "40 1 181.44\n",
- "41 52 176.24\n",
- "42 39 140.89\n",
- "43 35 140.42\n",
- "44 31 139.87\n",
- "45 51 137.57\n",
- "46 55 129.23\n",
- "47 84 114.21\n",
- "48 29 111.28\n",
- "49 26 107.10\n",
- "50 32 86.47\n",
- "51 34 81.97\n",
- "52 49 61.83\n",
- "53 36 59.13\n",
- "54 48 30.65\n",
- "55 25 24.19"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df = df.set_index(\"Route\")\n",
- "\n",
- "# let's plot for top 5 routes alone\n",
- "df.head(5)[\"daily\"].plot.bar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Route\n",
- "80 10211.79\n",
- "2 4808.03\n",
- "6 4537.02\n",
- "10 4425.23\n",
- "3 2708.55\n",
- "other 29296.56\n",
- "Name: daily, dtype: float64"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# let's use slicing to aggregate the rest of the data\n",
- "s = df[\"daily\"].iloc[:5]\n",
- "s[\"other\"] = df[\"daily\"].iloc[5:].sum()\n",
- "s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# let's plot the bars\n",
- "ax = (s / 1000).plot.bar(color = \"k\")\n",
- "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "conn.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
- "- This set of data is used in beginning Machine Learning Courses\n",
- "- You can train a ML algorithm to use the values to predict the class of iris\n",
- "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 1: Downloading IRIS dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "# use requests to get this URL\n",
- "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
- "response = requests.get(url)\n",
- "\n",
- "# check that the request was successful\n",
- "response.raise_for_status()\n",
- "\n",
- "# open a file called \"iris.csv\" for writing the data locally\n",
- "file_obj = open(\"iris.csv\", \"w\")\n",
- "\n",
- "# write the text of response to the file object\n",
- "file_obj.write(response.text)\n",
- "\n",
- "# close the file object\n",
- "file_obj.close()\n",
- "\n",
- "# Look at the file you downloaded. What's wrong with it?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2: Making a DataFrame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>5.1</th>\n",
- " <th>3.5</th>\n",
- " <th>1.4</th>\n",
- " <th>0.2</th>\n",
- " <th>Iris-setosa</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.7</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "0 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "1 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "2 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "3 5.0 3.6 1.4 0.2 Iris-setosa\n",
- "4 5.4 3.9 1.7 0.4 Iris-setosa"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# read the \"iris.csv\" file into a Pandas dataframe\n",
- "iris_df = pd.read_csv(\"iris.csv\")\n",
- "\n",
- "# display the head of the data frame\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 3: Our CSV file has no header. Let's add column names.\n",
- "- Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "4 5.0 3.6 1.4 0.2 Iris-setosa"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Attribute Information:\n",
- "# 1. sepal length in cm\n",
- "# 2. sepal width in cm\n",
- "# 3. petal length in cm\n",
- "# 4. petal width in cm\n",
- "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
- "\n",
- "# These should be our headers \n",
- "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
- "\n",
- "iris_df = pd.read_csv(\"iris.csv\",\n",
- " names = [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"])\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 4: Connect to our database version of this data!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>type</th>\n",
- " <th>name</th>\n",
- " <th>tbl_name</th>\n",
- " <th>rootpage</th>\n",
- " <th>sql</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>table</td>\n",
- " <td>iris</td>\n",
- " <td>iris</td>\n",
- " <td>2</td>\n",
- " <td>CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " type name tbl_name rootpage \\\n",
- "0 table iris iris 2 \n",
- "\n",
- " sql \n",
- "0 CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"... "
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
- "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
- "Break any ties by ordering by the shortest sepal width."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.8</td>\n",
- " <td>4.0</td>\n",
- " <td>1.2</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>5.7</td>\n",
- " <td>3.8</td>\n",
- " <td>1.7</td>\n",
- " <td>0.3</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>5.7</td>\n",
- " <td>4.4</td>\n",
- " <td>1.5</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>5.5</td>\n",
- " <td>3.5</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.5</td>\n",
- " <td>4.2</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>5.4</td>\n",
- " <td>3.4</td>\n",
- " <td>1.7</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>5.4</td>\n",
- " <td>3.4</td>\n",
- " <td>1.5</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>5.4</td>\n",
- " <td>3.7</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.7</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.3</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "0 5.8 4.0 1.2 0.2 Iris-setosa\n",
- "1 5.7 3.8 1.7 0.3 Iris-setosa\n",
- "2 5.7 4.4 1.5 0.4 Iris-setosa\n",
- "3 5.5 3.5 1.3 0.2 Iris-setosa\n",
- "4 5.5 4.2 1.4 0.2 Iris-setosa\n",
- "5 5.4 3.4 1.7 0.2 Iris-setosa\n",
- "6 5.4 3.4 1.5 0.4 Iris-setosa\n",
- "7 5.4 3.7 1.5 0.2 Iris-setosa\n",
- "8 5.4 3.9 1.7 0.4 Iris-setosa\n",
- "9 5.4 3.9 1.3 0.4 Iris-setosa"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.read_sql(\"\"\"\n",
- " SELECT *\n",
- " FROM iris\n",
- " WHERE class = 'Iris-setosa'\n",
- " ORDER BY `sep-length` DESC, `sep-width` ASC\n",
- " LIMIT 10\n",
- "\"\"\", iris_conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Lecture 36: Scatter Plots\n",
- "**Learning Objectives**\n",
- "- Set the marker, color, and size of scatter plot data\n",
- "- Calculate correlation between DataFrame columns\n",
- "- Use subplots to group scatterplot data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set the marker, color, and size of scatter plot data\n",
- "\n",
- "To start, let's look at some made-up data about Trees.\n",
- "The city of Madison maintains a database of all the trees they care for."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " <th>height</th>\n",
- " <th>diameter</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>1.9</td>\n",
- " <td>1.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>1.8</td>\n",
- " <td>1.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2</td>\n",
- " <td>1.8</td>\n",
- " <td>0.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2</td>\n",
- " <td>2.5</td>\n",
- " <td>1.5</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " age height diameter\n",
- "0 1 1.5 0.8\n",
- "1 1 1.9 1.2\n",
- "2 1 1.8 1.4\n",
- "3 2 1.8 0.9\n",
- "4 2 2.5 1.5"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trees = [\n",
- " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
- " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
- " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
- " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
- " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
- " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
- " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
- " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
- " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
- " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
- " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
- " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
- " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
- " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
- "]\n",
- "trees_df = DataFrame(trees)\n",
- "trees_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Scatter Plots\n",
- "We can make a scatter plot of a DataFrame using the following function...\n",
- "\n",
- "`df_name.plot.scatter(x = \"x_col_name\", y = \"y_col_name\", \\\n",
- " color = \"red\", marker = \"*\", s = 50)`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot the trees data comparing a tree's age to its height...\n",
- " - What is `df_name`?\n",
- " - What is `x_col_name`?\n",
- " - What is `y_col_name`?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"g\") \n",
- "# TODO: change y to diameter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now plot with a little more beautification...\n",
- " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
- " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
- " - Change the size (any int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot with some more beautification options.\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) # D for diamond"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Tree Age vs Height')"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Add a title to your plot.\n",
- "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) # D for diamond\n",
- "ax.set_title(\"Tree Age vs Height\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Correlation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " <th>height</th>\n",
- " <th>diameter</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>age</th>\n",
- " <td>1.000000</td>\n",
- " <td>0.797468</td>\n",
- " <td>0.854578</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>height</th>\n",
- " <td>0.797468</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.839345</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>diameter</th>\n",
- " <td>0.854578</td>\n",
- " <td>0.839345</td>\n",
- " <td>1.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " age height diameter\n",
- "age 1.000000 0.797468 0.854578\n",
- "height 0.797468 1.000000 0.839345\n",
- "diameter 0.854578 0.839345 1.000000"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# What is the correlation between our DataFrame columns?\n",
- "corr_df = trees_df.corr()\n",
- "corr_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.7974683544303798"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# What is the correlation between age and height (don't use .iloc)\n",
- "# Using index in this case isn't considered as hardcoding\n",
- "corr_df['age']['height']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Variating Stylistic Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Option 1:\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = \"diameter\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Option 2:\n",
- "# this way allows you to make it bigger\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Use subplots to group scatterplot data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Re-visit the Iris Data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>145</th>\n",
- " <td>6.7</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>146</th>\n",
- " <td>6.3</td>\n",
- " <td>2.5</td>\n",
- " <td>5.0</td>\n",
- " <td>1.9</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>147</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.0</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>148</th>\n",
- " <td>6.2</td>\n",
- " <td>3.4</td>\n",
- " <td>5.4</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>149</th>\n",
- " <td>5.9</td>\n",
- " <td>3.0</td>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>150 rows × 5 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "4 5.0 3.6 1.4 0.2 Iris-setosa\n",
- ".. ... ... ... ... ...\n",
- "145 6.7 3.0 5.2 2.3 Iris-virginica\n",
- "146 6.3 2.5 5.0 1.9 Iris-virginica\n",
- "147 6.5 3.0 5.2 2.0 Iris-virginica\n",
- "148 6.2 3.4 5.4 2.3 Iris-virginica\n",
- "149 5.9 3.0 5.1 1.8 Iris-virginica\n",
- "\n",
- "[150 rows x 5 columns]"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How do we create a *scatter plot* for various *class types*?\n",
- "First, gather all the class types."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# In Pandas\n",
- "varietes = list(set(iris_df[\"class\"]))\n",
- "varietes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# In SQL\n",
- "varietes = list(pd.read_sql(\"\"\"\n",
- " SELECT DISTINCT class\n",
- " FROM iris\n",
- "\"\"\", iris_conn)[\"class\"])\n",
- "varietes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [],
- "source": [
- "# If you want to continue using SQL instead, don't close the connection!\n",
- "iris_conn.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n",
- "iris_df[iris_df[\"class\"] == 'Iris-setosa'].plot.scatter(x = \"pet-width\", y = \"pet-length\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Write a for loop that iterates through each variety in classes\n",
- "# and makes a plot for only that class\n",
- "\n",
- "# For each class add a color and a marker style\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "\n",
- "for i in range(len(varietes)):\n",
- " variety = varietes[i]\n",
- " \n",
- " # make a df just of just the data for this variety\n",
- " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
- " \n",
- " #make a scatter plot for this variety\n",
- " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
- " label = variety, color = colors[i], marker = markers[i])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Did you notice that it made 3 plots?!?! What's decieving about this?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### We can make Subplots in plots, called an AxesSubplot, keyword ax\n",
- "1. if AxesSuplot ax passed, then plot in that subplot\n",
- "2. if ax is None, create a new AxesSubplot\n",
- "3. return AxesSubplot that was used"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# complete this code to make 3 plots in one\n",
- "\n",
- "plot_area = None # don't change this...look at this variable in line 12\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "for i in range(len(varietes)):\n",
- " variety = varietes[i]\n",
- " \n",
- " # make a df just of just the data for this variety\n",
- " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
- " \n",
- " #make a scatter plot for this variety\n",
- " plot_area = variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
- " label = variety, color = colors[i], marker = markers[i], \\\n",
- " ax = plot_area)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's focus on \"Iris-virginica\" data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>100</th>\n",
- " <td>6.3</td>\n",
- " <td>3.3</td>\n",
- " <td>6.0</td>\n",
- " <td>2.5</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>101</th>\n",
- " <td>5.8</td>\n",
- " <td>2.7</td>\n",
- " <td>5.1</td>\n",
- " <td>1.9</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>102</th>\n",
- " <td>7.1</td>\n",
- " <td>3.0</td>\n",
- " <td>5.9</td>\n",
- " <td>2.1</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>103</th>\n",
- " <td>6.3</td>\n",
- " <td>2.9</td>\n",
- " <td>5.6</td>\n",
- " <td>1.8</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>104</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.8</td>\n",
- " <td>2.2</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "100 6.3 3.3 6.0 2.5 Iris-virginica\n",
- "101 5.8 2.7 5.1 1.9 Iris-virginica\n",
- "102 7.1 3.0 5.9 2.1 Iris-virginica\n",
- "103 6.3 2.9 5.6 1.8 Iris-virginica\n",
- "104 6.5 3.0 5.8 2.2 Iris-virginica"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_virginica = iris_df[iris_df[\"class\"] == \"Iris-virginica\"]\n",
- "assert(len(iris_virginica) == 50)\n",
- "iris_virginica.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's learn about *xlim* and *ylim*\n",
- "- Allows us to set x-axis and y-axis limits\n",
- "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
- "- You need to be careful about setting the UPPER-BOUND"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = 0, ylim = 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "\n",
- "# What is wrong with this plot?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the maximum pet-len?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "6.9"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_virginica[\"pet-length\"].max()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.0, 6.0)"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ax.get_ylim()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's include assert statements to make sure we don't crop the plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "ename": "AssertionError",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m/var/folders/k6/kcy8b4f57hx9f1wh4sbs8mn40000gn/T/ipykernel_26032/2630924870.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mxlim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m figsize = (3, 3))\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0miris_virginica\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pet-length\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mAssertionError\u001b[0m: "
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now let's try all 4 assert statements\n",
- "\n",
- "```\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 7), ylim = (0, 7),\n",
- " figsize = (3, 3))\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Time-Permitting\n",
- "Plot this data in an interesting/meaningful way & identify any correlations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>name</th>\n",
- " <th>grade</th>\n",
- " <th>gpa</th>\n",
- " <th>attendance</th>\n",
- " <th>height</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Cole</td>\n",
- " <td>C</td>\n",
- " <td>2.0</td>\n",
- " <td>4</td>\n",
- " <td>68</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Cynthia</td>\n",
- " <td>AB</td>\n",
- " <td>3.5</td>\n",
- " <td>11</td>\n",
- " <td>66</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Alice</td>\n",
- " <td>B</td>\n",
- " <td>3.0</td>\n",
- " <td>10</td>\n",
- " <td>60</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Seth</td>\n",
- " <td>BC</td>\n",
- " <td>2.5</td>\n",
- " <td>6</td>\n",
- " <td>72</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " name grade gpa attendance height\n",
- "0 Cole C 2.0 4 68\n",
- "1 Cynthia AB 3.5 11 66\n",
- "2 Alice B 3.0 10 60\n",
- "3 Seth BC 2.5 6 72"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "students = pd.DataFrame({\n",
- " \"name\": [\n",
- " \"Cole\",\n",
- " \"Cynthia\",\n",
- " \"Alice\",\n",
- " \"Seth\"\n",
- " ],\n",
- " \"grade\": [\n",
- " \"C\",\n",
- " \"AB\",\n",
- " \"B\",\n",
- " \"BC\"\n",
- " ],\n",
- " \"gpa\": [\n",
- " 2.0,\n",
- " 3.5,\n",
- " 3.0,\n",
- " 2.5\n",
- " ],\n",
- " \"attendance\": [\n",
- " 4,\n",
- " 11,\n",
- " 10,\n",
- " 6\n",
- " ],\n",
- " \"height\": [\n",
- " 68,\n",
- " 66,\n",
- " 60,\n",
- " 72\n",
- " ]\n",
- "})\n",
- "students"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 0.3333333333333333\n",
- "1 0.25\n",
- "2 0.0\n",
- "3 0.5\n",
- "Name: height, dtype: string"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Min, Max, and Overall Difference in Student Height\n",
- "min_height = students[\"height\"].min()\n",
- "max_height = students[\"height\"].max()\n",
- "diff_height = max_height - min_height\n",
- "\n",
- "# Normalize students heights on a scale of [0, 1] (black to white)\n",
- "height_colors = (students[\"height\"] - min_height) / diff_height\n",
- "\n",
- "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
- "height_colors = height_colors / 2 \n",
- "\n",
- "# Color must be a string (e.g. c='0.34')\n",
- "height_colors = height_colors.astype(\"string\")\n",
- "\n",
- "height_colors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='attendance', ylabel='gpa'>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gpa</th>\n",
- " <th>attendance</th>\n",
- " <th>height</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>gpa</th>\n",
- " <td>1.000000</td>\n",
- " <td>0.976831</td>\n",
- " <td>-0.464758</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>attendance</th>\n",
- " <td>0.976831</td>\n",
- " <td>1.000000</td>\n",
- " <td>-0.635586</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>height</th>\n",
- " <td>-0.464758</td>\n",
- " <td>-0.635586</td>\n",
- " <td>1.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gpa attendance height\n",
- "gpa 1.000000 0.976831 -0.464758\n",
- "attendance 0.976831 1.000000 -0.635586\n",
- "height -0.464758 -0.635586 1.000000"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "students.corr()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots_template.ipynb b/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots_template.ipynb
deleted file mode 100644
index 7fa3c61..0000000
--- a/f22/meena_lec_notes/lec-37/lec_36_plotting2_scatter_plots_template.ipynb
+++ /dev/null
@@ -1,833 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "\n",
- "import sqlite3\n",
- "import os\n",
- "\n",
- "import matplotlib\n",
- "# new import statement\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "import requests\n",
- "matplotlib.rcParams[\"font.size\"] = 12"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Wrapping up bus dataset example"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### What are the top routes, and how many people ride them daily?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"bus.db\"\n",
- "# assert existence of path\n",
- "assert os.path.exists(path)\n",
- "\n",
- "# establish connection to bus.db\n",
- "conn = sqlite3.connect(path)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "\"\"\", conn)\n",
- "\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# let's extract daily column from df\n",
- "df[\"daily\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# let's create a bar plot from daily column Series\n",
- "df[\"daily\"].plot.bar()\n",
- "\n",
- "# Oops wrong x-axis labels!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = ???\n",
- "\n",
- "# let's plot for top 5 routes alone\n",
- "???"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# let's use slicing to aggregate the rest of the data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# let's plot the bars\n",
- "ax = (s / 1000).plot.bar(color = \"k\")\n",
- "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "conn.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
- "- This set of data is used in beginning Machine Learning Courses\n",
- "- You can train a ML algorithm to use the values to predict the class of iris\n",
- "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 1: Downloading IRIS dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# use requests to get this URL\n",
- "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
- "response = ???\n",
- "\n",
- "# check that the request was successful\n",
- "???\n",
- "\n",
- "# open a file called \"iris.csv\" for writing the data locally\n",
- "file_obj = open(\"iris.csv\", ???)\n",
- "\n",
- "# write the text of response to the file object\n",
- "file_obj.write(???)\n",
- "\n",
- "# close the file object\n",
- "file_obj.close()\n",
- "\n",
- "# Look at the file you downloaded. What's wrong with it?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2: Making a DataFrame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# read the \"iris.csv\" file into a Pandas dataframe\n",
- "iris_df = ???\n",
- "\n",
- "# display the head of the data frame\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 3: Our CSV file has no header. Let's add column names.\n",
- "- Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Attribute Information:\n",
- "# 1. sepal length in cm\n",
- "# 2. sepal width in cm\n",
- "# 3. petal length in cm\n",
- "# 4. petal width in cm\n",
- "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
- "\n",
- "# These should be our headers \n",
- "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
- "\n",
- "iris_df = pd.read_csv(\"iris.csv\",\n",
- " ???)\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 4: Connect to our database version of this data!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
- "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
- "Break any ties by ordering by the shortest sepal width."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.read_sql(\"\"\"\n",
- " SELECT\n",
- " FROM\n",
- " WHERE \n",
- " ORDER BY \n",
- " LIMIT 10\n",
- "\"\"\", iris_conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Lecture 36: Scatter Plots\n",
- "**Learning Objectives**\n",
- "- Set the marker, color, and size of scatter plot data\n",
- "- Calculate correlation between DataFrame columns\n",
- "- Use subplots to group scatterplot data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set the marker, color, and size of scatter plot data\n",
- "\n",
- "To start, let's look at some made-up data about Trees.\n",
- "The city of Madison maintains a database of all the trees they care for."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "trees = [\n",
- " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
- " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
- " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
- " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
- " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
- " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
- " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
- " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
- " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
- " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
- " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
- " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
- " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
- " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
- "]\n",
- "trees_df = DataFrame(trees)\n",
- "trees_df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Scatter Plots\n",
- "We can make a scatter plot of a DataFrame using the following function...\n",
- "\n",
- "`df_name.plot.scatter(x = \"x_col_name\", y = \"y_col_name\", \\\n",
- " color = \"red\", marker = \"*\", s = 50)`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot the trees data comparing a tree's age to its height...\n",
- " - What is `df_name`?\n",
- " - What is `x_col_name`?\n",
- " - What is `y_col_name`?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: change y to diameter"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now plot with a little more beautification...\n",
- " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
- " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
- " - Change the size (any int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot with some more beautification options.\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) \n",
- "# D for diamond"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Add a title to your plot.\n",
- "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) \n",
- "# D for diamond\n",
- "ax.set_title(\"Tree Age vs Height\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Correlation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# What is the correlation between our DataFrame columns?\n",
- "corr_df = trees_df.corr()\n",
- "corr_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# What is the correlation between age and height (don't use .iloc)\n",
- "# Using index in this case isn't considered as hardcoding\n",
- "corr_df['age']['height']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Variating Stylistic Parameters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Option 1:\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = \"diameter\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Option 2:\n",
- "# this way allows you to make it bigger\n",
- "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Use subplots to group scatterplot data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Re-visit the Iris Data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How do we create a *scatter plot* for various *class types*?\n",
- "First, gather all the class types."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# In Pandas\n",
- "varietes = list(set(iris_df[\"class\"]))\n",
- "varietes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# In SQL\n",
- "varietes = list(pd.read_sql(\"\"\"\n",
- " SELECT DISTINCT class\n",
- " FROM iris\n",
- "\"\"\", iris_conn)[\"class\"])\n",
- "varietes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# If you want to continue using SQL instead, don't close the connection!\n",
- "iris_conn.close()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Write a for loop that iterates through each variety in classes\n",
- "# and makes a plot for only that class\n",
- "\n",
- "# For each class add a color and a marker style\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "\n",
- "for i in range(len(varietes)):\n",
- " ???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Did you notice that it made 3 plots?!?! What's decieving about this?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### We can make Subplots in plots, called an AxesSubplot, keyword ax\n",
- "1. if AxesSuplot ax passed, then plot in that subplot\n",
- "2. if ax is None, create a new AxesSubplot\n",
- "3. return AxesSubplot that was used"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# complete this code to make 3 plots in one\n",
- "\n",
- "plot_area = None # don't change this...look at this variable in line 12\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "for i in range(len(varietes)):\n",
- " ???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's focus on \"Iris-virginica\" data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_virginica = ???\n",
- "assert(len(iris_virginica) == 50)\n",
- "iris_virginica.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's learn about *xlim* and *ylim*\n",
- "- Allows us to set x-axis and y-axis limits\n",
- "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
- "- You need to be careful about setting the UPPER-BOUND"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = ???, ylim = ???)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "\n",
- "# What is wrong with this plot?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the maximum pet-len?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax.get_ylim()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's include assert statements to make sure we don't crop the plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now let's try all 4 assert statements\n",
- "\n",
- "```\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 7), ylim = (0, 7),\n",
- " figsize = (3, 3))\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Time-Permitting\n",
- "Plot this data in an interesting/meaningful way & identify any correlations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "students = pd.DataFrame({\n",
- " \"name\": [\n",
- " \"Cole\",\n",
- " \"Cynthia\",\n",
- " \"Alice\",\n",
- " \"Seth\"\n",
- " ],\n",
- " \"grade\": [\n",
- " \"C\",\n",
- " \"AB\",\n",
- " \"B\",\n",
- " \"BC\"\n",
- " ],\n",
- " \"gpa\": [\n",
- " 2.0,\n",
- " 3.5,\n",
- " 3.0,\n",
- " 2.5\n",
- " ],\n",
- " \"attendance\": [\n",
- " 4,\n",
- " 11,\n",
- " 10,\n",
- " 6\n",
- " ],\n",
- " \"height\": [\n",
- " 68,\n",
- " 66,\n",
- " 60,\n",
- " 72\n",
- " ]\n",
- "})\n",
- "students"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Min, Max, and Overall Difference in Student Height\n",
- "min_height = students[\"height\"].min()\n",
- "max_height = students[\"height\"].max()\n",
- "diff_height = max_height - min_height\n",
- "\n",
- "# Normalize students heights on a scale of [0, 1] (black to white)\n",
- "height_colors = (students[\"height\"] - min_height) / diff_height\n",
- "\n",
- "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
- "height_colors = height_colors / 2 \n",
- "\n",
- "# Color must be a string (e.g. c='0.34')\n",
- "height_colors = height_colors.astype(\"string\")\n",
- "\n",
- "height_colors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "students.corr()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-37/.ipynb_checkpoints/lec_36_plotting2_scatter_plots-checkpoint.ipynb b/f22/meena_lec_notes/lec-37/lec_37_plotting2_scatter_plots.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-37/.ipynb_checkpoints/lec_36_plotting2_scatter_plots-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-37/lec_37_plotting2_scatter_plots.ipynb
diff --git a/f22/meena_lec_notes/lec-37/.ipynb_checkpoints/lec_36_plotting2_scatter_plots_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-37/lec_37_plotting2_scatter_plots_template.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-37/.ipynb_checkpoints/lec_36_plotting2_scatter_plots_template-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-37/lec_37_plotting2_scatter_plots_template.ipynb
diff --git a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1-checkpoint.ipynb b/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1-checkpoint.ipynb
deleted file mode 100644
index 2080466..0000000
--- a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1-checkpoint.ipynb
+++ /dev/null
@@ -1,2286 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Today's lecture: pie, bar, and scatter plots\n",
- "- matplotlib is similar to MATLAB\n",
- "- matplotlib integrates with pandas, just like sqlite3 integrates with pandas\n",
- "- Series.plot.PLOT_FN(...)\n",
- "- DataFrame.plot.PLOT_FN(...)\n",
- "- Example PLOT_FNs: pie, scatter, bar, line"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import sqlite3\n",
- "import os\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Without this Jupyter notebook cannot display the \"first\" plot in older versions of \n",
- "# Python / mathplotlib / jupyter\n",
- "# %matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# matplotlib font size settings\n",
- "#matplotlib.rcParams #Uncomment to see the settings available in matplotlib\n",
- "#matplotlib.rcParams[\"font.size\"] #Uncomment to see the default setting for font.size\n",
- "matplotlib.rcParams[\"font.size\"] = 18 #Uncomment when you reach input cell which has the following question: \n",
- "### How to change font inside the figure? How to change size of the figure?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a pandas Series\n",
- "1. pandas Series can be made using list or dictionary.\n",
- "2. pandas Series has both index (similar to dictionary key) and integer position (similar to list index).\n",
- "3. While creating a brand new list, index and integer position are the same."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 5000000\n",
- "1 3000000\n",
- "2 2000000\n",
- "dtype: int64"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s = Series([5000000, 3000000, 2000000])\n",
- "s"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Pie plot\n",
- "- gives you a sense of ratio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='None'>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.pie()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What's wrong with the above plot?\n",
- "\n",
- "- The labels are all wrong.\n",
- "- From where are you getting 0, 1, and 2 as labels? ---> let's fix just this\n",
- "- It is difficult to read the actual numbers: we can only see the relative portions, not the absolute amounts\n",
- "- It says \"None\" to the left.\n",
- "- The font is tiny.\n",
- "- No indication of what is being plotted here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:ylabel='None'>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series({\"Police\": 5000000, \"Fire\": 3000000, \"Schools\": 2000000})\n",
- "s.plot.pie() # Doesn't say the absolute numbers for each department"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plot\n",
- "- A lot of times bar plot is better\n",
- "- You can see absolute numbers in bar plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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SLVqay4F7Qnd9gm5/pXPntO9H9/nu8gzv5fky8MT5GkYbdh1Ft3WAZtM3gcfM1zC6ofaRdBuyafZ8FvijJC8G3gsU8Ik57femuxC9yzO8l+cNwMOSnALcf3TsTkkeA5wN7A/8+UC1acfeBjw2yYnA7UbHdktyb+A0upknbxmqOC3qeLphrdcDjwVOqqotAEn2AJ4MfG6w6qbI5fHLNJpK9pfAnnSzFbb9h7weeF5VvXeg0tRDklcD/5vu/qy7jZ4zeryyqk4csDwtYrTX0v2An1XVZXOO3wo4HPjmtkDflRneOyHJneh+xb4P3V/6C4EPVdWq+LWtdUkeCPw3tv/8Tqkq96jXzDO8taqMVlW+CDivqv556Hqk5XLMexmSHJhkwUU4SZ6QZP0US1JPVXUN3XDJbw1di3YsyY1JfrPExw1D1z0N7m2yPH9G95f/Ywu0vwj4Ad2sE82ei+j2n9HsO5mbridpDsN7eR7B4vfN+xTdjSs0m94O/EmSd1TVvw9djBZWVc8cuoZZZXgvzx3oFgss5Aq6neo0m35Ot6T6giR/R3eh8trxTlV18rQLk/oyvJfnauDui7Tfgy4gNJveO+frYxfoU3S/smsGJbk73eZh23bzvBg4o6ouGq6q6XK2yTIkOY1uPunBVXX5WNudgG8D51TVk4aoT4tLcmifflW1KhZ7tGa0uOo4YPexphuB11TV8dOvavoM72VI8gDgS8BPgTcC36A7U/sduouVtwUe4XxhaWUl+UO6nQO/QLfS+dujpoPodot8OHB0Vb1nmAqnx/BepiT/GXgPcHtuuhoe4Erg2VW10EwUScuU5Kt0q5h/t6puGGvbg26Tqj2r6kFD1DdNjnkvU1V9PMk6ug2O7kkX3BcAn6qqXw5anLaT5OmjL0+pqprzelFesJxJ9wVeOh7cAFV1Q5IPAidNv6zpM7x3wiikPzp0Hdqh99L9dvRBurO2ba+z8Ld4wXJGXU+37etCbjnqs8szvLUaPIru9li3ohvWOnzYcrQTvgI8J8m7xm8YneQOdOsrzhuksilzzLuHJJ+lOxN7zOhXs8/2+LaqqiMmXJp6SvIb4KiqOnX0ej+6hVavrqrvDlqcekvySOAzdFNx/5buZt/QXbB8Ft2Z9xFVde78P2HX4Zl3P3fjpi1Dt732X722jA+R7AU8lW7mguHdiKo6J8mT6PZkf9FY82XAM1ZDcIPh3UtVrV/staTpqaqPJfkE8CDgQLp/mC8CvlZVNw5a3BQZ3pKaMwrpr7CKb1dneEtqjsvjDe9ekrx7Gd9WVfXsFS9GO+Nxo+0LAPalu25x5GjF7LiqqjdPrTL1tsjy+NcncXm8bpJkOeNoVVXj/3NpIMv4DP38ZpDL429ieGtV6LsZ1VxuTDV7XB5/E4dNtCoYxLsMl8ePGN47Kcnt6aYrAVzinVmkiXJ5/Ig3IF6mJL+d5HN0d805b/S4IsnZSe4/bHXSLmvb8vib3anK5fHaoSQHA18E9gY+zvYXTZ5Ad0uth1fVd4apUNo1uTz+Job3MiT5CHAYcGhVfWus7WDgHOCsqnryAOVJu7QkT6BbHv9bY02XAcdU1cenX9X0Gd7LkORK4B1V9YoF2l8NPLeq1ky3Mml1SLIbLo/XMtyCxe8e/2+jPpImwOXxnnkvS5LvAJdV1WMXaP8ksK6qDppuZdLqkWRfutsQ3uymGlV12fQrmi5nmyzPycBjkpya5KAku48eByd5P/Bouru1SFpBSXZLclySH9FdtNwCXDLPY5fnmfcyJNkdOBU4km5/jG3jbLvRnQV8CPiD1TT+Jk1DktcDLwa+A5wFzLuuoqpeNc26hmB4L1GStXQ7mV1Jd7HkScB6brpo8tGq+vRgBUq7sCQ/Br5RVY8bupahecGyp9HV7bcDR3PTGNsXgSdW1dbBCpNWl9sCZwxdxCxwzLu/Y+hWb10OfAT4Ft0OZn8zZFHSKvMt4M5DFzELHDbpKclmYB/goVX189GxdwLPBNZW1dXDVSetDkkeT7ey8sFV9YOh6xmSwyb93Rs4YVtwj7wVeDZwL+DLg1Ql7cKSzHdjhUuB7yY5nW5myW/G2quqTpx4cQPzzLun0Wb+R1XV++ccW0O3MdURVXXWYMVJuyhvhLIwz7yXZvxfum2vb7ZIQNKKOHDHXVYnw3tp5t4DERa/D6L3QJR2UlVdOnQNs8phk568B6I0vCS3A/avqvMXaL8/8IOq+ul0K5s+z7z7O3zoAiTxeuCBo8d83kO3WdVzp1bRQAzvnrwHojQTDgfet0j7PwJHTamWQblIR1JL7kJ304WF/HDUZ5dneEtqyTXAAYu0HwD8akq1DMrwltSS84BnJLnleMPo2NNZJQvmDG9JLflzYH/gC0mekuQeSe6e5CnAF0Ztbxi0wilxqqCkpiR5DvCXwH+Yexi4HnhhVf31IIVNmeEtqTlJ7gr8PnAPuuC+APhwVf1o0MKmyPCW1KwkewAPAe4KfLeqvjNwSVPjmLekmZbksCRvGduagiTrga8C5wIfBM5P8u4BShyE4S1p1j0T+L2qunzs+MnAIXQXKt8MfJduJsozplveMBw2kTTTknwHOKuqjplz7D50YX1OVR02OrYP8HXgR1V1xBC1TpNn3pJm3Z2B748dO4xuR893bTtQVb8ETgXuP7XKBmR4S5p1ewG/HDv24NHz+J5DPwBuPfGKZoDhLWnWXQYcNHbsEcAV89zHcl/g6mkUNTTDW9KsOxd4epJDAJI8Ebgn8Ml5+h4CrIq53l6wlDTTkhwIfBvYG/h34PbAr4EHzZ3XnWR3umGTf6iqFwxR6zR55i1pplXVJcChwJl04f1J4LB5FuQcPmo/Y7oVDsMzb0lqkGfektQgw1uSGmR4S1KDDG9JatD/A0lY0uxTNUCUAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we set the x-axis, y-axis labels, and title?\n",
- "- plot_FN(...) returns what is called as AxesSubplot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the type returned by a plot function?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "matplotlib.axes._subplots.AxesSubplot"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "type(s.plot.bar())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = s.plot.bar()\n",
- "ax.set_ylabel(\"Dollars\")\n",
- "ax.set_xlabel(\"City Agency\") # this is self-explanatory, so we will skip this for other example plots\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What is this 1e6? Can we make the y-axis values more readable?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Recall that you can easily apply element-wise operation on a Series."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Police 5.0\n",
- "Fire 3.0\n",
- "Schools 2.0\n",
- "dtype: float64"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s / 1000000"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar()\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### The x-axis tick labels are difficult to read. Can we rotate them to make it more readable?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "How can we extract the indices from a Series?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Police', 'Fire', 'Schools'], dtype='object')"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s.index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar()\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")\n",
- "ax.set_xticklabels(list(s.index), rotation = 45)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to change font inside the figure? \n",
- "- Need to import matplotlib\n",
- "- Using matplotlib.rcParams[\"font.size\"] = ????"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar()\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we change the figure size?\n",
- "- figsize argument to plot_FN(...)\n",
- "- argument to figsize should be a tuple with two values: width and height"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 108x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar(figsize = (1.5, 4))\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we make the bars horizontal?\n",
- "- We have to switch figsize arguments\n",
- "- We have to change y-label to x-label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5))\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we change bar color?\n",
- "- color parameter in plot_FN(...)\n",
- " - 3 choices for arguments: \n",
- " - full name of color\n",
- " - single letter representation of the color\n",
- " - grayscale (string value between \"0\" and \"1\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"red\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"b\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"k\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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/Fh+yHwY8JumP7a07CILOQ91vbqSh6aPp58q4p3dwpy2NYkIKN8xJ2yCFE+vIWxEzm2hm55nZ3kBv4N/Ar9sy/A2CoHNTVfFJ2q6Cicli+LAQ3OP7TbhD34GSlszJX0+PbAzuKPlgSXPsBSUtDPwK713eWkfeeZDUM+Wdg5nNBEoLOEvXIX8QBJ2QWgyY/4qbs9yGr7p+DKyKe1BfB7jCzMYCSDoWOB8YK+kKYDLeG9wVOAT3MF8zZvZFMpK+GXhc0lD8TZF98FXaM0qrtG3JW4H+wFBJw/He6wx8weQw4FEze6GVskEQdCXMrNUP3qs7Hzf1mAp8ji9CjMCV2QI5+e/FV0ln4sPLi3AzFfDFEQMG5bQ1KKW1lMVvneqclup8Ajisgrw15QUuJ43Y0+/VgQvxHt404KP0/VSgZ7XzZGb06dPHgiBoDsAoq+E5NbNwL9lI+vbta+FsKAiaQ0Pt+IIgCLobofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgcofiCICgctezOEtTIlClTGDx4cLPFCJpE+AfpOnTbHp+kfpJM0oBmyxIEQeeiyyq+jGLL/eB78AVBEMxDdxjqXgvclRN/D3AO7lc3CIJgDt1B8Y0xs6vqLZy2xF/czGY0UKYgCDoxXXaoW428Ob5snKSfSXoO36X5+EyefSQ9KGm6pI8lPSrph004hCAI5hPdocfXQ1KvsrhZVcoci/vuvQh4E3gVQNJpwInA3cDJwGxgd+BGSUeZ2fkNlDsIgibRHRTf4PTJcj3uP6MSXwPWM7O3SxGSNsOV3plmdkIm77mSbgHOlHSFmU3PViTpcOBwgJ49e9Z9EEEQdBzdQfENBW4si3sTKO8FZrkiq/QS++GOjobl9CBvwz3FfQtfNJmDmQ1NMtC7d+9wYBIEXYDuoPjGm9l95ZGS+rVS5sWcuPUBAeNaKbdCmyQLgqBT0h0UXz18nBMnvMf3PeCLCuWenW8SBUHQYRRV8eUxHtgReMXMnm+2MEEQzD+6rTlLHVyZwjMkLVieKGn5DpYnCIL5RPT4Emb2uKSB+Arxk5JuBKYAKwF9gO8DizRRxCAIGkQovgxmdqqk0cDPcVu/xYG3gWeAY6qV7927d+zQEQRdgC6r+MxsJL4gUXN6tTIpz53Ane0WMAiCTkvM8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDhC8QVBUDi67Lu6nZEpU6YweHC5+48gCOplfm360aV7fMlV5OVlcZMkjWyOREEQdAU6TPFlfNpmPzMkjZZ0TN7mn0EQBPODZgx1rwXuwreH6g0MAM4BNiS5aWwn6+K+M4IgCHJphuIbY2ZXlX5IGgI8Dxwm6WQze6s9lZtZNWfiQRAUnKbP8ZnZNOARvAe4hqSFJP1G0nOSZkp6V9LNkr5eS32V5vgkbSrpRklvSZol6VVJ10pasyzftpLukfRBav9pSUc24liDIOgcNH1VV5KAtdLPqcDVwN7AvcAQYEXgZ8Ajkr5jZk/U0cYPgOHAR8DFwEup3h2AjYAJKd/hwIXAf4HTU/7tgCGS1jSzX9V5mEEQdCKaofh6SOqF9/BWAo4GvoErmxZc6d0A7GtmBiDpemAMcC7wnbY0JqkHcBnwIbCpmb2eST5V0gIp30qp/uvM7EeZPBdI+hvwC0kXmtmEsvoPJ81N9uzZsy2iBUHQJJox1B0MvIM78XkKOAS4DdgN2D3lOb2k9ADM7GngDmBLScu1sb0dgF7A2WVKr1T37PT1h8CiwCWSemU/wO34udomp/xQM+trZn179OjRRtGCIGgGzejxDQVuxFdePwJeNLP3ACStDszGFzvKeQbYFVgdV5y1snYKqw2R10/hfa3kWaEN7QZB0ElphuIbb2aVlEurHtDqpFRnNROXUr4DgTcq5JnYEImCIGgqTV/cKGMCPjRdH3i6LG2DFL7cxjpfSOGm+IJJJcancGorijkIgm5A081Zyrglhb9Lq70ASNoI2AV40MzaMswFuAdfLf5lWsD4Epl2bgBmAYMlLZaTr6ekRdvYdhAEnZBO1eMzs3sl3QDsCywt6Q7mmrPMBH5eR50fSzoUuAl4RlLJnGU5vHf5F+BWM3tN0k9wc5fnJV0JTE75vo4vvmwATGrXQQZB0HQ6leJL7IebrgwAzsYXQB4ATjazsfVUaGa3SdoSOAE4FFgCeAt4EBibyXeZpBeB44EjgKXw3uILwMnAm62107t37/m2m0QQBI1DGauRoJ307dvXRo0a1WwxgqCQSBptZn1rydvZ5viCIAjmO6H4giAoHKH4giAoHKH4giAoHKH4giAoHLGq20AkTWfumyJBY+iFmxQFjaO7ntPVzKymTUw6ox1fV+aFWpfTg9qQNCrOaWOJcxpD3SAICkgoviAICkcovsYytNkCdEPinDaewp/TWNwIgqBwRI8vCILCEYovCILCEYovCILCEYqvnUhaQNJxksYlB+SvSjpb0uLNlq0rImkdSadK+q+kdyRNl/SkpBPjnDYOST0kvSzJJP292fJ0NKH42s9f8V2cn8N9BN+I7xR9e8lnb9AmDgGOw/2vnAr8Cn8b5jTg4Ty3AEFdnIq/wVFI4s2NdiBpQ1zZ/cPM9szEv4w7J98XuKZJ4nVVbgLONLMPM3EXShoPnIjvoF24HkojkbQZcCzwa3yX88IRPZL28X+4W8pzyuIvAj4G9u9ogbo6ZjaqTOmVuD6FG3WkPN0NSQvi9+fdwD+aLE7TiB5f+9gcd4D+WDbSzGZKejKlB41hlRS+1VQpuj7HAesBe1bL2J2JHl/76I374Z2Vk/Y60EvSIh0sU7cj9VJOAT4npg7qRtLqwGDgVDOb1GRxmkoovvbRA/fFm8fMTJ6gfZwDbAGcYmax7Vf9DAFexhfjCk0MddvHx8DyFdK+kskT1Imk3wNHAUPN7Mxmy9NVkbQ/sD2wlZl91mx5mk30+NrHFHw4u2hO2sr4MPjTDpap2yBpEHAScBlwZHOl6bqk+/MvwF3Am5LWkrQWsFrK0jPFLdUsGTuaUHzt43H8HP5PNlLSV4BNgHCyWyeSBgIDgSuAwyx202gPiwHLATsB4zOfkSl9//T7sGYI1wxiqNs+rgdOwG2i/pOJ/zE+t3d1E2Tq8kg6BRgEXAkcbGazmytRl+cjYK+c+OWAC3DTlkuApztSqGYS21K1E0nn4XNQN+NDifXxNzceAr4bD23bkPQz3ED5FeBk3Fwoy1tmdm+HC9YNkdSCL3acb2ZHNVmcDiV6fO3nWGAScDg+lJgKnIevQIbSazsl28evAcNy0h8AQvEF7SJ6fEEQFI5Y3AiCoHCE4guCoHCE4guCoHCE4guCoHCE4guCoHCE4guCoHCE4guCoHCE4gsagqQByXFNv0xcvxQ3oGmCVUHSjpI+l7ReA+ucJGlkWdxISZPK4i6XZGVxg9I5a2mUPO1F0i2S/tVsORpJKL4CkFFApc8Xkt6X9IykYenhV7Pl7GgkLYTvWnK1mY3LxA/InKvjK5TdJJPn8g4SuVkMBPpJ2qXZgjSKUHzF4lrgAGAA7rjnfqAf8E/gniJtS5TYC3+3utLGnDOBgyukHcrczWbLWRff+64eTsN3U5lcZ/mGY2ZP4Tu5nNxkURpGKL5iMcbMrjKzK83sAjM7BlgDf/C3xRVjp0fSEg2q6qfA0+nBzuNmYANJ5duOLQr8iArOesxsVr37MJrZ52Y2sxNuw3Ul0FdSn2YL0ghC8RUcM/vCzH4JPAjsKGnLbLqkFklXSnpL0ixJEySdIamuLfWTA/YTJf1b0puSPpX0iqQhkpbNadvSvNc+kkZL+gTfBAJJq0q6VNLkJNvbkh6WdFANcqwIbInvqFOJ24F3mLfXtyuwDL5Bal7d88zx1UqlOb5ar0Om/Lop/bWU/ylJ389p70BJj0n6QNJHkiZKulrScmVZS+cpb3urLkfszhKUuARXBDvhShBJq+Ee5Hri/hpexIfGvwP+V9I2ZvZ5G9tZBHcSPhy4Fd8rbnN86LilpD45vaXd8K2+hgAXAtPS/Ny9+E7XFyTZegIbA98hf2eXLFun8LFW8nyG76l4sKRfmNknKf4Q4AngySptNIQ6r8MwXP4/4+f8WOAWSeuUHA3Jt6Mfhu8leQrwCb4rzvdwlwrvlCozs7fS4ky/+XCIHU4ovqBEaRPKdTJxZ5B27jWz0j/+BZL+BBwPHIQrzLYwC1gpo0TAHYY/DFyMK7kbyspsCGxsZs+XIiRtjM+l/cbMzmqjDAAbpHBClXyX4kpjd+AaSasA2wHH1NFmvdRzHaYCO5eGzJJG4MrzCFxhAuwBTMf3jcwqzkpzeROAb7bzWDoFMdQNSkxL4ZLgQ1JgF+CJzMNW4kx8g9Dd29qIOZ+kNhaUtJSkXkDJXCLvwbozq/QSJafj/SVVcvjUGqWh3HtV5B2LuxAoDXcPwntSHeLmsh3X4W/ZeUIzexxXcmtn8nyI7xS+U42r+u8CX5W0WBsOoVMSii8osWQKSwpwOeCrwLPlGc3sPeANfGGkzUjaW9Kj+NDqfXxINTElL51T5MUcGSYDp+Orp2+k+b+zJNXqxL2kFGp54C8DtklDzgHArekcdAT1XoeJOXHvAdl51DPw1eNbgHckDZd0WCuLR6Vz1dkWXtpMKL6gxMYpLPmtnS92fZL2wH2VgA8Xd8aHjjumuLx7MtdFp5mdhPdgjsWHYYcBj0n6Yw2ilOavlqkh7zX4EP0iYC18+NtR1HsdvqhWn5mNx4f8O+FzfavhxzhO0po5ZZcBZphZJTOeLkMovqDEoSm8M4Vv40OjDcszSloaWIn8XkU1DsDt3/qb2RAzu8PM7qNOuzUzm2hm55nZ3kBv4N/Ar2sY/j6TwrVbzeVtfICbtmwHvErHbn0/v64DMMf05i4z+6WZ9cWVYG/gFznZ12LueevShOIrOGme7c8k0w4zewgg+Qu5HdhU0o5lxX6L3zs319HkF/hQac69l+aXTmqj3D0lLZyNSz2R0lxg3pA5ywMp3KLGJv8ADAaO6khfKvPxOpDmVssZk8JlyvKuiPcIH5inRBckVnWLxWbJhAFgCXxVdDf8hr4HN8rNcgLey7lF0gXAS8BWwD54z6qayUgeNwF7Av+SdAWwcJKhrXaB/YGhkobjw/MZQB98uPuomb3QWmEzeyfZ2n0PXxltFTN7mua5X5wf1wH8bZ0PUx2vAkvhc5iGGyxn2SmFN9bZVqciFF+x+L/0mY0ritfwf/Brzezu8sxmNlnSN4FTcafTS6UyZwKn1WHDh5ldlybPj8NtzN7HezS/xVcNa+Up/M2JfsB+wIK4S8ozgLNrrGMIcH2yHRzdhrY7lPlxHRJDgL1xE5dl8PP/BHC0mY0oy7s/MKozn6e2EF7WgsIiaUFcgT5pZvtXy19UJG2CD4F3M7PbmixOQwjFFxSaNG92J7BRjq1ggG9LBfQ0s/7NlqVRhOILgqBwxKpuEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASFIxRfEASF4/8D97S8vNz5GFUAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"0.5\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we mark gridlines?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"0.5\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")\n",
- "ax.grid()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we erase the top and right-hand side margin?\n",
- "- ax.spines ---> gives list of spines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = \"0.5\")\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")\n",
- "# ax.spines\n",
- "ax.spines[\"top\"].set_visible(False)\n",
- "ax.spines[\"right\"].set_visible(False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we capture subplots? \n",
- "- from matplotlib import pyplot as plt\n",
- "- returns a tuple of figure, AxesSubplot\n",
- "- we can use it to write a function that applies all the plot add-on aspects for all the plots in a report that we are writing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(<Figure size 432x288 with 1 Axes>, <AxesSubplot:>)"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ret_val = plt.subplots() \n",
- "ret_val"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's refactor the bar plot code ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "def get_ax(height = 3):\n",
- " # Tuple unpacking\n",
- " _, ax = plt.subplots(figsize = (4, height))\n",
- " ax.spines[\"top\"].set_visible(False)\n",
- " ax.spines[\"right\"].set_visible(False)\n",
- " return ax\n",
- "\n",
- "ax = get_ax(1.5)\n",
- "(s / 1000000).plot.barh(color = \"0.5\", ax = ax)\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## bus.db examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"bus.db\"\n",
- "assert os.path.exists(path)\n",
- "conn = sqlite3.connect(path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Recap on exploring SQL database\n",
- "- pd.read_sql(QUERY, CONNECTION)\n",
- "- QUERY: SELECT * from sqlite_master\n",
- "- QUERY: SELECT * from boarding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>type</th>\n",
- " <th>name</th>\n",
- " <th>tbl_name</th>\n",
- " <th>rootpage</th>\n",
- " <th>sql</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>table</td>\n",
- " <td>boarding</td>\n",
- " <td>boarding</td>\n",
- " <td>2</td>\n",
- " <td>CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>index</td>\n",
- " <td>ix_boarding_index</td>\n",
- " <td>boarding</td>\n",
- " <td>3</td>\n",
- " <td>CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>table</td>\n",
- " <td>routes</td>\n",
- " <td>routes</td>\n",
- " <td>55</td>\n",
- " <td>CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>index</td>\n",
- " <td>ix_routes_index</td>\n",
- " <td>routes</td>\n",
- " <td>57</td>\n",
- " <td>CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " type name tbl_name rootpage \\\n",
- "0 table boarding boarding 2 \n",
- "1 index ix_boarding_index boarding 3 \n",
- "2 table routes routes 55 \n",
- "3 index ix_routes_index routes 57 \n",
- "\n",
- " sql \n",
- "0 CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ... \n",
- "1 CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ... \n",
- "2 CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"... \n",
- "3 CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in... "
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.read_sql(\"\"\"\n",
- "SELECT * FROM sqlite_master\n",
- "\"\"\", conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1163</td>\n",
- " <td>27</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>1.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>1163</td>\n",
- " <td>47</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>0.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2</td>\n",
- " <td>1163</td>\n",
- " <td>75</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>0.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>3</td>\n",
- " <td>1164</td>\n",
- " <td>6</td>\n",
- " <td>43.106465</td>\n",
- " <td>-89.340021</td>\n",
- " <td>10.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4</td>\n",
- " <td>1167</td>\n",
- " <td>3</td>\n",
- " <td>43.077867</td>\n",
- " <td>-89.369993</td>\n",
- " <td>3.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>5</td>\n",
- " <td>1167</td>\n",
- " <td>4</td>\n",
- " <td>43.077867</td>\n",
- " <td>-89.369993</td>\n",
- " <td>2.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>6</td>\n",
- " <td>1167</td>\n",
- " <td>10</td>\n",
- " <td>43.077867</td>\n",
- " <td>-89.369993</td>\n",
- " <td>0.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>7</td>\n",
- " <td>1167</td>\n",
- " <td>38</td>\n",
- " <td>43.077867</td>\n",
- " <td>-89.369993</td>\n",
- " <td>1.36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>8</td>\n",
- " <td>1169</td>\n",
- " <td>3</td>\n",
- " <td>43.089707</td>\n",
- " <td>-89.329817</td>\n",
- " <td>18.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>9</td>\n",
- " <td>1169</td>\n",
- " <td>37</td>\n",
- " <td>43.089707</td>\n",
- " <td>-89.329817</td>\n",
- " <td>1.35</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 0 1163 27 43.073655 -89.385427 1.03\n",
- "1 1 1163 47 43.073655 -89.385427 0.11\n",
- "2 2 1163 75 43.073655 -89.385427 0.34\n",
- "3 3 1164 6 43.106465 -89.340021 10.59\n",
- "4 4 1167 3 43.077867 -89.369993 3.11\n",
- "5 5 1167 4 43.077867 -89.369993 2.23\n",
- "6 6 1167 10 43.077867 -89.369993 0.11\n",
- "7 7 1167 38 43.077867 -89.369993 1.36\n",
- "8 8 1169 3 43.089707 -89.329817 18.90\n",
- "9 9 1169 37 43.089707 -89.329817 1.35"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pd.read_sql(\"\"\"\n",
- "SELECT * FROM boarding LIMIT 10\n",
- "\"\"\", conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are the top routes, and how many people ride them daily?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>181.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3</td>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4</td>\n",
- " <td>2656.99</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5</td>\n",
- " <td>1634.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>6</td>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>10</td>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>11</td>\n",
- " <td>392.43</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>12</td>\n",
- " <td>329.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>13</td>\n",
- " <td>615.20</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>14</td>\n",
- " <td>1373.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>15</td>\n",
- " <td>2179.98</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>16</td>\n",
- " <td>1258.93</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>17</td>\n",
- " <td>294.55</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>18</td>\n",
- " <td>1039.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>19</td>\n",
- " <td>827.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>20</td>\n",
- " <td>545.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>21</td>\n",
- " <td>590.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>22</td>\n",
- " <td>995.21</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>25</td>\n",
- " <td>24.19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>26</td>\n",
- " <td>107.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>27</td>\n",
- " <td>298.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>28</td>\n",
- " <td>1868.31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>29</td>\n",
- " <td>111.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>30</td>\n",
- " <td>687.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>31</td>\n",
- " <td>139.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>32</td>\n",
- " <td>86.47</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>33</td>\n",
- " <td>206.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>34</td>\n",
- " <td>81.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>35</td>\n",
- " <td>140.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>36</td>\n",
- " <td>59.13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>37</td>\n",
- " <td>319.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>38</td>\n",
- " <td>1955.85</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>39</td>\n",
- " <td>140.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>40</td>\n",
- " <td>602.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>44</td>\n",
- " <td>416.90</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>47</td>\n",
- " <td>379.89</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>48</td>\n",
- " <td>30.65</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>49</td>\n",
- " <td>61.83</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>50</td>\n",
- " <td>748.75</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>51</td>\n",
- " <td>137.57</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>52</td>\n",
- " <td>176.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>55</td>\n",
- " <td>129.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>56</td>\n",
- " <td>477.44</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>57</td>\n",
- " <td>464.86</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>58</td>\n",
- " <td>362.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>67</td>\n",
- " <td>729.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>70</td>\n",
- " <td>710.80</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>71</td>\n",
- " <td>497.09</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>72</td>\n",
- " <td>636.95</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>73</td>\n",
- " <td>448.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>75</td>\n",
- " <td>435.35</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>80</td>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>81</td>\n",
- " <td>371.76</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>54</th>\n",
- " <td>82</td>\n",
- " <td>219.48</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>55</th>\n",
- " <td>84</td>\n",
- " <td>114.21</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route daily\n",
- "0 1 181.44\n",
- "1 2 4808.03\n",
- "2 3 2708.55\n",
- "3 4 2656.99\n",
- "4 5 1634.69\n",
- "5 6 4537.02\n",
- "6 10 4425.23\n",
- "7 11 392.43\n",
- "8 12 329.51\n",
- "9 13 615.20\n",
- "10 14 1373.81\n",
- "11 15 2179.98\n",
- "12 16 1258.93\n",
- "13 17 294.55\n",
- "14 18 1039.57\n",
- "15 19 827.53\n",
- "16 20 545.91\n",
- "17 21 590.86\n",
- "18 22 995.21\n",
- "19 25 24.19\n",
- "20 26 107.10\n",
- "21 27 298.07\n",
- "22 28 1868.31\n",
- "23 29 111.28\n",
- "24 30 687.13\n",
- "25 31 139.87\n",
- "26 32 86.47\n",
- "27 33 206.53\n",
- "28 34 81.97\n",
- "29 35 140.42\n",
- "30 36 59.13\n",
- "31 37 319.82\n",
- "32 38 1955.85\n",
- "33 39 140.89\n",
- "34 40 602.92\n",
- "35 44 416.90\n",
- "36 47 379.89\n",
- "37 48 30.65\n",
- "38 49 61.83\n",
- "39 50 748.75\n",
- "40 51 137.57\n",
- "41 52 176.24\n",
- "42 55 129.23\n",
- "43 56 477.44\n",
- "44 57 464.86\n",
- "45 58 362.59\n",
- "46 67 729.54\n",
- "47 70 710.80\n",
- "48 71 497.09\n",
- "49 72 636.95\n",
- "50 73 448.87\n",
- "51 75 435.35\n",
- "52 80 10211.79\n",
- "53 81 371.76\n",
- "54 82 219.48\n",
- "55 84 114.21"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "\"\"\", conn)\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Let's take the daily column out as a Series ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = df[\"daily\"]\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Oops, too much data. Let's filter down to top 5 routes. How can we do that in SQL?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>80</td>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6</td>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>10</td>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>3</td>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route daily\n",
- "0 80 10211.79\n",
- "1 2 4808.03\n",
- "2 6 4537.02\n",
- "3 10 4425.23\n",
- "4 3 2708.55"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "LIMIT 5\n",
- "\"\"\", conn)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = df[\"daily\"]\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Huh, wat exactly is route 0? Where is that coming from?\n",
- "- Oops, it is coming from dataframe row index!\n",
- "- Let's fix that: we can use df.set_index(...)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df = df.set_index(\"Route\")\n",
- "s = df[\"daily\"]\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Wouldn't it be nicer to have an \"other\" bar to repsent other routes?\n",
- "- we have to now get rid of LIMIT clause\n",
- "- we have to deal with other routes using pandas"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Route</th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>80</th>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " daily\n",
- "Route \n",
- "80 10211.79\n",
- "2 4808.03\n",
- "6 4537.02\n",
- "10 4425.23\n",
- "3 2708.55"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "\"\"\", conn)\n",
- "\n",
- "df = df.set_index(\"Route\")\n",
- "s = df[\"daily\"]\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We are back to plotting all route bars ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we slice a pandas dataframe?\n",
- "- Recall that .iloc allows us to do slicing.\n",
- "- For reproducing previous 5-route plot, we just need to take first 5 route details and populate into a series s.\n",
- "- For the \"other\" part, we want all the rows in dataframe after row 5 summed up together.\n",
- "- What should start and end in start:end be for getting the above two slices?\n",
- "- Once we compute \"other\" count, we can add that back to the series s."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Route\n",
- "80 10211.79\n",
- "2 4808.03\n",
- "6 4537.02\n",
- "10 4425.23\n",
- "3 2708.55\n",
- "other 29296.56\n",
- "Name: daily, dtype: float64"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s = df[\"daily\"].iloc[:5]\n",
- "s[\"other\"] = df[\"daily\"].iloc[5:].sum()\n",
- "s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's fix the plot asthetics ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Rides/Day (Thousands)')"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = get_ax()\n",
- "(s / 1000).plot.bar(color = \"k\", ax = ax)\n",
- "ax.set_ylabel(\"Rides/Day (Thousands)\")\n",
- "# Where did the xlabel come from? It comes from \"set_index call on the dataframe\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Scatter plot\n",
- "- copy paste the data from trees.txt\n",
- "- When we have a series to plot:\n",
- " - s.plot.bar()\n",
- " - index => x-axis\n",
- " - values => y-axis\n",
- "- When we have a data frame:\n",
- " - df.plot.scatter(x = column_name, y = column_name)\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " <th>height</th>\n",
- " <th>diameter</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>1.9</td>\n",
- " <td>1.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>1.8</td>\n",
- " <td>1.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2</td>\n",
- " <td>1.8</td>\n",
- " <td>0.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2</td>\n",
- " <td>2.5</td>\n",
- " <td>1.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>2</td>\n",
- " <td>3.0</td>\n",
- " <td>1.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>2</td>\n",
- " <td>2.9</td>\n",
- " <td>1.7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>3</td>\n",
- " <td>3.2</td>\n",
- " <td>2.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>3</td>\n",
- " <td>3.0</td>\n",
- " <td>2.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>3</td>\n",
- " <td>2.4</td>\n",
- " <td>2.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>2</td>\n",
- " <td>3.1</td>\n",
- " <td>2.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>4</td>\n",
- " <td>2.5</td>\n",
- " <td>3.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>4</td>\n",
- " <td>3.9</td>\n",
- " <td>3.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>4</td>\n",
- " <td>4.9</td>\n",
- " <td>2.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>4</td>\n",
- " <td>5.2</td>\n",
- " <td>3.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>4</td>\n",
- " <td>4.8</td>\n",
- " <td>4.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " age height diameter\n",
- "0 1 1.5 0.8\n",
- "1 1 1.9 1.2\n",
- "2 1 1.8 1.4\n",
- "3 2 1.8 0.9\n",
- "4 2 2.5 1.5\n",
- "5 2 3.0 1.8\n",
- "6 2 2.9 1.7\n",
- "7 3 3.2 2.1\n",
- "8 3 3.0 2.0\n",
- "9 3 2.4 2.2\n",
- "10 2 3.1 2.9\n",
- "11 4 2.5 3.1\n",
- "12 4 3.9 3.1\n",
- "13 4 4.9 2.8\n",
- "14 4 5.2 3.5\n",
- "15 4 4.8 4.0"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trees = [\n",
- " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
- " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
- " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
- " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
- " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
- " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
- " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
- " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
- " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
- " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
- " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
- " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
- " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
- " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
- "]\n",
- "df = DataFrame(trees)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We will continue this example in the next lecture ..."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1_template-checkpoint.ipynb
deleted file mode 100644
index 4cb05fd..0000000
--- a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/plotting1_template-checkpoint.ipynb
+++ /dev/null
@@ -1,654 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Today's lecture: pie, bar, and scatter plots\n",
- "- matplotlib is similar to MATLAB\n",
- "- matplotlib integrates with pandas, just like sqlite3 integrates with pandas\n",
- "- Series.plot.PLOT_FN(...)\n",
- "- DataFrame.plot.PLOT_FN(...)\n",
- "- Example PLOT_FNs: pie, scatter, bar, line"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import sqlite3\n",
- "import os\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# matplotlib font size settings\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a pandas Series\n",
- "1. pandas Series can be made using list or dictionary.\n",
- "2. pandas Series has both index (similar to dictionary key) and integer position (similar to list index).\n",
- "3. While creating a brand new list, index and integer position are the same."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s = Series([5000000, 3000000, 2000000])\n",
- "s"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Pie plot\n",
- "- gives you a sense of ratio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What's wrong with the above plot?\n",
- "\n",
- "- The labels are all wrong.\n",
- "- From where are you getting 0, 1, and 2 as labels? ---> let's fix just this\n",
- "- It is difficult to read the actual numbers: we can only see the relative portions, not the absolute amounts\n",
- "- It says \"None\" to the left.\n",
- "- The font is tiny.\n",
- "- No indication of what is being plotted here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s = Series({\"Police\": 5000000, \"Fire\": 3000000, \"Schools\": 2000000})\n",
- "s"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plot\n",
- "- A lot of times bar plot is better\n",
- "- You can see absolute numbers in bar plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we set the x-axis, y-axis labels, and title?\n",
- "- plot_FN(...) returns what is called as AxesSubplot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the type returned by a plot function?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What is this 1e6? Can we make the y-axis values more readable?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Recall that you can easily apply element-wise operation on a Series."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### The x-axis tick labels are difficult to read. Can we rotate them to make it more readable?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "How can we extract the indices from a Series?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to change font inside the figure? \n",
- "- Need to import matplotlib\n",
- "- Using matplotlib.rcParams[\"font.size\"] = ????"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we change the figure size?\n",
- "- figsize argument to plot_FN(...)\n",
- "- argument to figsize should be a tuple with two values: width and height"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we make the bars horizontal?\n",
- "- We have to switch figsize arguments\n",
- "- We have to change y-label to x-label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we change bar color?\n",
- "- color parameter in plot_FN(...)\n",
- " - 3 choices for arguments: \n",
- " - full name of color\n",
- " - single letter representation of the color\n",
- " - grayscale (string value between \"0\" and \"1\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we mark gridlines?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we erase the top and right-hand side margin?\n",
- "- ax.spines ---> gives list of spines"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we capture subplots? \n",
- "- from matplotlib import pyplot as plt\n",
- "- returns a tuple of figure, AxesSubplot\n",
- "- we can use it to write a function that applies all the plot add-on aspects for all the plots in a report that we are writing"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's refactor the bar plot code ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_ax(height = 3):\n",
- " # Step 1: Call plt.subplots, make sure to set figsize\n",
- " # Setp 2 & 3: Set visibility of top and right spines to False\n",
- " # Step 3: return AxesSubplot\n",
- " pass\n",
- "\n",
- "ax = get_ax(1.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## bus.db examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"bus.db\"\n",
- "assert os.path.exists(path)\n",
- "conn = sqlite3.connect(path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Recap on exploring SQL database\n",
- "- pd.read_sql(QUERY, CONNECTION)\n",
- "- QUERY: SELECT * from sqlite_master\n",
- "- QUERY: SELECT * from boarding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.read_sql(\"\"\"\n",
- "SELECT * from\n",
- "sqlite_master\"\"\", conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.read_sql(\"\"\"\n",
- "SELECT * from\n",
- "boarding\"\"\", conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are the top routes, and how many people ride them daily?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT ???\n",
- "FROM ???\n",
- "GROUP BY ???\n",
- "\"\"\", conn)\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Let's take the daily column out as a Series ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Oops, too much data. Let's filter down to top 5 routes. How can we do that in SQL?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT ???\n",
- "FROM ???\n",
- "GROUP BY ???\n",
- "ORDER BY ???\n",
- "LIMIT ???\n",
- "\"\"\", conn)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Huh, what exactly is route 0? Where is that coming from?\n",
- "- Oops, it is coming from dataframe row index!\n",
- "- Let's fix that: we can use df.set_index(...)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Wouldn't it be nice to have an \"other\" bar to represent other routes?\n",
- "- we have to now get rid of LIMIT clause\n",
- "- we have to deal with other routes using pandas"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT ???\n",
- "FROM ???\n",
- "GROUP BY ???\n",
- "ORDER BY ???\n",
- "\"\"\", conn)\n",
- "\n",
- "df = df.set_index(\"Route\")\n",
- "s = df[\"daily\"]\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We are back to plotting all route bars ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we slice a pandas dataframe?\n",
- "- Recall that .iloc allows us to do slicing.\n",
- "- For reproducing previous 5-route plot, we just need to take first 5 route details and populate into a series s.\n",
- "- For the \"other\" part, we want all the rows in dataframe after row 5 summed up together.\n",
- "- What should start and end in start:end be for getting the above two slices?\n",
- "- Once we compute \"other\" count, we can add that back to the series s."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's fix the plot asthetics ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Scatter plot\n",
- "- copy paste the data from trees.txt\n",
- "- When we have a series to plot:\n",
- " - s.plot.bar()\n",
- " - index => x-axis\n",
- " - values => y-axis\n",
- "- When we have a data frame:\n",
- " - df.plot.scatter(x = column_name, y = column_name)\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "trees = [\n",
- " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
- " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
- " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
- " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
- " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
- " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
- " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
- " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
- " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
- " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
- " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
- " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
- " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
- " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
- "]\n",
- "df = DataFrame(trees)\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We will continue this example in the next lecture ... "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots.ipynb b/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots.ipynb
deleted file mode 100644
index 9e7f478..0000000
--- a/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots.ipynb
+++ /dev/null
@@ -1,2642 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import sqlite3\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt\n",
- "matplotlib.rcParams[\"font.size\"] = 16"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 1: Write a function that converts any Fehrenheit temp to Celcius\n",
- "C = (5/9) * (f-32)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "100.0\n",
- "0.0\n",
- "19.444444444444446\n"
- ]
- }
- ],
- "source": [
- "def f_to_c(f):\n",
- " return (5/9) * (f-32)\n",
- "\n",
- "# test it by making several calls\n",
- "print(f_to_c(212))\n",
- "print(f_to_c(32))\n",
- "print(f_to_c(67))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2a: What is the name of the only table inside of iris-flowers.db?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>type</th>\n",
- " <th>name</th>\n",
- " <th>tbl_name</th>\n",
- " <th>rootpage</th>\n",
- " <th>sql</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>table</td>\n",
- " <td>iris</td>\n",
- " <td>iris</td>\n",
- " <td>2</td>\n",
- " <td>CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " type name tbl_name rootpage \\\n",
- "0 table iris iris 2 \n",
- "\n",
- " sql \n",
- "0 CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"... "
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
- "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2b: Save & display all the data from this table to a variable called \"iris_df\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>145</th>\n",
- " <td>6.7</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>146</th>\n",
- " <td>6.3</td>\n",
- " <td>2.5</td>\n",
- " <td>5.0</td>\n",
- " <td>1.9</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>147</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.0</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>148</th>\n",
- " <td>6.2</td>\n",
- " <td>3.4</td>\n",
- " <td>5.4</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>149</th>\n",
- " <td>5.9</td>\n",
- " <td>3.0</td>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>150 rows × 5 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "4 5.0 3.6 1.4 0.2 Iris-setosa\n",
- ".. ... ... ... ... ...\n",
- "145 6.7 3.0 5.2 2.3 Iris-virginica\n",
- "146 6.3 2.5 5.0 1.9 Iris-virginica\n",
- "147 6.5 3.0 5.2 2.0 Iris-virginica\n",
- "148 6.2 3.4 5.4 2.3 Iris-virginica\n",
- "149 5.9 3.0 5.1 1.8 Iris-virginica\n",
- "\n",
- "[150 rows x 5 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_df = pd.read_sql(\"SELECT * FROM iris\", iris_conn)\n",
- "iris_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 3: Scatter plot to visualize relationship between `pet-width` and `pet-length`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# complete this code to make 3 plots in one\n",
- "\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "\n",
- "# getting unique class column values\n",
- "varietes = list(set(iris_df[\"class\"]))\n",
- "\n",
- "plot_area = None\n",
- "for i in range(len(varietes)):\n",
- " variety = varietes[i]\n",
- " \n",
- " # make a df just of just the data for this variety\n",
- " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
- " \n",
- " #make a scatter plot for this variety\n",
- " plot_area = variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
- " label = variety, color = colors[i],\n",
- " marker = markers[i], \\\n",
- " ax = plot_area)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Let's focus on \"Iris-virginica\" data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-length</th>\n",
- " <th>sep-width</th>\n",
- " <th>pet-length</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>100</th>\n",
- " <td>6.3</td>\n",
- " <td>3.3</td>\n",
- " <td>6.0</td>\n",
- " <td>2.5</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>101</th>\n",
- " <td>5.8</td>\n",
- " <td>2.7</td>\n",
- " <td>5.1</td>\n",
- " <td>1.9</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>102</th>\n",
- " <td>7.1</td>\n",
- " <td>3.0</td>\n",
- " <td>5.9</td>\n",
- " <td>2.1</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>103</th>\n",
- " <td>6.3</td>\n",
- " <td>2.9</td>\n",
- " <td>5.6</td>\n",
- " <td>1.8</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>104</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.8</td>\n",
- " <td>2.2</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-length sep-width pet-length pet-width class\n",
- "100 6.3 3.3 6.0 2.5 Iris-virginica\n",
- "101 5.8 2.7 5.1 1.9 Iris-virginica\n",
- "102 7.1 3.0 5.9 2.1 Iris-virginica\n",
- "103 6.3 2.9 5.6 1.8 Iris-virginica\n",
- "104 6.5 3.0 5.8 2.2 Iris-virginica"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "iris_virginica = iris_df[iris_df[\"class\"] == \"Iris-virginica\"]\n",
- "assert(len(iris_virginica) == 50)\n",
- "iris_virginica.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create scatter plot to visualize relationship between `pet-width` and `pet-length`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's learn about *xlim* and *ylim*\n",
- "- Allows us to set x-axis and y-axis limits\n",
- "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
- "- You need to be careful about setting the UPPER-BOUND"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = 0, ylim = 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "\n",
- "# What is wrong with this plot?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the maximum `pet-length`?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "6.9"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# How do we extract `pet-length` column Series?\n",
- "iris_virginica[\"pet-length\"].max()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For every set method, there is a corresponding get method. Try `ax.get_ylim()`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0.0, 6.0)"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ax.get_ylim()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's include assert statements to make sure we don't crop the plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ran into AssertionError while checking axes limits\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "try:\n",
- " assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]\n",
- "except AssertionError:\n",
- " print(\"Ran into AssertionError while checking axes limits\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now let's try all 4 assert statements\n",
- "\n",
- "```\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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Slkl6QtIOSY9KukLSzJR2OE7NkFZk/wFcImmfWJlJmgN8hNBXK8fHgT7gIuAtwPXA+4E7ErJIHKcuGM2e0YuAh4H7o7J/BvYDnmdwVeFSnGRmL8Q+3y3pReDfgYWEaKXj1BWpvIeZbQQ6gSuAZmAtQajXAkeZ2ZYRzn8hofiX0fseaWxxnFphNMt0vyzpGuBnBGE8DTxsZi+N0oZjo/dfj/J8x5nQjGYw+hLgKcK42C3R+1OSPjWKa+1BaGLeaWZdJeosltQlqeuFF5IcoeNMbNIORl8KLAG+Q9hw4qDo/bvApZKWpLjWDGA50AucXaqemS01s04z69x1113TmOs4E4K0zcVzCYPR58fK1gAroj3LFhNEWBZJU4AfAfsAx5pZcVa/49QNaZuLr6b0YPRPouNlkdQMLAMOB95qZr47p1PXpBXZ/cBhJY4dxmBYP5FoLOxm4HjgFDP7Rcrvd5yaI21z8UPADyT1At8D/gDMJSQLvxc4JT6oHK1oFec64DTgs8A2SUfGjq33ZqNTj8is8rVvJBVEk3SSisrNzIaIWNI6YK8Sl7/UzJaU+/7Ozk7r6koMQjpO1ZH0gJl1Fpen9WSXMYYVqcysY7TnOk6tknZm9JKM7HCcusWTch0nY1xkjpMxLjLHyRgXmeNkjIvMcTLGReY4GeMic5yMcZE5Tsa4yBwnY1xkjpMxLjLHyRgXmeNkjIvMcTLGReY4GeMic5yMcZE5Tsa4yBwnY1xkjpMxLjLHyRgXmeNkjIvMcTLGReY4GeMic5yMcZE5Tsa4yBwnY1xkjpMxLjLHyRgXmeNkjIvMcTLGReY4GeMic5yMcZE5TsbkLjJJe0q6VdIWSS9J+r6k9rztcJy8yFVkkqYBK4D9gPcAfwPsC6yUND1PWxwnL9LuGT1WzgX2AV5nZmsBJK0Gfgf8HfDFnO1xnMzJu7l4MvCLgsAAzOxx4F7glJxtcZxcyFtkBwAPJ5SvAfbP2RbHyYW8RbYLsCmh/EWgLWdbHCcX8u6TAVhCmUpVlrQYWBx97JaU5AnrlTnAhmobkSO1fr97JRXmLbJNBG9WTBvJHg4zWwosBZDUZWad2Zk3sfD7rQ/ybi6uIfTLitkfeCRnWxwnF/IW2Y+AIyXtUyiQ1AEsiI45Tt2Rt8huANYByyWdIulkYDnwFPCVCs5fmqFtExG/3zpAZklxiAy/MKRQXQ28iRDw+Bnw/81sXa6GOE5O5C4yx5lsTIgs/LEkDUuaIulzkp6VtEPSfZKOydrm0SLpHZKWSXoisvdRSVdImlnBuVbidUgOpqdG0sIS9m6u4Nyaeq7lqMY42RBiScPdhKRhAy4nJA0fZGbbRrjE14C3AecDjwHnAbdLOsrMVmVm+Oj5OPAkcBGwHngDsAQ4TtKfmln/COd/g+H919+Os43jzYeAX8Y+91ZwTq0919KYWVVfwIeBPmB+rGxvwoP46AjnHkwQ5dmxsibgUeBH1b63EjbvmlB2ZnQfi0Y414DLq30PKe51YWTzCSnPq7nnWu41EZqLY0kaPhnoAb4TO7cXuAU4UVLr+Js7NszshYTiwn/5PfK0ZQJTc8+1HBNBZGNJGj4AeNzMtiec2wLMH7t5uXBs9P7rCuq+X1K3pO2SVkh6Y5aGjRM3S+qTtFHStyrob9fLcwUmQJ+MsSUNlzu3cHxCI2kP4DLgTjPrGqH6TcBtwDOEPLnzgRWS3mRmd2Vq6OjYAnwBuBt4idD/vAi4T9IbzOz5EufV/HONMxFEBimThovqjPbcqiNpBmEwvhc4e6T6ZvY3sY/3SFpOaAVcDhydiZFjwMx+BfwqVnS3pJ8D/0MIhnyqxKk1/VyLmQjNxdRJwzFeLHNu4fiERNIUQirZPsCJZrY+7TXM7GXgx8Bh42xeZpjZg4RoaDmba/a5JjERRDaWpOE1wN7RMEDxuTuBtcNPqT6SmoFlwOHAW83sobFcjuT/+hOZkWyuyedaiokgsrEkDf8IaAZOi53bBLwT+KmZdY+7tWNEUgNwM3A8cIqZ/WIM13oVYSzp/nEyL3MkdQKvpbzNNfdcy1LtMQRgOuE/00OEkP3JwP8SBiBnxOrtRei7XFJ0/i2EZuU5hD/cW4FXgEOrfW8l7vd6Bgfcjyx6zSt1r4RB7BuAMwjjT++Jfmc7gTdW+75K3OvN0X3+JbAI+BhhUuaTwJx6eq5lfw/VNiD6hbYTmk8vAS8DPwQ6iup0RH+cS4rKpxJWuXouegj3AwurfU9l7nVddB9JryWl7hU4iTB2uIEwhrSR8B//8GrfU5l7/SSwmhBl7CHMtlgKvKbenmu5lycIO07GTIQ+mePUNS4yx8kYF5njZIyLzHEyxkXmOBnjInOcjHGR1SDRtP4lUfZIlt9hkhZWUNckLYl9frukj5a55gnjauwEx0VWmywEPk22z+9B4KjoPS1vB4aJbLIyUaa6OBMMM3sJGHVepTOIe7KciJp3JulASSujmc3PSros3uyTNEfS9ZKejmZA/ybadGPgOgQvBtBTWAGqzPfuKqlf0rtjZSdF590UK5smaaekv48+D2suSmqUdHlk93ZJd0k6oOj7vkHIq9wjtjrVuiKzpkm6VtIGSS9IuknSrAp/lTWHe7L8+SHwdeAK4ETgYqAfWBJl1d9LyNtbAjwe1bleUquZ/QvwVWAe8LeEiZp95b7MzF5Q2AlnEWFmNdHPO4DjYlXfSMh8X1nmcksIM5u/CPwU6GT4TInPALsS5oudHJUVZ81fQ5jhfQbwOuCq6D7eU+5eapZqJ09OlhfhD9SATxSV30BIip5FENwrwL4JdTYATUXXaqrwu68hrJlR+LyKsCyAEbYWBvgn4NlYnYXR8YXR5zZgK/DlomtfyPBk5m8A6xPsKFzz34vKr43uW9V+Tlm8vLmYP98t+nwLMAN4PfAWQrb545KaCi/gdmA2IywsFDXnmmKvwnT9lUCHpL0lzQYOAr5JmKG8KKqziPJe7EDCtKQk+9Py46LPDwGtwNxRXGvC4yLLnz+U+LwHsBtwDGFaSPz1vajO7BGu/fui8wrNr7sITdLjCN5kE2HO3krCoqqvAg6lvMheM4L9aShePqDQnJwyimtNeLxPlj9zCRNS458BnibMEXuesOBrEo+OcO2TCB6hwOMAZrZZ0iqCt9oC3GVmJmkFoam2EGikvMiejdm7JsF+pwQusvw5ndD/KfAuQl/nYeAnwAeBJ630cmkw+J9/KqE/B4CVXytkJfBXBJFdFyubQ1g56imLLTCbwGpgW2T/iiL7k+ybWuZakwoXWf6cG4Xsf0mIHJ5DCBpslnQ1YR2Le6KfHyX0g/YjLDFQWFG5sMDQxyT9F9BnI6/ZuIIw/X93Io9lIfK4hjC9/8ZyJ8fs+wdJLxOii4cRopzFPALsIun9QBfwygj/AOqbakdeJsuLwYjg6wl/5DsIU+s/AzTE6rUR9m97nLB+x/PAPYQ93Ap1Ggne6HlCX8sq+P6ZhH7ac0Xl10R2nVVUvpBYdDH2vZdHdu8g9PX2Z3h0cTrwbULfz4B1Rdc8oei7zorKO6r9nLJ4+fIDOREbRG62sK67M0nw6KLjZIyLzHEyxpuLjpMx7skcJ2NcZI6TMS4yx8kYF5njZIyLzHEyxkXmOBnzfy3XjUOR8SqDAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 216x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 7), ylim = (0, 7),\n",
- " figsize = (3, 3))\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Close the database connection.\n",
- "iris_conn.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting Applications\n",
- "\n",
- "**Learning Objectives**\n",
- "\n",
- "- Make a line plot on a series or on a DataFrame\n",
- "- Apply features of line plots and bar plots to visualize results of data investigations\n",
- "- Clean Series data by dropping NaN values and by converting to int\n",
- "- Make a stacked bar plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Line plots\n",
- "- `SERIES.plot.line()` each value in the Series becomes y-value and each index becomes x-value\n",
- "- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot\n",
- "- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!\n",
- "\n",
- "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plotting line from a Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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P7+1KjYqx3PXfxdz3fiL71MQuKDsPHuWBD5fQvHZlRv4u+prJBUtBIJLPwXTfDFU1K8Xy6g0df9NMzivt6vua2D18WXNmrNzDpaPnMnXJdjWxO4mMrBzumZhIZrZj/JB4yscW7/mdcKYgEMkjJ8cxYtJS9h4+xhuD46kRQjNUxZQuxf2XNOOL4RfSuGZFRkxaxu3//oWdB9XELpC/fLmapdsOMuradjSJ0mZywVIQiOQx7rsNzFmbzNNXt6Jj3MmbyXmlWZ3KfHL3BTxzdSsW/prC5WPm8d+FWzRfch7Tl+3k3z9u5vcXNubKttHbTC5YCgIRv/kb9jF65jr6tq/LTV0ael3OSZUuZdx+YWNmjOhGhwbVePrTFdzwj4X8mnzk1HeOcOv3HObxyUkkNKzO41e09LqcsKAgEMHXTG74B0toUqsSL4bRDFUNalTgv7/vzKgB7Vi96xBXvPo9b87dSFZ2jteleSLteBbDJiZSIbY0rw+KJ0aN/IKiV0miXmZ2Dve+n8jRzGzeHBJPxUI2k/OKmXHduQ2Y9VB3ujevxV+/WkP/cfNZtTO6mtg553h8ynJ+9TeTO7NqOa9LChsKAol6L365hsVbDvC3Ae1oWvv0m8l5pU6Vcrx1UyfGDY5nd+ox+r7+A3+fsTZqmti9t2ALny3bycOXt+CCpmomVxAKAolqXyTt4l/zN3HrBY3o076u1+WcNjPjyrZnMXNEd/p2qMvY2Ru46rUfWLwlspvYJW49wAtfrOKSlrUZ1v1sr8sJOwoCiVob9h7h0U+W0TGuGk9eGVkzVFWvGMvo6zrw79vO5WhGNte++SPPfraStOOR18QuJS2D+yYmUqdKOUZfp2ZyhaEgkKiUnpHFPRMXUzamNG8Miie2TGT+KlzcojbfjOjGTV0a8u78zfR6ZR7fr0/2uqwik53jeODDJexLy2D84E5UrRDjdUlhKTL/94uchHOOJ6csZ/3eI7x6QwfqllAzOa9UKluG5/q14aOh5xNbuhQ3/fNnHvl4Ganp4d/E7rVv1/P9+n0827c1betX9bqcsKUgkKgz4aetfLp0Jw9e0pyLmpV8MzmvdG5cgy8fuIhhF5/NlCU7uHTMXL5eEb5N7L5bu5fXZq9nQHx9bji3gdflhDUFgUSVZdsO8vxnq7i4RS3u7xl9M1SViynNY71bMu3ertSqVJa7JyzmnomL2Xv4mNelFciOg0d5cNJSWtSpzAv924TNdR+hSkEgUeNAWgb3TEykVuWyjInyk4pt6lVl2n1deaRXC2at3stlo+cxeXF4NLE7npXNPRMTyc52jB/SSc3kioCCQKJCTo5jxEe+GarGDY6negg1k/NKTOlS3NujKV8Ov4imtSvx8MfLuOXdX9h+IN3r0k5q5BerWbbtIC8NbEfjmhW9LiciKAgkKrw+ZwPfrU3m6T6taN+gmtflhJSmtSvx8dDzebZvaxZtTqHXmHm8t2BzSDaxm7Z0B+8t2MKdFzWmdxs1kysqCgKJeN+vT2bMrHX071CXIedphqpASpUybrmgEd882I34htV5ZtpKrntrARtDqInduj2HeXzycs5tVJ1He6uZXFFSEEhE881QtZRmtSvxlzBqJueVBjUq8N7tnXl5YHvW7z3CFa9+zxtzNpDpcRO7I8ezuHvCYiqWLaNmcsVAr6ZErIwsXzO545nZjB/SiQqx4dVMzitmxrWd6jPzoW5c0rI2L32zlv5vzGfFjlRP6nHO8djkJDbvS2PsjR2pU0XN5IqagkAi1l++XM2SrQcZdW17ztYMVQVWu3I5xg/pxPjB8ew5dJx+b8xn1NdrOJZZsk3s/v3jZr5I2sUferXg/LPPKNHHjhYKAolIuTNU3da1EVe100nF03FF27OY9VA3rulYj3HfbeTK175n0eaUEnnsxVsOMPKL1Vx6Tm3u7qZmcsVFQSARZ8Ne3wxV8XHVeOKKyGom55VqFWJ5aWB73ru9M8czcxj41gL+NG0FR4qxid3+I8e57/1EzqpWjr8PjO7rPoqbgkAiStrxLO6ekEi5mNK8MThym8l5pVvzWswY0Y1bzm/Eewu30GvMPOauK/omdr5mckvZr2ZyJUK/JRIxnHM84Z+hauyNHTmramQ3k/NKxbJl+HPf1nw89HzKxZTiln/9zEMfLeVgekaRPcars9bxw4Z9PN+vNW3qqZlccVMQSMT478ItTF+2k4cua05XzVBV7BIa1eCL4RdxX4+mTFu6k0tHz+XL5btOe7tz1u7ltdkbGNipPtefq+s+SoKCQCLCkq0HeP7zVfRsWZt7Lo6+ZnJeKRdTmj/0asH0+7pSp0o57pmYyN3/XczeQ4VrYrf9QDojJi3lnLOq8Hz/NkVcrZyIgkDCXkpaBvf+/xmq2uukogda163KtHu78ljvlsxeu5dLR8/lo0XbCtTE7jfN5AbHUy5GzeRKioJAwlp2juPBSUvZdySDcYPjqVZBzeS8UqZ0KYZdfDZfPXARLc+swqOfJHHzv35mW0pwTeye/3wVSdtTefm69jRSM7kSdcogMLNrzWyymW0xs6NmttbMXjSzyvnGVTezd8xsn5mlmdksM2sbYHvlzOwlM9vl394CM+tWlE9KosfY2euZty6ZP/VtRbv61bwuR4Cza1Xiw7u68Hy/1iRuOcDlY+bx7vxNZJ+kid3UJduZsHArQ7s1oVfrM0uwWoHg9gj+AGQDTwK9gfHAMGCmmZUCMF8Dl+n+9fcDA4AYYI6Z1c+3vX8CdwLPAFcDu4BvzKzD6T4ZiS5z1yXz6rfruaZjPQZ11knFUFKqlHHT+Y2Y8VB3OjeuwbOfrWLgmz+yYe/h/xm7dvdhnpiynM6Na/BIrxYeVCt2qmN4ZlbLOZecb9nNwH+AS5xzs82sH/Ap0NM5N8c/piqwCZjgnBvuX9YeWArc7px717+sDLASWOuc6xtM0QkJCW7RokVBP0mJPDsOHuXq176nduVyfHpvV01OEsKcc0xdsoPnPl9F+vFshl/SlKHdzyamdCkOH8uk3+vzOXQsiy+HX0ht9REqNma22DmXEGjdKfcI8oeA3y/+23r+277AztwQ8N8vFfgM6Jfnfn2BTGBSnnFZwIdALzMre6p6RHJPKmZmO8YPiVcIhDgz45r4+swc0Z3LWtfh5Rnr6DP2B5ZvT+WxyUlsSUnn9UEdFQIeKuzJ4u7+29X+29bAigDjVgJxZlYpz7hNzrn8Z49WArGAvvcnp5Q7Q9Woa9vRRM3kwkatymV5Y1A8b93Uif1pGfR5/Qe+XL6bR3q1oEsTNZPzUoH78ppZPeA5YJZzLvf4TA1gc4DhuZ2pqgNH/OMOnGRcjZM87l3AXQBxcToeHK1yZ6j6/YWNubKtmsmFo16tz6RL4zN4acYaDGNotyZelxT1ChQE/k/204As4La8q4BAJxvyf6E72HH/wzn3NvA2+M4RBFOvRJb1/hmqEhpW5/ErNENVOKtaIYYX+v/PlwrFI0EfGjKzcvi+GdQE6OWc255ndQqBP81X998eCHJcyfS2lbDzfzNU+ZrJaYYqkaIT1G+TmcUAk4HOwJXOueX5hqzEd/w/v1bAVufckTzjGptZhQDjMoANwRYu0cM5x+OTk9i0L43XNEOVSJEL5oKyUsBE4BKgn3NuYYBh04F6ZtY9z/2qAH386/KOiwEG5hlXBrgemOGcO16YJyGR7T8/bubzpF08fHkLLjhbzeREilow5wjewPfGPRJIM7MuedZt9x8img4sACaY2SP4DgU9ge/Y/6jcwc65pWY2CXjFv5exCd/FaY2BwUXwfCTCJG49wMgvV3NJy9oM664ZqkSKQzCHhq7w3z6F780+788dAM65HHxXCc8ExgFT8V2N3MM5ty3f9m4D3gVeAL4AGgC9nXOJp/VMJOLsP3KceycmcmbVcoy+TjNUiRSXU+4ROOcaBbMh51wKcLv/52TjjgIP+X9EAsptJrc/LYMpwy7QDFUixUhfvZCQ9Oq36/l+/T6e7asZqkSKm4JAQs6ctXsZO3s9A+Lrc8O5DbwuRyTiKQgkpOTOUNWiTmVe6N8GX2NbESlOCgIJGb+ZoWpIJzWTEykhBe41JFJccmeoenNIJxprhiqREqM9AgkJny7ZwYSFW7mrWxN6t9EMVSIlSUEgnlu3xz9DVaMaPKoZqkRKnIJAPPV/zeTK8PqgjpRRMzmREqffOvGMc47HPkli8740xt6oGapEvKIgEM+8O38zXyzfxSO9WnL+2ZqhSsQrCgLxxOItKfzly9Vcek4d7u6uGapEvKQgkBK378hx7p24hLrVyvP369rrojERj+k6AilR2TmOBz5cQkq6v5lceTWTE/Ga9gikRL0yax3zN+zn+X5qJicSKhQEUmJmr9nD2NkbGNipPtefG+d1OSLipyCQErEtJZ0Rk5ZxzllVeL5/G6/LEZE8FARS7I5l+prJ5TjHm0PiKRejZnIioUQni6XYPff5KpbvSOXtmzrR8Aw1kxMJNdojkGI1JXE77/+0laHdm3B5azWTEwlFCgIpNmt2H+LJqcs5r3ENHrlczeREQpWCQIrF4WOZDJuQSOVyMYxVMzmRkKZzBFLknHM8+kkSW1PSef+O86hdWc3kREKZPqZJkfvnD5v4asVuHu3VgvOaqJmcSKhTEEiRWrQ5hb9+tYbLW9Xhrm5qJicSDhQEUmT2HTnOve8nUq96eV4aqGZyIuFC5wikSGRl53D/+0s4mJ7J1Hs6q5mcSBhREEiRGD1zHQt+3c9L17ajVd0qXpcjIgWgQ0Ny2mat2sO47zZyw7kNGJjQwOtyRKSAFARyWrbuT+ehj5bSum4V/ty3tdfliEghKAik0I5lZnPP+4sBGD+4k5rJiYQpnSOQQnv2s5Ws2HGId25OIO6MCl6XIyKFpD0CKZRPFm/ng5+3Mezis7m0VR2vyxGR06AgkAJbvesQT01dzvlNzuDhy5p7XY6InCYFgRTIoWOZDJuwmKrlY3jtRjWTE4kEOkcgQXPO8ejHSWw7cJQP7uxCrcplvS5JRIqAPs5J0N75fhNfr9zN471b0rlxDa/LEZEiElQQmFl9MxtrZgvMLN3MnJk1CjCuupm9Y2b7zCzNzGaZWdsA48qZ2UtmtsvMjvq3260Ino8Uk583pfDXr9fQu/WZ3HFRY6/LEZEiFOweQVPgOuAA8H2gAebrMDYd6A3cDwwAYoA5ZlY/3/B/AncCzwBXA7uAb8ysQwHrlxKw9/Ax7ns/kbgaFXhpYDs1kxOJMMGeI5jnnKsDYGZ3AJcHGNMXuBDo6Zyb4x+7ANgEPAoM9y9rDwwCbnfOvetfNhdYCTzn346EiNxmcoeOZfLe7ztTuZyayYlEmqD2CJxzOUEM6wvszA0B//1Sgc+AfvnGZQKT8ozLAj4EepmZzkCGkJdnrOOnTSmM7N+WlmeqmZxIJCrKk8WtgRUBlq8E4sysUp5xm5xz6QHGxeI7DCUhYOaqPbw5dyM3do5jQKf8R/dEJFIU5ddHawCbAyxP8d9WB474xx04ybiAX0cxs7uAuwDi4uJOp045heNZ2bwxZyPj5mygTb0q/KlPK69LEpFiVJRBYIA7wfLCjPsN59zbwNsACQkJge4vRWDJ1gM8NjmJdXuO0L9DXf7Up7WayYlEuKIMghQCf5qv7r89kGdcoI/01fOslxKWnpHF32es41/zN3FmlXL869YEerZUDyGRaFCUQbCSwN8magVsdc4dyTPud2ZWId95glZABrChCGuSIMzfsI/HpySxLeUog8+L4/ErWurbQSJRpChPFk8H6plZ99wFZlYF6ONfl3dcDDAwz7gywPXADOfc8SKsSU4i9Wgmj09OYvA7P1HajA/v6sLI37VVCIhEmaD3CMzsWv8fO/lvrzCzZCDZOTcX3xv8AmCCmT2C71DQE/iO/Y/K3Y5zbqmZTQJeMbMYfNcZDAMaA4NP8/lIkGas3M0fP13BviPHGdqtCQ9e2pzysToXIBKNCnJo6ON8fx/nv50LXOycyzGzq4GX/evK4QuGHs65bfnuexswEngBqAYsA3o75xILVr4U1L4jx/nz9JV8nrSLlmdW5p1bEmhXv5rXZYmIh8y58PsCTkJCglu0aJHXZYQV5xyfLt3Bs5+tIv14Nvf3bMrQ7mcTW0Z9B0WigZktds4lBFqnNtRRYOfBozw1dTlz1ibTMa4aowa0o1mdyl6XJSIhQkEQwXJyHBN/3spfv1xNjoNnrm7FLRc0onQpNY0Tkf+jIIhQvyYf4fHJy/l5cwoXNq3Ji9e0pUENTTAvIv9LQRBhsrJzeOeHTYyZuY7YMqUYNaAdAxPqq3W0iJyQgiCCrNp5iEcnL2PFjkNc3qoOz/dvQ50q5bwuS0RCnIIgAhzPyub12RsY/91GqlWI4Y1B8VzZ9kztBYhIUBQEYW7xlhQe/SSJjclpXBNfj6evakX1irFelyUiYURBEKbSjmfx0jdr+c+CzZxVpRzv3nYuPVrU9rosEQlDCoIw9P36ZJ6YspztB45y8/kNebR3SyqV1T+liBSO3j3CSGp6Ji98sYqPF2+nSc2KfDT0fDo3DjiPj4hI0BQEYeLrFbt5etoKUtIyGHbx2TxwSTNNGCMiRUJBEOL2Hj7Gn6ev5Mvlu2l1VhXevfVc2tSr6nVZIhJBFAQhyjnH5MQdPP/5Ko5mZPNIrxbc1a0JMaXVJE5EipaCIARtP5DOk1NXMG9dMp0aVudvA9rRtHYlr8sSkQilIAghOTmO/y7cwt++XgPAs31bc1OXhpRSkzgRKUYKghCxMfkIj32SxKItB7ioWU3+8js1iRORkqEg8Fhmdg5vz/uVV79dT/mY0rw8sD0D4uupPYSIlBgFgYdW7Ejl0U+SWLXrEFe0OZNn+7WmdmU1iRORkqUg8MCxzGxe/XY9b8/7leoVYhk/OJ4r2p7ldVkiEqUUBCXsl80pPPZJEr/uS2Ngp/r88apWVK0Q43VZIhLFFAQl5MjxLEZ9vYb3FmyhXrXyvHd7Z7o1r+V1WSIiCoKSMHddMk9OWc7O1KPcekEjHunVgopqEiciIULvRsXoYHoGz32+iimJOzi7VkU+Hno+CY3UJE5EQouCoBg45/hqxW6embaCA+mZ3NejKff1bKomcSISkhQERWzvoWM8PW0F36zcQ5t6VfjP7Z1pXVdN4kQkdCkIiohzjo8Xb+eFz1dxLCuHx3q35M6LGlNGTeJEJMQpCIrAtpR0npiynB827KNzoxr8dUBbmtRSkzgRCQ8KgtOQneN4b8FmRn29llIGz/drzeDz1CRORMKLgqCQNuw9zKOfJJG49SDdm9fiL9e0pV618l6XJSJSYAqCAsrMzuHN7zYydvYGKpQtzZjr29O/g5rEiUj4UhAUwPLtqTzyyTLW7D7MVe3O4tm+ralZqazXZYmInBYFQRCOZWYzZtY6/jHvV2pWKstbN3WiV+szvS5LRKRIKAhO4adf9/P4lOVs2pfG9QkNePKqc6haXk3iRCRyKAhO4PCxTP729RomLNxKgxrlmXjHeXRtWtPrskREipyCIIA5a/by1NTl7Dp0jNu7NuYPvZpTIVYvlYhEJr275ZGSlsHzn69i6pIdNKtdicnDLiA+rrrXZYmIFCsFAb72EJ8n7eLP01eSejST4Zc0494eZ1O2jJrEiUjk8yQIzKwBMAa4DDBgFvCgc25rSdey59Axnpq6glmr99CuflUm3HEe55xVpaTLEBHxTIkHgZlVAGYDx4FbAAe8AMwxs3bOubSSqMM5x6RftjHyy9VkZOXw5JUtub2rmsSJSPTxYo/gTqAJ0MI5twHAzJKA9cBQYHRxF7B1fzqPT0nix437Oa9xDf42oB2NalYs7ocVEQlJXgRBX2BhbggAOOc2mdl8oB/FGATZOY5352/i5RlrKVOqFCN/14Ybz41TkzgRiWpeBEFrYFqA5SuBgcX1oKnpmdzy7s8s3XaQni1rM/J3bTirqprEiYh4EQQ1gAMBlqcAJ/yuppndBdwFEBcXV+AHrVK+DA3PqMBtXRvRt31dNYkTEfHz6uujLsCyk74zO+feBt4GSEhICHT/kzIzXr2hY0HvJiIS8bz4iswBfHsF+VUn8J6CiIgUIy+CYCW+8wT5tQJWlXAtIiJRz4sgmA50MbMmuQvMrBHQ1b9ORERKkBdB8A9gMzDNzPqZWV983yLaBrzlQT0iIlGtxIPAf+VwT2Ad8F9gIrAJ6OmcO1LS9YiIRDtPvjXk7yk0wIvHFhGR31JjHRGRKKcgEBGJcuZcga/N8pyZJQNbCnn3msC+Iiwn0un1Khi9XgWj16tgTuf1auicqxVoRVgGwekws0XOuQSv6wgXer0KRq9Xwej1Kpjier10aEhEJMopCEREolw0BsHbXhcQZvR6FYxer4LR61UwxfJ6Rd05AhER+a1o3CMQEZE8FAQiIlEuKoLAzBqY2Sdmlmpmh8xsipkVfJqzKGFm9c1srJktMLN0M3P+DrGSj5lda2aTzWyLmR01s7Vm9qKZVfa6tlBkZr3MbLaZ7Taz42a23cw+MrNWXtcWLszsa//v5AtFtc2IDwIzqwDMBloCtwA3Ac2AOWZW0cvaQlhT4Dp8EwV973Etoe4PQDbwJNAbGA8MA2aaWcT/fhVCDWAxcB9wOfAEvvlJFppZQy8LCwdmdiPQvqi369VUlSXpTqAJ0MI5twHAzJKA9cBQYLSHtYWqec65OgBmdge+X1gJrI9zLjnP3+eaWQrwH+BifB9CxM859wHwQd5lZvYzsAa4Fvi7F3WFAzOrBowBRgDvF+W2o+ETS19gYW4IADjnNgHzgX6eVRXCnHM5XtcQLvKFQK5f/Lf1SrKWMLbff5vpaRWhbxSw0h+mRSoagqA1sCLA8pX4pscUKWrd/berPa0ihJlZaTOLNbNm+Cak2g186HFZIcvMLgRuBu4pju1Hw6GhGviOdeeXAlQv4VokwplZPeA5YJZzbpHX9YSwn4BO/j9vwDcx1V4P6wlZZhaDLyxfds6tLY7HiIY9AoBAV81ZiVchEc3MKuGbdjULuM3jckLdTUAXYBBwCN/J9UaeVhS6HgPKAyOL6wGiYY/gAL69gvyqE3hPQaTAzKwcMB3fFxO6O+e2e1xSSHPO5R42+8nMvsI3j/njwN2eFRWC/F9zfwq4AyhrZmXzrC7rP4F82DmXfTqPEw17BCvxnSfIrxWwqoRrkQjk33WfDHQGrnTOLfe4pLDinDuI7/BQU49LCUVNgHLABHwfXHN/wPfV5QNA29N9kGgIgulAFzNrkrvAvwva1b9OpND81wpMBC4B+jnnFnpcUtgxszr4rvPZ6HUtIWgp0CPAD/jCoQe+ED0tEd90zn/R2DLgKPBHfOcLngcqA+2cc0c8LC9kmdm1/j9egm93/R4gGUh2zs31rLAQY2bj8b0+I4HP863erkNEv2VmU4FEIAnfuYHm+L4XfybQ2Tm3zsPywoaZOWCkc+6PRbK9SA8C+P/H2cYAl+E7Sfwt8KBzbrOXdYUy/3+0QOY65y4uyVpCmZltBk50Reyzzrk/l1w1oc/MHsN31frZQCywDfgOeFG/j8FTEIiISJGKhnMEIiJyEgoCEZEopyAQEYlyCgIRkSinIBARiXIKAhGRKKcgEBGJcgoCEZEo9/8APrtOUsjddAUAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# when you make a series from a list, the default indices 0, 1, 2, ...\n",
- "s = Series([0, 100, 300, 200, 400])\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series([0, 100, 300, 200, 400], index = [0, 20, 21, 22, 1])\n",
- "s.plot.line() # oops this produces a crooked line plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Let's fix it by sorting the Series values based on the indices\n",
- "s.sort_index().plot.line()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Craft breweries example"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, '# Craft Breweries')"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# You can make a series from a list and add indices\n",
- "s = Series([1758, 2002, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764], \\\n",
- " index=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])\n",
- "# We can save the AxesSubplot and \"beautify\" it like the other plots...\n",
- "ax = s.plot.line()\n",
- "ax.set_title(\"Craft Breweries in the USA\")\n",
- "ax.set_xlabel(\"Year\")\n",
- "ax.set_ylabel(\"# Craft Breweries\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Be careful! If the indices are out of order you get a mess\n",
- "# pandas plots each (index, value) in the order given\n",
- "s = Series([1758, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764, 2002], \\\n",
- " index=[2010, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2011])\n",
- "# TODO: fix this crooked line plot\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Fix: call sort_index()\n",
- "s.sort_index().plot.line()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Temperature example\n",
- "Plotting lines from a DataFrame\n",
- "\n",
- "- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot\n",
- "- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
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- " .dataframe thead th {\n",
- " text-align: right;\n",
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- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>26</td>\n",
- " <td>11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>31</td>\n",
- " <td>15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>43</td>\n",
- " <td>25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>57</td>\n",
- " <td>36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>68</td>\n",
- " <td>46</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>78</td>\n",
- " <td>56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>82</td>\n",
- " <td>61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>79</td>\n",
- " <td>59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>72</td>\n",
- " <td>50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>59</td>\n",
- " <td>39</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>44</td>\n",
- " <td>28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>30</td>\n",
- " <td>16</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " high low\n",
- "0 26 11\n",
- "1 31 15\n",
- "2 43 25\n",
- "3 57 36\n",
- "4 68 46\n",
- "5 78 56\n",
- "6 82 61\n",
- "7 79 59\n",
- "8 72 50\n",
- "9 59 39\n",
- "10 44 28\n",
- "11 30 16"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# This DataFrame is made using a dict of lists\n",
- "# City of Madison normal high and low (degrees F) by month\n",
- "temp_df = DataFrame( {\n",
- " \"high\": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],\n",
- " \"low\": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}\n",
- ")\n",
- "\n",
- "# Q: do \"high\" and \"low\" become rows or columns within the DataFrame? \n",
- "# A: columns\n",
- "temp_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Let's create line plots\n",
- "temp_df.plot.line() # not a nice plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### A Line Plot made from a DataFrame automatically plots all columns\n",
- "\n",
- "The same is true for bar plots; we'll see this later."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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SKS8RmCIiq7FGcpNzzQ7YhsUbXvu+7Gi5Q0RqOZpKYMuo49jpo2lkHE9DNNCBhR6mYbtKLgGmO2MMvPkT27i6BFtRzRL99YBOvXPzg235GqDTefar4+w30yf9Kyf9GM50knSOL42tGKzBFtb7sMFW3uXMObllSWfakdc+l2Hnyh7Cehy+xI6UHonPNB5n/yLOtj3YaXh/Yt2C7zr71/VzjauxxmI3tqDagn0pHwIKXeA99vymaX62FcRWSJY59/Ag1p08DGeKlNe+6Z2jtbNtoJ9tAzl7emEfJ60PdlDZQufa27CF6Vnzqi/knvi7ps95ErBGdTW2UF7jHJPg7zdiC/nXsQV/iu9vxY4V+ZDTLdHZ2Mrjqd95gfkrDuutmuU8j2NY1+w32GAscc5+1bCBdBZiKwRHsS7yd4FSAeaNkaQ/9a6Pn/3T3ZbO+Q2BT6c7a98LfVbOMW2x7+cxbKVnONZgr+c8U++cZ/0YNjDPZmxZsRU72O0KP9fKjfUyrHby5B5smVTbz77TSH8K51nbOD218rkLfN898+0N8L909pnrbE9vmmdronDqnSfAg6KEFafVdil2Lvtht/WEC7HhY0cAfY0xI91VoyiRiYi8gq30lTHnHtugBIj22Sshxelj9E27BTtAbUpWMvSKogRMc2CIGvrgoX32Sqj5VUT2Y0cLn8TGOb8MO7r3ERd1KYoSoRhjLnVbQ7ShLXsl1IzEjsjvDtyLDSbyOTbu+CL3ZCmKomQdtM9eURRFUaKcqHTjFypUyJQtW9ZtGYqiKIoSNubPn7/bGOM3OmdUGvuyZcsyb16kRcpUFEVRlNAhIhvS26Z99oqiKIoS5aixVxRFUZQoR429oiiKokQ5auwVRVEUJcpRY68oiqIoUY4ae0VRFEWJcqJy6p2iKIoSeg4ePMjOnTtJTk52W0pUEx8fT1JSEnny5MnwOdTYK4oSMRhjWLH9EJOX7+DAsWTqls5P/TL5KZInu9vSFB8OHjzIjh07KFGiBImJiYiI25KiEmMMx44dY8uWLQAZNvhq7BVFcZUTKanMWruXyct3MHn5TrbsPwZAQmwMQ/5YB0CJfInUL5P/1Kdq0dzExWovpJvs3LmTEiVKkCNHDrelRDUiQo4cOShRogRbt27NPMZeRJoBzwB1gOzAauA9Y8xwr33yA68CNwCJwEzgP8aYJeHWqyhK8Nlz+ARTV+5i8vIdTF+1iyMnU8keH0PzioW4t21F2lZNIl+OBJZtO8j8DftYsGEfs9ft4bvFWwFIjI+lTql8p4x/3dL5yJcjweVflbVITk4mMTHRbRlZhsTExIvqLgmrsReRWsAkYBZwG3AU6AQME5FsxpgPxfqCvgPKYVdJ2wc8AUwVkTrGmM3h1KwoysVjjGH1zsNMWr6Tyct3sGDjPtIMJOXOxvV1StCuWhLNKhYie3zsGcfVKZWPOqXycWvzchhj2Hrg+CnjP3/DPj78fQ2paXYxr4pJuajvuP3rlclP+UI5iYlR13IoUdd9+LjYex3ulv0tQCzQ3hhz2En7TURqA72AD4HrgeZAW2PMVAARmQmsAx4F7guzZkVRMkByahpz1+21Bn7FDjbsOQrAJcXzcE/bSrSrlkSN4nkDNsgiQol8iZTIl8j1tYsDcPRkCos3HWDBRmv8f1m2nc/nbQIgX4546pXOT73S+ahXJj91SuUjR4L2XCpZk3Dn/AQgGTjmk74fyO/8fz2w1WPoAYwxB0Tke6ADauwVJWI5cDSZaat2Mmn5Tqat3Mmh4ykkxMVwaYWC9G9RnsuqJlE8X/BcvzkS4mhaoSBNKxQEIC3NsHb3ERZs2HeqAjBlxU4AYmOEasVyU7+0bfnXL5OfEvl0YJmSNQi3sR8J3Am8IyLPY934NwOXAT2dfS4B/vFz7FKgl4jk8vIKKIriMut2H2Hy8h1MWr6Duev3kZpmKJQrgasuKcpl1YrQolIhcmYLT1ETEyNUTMpFxaRcdG5YCoD9R0+ycNP+U67/8fM3M2qmXRysSJ5s1u3vuP8vKZ6XhDgd+KdEH2E19saYf0SkNfA1cJeTnAzcYYz5zPleAFjv5/C9zt/8wFnGXkQGAAMASpcuHTTNiqKcSUpqGgs27j9l4NfsOgJAlSK5ub1ledpVL0Kdkvkipr88X44E2lRJok2VJMDqX7H90KmW//wN+5i4ZDsACXEx1C6Z17b8HQ9AoVzZ3JSvhJGBAwfy7LPPkpycTFycf/M4bdo02rRpw9SpU2ndunXQzx8qwj1ArxLwJbaVfgfWnd8B+EhEjhtjxgICGH+Hn+vcxpjBwGCABg0a+DteUZQMcuh4MtNX7Wby8h1MWbmT/UeTiY8VGpcrSM8mZbisWhFKFcgcU7DiYmOoUSIvNUrkpVfTsgDsOHj8VMt//sZ9DJ+xjo9T1wJQtmAO6jmt/zZVkygRxG4IJfNRr149Zs6cSfXq1d2WckGE243/ArYlf50xxjOHYLKIFATeFpFx2BZ8AT/Hevr094VepqIom/YedVrvO5m9bg/JqYZ8OeJpUyWJdtWK0LJyIXJnj3dbZlAokic7V9csxtU1iwFwPDmVf7YcONXyn75qF18t2EJ8rNCtUWnuaVuJwrm1xZ8VyZMnD02aNHFbxgUT7s6pmsBiL0PvYQ5QEEjCtvov8XNsdWCj9tcrSmhISzMs2LiPV39ZwZVvTqfFK1MZ+P0yth04Rr9m5fji9qbMe6odb3apw7W1ikWNofdH9vhYGpQtwO2tKjC4VwPmPtWOKQ+1olP9UoyZvZFWr07ltV9WcuCYhomNRtatW8e1115Lrly5KFOmDIMGDSItLQ2wbnwRYdq0aaf2T01N5emnn6ZYsWLkyJGDtm3bsmLFCkSEgQMHXtD5Q0W4W/bbgToikmCMOemV3hg4jm3Vfwf0FZFWxpjfAUQkD9Ae+DTMehUl6lmx/SDDZ6xjyoqd7D58ktgYoWHZ/Dx9bTUuq1aEcoVyui3RdUSE8oVz8eJNNRnQsjxv/LaK96auZvSsDdzZugK9m5YlMSH2/CeKcp79finLth50VUP14nl4pr2/9mLg3HjjjfTt25f//Oc/fP/99zzzzDOUKlWKvn37+t3/mWee4YUXXuCRRx6hXbt2LFiwgOuvvz5o5w8G4Tb27wHjge9F5ANsn/31QFfgTWPMSRH5Dhsxb4yIPMLpoDoCvBJmvYoStRhjGDdnEwO/X0q22BhaV02iXbUkWldOIm+O6G21XyzlCuXk3a51ub1leV77dSUv/bSCEX+u477LKtG5QSniNYxvpuehhx46ZXjbtWvHlClTGDdunF9jvG/fPt566y3uuOMOXn75ZQAuv/xy4uPjeeihhy76/MEi3KPxJ4jINcBjwFBsuNw1wN3Ax84+aSJyHfAa8IGzz0ygjTFmUzj1Kkq0cuRECk9+vYRvF22lRaVCvNmljo46v0BqlMjLyL6NmLNuL6/8vIKnvv6HIdPX8p/LK9O+VvGImY0QTi62RR0pXHvttWd8r1GjBgsXLvS775IlSzhy5Ag333zzGemdOnVK19hfyPmDRdjDSRljfgJ+Os8+e4F+zkdRlCCycvsh7ho7n3W7j/DQ5ZW5u03FLGmYgkWjcgUYf0dTpq7cySs/r+T+zxbx0e9refTKKrSuUliD9mRCChQ4c4x4tmzZOH78uN99t23bBkBSUtIZ6UWKFAnK+YOF+psUJQvxxbxNdHh/BgePpzCmf2PuvaySGvogICK0rVqEife14O1b6nDkRAp9R86l88czmbt+7/lPoGRaihWzMzh27tx5RvqOHTvckJMuauwVJQtw9GQKD32xmEcn/E3dUvn58b7mXFqhkNuyoo6YGKFDnRJMfqgVz91Qgw17jnLzRzPpO2KO6wPXlNBQs2ZNcubMyfjx489I9/3uNroqhKJEOat3HuLOMQtYvesw911Wifsvq0SstuZDSnxsDD2alKFjvZKM/Gs9H/2+hmve+YPraxfnwcsrU1ZnOEQN+fPn54EHHuCFF14gd+7cp0bjDxs2DICYmMhoU6uxV5Qo5uuFm3nyq3/IkRDLJ/0a0aJSYbclZSkSE2K5s3UFujUuzeDpaxg+Yz0Tl2yjc8NS3H9ZJYrkye62RCUIPPvssxhjGDZsGO+88w6NGzdm5MiRNGvWjLx587otDwAxJvoiyzZo0MDMmzfPbRmK4hrHk1MZ+N1SPpu7iUblCvBu17pqWCKAnYeO896U1Yybs5HYGKH3pWW5s1UF8uVIcFvaBbN8+XKqVavmtoyIZfz48XTu3Jnp06fTokWLoJzzfPdcROYbYxr426Yte0WJMtbuOsxdYxewYvsh7mpdgQcvr0yczv2OCJJyZ2dQhxrc1qI8b/62isHT1/Lp7I3c3rI8fZuVC9vqgEpwmT17Nj/++CONGzcme/bszJ8/n5deeokmTZrQvHlzt+UBauwVJar4bvFWnvjybxLiYhjRt+Gpld6UyKJUgRy80aUOA1qV57VfVvHar6sY+dd67m1bia6NSusyu5mMXLlyMX36dN5//30OHjxIUlISnTt35sUXX4yYqZfqxleUKOB4cirP/biMMbM2Ur9Mft7tWpfiujpbpmH+Brsmway1eymZP5H/tKvMDXVLRPRASnXjh5+LceNr9VFRMjkb9hyh44d/MWaWdQd/NqCJGvpMRv0y+Rl3WxM+6deIfDnieWj8Yq5+ezq/Lt1ONDbIlPCjbnxFycT8tGQbj074m5gYYWivBrSrnn7ULiWyERFaVi5M84qF+Omf7bz+60oGjJ5P3dL5eOTKKhoXQbko1NgrSibkREoqL05cwci/1lO7VD7e71aXkvlzuC1LCQIxMcK1tYpx5SVF+HLBZt6a9C/dhsymRaVCPHJlFWqVzOe2RCUTosZeUTIZm/Ye5Z5PF7B48wH6NSvH41dX1QFdUUhcbAxdGpamQ50SjJm1gfenrub69/7kmppFefDyKlRMyuW2RCUTocZeUTIRvy7dzsPjF2OAj3rU46oaxdyWpISY7PGx9G9Rni4NSzH0j3UM/WMtP/+znU71S3J/u8qU0PEZSgCosVeUTEByahov/7SCoTPWUbNEXt7vVo/SBdVtn5XInT2e/1xemV5Ny/D+1DWMmbWBbxZu5eErKzOgZQW35SkRjhp7RYlwtuw/xj2fLmDhxv30alqGp66tRra4WLdlKS5RMFc2/tu+Ore2KMeg75fywsQVHD2Zyv2XVYqYOd1K5BFwR5+IJIhIExG5SUS6i8iVIlI2hNoUJcszZcUOrn3nD/7dcZj3utVlUIcaaugVAErkS+SD7vXpVL8kb036l9d/XaXT9C6SgQMHRm2F6ZwtexGJBW4E+gOtgATA+04YEdkCjAOGGGNWh0qoomQlUlLTeO3XVXz0+xqqFcvDB93rUU5XSlN8iI0RXulYi/hY4b2pq0lOS+Pxq6pGrcFSMk66xl5EOgIvAaWAX4CngYXALuAYUAAoBzQGbgAeFJGRwNPGmB0hVa0oUcz2A8e5d9wC5q7fR9dGpXmmfXWyx2trXvFPTIzw/A01iY0RPv59LSmphqevraYGXzmDc7nx3wXeB4oaYzoYY143xkwxxiwxxqw2xswxxnxujHnQGFMFaA4UBG4Lh3BFiUamr9rFNe/8wdKtB3n7ljq8eFNNNfTKeYmJEf7XoQZ9Li3LsBnrePb7ZerSDwIHDx7knnvuoXjx4mTLlo0qVarw5ptvnrq3qamp5MuXj+eee+7UMUuWLEFEzloAp2TJkjz66KNh1e/Nudz45Y0xxwM9kTFmNnCTiGS7eFmKkrVITTO8NWkV701dTeWk3LzfvZ7Oo1YuCBHhmfbViYsRhs5YR3JqGv/rUIOYcMbX/+lx2L4kfNfzR9GacPVLF32atLQ0rr32WhYsWMCgQYOoWbMmP/74Iw8++CC7du3ihRdeIDY2lpYtWzJlyhSefvppAKZMmUJiYiJz5szhyJEj5MyZk5UrV7JlyxbatGlz0boySrote29DLyKlRSTe334iEicipb2OOxFciYoS3ew8eJzuQ2fx7pTVdKpXkm/ubqaGXskQIsJT11bjjlYVGDt7I09+vYS0NG3hZ4SJEycyY8YM3n//fR566CGuuOIK3n77bW699VZef/11du/eDUCbNm2YOXMmJ05Y0zd16lR69+5NQkICM2bMOJUWFxcXtHXtM0KgU+/WAU2BOX621XbS1deoKBfIX6t3c99nizh8IpnXbq5Np/ol3ZakZHJEhMeuqkJ8rPDulNUkpxpe6VQrPCvoBaFFHSlMnz6dmJgYunbtekZ6jx49GDZsGDNnzqR9+/a0adOG48eP89dff9GqVSt+//13RowYwdq1a5kyZQpXXnklU6ZMoWHDhuTK5V4lPlBjf65cEg+kBUGLomQZUtMM701ZzVuTV1GhcC4+va0xlYvkdluWEiWICA9dUYW4mBjenLSK1LQ0Xru5NnGxGlY5UPbu3UuBAgXIlu3MnumiRYue2g5Qu3ZtChYsyNSpU8mTJw8HDx6kVatWrFixgq+++gpjDNOmTeO229wdznau0fj5sCPuPZQQkfI+uyUCvYHtwZemKNHJ7sMneOCzRcxYvZub6pbgfzfUIGc2jW+lBJ/721UiLlZ49ZeVpKQZ3upSRw1+gBQoUIC9e/dy8uRJEhISTqVv327NXcGCBQFbsWrVqhVTpkwhd+7c1KlTh/z589O2bVuefvpp/vzzT3bt2uVqfz2cezT+/cBq4F/AABOc/70/fwO3A4NDK1NRooN56/dyzdt/MHf9Xl66qSavd66thl4JKXe3qcgTV1flh7+3ce+4hSSnqiM2EFq1akVaWhrjx48/I33s2LEkJCTQpEmTU2lt2rRhzpw5/PDDD7Rt2xaA+vXrkzNnTgYOHEhCQgLNmjULq35fzlXKfAOsx7rwhwPPAWt89jkBLDPG/B0KcYoSTYyft4knv15CiXyJjOrXiGrF8rgtScki3N6qArExwnM/LufusQt4r1s9XSnxPFx99dU0b96cO+64g127dnHJJZcwceJEhg4dyhNPPEGhQoVO7du2bVuSk5OZPn06jz32GMCpkfo//PADLVu2JDHR3QWL0jX2xpjFwGIAETHAj8aY3eESpijRQmqa4eWfVzB4+lqaVSzI+93qkS9HwvkPVJQg0r9FeeJjY3jmu6XcOWY+H/Sop6GXz0FMTAw//vgjTz75JC+//DJ79uyhbNmyvPHGGzzwwANn7Fu9enWKFCnCnj17zhhx37ZtW3744QfXXfgAEo2BFxo0aGDmzZvntgxF4dDxZO7/bBFTVuykZ5My/Ld9deK1z1RxkbGzN/DU1//QqnJhPu5ZP8NBm5YvX061atWCrE45F+e75yIy3xjTwN+2cw3QmwLcZYxZ4fx/Lowx5rKA1CpKFmHjnqP0/2Qua3Yd4X8dLqFn07JuS1IUujcuQ1yM8PhXS+g/ah5DejUgMUFb+NHOuZoY3tPtYpzv6X20qaIoXsxau4cO789gx8ETjO7XSA29ElF0aViaVzvV5s81u+k3ci5HT6a4LUkJMefqs2/j9X/rsKhRlChg3JyN/N83/1C6YA6G9W6oq9UpEUmn+iWJjxX+8/ki+gyfy/C+DcmlM0OiFm2RK0qQSElN49nvl/LEV0u4tGIhvr6rmRp6JaLpUKcE73Sty/yN++g9fA6Hjie7LUkJEQEbexEpISJviMg8EVknIjWc9AdEpHHoJCpK5HPgWDL9Rs1jxJ/r6dusLMN7NyBvot/lJBQloriuVnHe61qXxZv202PYHA4cC9zgR+MA70jlYu91QMZeRC4BlgA9ga1AacAzd6gMNgCPomRJ1u0+wo0f/Mlfq3fz4k01eab9JRqlTMlUXF2zGB90r8eyrQfoMXQ2+4+ePO8x8fHxHDt2LAzqFIBjx44RH5/xBkSgJdLrwHKgHHATZw7e+wto4u8gRYl2/lq9mxve/5N9R04ypn9jujYqff6DFCUCueKSonzcsz4rtx+i25DZ7D1yboOflJTEli1bOHr0qLbwQ4gxhqNHj7JlyxaSkpIyfJ5AR2M0B7oaYw6LiO8cjR1A0QwrUJRMyuhZGxj43VLKF8rJsN4NKV0wh9uSFOWiaFu1CEN6N2DAJ/PoNmQWY/s3pmCubH73zZPHRoDcunUrycna1x9K4uPjKVKkyKl7nhECNfbnCqZcCFBfjpJlSE5NY9D3yxg9awNtqybx9i11yJ1d++eV6KBV5cIM79OQW0fNpeuQWYzt34TCudM3+BdjgJTwEagbfw7QN51tnYE/gyNHUSKbA0eT6TNiDqNnbWBAy/IM6dVADb0SdTSrWIgRfRqxed8xbhk8k50Hj7stSblIAjX2/wPai8iv2EF6BmgnIqOAG4HnQ6RPUSKG1TsPc8MHfzJ33T5e7VSLJ6+pRmyMnP9ARcmENK1QkFH9GrH9wHG6DJ7FtgPqwM3MBGTsjTG/AzdgB+gNxw7QewloAdxgjJkdKoGKEglMX7WLGz/4k4PHkvn0tsbc3KCU25IUJeQ0LFuAT25tzO5DJ+jy8Sy27FeDn1kJeH6QMeZHY0wloDJ2wF41Y0x5Y8xPIVOnKC5jjGHEn+voM2IOJfIl8u09zWhQtoDbshQlbNQvk5/R/Ruz/+hJunw8k017j7otSckAFzwZ2Biz2hjzlzFmZSgEKUqkcDIljSe/XsKz3y/jsmpF+PLOSymZX0fcK1mPOqXyMbZ/Ew4dT6HLxzPZsOeI25KUC+RCIujlEZFbRORREfmvz+f/LuSiInKNiEwXkcMictCJytfWa3t+ERkqIrtF5IiITBKRmhdyDUW5GPYdOUnPYbMZN2cTd7WuwMc96pNT44YrWZiaJfPy6W2NOZacSpePZ7F212G3JSkXQEDr2YtIM+B7IF86uxhjTEBrJIrI7cB7zmcitsJRB1hqjPlBRASYjh0f8AiwD3gCuASoY4zZfL5r6Hr2ysWwasch+o+ax/aDx3mlYy1uqFvCbUlZj5NH4fgByF0URAdBRhIrth+k+5DZxMYIn97WhIpJudyWpDicaz37QI39XCAWuA1YYow5fyxF/+cpi43E94Qx5q109ukAfAO0NcZMddLyAuuAMcaY+853HTX2SkaZsmIH941bRPb4WAb3qk+90vndlpR1MAY2zoJFY2HpN3DyEMTnhIIVoGBFKFQJClY6/T27zu92i393HKLrkNmA4dPbmlC5SG63JSkEx9gfBjobYyZepJBBwENAQWOM34mbIjIMuMoYU8InfRTQ2hhT5nzXUWOvXCjGGIb+sY4XflpO9WJ5GNKrAcXzJbotK2twYDMsHgeLPoW9a62Bv+RGKFbbft+zGvb8C/s3gvGK75WrqFMJqGj/FqxkKwT5SkOsxj4INat3HqbbkFmkpBnG9m9MtWJa+XKbcxn7QDshNwL+QyhdGM2BFcAtTj9/GWA98KYx5n1nn0uAf/wcuxToJSK5jDHaWaQEjRMpqTz99T+Mn7+Zq2sU5fXOtcmRoP3zISX5GKz40bbi10wFDJRpDi0fgWrXQzY/ruGUE7B3nTX8u/+FPWvs/8u+g2N7T+8XEwf5yzmegAqON8DxDOQsrN0CQaJiUi4+v70p3YbMouuQWYy5tTE1SuR1W5aSDoG27LsADwKXG2MOZvhiIiuA4sAJ4ElgDXAzcAfwgDHmbRFZBSwwxtzic2x/YAhQ2hizyc+5BwADAEqXLl1/w4YNGZWpZCF2Hz7BnWPmM3f9Pu67rBIPXFaJGA2UExqMgS3zrYFf8iWcOAB5S0OdrlD7FihQPuPnPrrX8QCsdioCnsrAGkg9cXq/bHltBaCQUwHwVAIKVIAEnWmRETbuOUrXIbM4dDyZMf0bU6tkPrclZVky5MYXkU98kloCuYGZwF6fbcYY0zsAIauASkBHY8xXXuk/AXWBYsAqYJ4xpqvPsbcBg0nH2HujbnwlEFZsP8itI+ex+/AJXru5Nu1rF3dbUnRyaDss/sy66XevhLhEqH491OkOZVtATAiXA05LhQObnEqA0x3g+f+gz1jfPCV9ugSc//OWgpiAxh9nWTbtPUq3obPYfzSZT/o1oq6OdXGFjLrxW2LD4nowwEGsm92XQNc33IM19r/5pP8KXIU19nsBf1FLPLlnX4DXUpR0+W3ZDh74bCG5sscx/o6m2hoJNiknYOVPthW/epLtay/VGNq/A5fcANnD5O6NiYX8Ze2nYrszt508CnvXnNklsPtf+PsLOOHlwIzNZr0OhSpC5atsJUW7As6gVIEcfD6gKV2HzKLnsDl8cmsjHdwaYaRr7I0xZUNwvaVAEz/pnjcnzdnnCj/7VAc2an+9cjEYY/jw9zW8+stKapXIy+BeDSiSJ7vbsqIDY2DbYsdNPx6O7YPcxaHZA9ZAFqrotsIzScgBRWvajzfGwJFdZ3cJbF8Cy7+Hf76C69+FvDol05vi+RL5fEBTbhk8k34j5/LlnZdSobBOy4sUAuqzD9rFRK4FfgBuNsZM8Er/BahujCklIjcAX2NH3v/ubM+DnXr3qTHm3vNdR934ij+OJ6fy5FdL+GrhFtrXLs6rnWqRPV7dsxfN4V2w5AtYOBZ2LrUt4arXQt3uUL5N9LjA09Jg3jD47b8QEw/XvAK1umgr34eNe45y04d/kpgQy1d3Nkt3eVwl+Fz01DuvExUFSgNnNYWMMdMDOF6AyUBt4ClgLdAJO3+/rzFmpIjEADOAUpwZVKcWUPt8/fWgxl45m52HjnP76Pks3Lifhy6vzD1tKyJaSGec1GT491dr4P/9BdJSoER9qNMNanSExCh24e5ZA9/cBZtmQdXr4Lo3IVeS26oiisWb9nPL4FlUTMrFZwOaaPTJMBGMefYlgDHYfvyzNnNhEfTyAC9ijXx+7FS8l4wxn3rtUwB4DbvSXnbsoMAHjTGLA7mGGnvFm3+2HGDAJ/PYdzSZNzrX5uqaxdyWlHnZsdQa+L8/h6O7IWcS1O5i3fRJ1dxWFz7SUmHm+zDlOTtN8No37FgE5RRTVuyg/6h5tKpcmCG9GhAXG8KBmAoQHGP/HdAUu6ztEuzUuTPwuNwjATX2ioeflmzjwS8Wkz9HPIN7NdB5wBnh6F5YMgEWjbF98jHxUOVqa+ArtoPYLNxq27kCvr4dti2CGp3gmlchh66K6OHT2Rt58usl3NKwFC/eVFO9aSEmGEF1WgD3GWNGB0+WooQWT0FTt3Q+Pu5Zn6TcOhAvYFJTYM0Ua+BX/gSpJ6FoLbj6FWvUchZ0W2FkkFQV+k+CGW/C7y/D+hlw/TtQ+Uq3lUUE3RqXZtuBY7w7ZTXF8yVy32WV3JaUZQnU2B8DdoZSiKIEk28WbuGpb5bQtmoSH3SvpwPxAmXXSjuafvHncHg75CgIDfvbvnjfUeuKJTYeWj1qDfzXd8CnnaFuT7jyBY3fDzx4eWW27j/OG7+tolje7NzcoJTbkrIkgRr7IUBP4JcQalGUoPDr0u08NH4xTcoVVEMfCCcOnx5Nv2UeSKw1XHW6Q6UrIC7BbYWZg2K1YcA0mPYi/Pk2rJ0GHd6H8q3cVuYqIsJLHWuy89BxnvhqCUl5stOqcmG3ZWU5Au2zHwA8jo1jP5GzI+hhjBkebHEZRfvssy5//LuLW0fOo3rxPIzp35hcOgr43GxfAl/0tsFlkqpbA1+rs44uv1g2zYVv7rBz9RvdDu0GZvlwvIeOJ9Pl41ls2HOEz29vquNnQkAwBuilnWeXgEfjhwM19lmTeev30nPYHMoUtNG88ubQlc/SxRhYMAp+egyy54MbP4LyrXXOeDA5eRQmD4LZH9rY+zd8CKUbu63KVXYcPM5NH/zFydQ0vr7rUkrmz9oVoGBzLmMf6FyIcuf5XMQKFopy8fyz5QB9R8ylWN7sjL61sRr6c3HisB1B/v39ULoJ3DEDKrRRQx9sEnLA1S9B7+9tXIIRV8Fvz9hQwlmUInmyM7JvQ04kp9J7+Bz2Hz3ptqQsQ1gj6IULbdlnLf7dcYgug2eRGB/L+Dua6jr052LHMhjf24aBbf0EtHw4eiLcRTInDsEvT1lvSuFq1pNSvI7bqlxj9to99Bw2h9ql8jL61sY6riZIBKNl7zlRLRG5R0SecaLpISIVRSR3MIQqyoWycc9RegybTWyMMLZ/YzX052LhWBjSFo7th17fQuvH1NCHi2y57ZS87hPg+H4YehlMe8m2+LMgjcsX5I0utZm7fh8PfrGItLToa3RGGgEZexHJJiLjgYXAO8B/sevSA7yCDX2rKGFl+4HjdB82ixMpaYy5tTFlC+V0W1JkcvKoDe/67V1QsoF122fxEeKuUelyuGumDSk87UVr9Hcud1uVK1xXqzhPX1uNiUu28/zErHkPwkmgLfvngXbY6XdFOL1KHcBPgEaQUMLKnsMn6D50FvuOJDOqbyOqFFXnkl92rbIGZdGn0PJR26LPXcRtVVmbxPxw02DoPBoObIGPW9qpemmpbisLO/1blKdfs3IMm7GOoX+sdVtOVBPovKSuwNPGmE9FxNfvtw4oG1RVinIODhxLptfwOWzZf4xRfRtRu1Q+tyVFJn9/Ad8/APHZoceXUPEytxUp3lS/Hko3hR8esCvprfjRjtgvWMFtZWHl6Wursf3gMZ6fuJxieRO5tpauXREKAm3ZFwTS87PEALqGoRIWjp5Mod/IuazacYiPetSncXkN23oWycfgu/vgq9tsoJc7Zqihj1RyFYYuY+CmIbBrBXzYDGYPtsvpZhFiYoQ3OtehQZn8/OeLRcxZd1YYFyUIBGrs12EXwvFHI2BlcOQoSvocT05lwCfzWbhxH+/cUpfWVTTwy1nsXg1DL7ejvpv/x077ylP8/Mcp7iFiAxndNQvKNoOfHoHRHWD/RreVhY3s8bEM6dWAUvkTue2TeazeechtSVFHoMb+E+BxEekOeGJnGhFpA/wHiJjoeUp0kpyaxr3jFjJj9W5e6aTL1Prln69gcGs4uBm6jbdR27LyinSZjTzF7Wj99m/DlgXwwaWwYLQNgJQFyJcjgZF9G5EQF0Pv4XPZcfC425KiikCN/SvAj8BoTofKnQFMAn42xrwbAm2KAkBamuHh8Yv5bdkOBnW4hE71S7otKbJIOQE/PgQT+to15W//Aypf4bYqJSOIQP0+cOdfdh7+d/fYhXUObnNbWVgoVSAHI/o0ZN/Rk/QdMZfDJ1LclhQ1BGTsjTGpxphbgFbA68BQ7BS8tsaY7iHUp2RxjDE8/e0/fLtoK49eVYVeTcu6LSmy2LsWhl0Oc4dC03ug70TIp6uKZXryl4Fe38FVL8O66fBBE1gyIUu08muUyMsH3euxcsch7hwzn+TUrDN+IZScN4KeiCQAs4DHjTG/hkXVRaIR9KIDYwwvTFzOkD/WcVfrCjx6VVW3JUUWy76Db++2rcEbPoKq17itSAkFu/+Fb+6EzXOh+g1w7RuQM/oHpn4xbxOPTvibTvVL8mqnWoiGcz4vFxVBzxhzEhv/Xv0pSlh5d8pqhvyxjt5Ny/DIlVXclhM5pJyEnx6HL3pCwYrWba+GPnopVAn6/gyXPWOn533Q2P6Ncjo3KMV/2lVmwvzNvDnpX7flZHoC7bP/DdBOQCVsDJuxjjd+W0XHeiV5pv0lWqv3sG+DXVBl9ofQ+A7o94t1+SrRTWwctHgQbv8dcheFz7rB13fY0MdRzH2XVaRLg1K8M/lfPpuTdWYnhIJAh+q+C4wRkTjgG2AbcIb/3xij4Y+UoPD53I3874dlXF2jKC93rElMjBp6AFZMtGukGwOdP4HqHdxWpISbIpdA/ynwx2sw/TVY+zvc9DGUa+m2spAgIjx3Yw12HDrOU9/8Q5E82WlTVafcZoSMrGfv9wBdz14JBt8v3sp9ny2kZaXCDOnVgIS4C1qrKTpJTYbJz8Jf79ogOTePhAK6qnSWZ8sCu1TxnjVw9cvQ6Da3FYWMIydS6DJ4Jmt2HuHz25tQq2Q+tyVFJOfqsw/U2Pc+3z7GmFEZ0BYS1NhnTiYv38Hto+dTr0x+RvVtRGJCxNQf3ePAZhjfFzbPgYb94YrnbfhbRQE4ftBGSlz1MzS41Rr92Hi3VYWEnYeOc9MHf3E8OZWv7mxG6YI53JYUcVy0sc9sqLHPfPy1ejd9Rs6latHcjO3fmNzZo7PAuiD+/Q2+GgCpJ+3yqDU6uq1IiUTSUq3n58+3rTv/5lGQo4DbqkLCml2H6fjhX+TPkcCXd15KgZwJ5z8oCxG09ewVJRQs2LiP/p/Mo1zBnIzq20gNfWoKTBoIYzvZqGoDfldDr6RPTCxcPsguorNxll3lcNcqt1WFhAqFczG0VwO27j9G/1FzOZ6c9VYKzCgBG3sRaSUiH4nIRBGZ4vOZHEqRSvSybOtB+gyfQ1LubIy+tRH5s3pN/eBWGNUeZrwJ9XpD/0lQqKLbqpTMQJ1u0PsHOHEIhraD1ZPcVhQSGpQtwNu31GHhpv3c/9lCUtOizzsdCgIy9iJyOzAV6Ajkw65n7/1RD4FywazZdZiew2aTM1scY/o3JilPFu+LXj0ZPmoB2xbBjYOt6z4+0W1VSmaidGO4bQrkKw1jb4ZZH0Zl1L2rahTjmeuq88vSHQz6finR2B0dbAKdevcQ8CnQzwmyoygXxaa9R+kxdDYiMLZ/Y0rmz8KDbdJSYdpLMP1VKFwVOo+CwhpESMkg+UpDv5/tSP2fH4edy+Ca1yEuurxmfZqVY+uB4wyevpbi+RK5vVUFtyVFNIEa+xLACDX0SjDYefA4PYbN5siJFD6/vSnlC+dyW5J7HNoBX94K6/+AOt3hmlchIafbqpTMTrZc0Hk0TH3ezsnfs8Z+j7Iwu49fVZWt+4/x4k8rKJo3Ox3qlHBbUsQSqPt9PqATe5WLZu+Rk/QYNptdh04wql8jqhXL47Yk91j7O3zUHDbPgw4fwA0fqKFXgkdMDFz2f3DTUJvHhrSBncvdVhVUYmKE1zvXpnG5Ajw8fjF/rdnttqSIJVBjfx/wgIhEZ5gmJSwcOp5M7+Fz2LDnKEN7N6Bu6fxuS3KHtFT4/RUYfQMk5rN9rHV18UglRNS6Gfr+BCnHYejlsOoXtxUFlWxxsQzu2YCyBXNy++j5rNx+yG1JEUm6xl5ENonIRhHZCHwHlASmisghT7rXZ0PYFCuZkmMnU7l15DyWbzvIhz3qcWmFQm5LcofDu2BMR+terdEJbpsKRaq7rUqJdkrWt3mtYHn4tAv8+U5UDdzLmyOekf0akSMhlj4j5rD9wHG3JUUc6QbVEZGRpBMa1x/GmL5B0nTRaFCdyOJESiq3fTKfGf/u4p2udbmuVnG3JbnD+j9t//zRvXDNK3ZqnS7wo4STk0ftcrnLvrFjRK57E+Kyua0qaCzbepDOH8+kZP5EvrijKXmyWMwOjaCnuEZKahr3fLqQn5du55WOtejcsJTbksJPWhr8+RZMeQ7yl7Wj7YvWdFuVklVJS4Ppr8C0F6FUY+gyFnIVdltV0Jjx7276jJhD4/IFGNGnUZZaX0Mj6CmukJZmePTLv/l56Xb+e131rGnoj+yBcV1sONPqHWDANDX0irvExEDrx+2CStv+tgP3tv/jtqqg0bxSIV7pVIs/V+/h0QmLdQ6+Q6BT7wAQkdpAFeCs6CfGmE+CJUrJ/BhjeOa7pXy1YAsPXV6Zfs3LuS0p/GycDRP6wpFdcM1rdiEbddsrkcIlN1pP07iuMOwK6DgEql7rtqqgcFO9kmw7cJxXf1lJ8XyJPHpVVbcluU5Axl5E8gE/Ak08Sc5f7yqTGnvlFK/8spLRszZwe8vy3NM2i4V7NcYuRzv5WchbEm79FYrXdVuVopxN8bp24N5n3eCz7naqXvMHo6JSelfrCmzZf4wPpq2hWL5EejYp47YkVwnUjf8CUBBoiTX0NwJtgbHAWqBRSNQpmZL3p67mw2lr6N64NI9fXRWJgoIjYI7tswXnb/8Hla+yi9iooVcimTzFoO9Eu9jS5EF2pcXkzD+aXUQYdP0lXFY1iWe+/Yef/9nutiRXCdTYX4k1+LOc75uNMdOMMb2AScD9oRCnZD5G/rmOV39ZyY11S/C/DjWylqHfPB8+ammXpr3qJegyxs6jV5RIJz4ROg6Ftv8HS76AkdfCocxvHONiY3i3W11ql8rHfeMWMn3VLrcluUagxr4YsNYYkwocB3J7bfsKiI6OHuWiGD9vEwO/X8YV1YvwaqdaxMRkEUNvjF1wZPiV9nu/X6DJnVHhClWyECLQ8mFbSd25DIa0ha2L3FZ10eRIiGNkn0ZUSMrFgNHzmLt+r9uSXCFQY78du9odwAagqde2LNYhq/jji7mbeOzLv2lRqRDvdqtLXGwWmehxbD980dMuOFKxHdz+uw1goiiZlWrtbYUVgeFXwbJv3VZ00eTNEc/oWxtRPF8i/UbMZcnmA25LCjuBlsgzOG3gRwPPiMjHIvI+8CoQXfEXlYAxxvD+1NU8+uXfNK9UmI971idbXKzbssLD1kUwuBWsmAhXPAddx0GOAm6rUpSLp1gtGDDVThP9ohdMeznTR9wrlCsbY25tTJ7EeHoNn82/O7JWWN1Ajf2zwM/O/68C72Nd912xoXTvDb40JdJJSzM8+/0yXv1lJTfUKc7QXg3IkXBBszkzJ8bAnCEw7HJITbZxxy+9V932SnSRKwn6/AC1u8K0F+w00pNH3VZ1URTPl8intzUmPjaG7kNns3FP5v49F8K5wuUOAj4yxmz1SosxxqSFS1xG0Qh6oedESioPfbGYH/7eRv/m5XjymmpZo4/++EH4/n5Y+hVUvBxu/Djqlg1VlDMwBv58GyYNhGK1rQcrT+YOeb1qxyE6fzyTXNniGH9HU4rlTXRbUlDIaAS9p7CL33hOEgski0i9IIv7WUSMiDznk55fRIaKyG4ROSIik0REQ49FAIdPpNBv5Fx++HsbT15Tlaevq541DP32JTC4te3DvOwZ6PaFGnol+hGB5g9YI79nNQxuA1vmu63qoqhcJDef9GvE/qPJ9Bg6m92HT7gtKeScy9j7K72DWqKLSFegtp90wXYPXIXtIugIxGNX3Svpu78SPnYdOsEtg2cya+1eXr+5NgNaVnBbUugxBuaPhCGXwckj0Pt7aPGgDTuqKFmFKlfbAFFxCTDiGlgywW1FF0WtkvkY3qchW/Yfo9ewORw4luy2pJDiWmnlROV7E3jQz+brgeZAT2PMOGPMz05aDPBo2EQqZ7BhzxE6ffQXa3YeYWivBnSsnwXqXScO2yAj398PZS6FO2ZA2WZuq1IUdyhyiY24V7yeXcFxynN2YZ1MSqNyBfi4ZwP+3XmIviPmcOREituSQoabTZNXgKXGmHF+tl0PbDXGTPUkGGMOAN8DHcKkT/Hiny0H6PjhXxw4lszY2xrTpmqS25JCz45ldpGQfyZAm6egx5dRtTqYomSInIWg17dQtydMfxXG97Ier0xKq8qFebdrXRZvPsCA0fM4npzqtqSQcL6h0+1FpIbzfww2Fv71IlLHd0djzPBALyoizYFe+HHhO1wC+FuGaSnQS0RyGWMOB3o95eL4a/VuBoyeT97EeD7r14iKSbnclhR6Fo6BHx+GbLltwVaupduKFCVyiEuA69+FpGrw69M2oNQt4yBf5lzZ8qoaxXi1UyoPfrGYez5dyIc96hEfZbFCzmfsn/KT9l8/aQYIyNiLSDzwMfCaMWZlOrsVANb7SfeEPsoPnGHsRWQAMACgdOnSgUhRAuCHv7fy4OeLKVcoJ6P6NaJo3rMWPIwuTh6FiQ/DorFQtgV0HAa5i7itSlEiDxFoejcUqgwT+tmIe7eMhVKZc6mUm+qV5MiJFP7v26U89MVi3uxSh9goGnh8LmMfqjVJHwMSgefPsY9w5op63ul+McYMBgaDnXp3MQIVy6i/1jPw+6U0KJOfob0akjdHvNuSQsuulfBFb9i1Alo+atf8jskiAYIUJaNUuhz6T4JPu9iY+te/C7VvcVtVhujZtCyHT6Ty8s8ryJktlhdurBk163uka+yNMRuCfTERKY31FvQHsolINq/N2ZxBe4ewLXh/ocjyO3/3BVubchpjDG/8top3p6zm8upFeLdrXbLHR7nRW/w5/PAfuyBIjy+h4mVuK1KUzEPhKnDbFBtt7+vbYedyuOy/mbKyfGfrChw+kcz7U9eQMyGOp66tFhUGP9zhzsoD2YExfrY97HzqYvvmr/CzT3Vgo/bXh46U1DSe/uYfPpu7iVsaluK5G2pEd5z75GPw06Ow4BMofSl0GpbpA4YoiivkKAA9v4aJj8Cfb1lPWcchdtxLJuPhK6pw5EQqQ2esI1f2OB5oV9ltSRdNusZeRL4FBhpjFgZyIhHJDtwFHDXGfJTObouANn7Sp2IrAMOA1dg59n1FpJUx5nfn/HmA9sCngehRLpzjyanc8+lCJi3fwb1tK/Lg5ZWjokabLrtXw/jesOMfaP6gHXEfmwXC/SpKqIiNh+vehKTqdnGoYVfaYDz5y7it7IIQEf57XXUOn0jhrUn/kitbHP1blHdb1kVxrpJtIzBLRBYBY7GL4fxtjDk1EVFEigONsEb4JmAL0C+9Expj9gPTfNMdg7LBGDPN+f4dMBMYIyKPYN32T2D77F8J8LcpF8CBo8n0/2Qu8zbsY1CHS+jVtKzbkkLLP1/Cd/fZwqnbeKjsz5GkKMoFIwKNB0ChijC+j52+2mUslGl63kMjiZgY4aWbanL0ZArP/bicnNni6Noo8w7+Ttc/a4y5F+s2nwMMBOYCx0Vkr4hsE5HjwCbsevaXAA8AtYwxcy5WlBN//zrgN+AD4GsgFWhjjNl0sedXzmT7geN0/ngmizcd4L2u9aLb0Ccfhx8etKOHk6rbIDlq6BUl+FRoC/2nQGJ+GNXeTmfNZMTFxvBWl7q0rlKYJ79ewreLtrgtKcOkuxDOGTuJJGCXuG0MFMf2u+8BVgDTQzGY72LQhXACZ/XOw/QebkNFDu5Zn0srFnJbUujYu9a2NLYttqvUXfaMbdkrihI6ju2z793aadD0Hrh8UKYbuHc8OZXew+cwb8M+PupRn8urR+Z03HMthBOQsc9sqLEPjAUb99Fv5FziYmIY2bchNUrkdVtS6Fj2LXx7D0gM3PiRjfOtKEp4SE2BX56EOR/b1SI7DYPsmau8OXTcLpqzfPshRvRpSLMIbBhldNU7JYqZumIn3YbMIm9iPF/e2TR6DX3KSfjpMTslqFAluH26GnpFCTexcXDNK3bw3tqpMPRy62nLROTOHs+ofo0oVzAnt30yj/kbMtcMcDX2WZAv52+m/yfzqJiUiwl3XEqZgjndlhQa9m+yYTxnfwSN74S+P2e6UcGKElU06Gen5x3ZaSPurfvDbUUXRL4cCYzu34ik3NnoM2IOS7cecFtSwKixz0IYY/j49zU8NH4xTcoX4LMBTSmcO9v5D8yMbJpjC5Pd/0LnT+Dql2w8b0VR3KVcS+g/GXImwegbYN4ItxVdEEm5szOmf2NyZ4uj17A5rN6ZOcK+qLHPIqSlGZ7/cTkv/rSC62oVY3ifhuTKFqVzyhd/ZsN2JuSE2yZDdV0oUVEiioIVoP9vUL4N/PAATHzU9utnEkrmz8HY25ogIvQYOptNe4+6Lem8qLHPApxMSePBLxYxdMY6+lxalnduqUu2uMw1GjYg0lLht2dsuM5SjW34zsJV3FalKIo/sueFbp/bEfpzPoaxnezI/UxCuUI5GX1rI44lp9J96Gx2HDzutqRzosY+yjlyIoVbR83lm0VbeeTKKjzTvjoxUbSS0ylOHILPutswnZ5+wRz+lldQFCViiImFK5+3i+esnwFD29nIlpmEasXyMKpfI/YcPkGPobPZe+Sk25LSJWBjLyKVRGSUiKwSkSPO35EiUjGUApWMs+fwCboNmcWfq3fzSsda3N2mYnSGv923AYZdAf/+Cle/Cte+ofPnFSUzUa8X9P7OtuyHtoU1U91WFDB1SuVjaO+GbNx7lF7DZ3PweLLbkvwSkLEXkdbAYmxUu1nYqHazsGFyl4hIqxDpUzLIpr1H6fTRTFZsP8THPRvQuWEptyWFhg0zbTjOg1ugxwQbpjMaKzSKEu2UudR2veUpAWM6wpwhbisKmKYVCvJRj/qs2HaIfiPmcvRk5I0/CDSC3nzgOHCl94pzIpIb+BWIT28ivxtk9aA6y7cdpPfwOZxISWNY7wY0KBul7uwFo+2ytPnLQNfPbSxuRVEyNycOwZe3waqfoMGtcPXLmcZT9+Pf27h33AKaVSzE0N4Nwj42KhhBdaoDL/suLWuMOQS8jI2Nr0QAs9buofNHM4kRYfwdTaPT0Kelwi9PwXf3QNlm0H+SGnpFiRay5YZbxkKzB2DeMBhzExzd67aqgLi2VjFe6liLP/7dzX3jFpKSmua2pFMEauw3A+lNUk7ArnanuMzP/2yj1/A5FMmbnS/vupTKRTLfOtLn5fgB+LQLzHwPGt0O3b+0C20oihI9xMTC5c/CDR/Bxlk2ZsaulW6rCojODUrxTPvq/LJ0B49M+Ju0tMgISR+osX8ZeFZESngnOt+fAV4ItjDlwhgzawN3jV3AJcXzMP72ppTIl+i2pOCzd60Ns7l2qg27ec0ruv68okQzdbpCnx/h5GE7Uv/fSW4rCoi+zcrx8BWV+XrhFv773T9Ewho0gZaUrYDcwBoRmQXsAIoATZz/WzuD+ACMMaZ3kHUq6WCM4e3J//LWpH9pWzWJ97vVIzEhCufQr/sDvuhp/+/5tY3CpShK9FOqEdw2FcZ1hU9vhiuehyZ3RvxA3LvbVOTQiRQ+/n0tObPF8fhVVV2dDRWosW+OXU9+G1DG+eB8B2jhta/7VZgsQmqa4b/f/sPY2RvpVL8kL95Uk/jYKAydMG8ETHwYClSAruNs9C1FUbIO+UpBv59twKxfnoBdy+Ga1yM6BLaI8PhVVTniGPzc2eK4p20l1/QEZOyNMeVCLUS5MI4np/LAZ4v4eel27mxdgUevrBJ9c+ijYFlMRVGCRLZc0Hk0THsBpr8Ke9bY7zkLuq0sXUSEQdfX4OiJVF77dRU5s8XRt5k75lQ7PDMZaWmGmWv38OZvq5i3YR//d111bm0ehXWxY/tgfF/bP9/0Hrh8kB20oyhK1iUmBto+DYWrwjd32RgbXT+DItXdVpYuMTHCK51qceRkCs9+v4ycCXGuxD25IGMvIqWAUkB2323GmCnBEqWczcY9R5kwfxNfLtjClv3HyJM9jrdvqUOHOiXOf3BmY/dqGNfFRsa7/l0bXUtRFMVDzU6Qvxx81hWGXQ4dh0GVq9xWlS5xsTG807Uu/UfN4/Gv/iZHtliuq1U8rBoCDapTHhgLNPIkOX+N878xxkRMsytaguocOZHCxCXbGD9/M3PW7UUEWlQqTKf6JbmiehGyx0fMLQ8ea6bC+N4QE2dddGWbua1IUZRI5cAW+KwbbFtsvX+X3hvRA/eOnkyh9/A5LNy4nyG9GtCmalJQz3+uoDqBGvspQBXgJWAFcFa0f2PM7xepM2hkZmNvjGHOur2Mn7+ZiUu2cfRkKuUK5aRT/ZLcVK8ExfJG4ZQ6D3OGwE+P2ZXquo6D/GXdVqQoSqRz8ih8cycs+wZqd4P2b0FcNrdVpcvB48l0HzKbVTsOMapfI5qUD96Yg3MZ+0Dd+A2BPsaYL4OmSjmDLfuP8eX8zUyYv5mNe4+SK1sc19cuTqf6JalfJn/0Db7zJjXZGvl5w6Dy1dBxiI2ipSiKcj4ScsDNI+H3V+zgvb1roMtYyFXYbWV+yZM9nlH9GvHgF4somuesHvGQEaix34yf1rxycRw7mcovS7czfv4m/lqzB2Pg0goFeaBdJa6qUZQcCVlg/OTRvfBFL1j/hw2Pedl/dSCeoigXhgi0fgwKV4av73QG7o2DojXdVuaXAjkTGNm30fl3DCKBWpMXgMdEZIox5kgoBUU7xhgWbNzPhPmb+GHxNg6dSKFUgUQeuKwyN9UrQakCOdyWGD52rbShbw9usWEx63R1W5GiKJmZS2603X/jusGwK+GmwVDtOrdVRQSBzrMfLSJVgfVOBL19Z++iUfPOxfYDx/lqoXXTr911hMT4WK6pWYxO9UvSuFwBYmKi2E3vj38nwYS+tm+t9w9QurHbihRFiQaK14UBU+3Avc+7W29h8wcjeuBeOAjI2ItIH+AJbBS9epzt0teoeX44npzKpOU7GD9vM3/8u4s0A43KFuCOlhW4plYxcmXLAm56X4yBWR/Cr09B0iXW1ZYv/HNOFUWJYnIXtTH1v7sXJg+Cncvh+vcgPnx95JFGoNbmWeBr4FZjzP7Qycn8GGP4e/MBJszfzHeLt3LgWDLF82bn7jYV6VivJGUL5XRbonuknISJD8GCT6DqdXDjxzYqlqIoSrCJT4SbhkBSNWvwD2yxjYvEfG4rc4VAjX1B4AM19Omz69AJvlm4hfHzN7Fqx2GyxcVwVY2idKpfkksrFCI2q7npfTmyGz7vCRv/ghYPQ5unbDQsRVGUUCECLR6y/fhf3Q4jroEeX0KeYm4rCzuBGvsZQDVgcgi1ZDpOpqQxZcVOJszfxNSVu0hNM9QtnY/nb6zBdbWKkzcx3m2JkcGOZTYi3qEdcNNQqHWz24oURclK1OgIiQXg8x4w7Aro+RUUcm9RGjcI1NjfD3whIvuAnzl7gB7GmLRgCotklm61bvpvF21l75GTJOXOxm0tytOpfgkqJun88DNY+TN8eSsk5IS+P0HJ+m4rUhQlK1Khje3HH9vJGvzuE7JUeRRoBD2PIU9vZ2OMiZjRZqGIoLf3yEm+XbSF8fM2s2zbQRJiY7i8ehE6NShJi4qFiIvGpWUvBmPgr3fgt2egWG245VPIG4Vx/BVFyVzsWQNjboLDO21I7krt3FYUNIIRQW8QWXTE/b4jJ3niqyVMXrGD5FRDzRJ5GdThEtrXKk7+nJG7lrKrpJyA7x+AxZ9C9Rvghg9tlCtFURS3KVgB+v0KYzva7sUOH0DtLm6rCjmBzrMfGGIdEUuexHi2HjhG76Zl6dSgJFWL5nFbUmRzeKftF9s0G1o/Aa0ey/LzWxVFiTByF4E+E+08/K8HwJGddhGdKOaCXe8ikgs7On+rMSY5+JIii9gY4du7m0V3bPpgsX0JjOtqR97fPNJGs1IURYlEsuex/fZfDYBfn4bDO6DdoKidJRTwrxKR60RkAXAAWAvUdNKHiki3EOmLCNTQB8CSCTY8ZVoq9PtJDb2iKJFPXDboNBwa3gZ/vWtXz0uNzjZsQMZeRG4AvgV2A49xej17gHWAhsrNqiQft/3zX95qF50YMNWGq1QURckMxMTCNa9C26fh78+sd/Jk9C0BE2jL/hlghDHmCuAtn23/ADWCKUrJJOxZA8PawfwR0Ox+6PODDVOpKIqSmRCBlo9A+3dgzWQY1R6O7HFbVVAJ1NhXAz53/vcdlb8P24evZCWWfg0ft4L9m6Dr53D5IIjVIEKKomRi6veGLmNhx1IYfiXs3+i2oqARqLE/CBRKZ1tZYFdQ1CiRT8oJmPgIjO8DSVXhjhlQ5Sq3VSmKogSHqtdAz2/sCP1hV1jDHwUEaux/A54QkXxeaUZEsgH3AD8FW5gSgexbb2u7cwZDk7vt1BVdsU5RlGijTFPo+zMgMPxq2PCX24oumnSNvYisFZHaztengKLASmAo1pX/OLAIKAkMDKlKxX2W/wAftYQ9a62b66oXIE6DCimKEqUUqQ63/gq5kuCTG2wZmIk5V8u+LJANwBizHruO/Q/A5dh17VsCs4DGxpitIVWpuEfKSfj5SRt8omB5uGM6VLvObVWKoiihJ18p6PeLnWn0RU+YP9JtRRkm4KA6xpjNwK0h1KJEGvs3wvi+sGUeNLodrvifnZeqKIqSVchZEHp/Z8cpfX+/jRLa8pFMFxn0fMY+S8bDV7Cr1X19uw2So9HwFEXJyiTktIt5fXcvTH3eRtu7+hU7Rz+TcD5j/6yI7A7gPMYYc97AOiLSCegKNACSgI3AV8ALxphDXvvlB14FbgASgZnAf4wxSwLQolwMqckweZBdsa5oTbh5lF04QlEUJSsTG28X9cqVBH++DUd2wY2DIT6728oC4nzGvg5wIoDzBOoBeBhr4J8ENgN1sYP72ojIpcaYNLGxab8DygH3YufxPwFMFZE6TneCEgoObIEJ/WDTLGjQD658MdNkZEVRlJAjYmOK5CoCvzwJR/fCLWMhe163lZ2X8xn7G4wxc4J4vfbGGO85+b+LyF5gFNAamAJcDzQH2hpjpgKIyExsWN5HgfuCqEfx8O8ku/pTygnoOAxqdnJbkaIoSmTS9G7IWdjG0h95LXT/0q6kF8GEdXkfH0PvYa7zt4Tz93rsinpTvY47AHwPdAitwixIaop124/tCLmKwoBpaugVRVHOR63O0O1zOx152OU2fHgEEwlr+bVy/i53/l6Cjbfvy1KgtLPErhIMDm6DTzrAH69D3Z7QfxIUquS2KkVRlMxBxXbQ53s4edhG29u60G1F6eKqsReREsAgYJIxZp6TXADbT+/LXudv/nTONUBE5onIvF27NHrveVk7DT5uAVsXwI0fQ4f3ICGH26oURVEyFyXqQ79fbfk58jpYM8VtRX5J19gbY2KC3F9/Bk4L/VsgBejrvQn/A/7OOanRGDPYGNPAGNOgcOHCwRMabaSlwtQXbUSoHAXhtqlQ+xa3VSmKomReClW0Bj9/WRjbGZZMcFvRWbjSsheR7NgR9+WBK31G2O/Ftu598bTo/bX6lUA4vBNG3wi/v2QN/G1T7GI2iqIoysWRpxj0+RFKNYIvb4VZH7qt6AzCbuxFJB74EmgEXONn7vxSbL+9L9WBjcaYwyGWGJ2s+wM+ag6bZsP179n5ogk53ValKIoSPSTmgx5fQbX28PPjMGkgmMiITRdWYy8iMcBY4DKggzFmlp/dvgNKiEgrr+PyAO2dbcqFkJYG01+FT66HbHlsa75ez0wX6lFRFCVTEJ/dBiOr3xdmvAnf3mNnPblMwLHxg8T7wM3A88AREWnitW2z487/Dhsxb4yIPMLpoDoCvBJmvZmbI7vhqwGwZjLUvBmuexOy5XZblaIoSnQTE2vL29xFYdqLcHQ3dBrh6iDocLvxr3b+PoU16N6f/gDGmDTgOuA34APga+wqe22MMZvCrDfzsmEmfNQC1s+wme6mIWroFUVRwoUItH4crn0D/v3VTnM+uvf8x4WIsLbsjTFlA9xvL9DP+SgXQloa/PU2TP4f5C8D/X+DYrXdVqUoipI1aXirjbb35a0w/Cro+RXkLRl2GZEQVEcJFkf3wrhb7KCQau1hwO9q6BVFUdym+vXQ82s4tM0G39m5/PzHBBk19tHCprnWbb92Klzzml2WNnset1UpiqIoAGWbQ9+JkJZiW/gbZ4f18mrsMzvGwMz3YcRVdlBIv1+g0W062l5RFCXSKFoTbv3VBjT7pENYo+2FezS+EkyO7YNv7oaVP0LV66DD+3aep6IoihKZ5C9rDf5390HB8K1FosY+s7JlPozvAwe32nXnm9yprXlFUZTMQM5C0PXTsF5SjX1mwRjYsRRW/QQrf7bGPm9J6PszlGrotjpFURQlglFjH8mknLDz5Ff9bA38gY02vUR9aPOUndKRw98yAoqiKIpyGjX2kcaRPTYAw6qfYPUUOHkI4hKhQhto+TBUvtJGZVIURVGUAFFj7zbGwO5VsPIn+9k8B0wa5C4GNTtC5auhfCuIT3RbqaIoipJJUWPvBqnJsHGmdc2vnAj71tn0orWg5SNQ5WooWhtidGakoiiKcvGosQ8Xx/bB6snWuK+eBMcPQGw2KNcSLr0HKl/lSghFRVEUJfpRYx9K9qyxrvlVP8OGv8Ck2hjJVdvb1nv51pAtl9sqFUVRlChHjX0wSU2xfe4eA797lU1Pqg7N7ocq19iR9OqeVxRFUcKIGvuL5fhBu178yp/tKPpjeyEmHso2g4b97ej5/GXdVqkoiqJkYdTYZ4R9G5y57z/ZefBpyZCYHypdYd3zFS7TRWgURVGUiEGNfSCkpdmIdZ7odTuX2vSClWyY2ipXQ8lGEKu3U1EURYk81Dqdj6N74f1GcGQXSCyUbgpXPG8NfMEKbqtTFEVRlPOixv585CgANTtD8bpQ8TINT6soiqJkOtTYB8JVL7itQFEURVEyjM4BUxRFUZQoR429oiiKokQ5auwVRVEUJcpRY68oiqIoUY4ae0VRFEWJctTYK4qiKEqUo8ZeURRFUaIcNfaKoiiKEuWIMcZtDUFHRHYBG4J82kLA7iCf82JRTecn0vSAagoU1RQYqikwsoKmMsaYwv42RKWxDwUiMs8Y08BtHd6opvMTaXpANQWKagoM1RQYWV2TuvEVRVEUJcpRY68oiqIoUY4a+8AZ7LYAP6im8xNpekA1BYpqCgzVFBhZWpP22SuKoihKlKMte0VRFEWJctTYK4oScYiIuK1BUaIJNfaKokQiAiAiWkYpShDQF0nJUni3GNWQRCYiUgv4WUQKGWPS9DkpysWjL5ESciLFJSsiscYYIyK5AYwxaW5rUvzSFmgKfCYiBdTgZ25EJM5tDf4QkVi3NYQTfYECwF+miNTCJ1IMqwePgXX+L+WmFmNMqojkAlaLyP+5qcUXT4Ho1vPz5PEIyT/vAc8AlYGvItXg+xqxCLl3p3S4qUdEYkWkKIAxJsVJu9ItPf4wxqQCiEgjt7XA2XYm2M8vol6eSERE4hwjkUNEuovIXSJSNVJahb4ZxETYXEqvF+oz4B4RKRRuDV6GNAZ4EVgEfBduHefCGJMiIjmBQSJSIJwFtVMhS3Wu/4KIjBORB0Wkfrg0eGmJcYzDO8C7QHng60gz+CIinmcmIg+CfffcNrAeHd5/XdLUGhgnIq0dDb8Co9x4/8+FiLwADA33O+dHR6xXWXmLiFQLdlkeke6VSMHrhc4NzABKAnmAEyLyX2C0MWaXy/o8GeQuR18iMAZYZYw55KI278zbA2iGLcD3h1uLlyFtjn1+nxtjFodbRwB0B54CRhhj9jrPN+SVNy+Px2wgO3AEuAlYJCIvGGO+DbUGOFWxTnHyToqIvOVsuhdr8G907kuMm5Vtr8pRDNAbeE1Echpj/ucx+OGudPtU2B4DigEHsHl9bji1OGx3/n4nIkscPdcbYyJtIZpVwCVALWPMNJeeXYxXWTka24U1XUTuMcYcDdqFjDH68fMBYp2/McBbwE9AQ6A+1s2YCgwCioRZV07nuiW90r4C9gAbga3AMeBloEIE3MduwEvAC0CMizpeBNKce3O92/clHY15gRXAEE/+C/H1Yr3+7wL8AlR2vrfHekCWADeG8R7kdLQUcr7HA484eXsaUMBJdyUveZULuYDHgS+xFdg0YJDXfuKCtlzAcmAt1ohtBJKBR4EkF/RUBQ4BJ4G73XheAWjM4eSraUB+l7WMdJ5Z61DYFddvdiR/sK3kDsB4oKvPtpedF/zZcBp8bIsrzckYRYE+wDKgpfM9J9b9eQx4Hyjo4v0bABx3KiJ3O2nxLmkpia20pQEfAflczlviMQhef2OA4Y7BL+S9LYQ6cgBDsWE7n/HZdnW4DT62UpgG3OVl2L0N/u9e6SGvEKWjMadjTH8B/ott3c9zdL/k/YzDoCXO6/9nnftTHYgFKgGvO7oeCVN+ivH6/zJgFrAAOAhc7sbzOl9eAZ5wyqgmbuUroBWwDrg5VM/IlRufWT7YFnOa82JX8M0IjsFPBQYCxcKkKTvQ3zHm72HdvoN9MyjwKpACXOd8D3tLCCgIfOLcw2mcbhWF9GVK7/yOwR8GnABuB7K7lK9i0tOJHZR2BHgsTFp6Y929h4EBnjzmtf0ap7BeBHQLg54EYDRwFLjbj8HfAEzBqQy59Pz+h614VOR0Ra0y8IGT15/12jccBj8X8H9Y79V/vK+N7ap9A9u6rh9iHd5lY33nnhQA6gC/OQb/yvSOCdOz6wvkBxK80mKcMv4rF/NUD6dMr+yTLuf6fkHXcOvHZYYPtgY/y3mBXwJyeDKH1z4e93D/MOrK5hirY861h3pti/f6fx7wY5g0+c2UWNf0cMeAfejRF6qXHKel49yjdtjWRX2v7YWxYxqOYz0Pbhn87MAfWJd9K05XALJjXcN/YitLQTUWeLUEPfnFMaI7gNU4Hg+ffHQVsAU7RiVo+cQ3D3C6MhgPfOrkb2+DHwc87Biu99x4bo6OT4BFnt/jlderAAudd3Kgv98cIj13ONdMAx7wfs6OvirAJuCFUOnhTEM/FFjs6PK87/WASViDf5mTlg34OFxlJ6e9ouux3r0qXs/uQWAn0M6lPNXP0VbJ+R7js/1e7LiCjF/DjR8WiR/fF8DrZUnEtmz2YGuFCb4PA7jTtxANtUZHVx9gH7ASqOG7H3acwfQw6PJ+0XMDhX2253MKyK3YroU43+OCeX8cDfOdlzcFW9F4A2ecA9bgj3aMSX8g0YX8dgm2W2GjU/jMAK7EtjoudV78lv7yZhCu7dsvHgc8BOxydBR00r1bP02C8byAXN7P3snHLb22eyo98cBYTrfwvfvwuwU77wSSt73SXsB6Q5I8+3jlvf7OtjTg6TBpLI7tTtiL9Xp4tHjeswRgDfB+GLSMw44ZuAUo4bPNY/APAa9hu0fTCJHHwfe9wTY8imIr2Mud9/8dbBdoQWwZ/3yI749fDyt2PNh24DOcyq3XtiLOfXv5YvJ9yDNiZvhw5mC83FjXk7cxzwH8gzUe/fBj8J3vITH458gguYFbnUw7Dqjota0QtnX4uVNAhqYf6ExD/zy2tbrGKXTKcbpmnx9rYLc5L1hQ75XXM4wDpmO7Da7Dtkr/5xiN74Hizn7FsOMe0gjDgL1zPMOCzjOchK24rcYOwNyKnR6YKwRa/PWLx2Fb+JudfHOWwfd93hm4blXsYLZ2Xtec6hSy1/neK+zMiSlYr8Pt+AwyuxgtF6g7EWjq9f1abEt5mNf9E6f8eA74AuvN+henpRbsfO4nvQS2OzEV6xXxLr9qYyuVIa18YCuQ6537431970ZKTWwlbiswl4tsrQZyn7CVsdw+mqoATzvP6CjwNna8QxpQNwyaqgO1OLOR9g62wjbEq5yqCoxw3suLykshe/CZ5cNpI5ELGIV1fS90/k/02i8HdqDSDmyLOls49Tn/twFuBDp6FYgxTkF43CmkH8UW4mOxLrPqIdTm/fJ8gR1g8jTQFduange04EyDPwLbZ/5qEHV4WjKJQBLWiDf31ol14R0FXvdKL4Ht6wypV4bTLaw4bCvsknT2uw5bYdqLrcBtw6nAEcQxF6TfL+4x+Jucgq9wsK7pnL+58w4dBlo7adWc37kMaO+b74H/cNpF3TGUzykdzbHYbpVNwBVe6R84ZcFwbMtLnN/yB7Z76HJHc5sQ5KNE7FiK27GDKPM66cWwBj8FW4l61PnMwpZpoWqMeJ7Vc9hWva9n76yGBlABnxZssPU4/w8CfnTKplH4DDTFethuxVay07BehzIh0ORdVg7HehaOYiu/H3DauA/Betj2AH87+20F6ly0hlDc7Ej+cGYt02MwcwFLgb+wLvn/Og9+DpDTa/8c2IFKacC1YdDqnWk/xbaYDzsZ8m/nRc/uFEje7sMJ2BH51UKhCWfsglfaIOwI8sbO90ewBn2z85K14LQ3pCC2v6xyCHRNdn7/Jt/zO9tfcO5dbT/Hh7ogzIV1W65wXt7Z2Fq7PzdxRWx/5xbgw4vM4xnpF38IazAu+NoBaGuB7So4gTM6GztqfJdTsLX3KRgfxUbTezhUzygAzT2xfdDzgWu80t938tphbGVlh7NfHHa8yE7POxEEDZ6yKrdzjX+de+i5b7Wd7UlYg7/XeRcGYufdZ/eXHzKgIw5baW/iPLeCXttGYbsUY5yPrxv9aqB8GJ/bl8479Bm28bMJO97jTj/7eryk5UKs6RNHRw+gk3PNFGAiztQ/bIPuFWyF/KFgaQr7i+P2h9N9hp6XJwH4FmsoPH1wE7D9J4ewLZEcXsfndB5CqAaYZefsvq6PsW64a4G6WBfQSqwhrefsk+gUSmnY6TZBn+LmvBC/YweLeO5jEezAO8/UOs8Aqi5AY6xhW4xt1fnt/siAjgQ/aXHYitpcrFfBY0i8K0ztnRer+cVc/wJ0elcml2G7Fm7Bdi2kYceCtMB/BTQeWylayAVOnyQC+8V9fmNLrME/yWmXfmWs4VrmXDsR21L+nTNHmIfaC+Nd0fAepNgJ2yBYgFdFHzvO4lnsoLTHvO75BGzZETTviFM2/IXt8mmKrTi3xVby1wBFnf2KYo38Ic4cvHtRg1GdfDwGW7lIc96lTc47X9R5v9KAHt75z/m/OnZE/s0heGbi+z+2srwDO/7FU+40dZ5LGnCL1zHh6g5qhR31fx2nvZ0VsLEQBuPVsAzJ9cPxIyPlw9l9hjFAI2zNr6mTNsHJwA2xRi0N6wbL4ed8wR5glgdbyXiA0wVyFaeQ6eOVactj+3dHc+Y0qVzOfqFo0Qu25p4G7Ma6EBOxhuFa7KC3ptiadH+v4z51jtmBV7/nReioi3V1tfezLcH5/eudl8q3j/cmbGurdQjzWEG8+tkdTeOwBbTHmE5w7tMqrNuzhb+8hK3178NrLEYG8njE9ItzfoNfCdti3YmtyK4ihO7n9PRhjWo2z/Pz2u4x+Avxcun7nKM21iDuIwj90T73rJVzX9p5lQXdsS38x32ea3GswU8DRgZBR26sV2oqtrVZB1tB/NK5xo/Y7ruvnfzXyevY0lhjthooG+RnltMpD1r7pL+JbWQkcmYFrj62wvYHkCcc+crr2rc4+aK6870i1gPzGadnerX0OSZoY63C9kMj4YP/PsN82LnGscD9Toa81OuYv53MvJMQTtNyCt1/sS2Z4l7pTZyX2aO3ip8McmswM8U5NN6B9SjMwRbS9/q8SA86BUIpr7TXscbuRy52gIkdOLmU0324X2CNu7eGbM7z3IY1Fldh++XaYF3nfxLE/m8ffc2wRvwuTreuy2I9M554B59jK5N1nP33OoVPc5/fkQNbcC7lAlqH6eTxiOkX5/wGvyQ2dsQH2BZzSGZunENfPLYFOgtnzA5nGvxuzvu4GJ8gMc67+alzbM2L0FAQqOIn3TPd1qOru/PMPIY+r3Pvcjrfk7BjUtK4iC4Z5558j/V+lvF5hjFe+ecrbBn6m/P9W+AHrGHdg5/usyA8L8/MlUmcWW5/Amz1+u4dfOhhbGu6dAj0xGLHTnQCrgdaeW3r6+SdWGzjyFOOe8qK9ljPZNWQ5O1QnDSSP5zZZ9jWOyM4N/5br5sf67y4Y7CFdKhc93mwNfbJOMF5ON3KqIMdYdvO2W+voyW3s70xtqVxZSi0+eisgm0BvogdSJYC3ON1v17GeiaqOd8LOvfz3mDcO6dg8XgKvsF2EXjc4b04PZgtHlsQbsYWjluwAwMnEaJ5/thKhmc0725sxSi7k97W0dQLW5ls5XXcz84xB70LQ2xLainQ8CLzeMT1i+Pf4J/g9Pxr31gAoXrv/HUHZcMGpNqIraD6M/hDnPdgAz598ljPSoZD0zrv+3fYPlzfykQ75z41xPbrpgFPeO4p0Nk5rpbXMUWxYX0z7O1zftM/2IrOqYHBPvt4DP6D2C6Zx7CG6w/s+KGgjtHxzkfAFVjjPRWniw47hukk1kt6RoUR65HYjNPtEUQ9ObGBzuZzuvKc4jzPqthK979YT9perGc2j3NsEWwZNZFQDVwMxUkj8XOOAqa1k5YDO3p8ivM9xsm0kzmzcA62kciJdTv/ymlD78mU2bC1wy+cguUQ1pXuqbkXcjLIH8HOuH50el7yRxy9LbF99afiXmOn1SRj+6bfxrqrdxKEGP1e16/onPMNrIv8Ua+Xay22YuEJTNEFWxHYh1cBTAgMmpNfnsC2qqc498HX8/Eu1iuSxyttHNa4vOP13GMyojOdPB5x/eJ+tLbyeh89sQXOGuAV5OufqzsoETtIdyu28PUY/Fhst8gEbEVgZDDLA2wrdRe2hexvEFl1bN/83869ethzL7EV8RlO+eA7MO5ix8jc7LxfZxlsn+c4DlsJKuJ893SFBL2y5skfXu/KlVjDOs15tonOu7YNuM3ruELYgbIzcBpMQdLjie3xO9a7UsvJ129iK/+LsZXsN5zv6zg9RqwythzfQShnT4XqxJH4SacwPIEzPcYpAJOxLedXsDXTWV4ZKhSRp+50XqRXPZnY+ZsNazhGYuf2r8Eauaud7Q2dF3sP6Uzlukhd2bBuc0/foMcQNXEydS+sp+ETrEG5z9neBtt6Xee8bBl2Z6ajK5/zsh7w/G5Ha19gJqfDG7/ovET3OvduGaenKIWqpVgD20J/Euv5SMa2Ijyej9GOtgpOQVXZyYPe/Zveg5ouOL+lk8cjpl/8PFr/cp5rnRBfN5DuoJxYb8c2rPfF0zos53yv5++ZXYSm2lgP1Ft4hd72PbeTz9Oc/HwZtvJxjZP353vpDOZUzS7YcrLKuc6NHWGeBjTz3i8j+fgcWhJ8zu39zK7CGvw/nHyeF1sxOog1woOxlYH9BLFcwpaDa7HdF9V98nUuR9d65xm1dJ7xNk7HI5njvIuhzfehPHkkftIpDL1bFPc6BeFKbD+Vx+0bqn7e/HgtquOkeQz9XE7X/m51MstJbGG93Nmndgg05cTWMv91MmYdn+1jgRXO/7mwtdKTwL1OWl7PJ0T37Aq8XJheOjZgvTPTsH3WJ7Ct7V7YfvJthGBQDme2MP7nPJfG2L76k8A9zrZ62C6ZudgK5SLn/5BEEvTJ4xHTL34OrVc6eW40tgskVIGgztcd5PEMJWLd0buc/OOZOrmQ05Xfi9aI9Ri865Q3JbzPizXmObDGy5PHemGNyy6s12o5NlpmqLqoqnF2jAp/M0iKOfexQ4ieW07nHV+CNdzt8Bof5OxzHdbg/4X1AubETrn1xBoYQxAHMOPHM+t9T7zy2xXYFv04J60lNorgR9guvzKhuGdnaA31BSLx46cw/BNrGDz9PYVxAmU430M93SeP8+DTsMZiMdawl/TRWhHbV/eIUzCGZPEdTsfaPold2eskdqDdTc72stga813O9yKcjn//KGEIP4stpHdhxwXkxLZ05jsFTkHsoLw3vfa/DVtQB2XZX+dZlOB0l4qn8L8c22rsgC2kfT0fLbEVktlYgxPS0MFe13SlXzwDWifidKWF6FqBdgfdhzO/2XmmE7CRGYcE+5lhDfo8YJRPeklsoJoF2Mr3nzjR3bBdZq2xhr8+Gez6CVBfPmxreStehpyz++27Yb0TIZmrzmkvaBqnPTNrsY2P7pwOh90Ua/B/xysanvM+Bjty5xme2XPslxPr7UvDrfj7blw0Ej6c3WfoMfitnDS/A1FCqMdj8I9hjZj3iPxwrwyVH9viO4Htc+rpvDibsAPubsIOhhnudUwS1h26gzCsC42NUpbi6FzlFNTpjmLFDpALSqse2yeYhm2FjsJZGtNr+zfAQq/n6uv5yM6Zo4NDFdDH1X7xC9Xq3KvvsC37bKHURuDdQS8DpfCpwAbzmTn54XdsRacYtv/XE5LX43GY6JQL60inkh/Ksgo7cPCw8575G+fgCYc9OVTvv3ONgdhooe9iK2GvY93hB7Fjmr7GjnO6F+tFm0A60ySDqMnjmf3vefZtgO3a6+2THpZ3MOQXiOQPLvUZnkNPXk7X/s6ZccKk5VVOx1Av5tyj2di+qaPONu+wtIXSK4hC9Mz+5PTyueXSe2mC+TJhXa5PO9fdgzXsqViXfW9nn3pYl2E353sRbBz1Y9iR0dlDoS2A++V6Hj+P1rKOQQvZICWf6wXSHXTQ2eeMpWNDoKUZpw37HGxF9m/gfmd7PHCDYyxeDEfe8aPxKqzB34D1QBZxjF1bbIV2P16x3kOkwbtceshJi8NWZgdxepXSRZz2AowmhN5GzvTMPuNnu6fhWNi5f/eF87md0uHGRSPpgwt9hufRc86M44KWN/CqfDjG7jrsAMZfCdGc0ECeGdaFdgD4X5iv7/F8JDuFXkdsf+t2bLfHbU5B/b7XMYWxo6ynuVBIR1QeP4/WkEYR83O9bzh/d9BrhGeGwqVY78tS7JiKKj7bSzkG9RkXn0995/4kYyuvR5z8NJ8QLWrjR0MebIs+zffdd7ZVxg6S9TzbkFceAym3OR2DP+xlpjFq7D0PIWx9hgHq8c44YVkm8zxa0nux8rmsrQR2vuyvvs8xDNfO6/WM7sGO8K6FdWP+5KQf4cy58/kI4cyO8+iNqDweKR8uoDsoTAY/Dj8rHWK7XK5yNN7iRh7y0lIIOwD1Huw89uZcRGyBDGrwa1w5exxBPrc1OdtKYLuNxhPmyH2nNLhx0Uj8EOY+wwD05OF0X9CjLt8b70w80Cs9LOMZzqPtLkdXW5fuyxve9wXr+WiOjVz2MX5mJLh13yItj7ucby64Oyjc2vCaRocdUDgTO1XLtcGUkfQ5j3EN2myJi9Dk8Ybmws4e2JpeRTIcnzgUAIwxRkQKYEdytjLGnHBZz0EReQE7sOuHCNAyyPn6XxFJMcY8Z4xJc1OXw0RsfO7p4b6wc18G4rzYIhJjjPkv1hU7Q0QSjDEnnfQ0r+NcuW+RlsfdxLkXYmxpPAYbI+EPY8w6l6XhaMIYkyIihbCrxXkiMrY0xqSKSKwxJtVNnW7jUy49IyKpxpjnnG2pzl/joqaBIpKA7Q7qgQ3nuyKceryRMN+LiEdEchpjjritw4OvoXATEcmD7Ut8BBuP+xWXJZ2BiMQZY1JcuG4ebMS1B4FBxpiBTrpA+Auc8xFpedxtRKQEduDpMmPMFV6VALd15cPG+9iNHQPS06kAuJLPI5VILJd8NKVhw14vdFOTtux9iLRCMFIMPUSWt8EfbhWAPrX5/3NaGP+LBIPhj0jL425jjNni5Ov3RKStMWaK25oAjDH7ReQK7KpxPxpj0pwWvRp6LyKxXHI0vYgdQPyVmy16D9qyVy6YSPI2RBJObf5p7IIy/Y0xw12WpASIiJTFTunqGqnGVN+7cxOJ9yeSNKmxV5QgIiJ5sUGIPopUo6GcG3WTK9GIGntFCRFqNBRFiRTU2CuKoihKlBPjtgBFURRFUUKLGntFURRFiXLU2CuKoihKlKPGXlEURVGiHDX2ipLFEJE+ImKcT2U/21t7bW8XQg39zqGtYiiuqyhZFTX2ipJ1OYSNCeBLL2dbKOkDnGXsFUUJDWrsFSXr8hXQwxPDH0BEEoGO2MWFFEWJEtTYK0rWZTRQBrskr4cbscv0nmXsRaSHiCwWkeMisltERotIMZ991ovIGBG5RUSWi8gREZknIs299pkGtAKaeXUXTPO5XCERGSsiB0Vkq4i8IyLZg/OzFSXrocZeUbIuG7BLA3u78nsBXwOHvXcUkQHYysFy4CbgceBK4HcRyeVz3hbAQ8D/AV2wlYcfnFXcAO4CFmJXcmvqfO7yOcdoYI1zrQ+Bu4EnMvYzFUXRVe8UJWvzCfC6iNwH5AfaYddPP4WIxAL/A6YZY27xSl8B/IHte3/H65A8QB1jzD5nv+3AXOAa4FNjzDIROQjEGWNmpaPrU2PMM87/k0SkMdAVeCad/RVFOQfasleUrM14IBvQHugObAcm++xTBUgCxnonGmNmYL0DrXz2n+kx9A5LnL+lL0DXjz7fl1zg8YqieKEte0XJwhhjDonIN1hXfllgrLNuuvduBZy/2/ycYrvXdg97fa5xwjnfhfS57/X5fgJbKVEUJQOosVcU5RNsSzoG6yr3xWN4i/rZVhSYFyJdiqIECXXjK4ryG/AF8JExZqmf7SuBHcAt3okicil2NP/vGbjmCSAxA8cpipIBtGWvKFkcY0wq/lv0p7aLyH+Bj0VkDDAGKAE8D/wLjMjAZZcBd4lIF+yo+0PGmJUZOI+iKAGgxl5RlPNijBksIkeBR4BvsVPzJgKPGmMOn/Ng/7yMHfg3FMiF9Q60Do5aRVF8EWOM2xoURVEURQkh2mevKIqiKFGOGntFURRFiXLU2CuKoihKlKPGXlEURVGiHDX2iqIoihLlqLFXFEVRlChHjb2iKIqiRDlq7BVFURQlyvl/47xImDFQHxUAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# You can also add ticks and ticklabels to a line plot\n",
- "\n",
- "ax = temp_df.plot.line(figsize = (8, 4))\n",
- "ax.set_title(\"Average Temperatures in Madison, WI\")\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")\n",
- "ax.set_xticks(range(12)) # makes a sequence of integers from 0 to 11\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation = 45)\n",
- "\n",
- "# This gets rid of the weird output\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Temp (Fahrenheit)')"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# We could explicitly pass arguments to the \"x\" and \"y\" parameters\n",
- "temp_df_with_month = DataFrame( \n",
- " {\n",
- " \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"],\n",
- " \"high\": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],\n",
- " \"low\": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}\n",
- ")\n",
- "\n",
- "ax = temp_df_with_month.plot.line(x = \"month\", y = [\"high\", \"low\"], figsize = (8, 4))\n",
- "ax.set_title(\"Average Temperatures in Madison, WI\")\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### We can perform a calculation on an entire DataFrame\n",
- "Let's change the entire DataFrame to Celcius"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>-3.333333</td>\n",
- " <td>-11.666667</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>-0.555556</td>\n",
- " <td>-9.444444</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6.111111</td>\n",
- " <td>-3.888889</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>13.888889</td>\n",
- " <td>2.222222</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>20.000000</td>\n",
- " <td>7.777778</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>25.555556</td>\n",
- " <td>13.333333</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>27.777778</td>\n",
- " <td>16.111111</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>26.111111</td>\n",
- " <td>15.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>22.222222</td>\n",
- " <td>10.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>15.000000</td>\n",
- " <td>3.888889</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>6.666667</td>\n",
- " <td>-2.222222</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>-1.111111</td>\n",
- " <td>-8.888889</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " high low\n",
- "0 -3.333333 -11.666667\n",
- "1 -0.555556 -9.444444\n",
- "2 6.111111 -3.888889\n",
- "3 13.888889 2.222222\n",
- "4 20.000000 7.777778\n",
- "5 25.555556 13.333333\n",
- "6 27.777778 16.111111\n",
- "7 26.111111 15.000000\n",
- "8 22.222222 10.000000\n",
- "9 15.000000 3.888889\n",
- "10 6.666667 -2.222222\n",
- "11 -1.111111 -8.888889"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# call the function on the dataframe\n",
- "celcius_df = f_to_c(temp_df)\n",
- "celcius_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " <th>freezing</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>-3.333333</td>\n",
- " <td>-11.666667</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>-0.555556</td>\n",
- " <td>-9.444444</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6.111111</td>\n",
- " <td>-3.888889</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>13.888889</td>\n",
- " <td>2.222222</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>20.000000</td>\n",
- " <td>7.777778</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>25.555556</td>\n",
- " <td>13.333333</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>27.777778</td>\n",
- " <td>16.111111</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>26.111111</td>\n",
- " <td>15.000000</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>22.222222</td>\n",
- " <td>10.000000</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>15.000000</td>\n",
- " <td>3.888889</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>6.666667</td>\n",
- " <td>-2.222222</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>-1.111111</td>\n",
- " <td>-8.888889</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " high low freezing\n",
- "0 -3.333333 -11.666667 0\n",
- "1 -0.555556 -9.444444 0\n",
- "2 6.111111 -3.888889 0\n",
- "3 13.888889 2.222222 0\n",
- "4 20.000000 7.777778 0\n",
- "5 25.555556 13.333333 0\n",
- "6 27.777778 16.111111 0\n",
- "7 26.111111 15.000000 0\n",
- "8 22.222222 10.000000 0\n",
- "9 15.000000 3.888889 0\n",
- "10 6.666667 -2.222222 0\n",
- "11 -1.111111 -8.888889 0"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# here is one way to add a horizontal line to our line plots\n",
- "celcius_df[\"freezing\"] = 0\n",
- "celcius_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# this plots each column as lines\n",
- "# with rotation for the tick labels\n",
- "ax = celcius_df.plot.line(figsize = (8, 4))\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Celcius)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation = 45)\n",
- "ax.grid()\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plots using DataFrames"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Bar Plot Example w/ Fire Hydrants\n",
- "\n",
- "- General review of pandas\n",
- "- Some new bar plot options"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
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- " <th>Y</th>\n",
- " <th>OBJECTID</th>\n",
- " <th>CreatedBy</th>\n",
- " <th>CreatedDate</th>\n",
- " <th>LastEditor</th>\n",
- " <th>LastUpdate</th>\n",
- " <th>FacilityID</th>\n",
- " <th>DataSource</th>\n",
- " <th>ProjectNumber</th>\n",
- " <th>...</th>\n",
- " <th>Elevation</th>\n",
- " <th>Manufacturer</th>\n",
- " <th>Style</th>\n",
- " <th>year_manufactured</th>\n",
- " <th>BarrelDiameter</th>\n",
- " <th>SeatDiameter</th>\n",
- " <th>Comments</th>\n",
- " <th>nozzle_color</th>\n",
- " <th>MaintainedBy</th>\n",
- " <th>InstallType</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>10104</th>\n",
- " <td>-89.439367</td>\n",
- " <td>43.040481</td>\n",
- " <td>286329</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-13T18:03:33.000Z</td>\n",
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- " <td>2018-08-13T18:04:02.000Z</td>\n",
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- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2018.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10105</th>\n",
- " <td>-89.426303</td>\n",
- " <td>43.067854</td>\n",
- " <td>286330</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-13T18:14:14.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-13T18:17:45.000Z</td>\n",
- " <td>HYDR-4253-4054</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2017.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10106</th>\n",
- " <td>-89.445461</td>\n",
- " <td>43.053305</td>\n",
- " <td>286729</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:37:18.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:39:24.000Z</td>\n",
- " <td>HYDR-3859-4055</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2000.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10107</th>\n",
- " <td>-89.388849</td>\n",
- " <td>43.068576</td>\n",
- " <td>286730</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:51:54.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:59:15.000Z</td>\n",
- " <td>HYDR-5052-4056</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2017.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10108</th>\n",
- " <td>-89.518896</td>\n",
- " <td>43.062062</td>\n",
- " <td>287129</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-20T18:54:01.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-20T19:27:16.000Z</td>\n",
- " <td>-2355-4057</td>\n",
- " <td>FASB</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>PRIVATE</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 25 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " X Y OBJECTID CreatedBy CreatedDate \\\n",
- "10104 -89.439367 43.040481 286329 WUJAG 2018-08-13T18:03:33.000Z \n",
- "10105 -89.426303 43.067854 286330 WUJAG 2018-08-13T18:14:14.000Z \n",
- "10106 -89.445461 43.053305 286729 WUJAG 2018-08-15T16:37:18.000Z \n",
- "10107 -89.388849 43.068576 286730 WUJAG 2018-08-15T16:51:54.000Z \n",
- "10108 -89.518896 43.062062 287129 WUJAG 2018-08-20T18:54:01.000Z \n",
- "\n",
- " LastEditor LastUpdate FacilityID DataSource \\\n",
- "10104 WUJAG 2018-08-13T18:04:02.000Z HYDR-3964-4053 TC \n",
- "10105 WUJAG 2018-08-13T18:17:45.000Z HYDR-4253-4054 TC \n",
- "10106 WUJAG 2018-08-15T16:39:24.000Z HYDR-3859-4055 TC \n",
- "10107 WUJAG 2018-08-15T16:59:15.000Z HYDR-5052-4056 TC \n",
- "10108 WUJAG 2018-08-20T19:27:16.000Z -2355-4057 FASB \n",
- "\n",
- " ProjectNumber ... Elevation Manufacturer Style year_manufactured \\\n",
- "10104 1-1830-19 ... NaN WATEROUS NaN 2018.0 \n",
- "10105 1-1830-19 ... NaN WATEROUS NaN 2017.0 \n",
- "10106 1-1830-19 ... NaN WATEROUS NaN 2000.0 \n",
- "10107 1-1830-19 ... NaN WATEROUS NaN 2017.0 \n",
- "10108 NaN ... NaN NaN NaN NaN \n",
- "\n",
- " BarrelDiameter SeatDiameter Comments nozzle_color \\\n",
- "10104 5.0 NaN NaN NaN \n",
- "10105 5.0 NaN NaN NaN \n",
- "10106 5.0 NaN NaN NaN \n",
- "10107 5.0 NaN NaN NaN \n",
- "10108 NaN NaN NaN NaN \n",
- "\n",
- " MaintainedBy InstallType \n",
- "10104 MADISON WATER UTILITY NaN \n",
- "10105 MADISON WATER UTILITY NaN \n",
- "10106 MADISON WATER UTILITY NaN \n",
- "10107 MADISON WATER UTILITY NaN \n",
- "10108 PRIVATE NaN \n",
- "\n",
- "[5 rows x 25 columns]"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# TODO: read \"Fire_Hydrants.csv\" into a DataFrame\n",
- "hdf = pd.read_csv(\"Fire_Hydrants.csv\")\n",
- "hdf.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['X', 'Y', 'OBJECTID', 'CreatedBy', 'CreatedDate', 'LastEditor',\n",
- " 'LastUpdate', 'FacilityID', 'DataSource', 'ProjectNumber',\n",
- " 'InstallDate', 'LifecycleStatus', 'Location', 'SymbolRotation',\n",
- " 'HydrantType', 'Elevation', 'Manufacturer', 'Style',\n",
- " 'year_manufactured', 'BarrelDiameter', 'SeatDiameter', 'Comments',\n",
- " 'nozzle_color', 'MaintainedBy', 'InstallType'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Extract just the column names\n",
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *colors* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "blue 5810\n",
- "Blue 1148\n",
- "Green 320\n",
- "Orange 74\n",
- "BLUE 45\n",
- "green 9\n",
- "Red 9\n",
- "orange 4\n",
- "GREEN 1\n",
- "white 1\n",
- "C 1\n",
- "ORANGE 1\n",
- "Name: nozzle_color, dtype: int64"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Make a series called counts_series which stores the value counts of the \"nozzle_color\"\n",
- "color_counts = hdf[\"nozzle_color\"].value_counts()\n",
- "color_counts # what is wrong with this data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "BLUE 7003\n",
- "GREEN 330\n",
- "ORANGE 79\n",
- "RED 9\n",
- "WHITE 1\n",
- "C 1\n",
- "Name: nozzle_color, dtype: int64"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# TODO: Clean the data ......use str.upper()\n",
- "\n",
- "color_counts = hdf[\"nozzle_color\"].str.upper().value_counts()\n",
- "color_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Fire hydrant count')"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Make a horizontal bar plot of counts of colors and have the colors match\n",
- "# use color list: [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"]\n",
- "ax = color_counts.plot.barh(color = [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"])\n",
- "ax.set_xlabel(\"Fire hydrant count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *style* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "PACER 3620\n",
- "M-3 1251\n",
- "MUELLER 1243\n",
- "WB-59 664\n",
- "K-11 351\n",
- "K-81 162\n",
- "W-59 151\n",
- "CLOW 2500 123\n",
- "CLOW MEDALLION 70\n",
- "CLOW 50\n",
- "CENTURIAN 35\n",
- "EDDY 27\n",
- "MUELLER 90 13\n",
- "MUELLER 86 13\n",
- "MUELLER SUPER CENTURIAN 12\n",
- "MUELLER 92 12\n",
- "MUELLER 93 11\n",
- "MUELLER 91 9\n",
- "MUELLER 89 9\n",
- "MUELLER CENTURIAN 9\n",
- "MUELLER 85 8\n",
- "MUELLER 87 7\n",
- "MUELLER 84 6\n",
- "M-2 6\n",
- "SUPER CENTURIAN 6\n",
- "M-1 5\n",
- "MUELLER 83 4\n",
- "MEDALLION 4\n",
- "PACER 1996 3\n",
- "SUPER CENTURION 3\n",
- "K-10 3\n",
- "PACER 90 3\n",
- "MUELLER 80 2\n",
- "MUELLER 79 2\n",
- "MUELLER CENTENIAL 2\n",
- "MUELLER 82 2\n",
- "PACERR 1\n",
- "2013 1\n",
- "CENTERIAN 1\n",
- "WATEROUS 1\n",
- "MUELLER 88 1\n",
- "WB-67 1\n",
- "KENNEDY 1\n",
- "MUELLER CENTURION 1\n",
- "MUELLER 94 1\n",
- "MUELLER 2004 1\n",
- "MUELLER 2006 1\n",
- "MUELLER 81 1\n",
- "GOLDEN 1\n",
- "Name: Style, dtype: int64"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Do the same thing as we did for the colors but this time for the \"Style\"\n",
- "style_counts = hdf[\"Style\"].str.upper().value_counts()\n",
- "style_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "style_counts.plot.bar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Grab the top 12 \n",
- "top12 = style_counts.iloc[:12]\n",
- "\n",
- "# and them add an index to our Series for the sum of all the \"other\" for \n",
- "top12[\"other\"] = style_counts.iloc[12:].sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Hydrant Type')"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot the results\n",
- "ax = top12.plot.bar(color = \"firebrick\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "ax.set_xlabel(\"Hydrant Type\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### In what *decade* were *pacers manufactured*?\n",
- "### Take a peek at the *Style* column data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 Pacer\n",
- "1 Pacer\n",
- "2 Pacer\n",
- "3 Pacer\n",
- "4 Pacer\n",
- " ... \n",
- "10104 NaN\n",
- "10105 NaN\n",
- "10106 NaN\n",
- "10107 NaN\n",
- "10108 NaN\n",
- "Name: Style, Length: 10109, dtype: object"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "hdf[\"Style\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which *column* gives *year* information?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['X', 'Y', 'OBJECTID', 'CreatedBy', 'CreatedDate', 'LastEditor',\n",
- " 'LastUpdate', 'FacilityID', 'DataSource', 'ProjectNumber',\n",
- " 'InstallDate', 'LifecycleStatus', 'Location', 'SymbolRotation',\n",
- " 'HydrantType', 'Elevation', 'Manufacturer', 'Style',\n",
- " 'year_manufactured', 'BarrelDiameter', 'SeatDiameter', 'Comments',\n",
- " 'nozzle_color', 'MaintainedBy', 'InstallType'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *year_manufactured* for *pacers* and *others*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 1996.0\n",
- "1 1995.0\n",
- "2 1996.0\n",
- "3 1995.0\n",
- "4 1996.0\n",
- " ... \n",
- "10050 2017.0\n",
- "10051 2017.0\n",
- "10052 2017.0\n",
- "10053 2017.0\n",
- "10054 NaN\n",
- "Name: year_manufactured, Length: 3458, dtype: float64"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Let's get the year manufactured for all of the \"Pacer\" hydrants.\n",
- "pacer_years = hdf[hdf[\"Style\"] == \"Pacer\"][\"year_manufactured\"]\n",
- "\n",
- "# Note: We can do this either way\n",
- "# pacer_years = hdf[\"year_manufactured\"][hdf[\"Style\"] == \"Pacer\"]\n",
- "\n",
- "pacer_years"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "18 1987.0\n",
- "22 1996.0\n",
- "23 1996.0\n",
- "71 1987.0\n",
- "72 1987.0\n",
- " ... \n",
- "10104 2018.0\n",
- "10105 2017.0\n",
- "10106 2000.0\n",
- "10107 2017.0\n",
- "10108 NaN\n",
- "Name: year_manufactured, Length: 6651, dtype: float64"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# then do the same for all the other data\n",
- "other_years = hdf[\"year_manufactured\"][hdf[\"Style\"] != \"Pacer\"]\n",
- "other_years"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *decade* for *pacers*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 1990.0\n",
- "1 1990.0\n",
- "2 1990.0\n",
- "3 1990.0\n",
- "4 1990.0\n",
- " ... \n",
- "10050 2010.0\n",
- "10051 2010.0\n",
- "10052 2010.0\n",
- "10053 2010.0\n",
- "10054 NaN\n",
- "Name: year_manufactured, Length: 3458, dtype: float64"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Round each year down to the start of the decade.\n",
- "# e.g. 1987 --> 1980, 2003 --> 2000\n",
- "pacer_decades = (pacer_years // 10 * 10)\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to convert the *decades* back to *int*?\n",
- "- `astype(...)` method\n",
- "- `dropna(...)` method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2000 1730\n",
- "1990 846\n",
- "2010 503\n",
- "1980 21\n",
- "1960 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Drop the NaN values, convert to int, and do value counts\n",
- "pacer_decades = pacer_decades.dropna()\n",
- "pacer_decades = pacer_decades.astype(int).value_counts()\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to *count the decades* for pacers?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1730 1\n",
- "846 1\n",
- "503 1\n",
- "21 1\n",
- "1 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pacer_decades_count = pacer_decades.value_counts()\n",
- "pacer_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Count the *decades* for others."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2010 1196\n",
- "1980 937\n",
- "1970 578\n",
- "1990 431\n",
- "1950 371\n",
- "1960 349\n",
- "2000 215\n",
- "1940 68\n",
- "1930 9\n",
- "1900 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Do the same thing for other_years. Save to a variable called \"other_decades\"\n",
- "other_decades = (other_years // 10 * 10).dropna().astype(int)\n",
- "other_decades_count = other_decades.value_counts()\n",
- "other_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Build a DataFrame from a dictionary of key, Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>pacer</th>\n",
- " <th>other</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>503</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>846</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1730</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1900</th>\n",
- " <td>NaN</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1930</th>\n",
- " <td>NaN</td>\n",
- " <td>9.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1940</th>\n",
- " <td>NaN</td>\n",
- " <td>68.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1950</th>\n",
- " <td>NaN</td>\n",
- " <td>371.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1960</th>\n",
- " <td>NaN</td>\n",
- " <td>349.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1970</th>\n",
- " <td>NaN</td>\n",
- " <td>578.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1980</th>\n",
- " <td>NaN</td>\n",
- " <td>937.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1990</th>\n",
- " <td>NaN</td>\n",
- " <td>431.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2000</th>\n",
- " <td>NaN</td>\n",
- " <td>215.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2010</th>\n",
- " <td>NaN</td>\n",
- " <td>1196.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " pacer other\n",
- "1 1.0 NaN\n",
- "21 1.0 NaN\n",
- "503 1.0 NaN\n",
- "846 1.0 NaN\n",
- "1730 1.0 NaN\n",
- "1900 NaN 1.0\n",
- "1930 NaN 9.0\n",
- "1940 NaN 68.0\n",
- "1950 NaN 371.0\n",
- "1960 NaN 349.0\n",
- "1970 NaN 578.0\n",
- "1980 NaN 937.0\n",
- "1990 NaN 431.0\n",
- "2000 NaN 215.0\n",
- "2010 NaN 1196.0"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plot_df = DataFrame({\n",
- " \"pacer\": pacer_decades_count,\n",
- " \"other\": other_decades_count,\n",
- "})\n",
- "plot_df # observe the NaN values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrant Count')"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# make a bar plot\n",
- "\n",
- "ax = plot_df.plot.bar()\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Ignore data from before 1950 using boolean indexing."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrant Count')"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = plot_df[plot_df.index >= 1950].plot.bar()\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Stacked Bar Chart\n",
- "`stacked` parameter accepts boolean value as argument"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = plot_df[plot_df.index >= 1950].plot.bar(stacked=True)\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "None"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots_template.ipynb b/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots_template.ipynb
deleted file mode 100644
index ffe8742..0000000
--- a/f22/meena_lec_notes/lec-38/lec_37_plotting3_line_plots_template.ipynb
+++ /dev/null
@@ -1,894 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import sqlite3\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt\n",
- "matplotlib.rcParams[\"font.size\"] = 16"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 1: Write a function that converts any Fehrenheit temp to Celcius\n",
- "C = (5/9) * (f-32)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def f_to_c(f):\n",
- " \n",
- "\n",
- "# test it by making several calls\n",
- "print(f_to_c())\n",
- "print(f_to_c())\n",
- "print(f_to_c())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2a: What is the name of the only table inside of iris-flowers.db?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Establish a connection to \"iris-flowers.db\" database\n",
- "iris_conn = ???\n",
- "???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 2b: Save & display all the data from this table to a variable called \"iris_df\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_df = ???\n",
- "iris_df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 3: Scatter plot to visualize relationship between `pet-width` and `pet-length`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# complete this code to make 3 plots in one\n",
- "\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "\n",
- "# getting unique class column values\n",
- "varietes = list(set(iris_df[\"class\"]))\n",
- "\n",
- "plot_area = None\n",
- "for i in range(len(varietes)):\n",
- " variety = varietes[i]\n",
- " \n",
- " # make a df just of just the data for this variety\n",
- " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
- " \n",
- " #make a scatter plot for this variety\n",
- " plot_area = variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
- " label = variety, color = colors[i],\n",
- " marker = markers[i], \\\n",
- " ax = plot_area)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Let's focus on \"Iris-virginica\" data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "iris_virginica = ???\n",
- "# assert that length of iris_virginica is exactly 50\n",
- "???\n",
- "iris_virginica.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Create scatter plot to visualize relationship between `pet-width` and `pet-length`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's learn about *xlim* and *ylim*\n",
- "- Allows us to set x-axis and y-axis limits\n",
- "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
- "- You need to be careful about setting the UPPER-BOUND"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = ???, ylim = ???,\n",
- " figsize = (3, 3))\n",
- "\n",
- "# What is wrong with this plot?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is the maximum `pet-length`?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# How do we extract `pet-length` column Series?\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For every set method, there is a corresponding get method. Try `ax.get_ylim()`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's include assert statements to make sure we don't crop the plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 6), ylim = (0, 6),\n",
- " figsize = (3, 3))\n",
- "\n",
- "#print(\"Ran into AssertionError while checking axes limits\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Now let's try all 4 assert statements\n",
- "\n",
- "```\n",
- "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
- "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
- "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
- "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
- " xlim = (0, 7), ylim = (0, 7),\n",
- " figsize = (3, 3))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Close the database connection.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting Applications\n",
- "\n",
- "**Learning Objectives**\n",
- "\n",
- "- Make a line plot on a series or on a DataFrame\n",
- "- Apply features of line plots and bar plots to visualize results of data investigations\n",
- "- Clean Series data by dropping NaN values and by converting to int\n",
- "- Make a stacked bar plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Line plots\n",
- "- `SERIES.plot.line()` each value in the Series becomes y-value and each index becomes x-value\n",
- "- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot\n",
- "- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!\n",
- "\n",
- "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plotting line from a Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# when you make a series from a list, the default indices 0, 1, 2, ...\n",
- "s = Series([0, 100, 300, 200, 400])\n",
- "s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s = Series([0, 100, 300, 200, 400], index = [0, 20, 21, 22, 1])\n",
- "s # oops this produces a crooked line plot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Let's fix it by sorting the Series values based on the indices\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Craft breweries example"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# You can make a series from a list and add indices\n",
- "s = Series([1758, 2002, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764], \\\n",
- " index=[2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])\n",
- "\n",
- "# We can save the AxesSubplot and \"beautify\" it like the other plots...\n",
- "\n",
- "# Set title to \"Craft Breweries in the USA\"\n",
- "\n",
- "# Set x-axis label to \"Year\"\n",
- "\n",
- "# Set y-axis label to \"# Craft Breweries\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Be careful! If the indices are out of order you get a mess\n",
- "# pandas plots each (index, value) in the order given\n",
- "s = Series([1758, 2408, 2898, 3814, 4803, 5713, 6661, 7618, 8391, 8764, 2002], \\\n",
- " index=[2010, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2011])\n",
- "# TODO: fix this crooked line plot\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Fix it here\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Temperature example\n",
- "Plotting lines from a DataFrame\n",
- "\n",
- "- `DATAFRAME.plot.line()` each column in the data frame becomes a line in the plot\n",
- "- ***IMPORTANT***: lines in line plots shouldn't be crooked, you need to sort the values based on increasing order of indices!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This DataFrame is made using a dict of lists\n",
- "# City of Madison normal high and low (degrees F) by month\n",
- "temp_df = DataFrame( {\n",
- " \"high\": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],\n",
- " \"low\": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}\n",
- ")\n",
- "\n",
- "# Q: do \"high\" and \"low\" become rows or columns within the DataFrame? \n",
- "# A: \n",
- "temp_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Let's create line plots\n",
- " # not a nice plot\n",
- " \n",
- "# Let's fix the aesthetics"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### A Line Plot made from a DataFrame automatically plots all columns\n",
- "\n",
- "The same is true for bar plots; we'll see this later.\n",
- "\n",
- "`ax.xticks(...)`: takes as argument a sequence of numbers and add ticks at those locations."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# You can also add ticks and ticklabels to a line plot\n",
- "# TODOs:\n",
- "# 1. Also add figure size as (8, 4)\n",
- "# 2. Add xticks - how many do we need?\n",
- "# 3. Add xticklables and rotate them by 45 degrees\n",
- "#[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
- "\n",
- "ax = temp_df.plot.line(???)\n",
- "ax.set_title(\"Average Temperatures in Madison, WI\")\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")\n",
- "ax.set_xticks(???) # makes a sequence of integers from 0 to 11\n",
- "ax.set_xticklabels(???, ???)\n",
- "\n",
- "# This gets rid of the weird output\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# We could explicitly pass arguments to the \"x\" and \"y\" parameters\n",
- "temp_df_with_month = DataFrame( \n",
- " {\n",
- " \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"],\n",
- " \"high\": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],\n",
- " \"low\": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]}\n",
- ")\n",
- "\n",
- "ax = temp_df_with_month.plot.line(x = ???, y = ???, figsize = (8, 4))\n",
- "ax.set_title(\"Average Temperatures in Madison, WI\")\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### We can perform a calculation on an entire DataFrame\n",
- "Let's change the entire DataFrame to Celcius"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# call the function on the dataframe\n",
- "celcius_df = ???\n",
- "celcius_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# here is one way to add a horizontal line to our line plots\n",
- "celcius_df[???] = ???\n",
- "celcius_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# this plots each column as lines\n",
- "# with rotation for the tick labels\n",
- "ax = celcius_df.plot.line(figsize = (8, 4))\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Celcius)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation = 45)\n",
- "ax.grid()\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plots using DataFrames"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Bar Plot Example w/ Fire Hydrants\n",
- "\n",
- "- General review of pandas\n",
- "- Some new bar plot options"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: read \"Fire_Hydrants.csv\" into a DataFrame\n",
- "hdf = ???\n",
- "hdf.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Extract just the column names\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *colors* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Make a series called counts_series which stores the value counts of the \"nozzle_color\"\n",
- "color_counts = ???\n",
- "color_counts # what is wrong with this data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Clean the data ......use str.upper()\n",
- "\n",
- "color_counts = ???\n",
- "color_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Make a horizontal bar plot of counts of colors and have the colors match\n",
- "# use color list: [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"]\n",
- "ax = ???\n",
- "ax.set_xlabel(\"Fire hydrant count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *style* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Do the same thing as we did for the colors but this time for the \"Style\"\n",
- "style_counts = ???\n",
- "style_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Grab the top 12 \n",
- "top12 = ???\n",
- "\n",
- "# and them add an index to our Series for the sum of all the \"other\" for \n",
- "top12[???] = ???"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot the results\n",
- "ax = ???(color = \"firebrick\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "ax.set_xlabel(\"Hydrant Type\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### In what *decade* were *pacers manufactured*?\n",
- "### Take a peek at the *Style* column data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "hdf[\"Style\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which *column* gives *year* information?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *year_manufactured* for *pacers* and *others*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Let's get the year manufactured for all of the \"Pacer\" hydrants.\n",
- "pacer_years = ???\n",
- "\n",
- "# Note: We can do this either way\n",
- "# pacer_years = hdf[\"year_manufactured\"][hdf[\"Style\"] == \"Pacer\"]\n",
- "\n",
- "pacer_years"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# then do the same for all the other data\n",
- "other_years = ???\n",
- "other_years"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *decade* for *pacers*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Round each year down to the start of the decade.\n",
- "# e.g. 1987 --> 1980, 2003 --> 2000\n",
- "pacer_decades = ???\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to convert the *decades* back to *int*?\n",
- "- `astype(...)` method\n",
- "- `dropna(...)` method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Drop the NaN values, convert to int, and do value counts\n",
- "pacer_decades = ???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to *count the decades* for pacers?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pacer_decades_count = ???\n",
- "pacer_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Count the *decades* for others."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Do the same thing for other_years. Save to a variable called \"other_decades\"\n",
- "other_decades = ???\n",
- "other_decades_count = ???\n",
- "other_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Build a DataFrame from a dictionary of key, Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plot_df = DataFrame(???)\n",
- "plot_df # observe the NaN values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make a bar plot\n",
- "\n",
- "ax = ???\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Ignore data from before 1950 using boolean indexing."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = ???\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Stacked Bar Chart\n",
- "`stacked` parameter accepts boolean value as argument"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = ???\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "None"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/lec_37_plotting3_line_plots-checkpoint.ipynb b/f22/meena_lec_notes/lec-38/lec_38_plotting3_line_plots.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-38/.ipynb_checkpoints/lec_37_plotting3_line_plots-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-38/lec_38_plotting3_line_plots.ipynb
diff --git a/f22/meena_lec_notes/lec-38/.ipynb_checkpoints/lec_37_plotting3_line_plots_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-38/lec_38_plotting3_line_plots_template.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-38/.ipynb_checkpoints/lec_37_plotting3_line_plots_template-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-38/lec_38_plotting3_line_plots_template.ipynb
diff --git a/f22/meena_lec_notes/lec-39/.ipynb_checkpoints/demo_lec_38-checkpoint.ipynb b/f22/meena_lec_notes/lec-39/.ipynb_checkpoints/demo_lec_38-checkpoint.ipynb
deleted file mode 100644
index 6409907..0000000
--- a/f22/meena_lec_notes/lec-39/.ipynb_checkpoints/demo_lec_38-checkpoint.ipynb
+++ /dev/null
@@ -1,2087 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "\n",
- "import sqlite3\n",
- "import os\n",
- "\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "import requests\n",
- "\n",
- "matplotlib.rcParams[\"font.size\"] = 15"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How do you plot a *bar chart* from a *pandas series*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Series.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are the *axes* for a *bar chart*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# index => x-axis\n",
- "# values => y-axis"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Continuing scatter plot example from Plotting 1 lecture\n",
- "\n",
- "### Scatter plot\n",
- "- copy paste the data from trees.txt - already done for you\n",
- "- When we have a series to plot:\n",
- " - s.plot.bar()\n",
- " - index => x-axis\n",
- " - values => y-axis\n",
- "- When we have a data frame:\n",
- " - df.plot.scatter(x = column_name, y = column_name)\n",
- "- Scatter plots enable us to understand relationship between columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>age</th>\n",
- " <th>height</th>\n",
- " <th>diameter</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>1.9</td>\n",
- " <td>1.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
- " <td>1.8</td>\n",
- " <td>1.4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2</td>\n",
- " <td>1.8</td>\n",
- " <td>0.9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>2</td>\n",
- " <td>2.5</td>\n",
- " <td>1.5</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " age height diameter\n",
- "0 1 1.5 0.8\n",
- "1 1 1.9 1.2\n",
- "2 1 1.8 1.4\n",
- "3 2 1.8 0.9\n",
- "4 2 2.5 1.5"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trees = [\n",
- " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
- " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
- " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
- " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
- " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
- " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
- " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
- " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
- " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
- " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
- " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
- " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
- " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
- " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
- " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
- "]\n",
- "df = DataFrame(trees)\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are some aspects of scatter plots that you can control with different variables? \n",
- "1. *X-axis*: age\n",
- "2. *Y-axis*: height\n",
- "3. *Color of plot points*\n",
- "4. *Size of plot points*: diameter\n",
- "5. *Shape of plot points*"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to vary *color* of plot points?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", color = \"r\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to vary *size* of plot points?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", s = 100)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to vary *shape* of plot points?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", marker = \"^\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", marker = \"o\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", marker = \"+\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How do we add *tree diameter* data to this *scatter plot*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Option 1:\n",
- "df.plot.scatter(x = \"age\", y = \"height\", s = \"diameter\") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#Option 2:\n",
- "df.plot.scatter(x = \"age\", y = \"height\", s = df[\"diameter\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 20.0\n",
- "1 30.0\n",
- "2 35.0\n",
- "3 22.5\n",
- "4 37.5\n",
- "5 45.0\n",
- "6 42.5\n",
- "7 52.5\n",
- "8 50.0\n",
- "9 55.0\n",
- "10 72.5\n",
- "11 77.5\n",
- "12 77.5\n",
- "13 70.0\n",
- "14 87.5\n",
- "15 100.0\n",
- "Name: diameter, dtype: float64"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df[\"diameter\"] * 25"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df.plot.scatter(x = \"age\", y = \"height\", s = df[\"diameter\"] * 25)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Not recommended: don't use same variable to represent multiple aspects of the plot!\n",
- "# Dummy example\n",
- "df.plot.scatter(x = \"age\", y = \"height\", s = df[\"diameter\"] * 25, c = \"diameter\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Not recommended: don't use same variable to represent multiple aspects of the plot!\n",
- "# Dummy example\n",
- "df.plot.scatter(x = \"age\", y = \"height\", s = df[\"diameter\"] * 25, c = \"diameter\",\n",
- " vmin = df[\"diameter\"].min() - 2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
- "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>5.1</th>\n",
- " <th>3.5</th>\n",
- " <th>1.4</th>\n",
- " <th>0.2</th>\n",
- " <th>Iris-setosa</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.4</td>\n",
- " <td>3.9</td>\n",
- " <td>1.7</td>\n",
- " <td>0.4</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "0 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "1 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "2 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "3 5.0 3.6 1.4 0.2 Iris-setosa\n",
- "4 5.4 3.9 1.7 0.4 Iris-setosa"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Used to work. Doesn't work anymore due to SSL cert issue\n",
- "#df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
- "\n",
- "#Let's use requests to download the iris.data csv file\n",
- "resp = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
- "resp.raise_for_status()\n",
- "iris_fh = open(\"iris.data\", \"w\")\n",
- "iris_fh.write(resp.text)\n",
- "iris_fh.close()\n",
- "\n",
- "df = pd.read_csv(\"iris.data\")\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-len</th>\n",
- " <th>sep-wid</th>\n",
- " <th>pet-len</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-len sep-wid pet-len pet-width class\n",
- "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "4 5.0 3.6 1.4 0.2 Iris-setosa"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_csv(\"iris.data\",\n",
- " names=[\"sep-len\", \"sep-wid\", \"pet-len\", \"pet-width\", \"class\"])\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " .dataframe tbody tr th {\n",
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- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-len</th>\n",
- " <th>sep-wid</th>\n",
- " <th>pet-len</th>\n",
- " <th>pet-width</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>145</th>\n",
- " <td>6.7</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>146</th>\n",
- " <td>6.3</td>\n",
- " <td>2.5</td>\n",
- " <td>5.0</td>\n",
- " <td>1.9</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>147</th>\n",
- " <td>6.5</td>\n",
- " <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.0</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>148</th>\n",
- " <td>6.2</td>\n",
- " <td>3.4</td>\n",
- " <td>5.4</td>\n",
- " <td>2.3</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>149</th>\n",
- " <td>5.9</td>\n",
- " <td>3.0</td>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-len sep-wid pet-len pet-width class\n",
- "145 6.7 3.0 5.2 2.3 Iris-virginica\n",
- "146 6.3 2.5 5.0 1.9 Iris-virginica\n",
- "147 6.5 3.0 5.2 2.0 Iris-virginica\n",
- "148 6.2 3.4 5.4 2.3 Iris-virginica\n",
- "149 5.9 3.0 5.1 1.8 Iris-virginica"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.tail()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How do we create a *scatter plot* for various *class types*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'}"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "classes = set(df[\"class\"])\n",
- "classes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='pet-width', ylabel='pet-len'>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df[df[\"class\"] == \"Iris-versicolor\"].plot.scatter(x = \"pet-width\", y = \"pet-len\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
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- },
- "output_type": "display_data"
- },
- {
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qOhvt3XFTlGqhXapxpcr9Za2g1t1xC9dxSDpG0p3AJmANcFDe/j1JHxhzpFZTqoV2qcaVKveXtbrCdRzAz8iucjqFba+y+j1wcv1Cs8FSLbRLNa5Uub+s1RUdcXwROD8iPgj870Hb7gPeUJeorKpUC+1SjStV7i9rdUUTx77AL2ps2wTsMrZwbCipFtqlGleq3F/W6ooWAK4B/gNwQ5VtXUDPmCOyIaVaaJdqXKlyf1krK5o4fgCcKamXrRXbknQkcDrZbdKtwVIttEs1rlS5v6xVFU0c5wIzgR8CL+dtvyKrubg4Iv6pjrGZmVmCCiWOyIo+Pi7pAuBIYAbwDHBDRPy+AfGZmVliCq8AKGlHsqRxCFkB4JPAy5IeiYgX6xyfVeGKYzMrU6HEIenfA9eQrTO+EngKeBOwkOwmhO+OiPvrHqW9whXHZla2opfjLgGeBfaPiHkRcVxEzAPm5O0X1TtA28oVx2aWgqKJows4IyIeq2zMn58BvKVegdn2XHFsZikomjgeIVt7o5pJwGM1tlkduOLYzFJQNHF8HviqpLdWNkqaR1bDsbhegdn2XHFsZikodFt1SXeR3XZkOtnE+FNkCz3tDvSRjUheERGH1CvQsWin26qDr6oys+aodVv1opfj/i5/WIlccWxmZSpaAPihRgViZmatofBCTp2kb+NmfrtmvS93NTOrULhyvFO40M7MrDqPOKpwoZ2ZWW1OHFW40M7MrDYnjipcaGdmVpsTRxUutDMzq82T4zV4aU8zs+qcOIbgQjszs+35VJWZmRXixGFtz4WcZvXlU1XW1lzIaVZ/HnFY23Ihp1ljOHFY23Ihp1ljOHFY23Ihp1ljOHFY23Ihp1ljeHLc2poLOc3qz4nD2p4LOc3qq6mnqiSdKOlXkvokbZL0oKQvSdpxiGNmS4oqj8uaGbuZmWWaPeKYDtwInA+sBw4BzgL2BD4xzLGnAbdVPH+6/uGZmdlwmpo4IuLiQU03StoF+LikT0ZEDHH4gxFxRwPD205P7wbuWbOeuTOnMmePKc18azOzZKUwx9EH1DxVVZYzrryXZXc89srzhfNncfaCA0uMyMwsDaVcjitpvKSdJR0GfAr47jCjDYBLJb0s6UlJF0hq2MX4Pb0btkkaAMtuf4ye3g2Nekszs5ZR1ojjeWDgMpdlwOeG2Hcz8G3gOuA54B3AYmB/YEGtgySdApwCMGvWrELB3bNmfc12n7Iys05XVuJ4G7Az2eT4GcC3gI9V2zEinmTbifObJPUC35E0NyLuqXHcEmAJQFdX13CjmW3MnTm1ULuZWScp5VRVRNwdESsi4gKyU1UflbR/gZe4Iv96cP2jgzl7TGHh/G1HKQvnz/Jow8yMNCbH786/7gc8NMJjYtDXujt7wYEsnDfbV1WZmQ2SQuI4NP/6cIFjTsy/rqxzLNuYs8cUJwwzs0GamjgkXQNcD9wHvEyWND4LXB4RD+X79AA3R8TJ+fOzgClkxX/PAUeQTab/a0Ssamb8ZmbW/BHHXcBJwGzgJeCPwBeAiwbFNL7i+WqyqvFFwE7AY2SV519reLRmZrYdDV8+0fq6urqiu7u77DDMzFqKpJUR0TW43etxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cQyhp3cDV3Sv8QJOZmYVUrjJYZK8dKyZWXUecVThpWPNzGpz4qhiqKVjzcw6nRNHFV461sysNieOKrx0rJlZbZ4cr8FLx5qZVefEMQQvHWtmtj2fqjIzs0KcOMzMrBAnDjMzK8SJw8zMCnHiMDOzQpw4zMysECcOMzMrxInDzMwKceIwM7NCnDjMzKwQJw4zMyvEicPMzApx4jAzs0KcOMzMrBAnDjMzK8SJw8zMCnHiMDOzQpw4zMyskKYmDkknSvqVpD5JmyQ9KOlLknYc5rhdJV0qaZ2kZyX9WNL0ZsVtZmZbNXvN8enAjcD5wHrgEOAsYE/gE0McdznwemAR0A+cC1wJHN6wSM3MrKqmJo6IuHhQ042SdgE+LumTERGDj5E0HzgKeHtE3JK3PQHcKeldEXF9wwNPTN/GzTy+7gX2mbYT0ydPLDscM+swzR5xVNMHDHWq6migdyBpAETEryU9nG/rqMTx03ueYPHyVUwYN44t/f2cd8JBHDd377LDMrMOUsrkuKTxknaWdBjwKeC71UYbuQOA1VXaH8i3dYy+jZtZvHwVm7b0s2HzS2za0s/py1fRt3Fz2aGZWQcp66qq5/PHrcDNwOeG2Hca2XzIYOvybVVJOkVSt6TutWvXjiHUdDy+7gUmjNv2I5swbhyPr3uhpIjMrBOVlTjeRjax/VlgAfCtYfavNhpRjfbsgIglEdEVEV277bbbqANNyT7TdmJLf/82bVv6+9ln2k4lRWRmnaiUxBERd0fEioi4gOxU1Ucl7V9j93XA1CrtU6k+Emlb0ydP5LwTDmLShHFMmbgDkyaM47wTDvIEuZk1VQqT43fnX/cDHqqyfTXVL7s9gOyS3I5y3Ny9OXTODF9VZWalSaFy/ND868M1tl8N7JlPpAMgqQt4Tb6t40yfPJE3z5zqpGFmpWjqiEPSNWSXz94HvEyWND4LXB4RD+X79AA3R8TJABFxu6RrgWWSTmNrAeCKTqzhMDMrW7NPVd0FnATMBl4C/gh8AbhoUEzjBx33PuBC4BKyUdJVZHMjZmbWZKpdPtE+urq6oru7u+wwzMxaiqSVEdE1uD2FOQ4zM2shThxmZlZIR5yqkrQWeHSUh88Anq5jOO3O/VWM+6sY91dxY+mzfSNiuwrqjkgcYyGpu9o5PqvO/VWM+6sY91dxjegzn6oyM7NCnDjMzKwQJ47hLSk7gBbj/irG/VWM+6u4uveZ5zjMzKwQjzjMzKwQJw4zMyukYxOHpDmSLpb0W0kvS7pphMftKulSSeskPSvpx5KmNzjc0o2mvyS9Je+rHkl/kfSgpDMlTWpCyKUa7e9XxfHjJK2UFJKObVCYyRhLf0l6r6S7JL0gqU/SNZJe1cBwSzeGv19dkq7L++kZSddLemvR9+/YxAG8ETgG+H3+GKnLgXcAi8hu2PgWOmNdkNH0118D+5PdzfgY4NvAZ4AfNyLAxIz292vAImDvukaUtlH1l6RFwD+TLbFwNFm//YE01hpqpML9JWkm2d3JdwAWAn+Tf3+dpH0LvXtEdOQDGFfx/RXATSM4Zj7ZcrVHVLQdkre9q+yfKcH+2q1K2yl5f+1b9s+UWn9V7D8NWAucnPfVsWX/PCn2F1lF9AbgI2XH3yL9dSrZchZTK9qm5W0fLfL+HTviiIj+4ffaztFAb0TcUvE6vyZbhOroesWWotH0V0SsrdL8m/zr7mOLKG2j/P0acA5wG/DLOoWTvFH213/Lv/6wnrG0glH21wSy5Sw2VrRtzNtU5IU6NnGM0gFkS9kO9kC+zYb3NrLFuB4sO5AUSToI+BBwWtmxtIC3kv0enSzpcUlbJN0p6W1lB5ao5cBfgG9I2l3S7mTrHK0D/m+RF3LiKGYasL5K+7p8mw1B0p7AF4EfRcRzZceTqG8C346InrIDaQF7Aq8HvgQsBt4DPA9cI2mPMgNLUUT8CXgncALQmz/eCxxV4+xATU4cxVWrmFSNdstJ2hH4F7Kh8adLDidJkt5H9ofwq2XH0iLGAZOBkyPixxFxDXA82Tn7T5QZWIok7UU2H7KS7NT60fn3/yZpVpHXavcrD+ptHbDdLYaBqVQfiRggScAysitBDo2IdSWHlBxJE4Dzya5AGydpKrBLvvlVkqZExIay4kvUM/nXmwYaIuI5SSuBN5QSUdo+R/Y3/8SI2AIg6Qayq9BOo8By3B5xFLOa6nMZteY+LHMhsABYEBHup+peBewDXED2H5R1wG/zbZex9aIC2+oBspH+4Ildkc2j2bYOAO4bSBoAEfEicB/ZZfMj5sRRzNXAnpIOG2iQ1AW8Jt9mg0j6AvBJ4AMRsaLseBK2kez8c+Xjv+fb/hb4HyXFlbKryJLEOwcaJO0K/Ee2Jl3b6lHgTflpYwAkTQTeBDxS5IU69lSVpJ3JCmggK7TaRdKJ+fOfR8RfJPUAN0fEyQARcbuka4Flkk4j+1/NucCKiLi+yT9CU42mvyS9H/g6sBR4QtK8ipd8qOiEXCsp2l8R8RIVp1zy15idf3tvRNzZhLBLM8p/j92Sfgr8QNLnyVa5Ox3YQlZs2rZG01/A98kKJH8i6TtkSffjwF4UvYNu2YUsJRbQzCYb5lZ7zM73eQRYOui4qcClZHMaz5FVrc4o++dJsb/IEkatY04q+2dKrb+GeI1OKAAc7b/HycB3gT7gBbLK6APL/nkS7q8jgVvI5oeeAW4G3lH0/X1bdTMzK8RzHGZmVogTh5mZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhNgaSTpF0fINee/ZIlo6V9AlJUfH8dZLOyu93VbnfSfnrTW5EvNY5nDjMxuYUsjuyNsKTZKtOFr1Vy+uAM8mKVc3qrmNvOWKWuojYDNxRdhxmg3nEYR1J0lJJ3ZKOl7Ra0iZJKyS9oWKfcZI+L6lH0mZJv5f0wYrtN5HdUO+D+SmgkHRSjfeblL/G+yva/i4/5riKtm9Kui3/frtTVZImSvqWpPWSnpF0IdmSoAPb3wH8v/zpw/nxjwwKZz9Jv5D0fP6zv7dg91mHc+KwTrYv2W3MzwHeD+wKXCtpUr79m2Sryy0B/jPwE+CSij/kHyO7nf7PyU4pzQf+rdobRcQm4C7g8IrmI4BNVdpuHSLmvye7Ud05ZHfM3Rf4bMX2u9m67Ox785j+y6DX+GfgZ3n7H4DLJO0zxHuabcOnqqyTzSBbI+RXAPkCQA8BJ0m6Hvgo8KGI+GG+//X5KmpnAldFxP2SngfWRsRITindSra8KXly6gK+R5448snsN5HdRn07kqYDpwJnRsQ38rZrgfsH9olsIaOB9dx/ExGPVHmpCyPikoqfuRc4FrhoBD+DmUcc1tGeGkgaABHxKNlSmoeQ3UW0n+wW1DsMPIBfAnMlja/1ovkprh0qHgP/zm4F3iDp1cA8svWxvwscnN8me2Cdl9tqvPSBwCTgpxUx91c+H6HrKo7vA54iW0TKbEScOKyTPVWjbS+y0ch44Fmy9R0GHkvJRup7DfG6Zww65oy8/Tay214fRjbKWBER9+XvMS9v+11ErK/xunvWiLvazzGUwa//IllCMhsRn6qyTrZ7jbb7yNYqeAk4lOrLkA71x3oJ2ep0A/4EEBHPSlpFliDmAtfm21fkbcPNb/y5IsZnKtqr/RxmDePEYZ1sd0lvq5jjmAUcTLZQ141kI45dI+IXQ7zGdv9bj4g/kSeLKm4lW+r0AOCLedstwH8lu0LrH4d4r3vJJtMXkK9xn58GW1AlJgbHZVYvThzWyZ4GfiTpy2Srx51NNpJYGhGbJF1EdsXReUA32R/iNwKvi4hF+WusBo6SdBTZKnQP5/MGtdxCtgb7RrIroCBLJhfk39cs9ouIPklLgK9IeolsZPQRslXwKg1Mjv9PSZcBf4mIe4fqCLMiPMdhnexR4HPAWcBlZEsBH5VfOgvZesznAAvJLrldSnZZ7i0Vr/FV4AHgX8gut33PMO85cCrq9sjWGQf4DbCBLOk8MczxpwOXkM2b/B+ykc0FlTvkk/ynkV2Oextb6zrM6sJLx1pHkrQUeFNEdJUdi1mr8YjDzMwKceIwM7NCfKrKzMwK8YjDzMwKceIwM7NCnDjMzKwQJw4zMyvEicPMzAr5/9P+yWrXL7vBAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "for variety in classes:\n",
- " df[df[\"class\"] == variety].plot.scatter(x = \"pet-width\", y = \"pet-len\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### When we call a plotting function, like scatter\n",
- "1. RULE 1: if AxesSuplot ax passed, then plot in that subplot\n",
- "2. RULE 2: if ax is None, create a new AxesSubplot\n",
- "3. RULE 3: return AxesSuplot that was used"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_area = None\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "for variety in classes:\n",
- " sub_df = df[df[\"class\"] == variety]\n",
- " plot_area = sub_df.plot.scatter(x=\"pet-width\", y=\"pet-len\",\n",
- " ax=plot_area, color=colors.pop(0),\n",
- " label=variety, marker=markers.pop(0))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Line plots\n",
- "- SERIES.plot.line()\n",
- "- DATAFRAME.plot.line()\n",
- " - each column in data frame becomes a line in the plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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8mrwFSu+KiN/JJm+ZmY6JQzv/rsmbVxpWK88Ht5/PmGvasGLnUfq+uoB3Fqq53Ok453jo4xVsP5zChBs7UT2qjNclhSwFgIjf01/6VoR6aVB7GobQilAlShg3da3PnJE96da4Kk9/Gc+1Exexfp+ay+XmnYVb+Hr1Xh65vAVdGlbxupyQpgAQAT5buov3f97OsJ6NuLxNaK4IVbtiOd75nxjG3dCBbYeS6f/aD7z27QZOZKi53Em/bDnMc7PWcnnrWtzWo6HX5YQ8BYAUe+v3+VaE6tKgCg/1De0VocyMAR3qMHdkLy5vU5uxc9Zz1YSFLN9xxOvSPLf/WBojpsRRt3I5XhzUrlg3eQuUAkCKtaTjGdz5/hLKlynJhCK0IlTVCmUYP6Qjb/8xhoSUE1zzxo88O3MNqSdyLs1dPGRkZnHPB0tJTEtn4tDORJct2Ps2wkXR+NsuUgCcczz8yQq2Hkxm/JCO1IgueitCXdaqJnNG9mLweXV5a8Fm+o1bwE+bil9zuZfnrOfnzYd55uq2tKytJm+BUgBIsfXuj1v5auUeHuzbgm6Ni+6KUNFlS/HcwHZMua0rWQ6GvP0zj01fSWIxaS43J34fE7/fxJAudbmu87lel1OkKACkWFqy7TDPzlxD75Y1ubNXeKwI1d3fXO72Hg358Jft9Bm7gO/W7jvzA4uw7YdSGDl1GW3qRPO3K9XkLa8UAFLsHEw6zp8nx3FOpXK8fH14rQhVrnQEj/dvxbQ/X0DFcqW49V+x3PvhUg4lHfe6tKA72eTNgIk3qclbfigApFjxNXlbSkJKOm/c1ImK5cJzsrBD3UrMuPtC7uvdlJkr93DZKwv4fNmusGon8eQXq1m9O5FXBnegbhU1ecsPBYAUK6/MWc+PGw/x9IDCa/LmldIlS3Bf72Z8eXcP6laJ5N4Pl3Hbv2PZczTV69LO2sexO/jw1x3cdXFjLm2pJm/5pQCQYuO7tfuYMG8jgzqfy+Dz6nldTqFpXiuKacO780T/lvy46SB9xi5gyuLtZBXRdhLxuxN54rNVdG9clZGXhfZ9G6FOASDFgm9FqOW0rB3N01e38bqcQhdRwritRyNm39eTNnUq8tj0ldz4j5/ZejDZ69LyJDEtnT9PXkKlSF+Tt4gCWKSnOFEASNhLS/c1ectyjklDOxXrycL6Vcsz5fauPDewLat3JXL5uAW8vWBzkWgu55zjL1OXszMhlddv7ES1CmrydrYUABL2Rn8Zz8pdR3l5UHvqVw2dJm9eMTOGdKnHnJG9uLBJNcbMXMPAN35k3d7Qbi739g+b+SZ+H4/0a0FMAzV5CwYFgIS1aXE7mbJ4O3f0akSf1qHZ5M0rtSqW5e0/xjB+SEd2JqTyh/E/8Mqc9RzPCL12Eos3H+KFr9dxRdta/O+FavIWLAoACVtr9yby2PSVdG1YhQf7aLIwN2bGle3PYc7IXvRvW5tx327gyvELWbo9wevSfrM/MY0RHyylfpVIXrhWTd6CSQEgYemYf0WoqLKlGF+Emrx5pUr50rx6Q0f+eUsMx9IyGDhxEU9/GU/KiQxP68rIzGLEB0s5lpbOG0M7EaUmb0GlfxUSdpxzPPSJf0WoIR2pEVX0mrx55ZIWNfnm/p7c2KUe7yzcwuWv/sCijQc9q+elb9bxy5bDPHtNW1rUUpO3YFMASNh5Z+EWZq3ay0N9m9O1UdFt8uaVqLKlGHNNWz4cdj4lDG78x2Ie+XQFR1MLt7ncN6v38ub8zdzYtR4DO6nJW0FQAEhY+XWrb0WoPq1qMqxneDR588r5jaoy696e3NGzEVNjd9DnlfnMiS+c5nLbDiXzwMfLaVunIn/9Q6tCec3iSAEgYePAsePcNTmOcyuX46VB4dXkzSvlSkfw6BUt+eyuC6gcWZrb34tlxJQ4DhZgc7m09EzufD+OEma8cVPxvm+joCkAJCycXBHqaGo6E2/qHLZN3rzS7txKfDHiQh64rBnfrN5H77Hzmb50Z4E0l/vr56tYsyeRV9XkrcApACQsjJ2znp82H+KZq9vQ6hxNFhaE0iVLcPelTfnqngtpWK0893+0nFv/9Su7jwSvudzUX3cwNXYnd1/ShItb1Aja80ruFABS5M2N38cb32/ihvPqMiimrtflhL2mNaP45M7u/PUPrfh582H6vLKA//y87ayby63efZRRn6/iwibVuK93syBVK6ejAJAi7eSKUK3PiebJq7QiVGGJKGHcemFDZt/Xk/Z1KzLqs1Xc8PbPbMlnc7mjqb77NipHlmbcDR3U5K2QKACkyEpLz+TPU5YAWhHKK/WqRvL+/3blxWvbsWZPIpe/uoBJ8zeRkZkV8HM45/jLx8vZfSSV12/qRFU1eSs0CgApsp6asZpVuxIZe30H6lXVZKFXzIzrz6vL3JG96NWsOs/PWss1bywifndiQI9/c8Fm5sTv47ErWtK5fuUCrlayO2MAmNkgM/vCzHaZWZKZLTGzITnGmJk9ZmY7zCzVzBaYWYdcnquVmX1rZilmttvMRpuZPrZJnn2yZCcf/LKD4Rc1pncrrQgVCmpGl+XNmzvz+o2d2HM0lasmLOTlb9adtrncT5sO8eLXa+nfrjZ/uqBB4RUrQGBnACOBJOB+4CpgHjDFzO7ONuYRYBTwAnClf/xcM/ut/aKZVQbmAg4YAIwGHgCeOvsfQ4qTNXsSeXz6Sro1qsoDl2myMJSYGf3b1WbO/b24qsM5jP9uI/1fW8iSbf/dXG5/Yhp3f7CUBtXKq8mbR+xM3+M1s2rOuYM5tk0BujnnGppZWWAf8LJzbrR/f3lgK/Cmc+4J/7ZHgYeA+s65RP+2h4AngVont51OTEyMi42NzdtPKGElMS2dq8YvJOVEJl/d04PqUbpeHMrmrdvP49NWsicxjVu6N+AvfZpTvkxJ0jOzuOntxazcdZTPR1xAs5pRXpca1sxsiXMuJuf2M54B5Dz4+y0FTn5JtzsQDUzN9phkYAbQL9tj+gGzcxzoPwTKAb3OVIfIyRWhdiSkMuHGTjr4FwEXN6/BNyN7cfP59Xn3x630fXUBP2w4wEuz1/HL1sM8N7CtDv4eyu8kcHcg3v/7FkAmsCHHmDX+fWQbtzb7AOfcdiAlxziRXP22ItTlLejSUCtCFRUVypRk9IA2TL2jG6UiSnDzO7/w1oLNDD2/Hld3rON1ecVaybw+wMwuxXcN/1b/pspAknMu50xPAhBpZqWdcyf8447k8pQJ/n2ner1hwDCAevXq5bVcCRMnV4S6vHUtbuuhFaGKoi4NqzDr3h6M/24D2w6lMEpN3jyXpwAwswbAFOBz59y/su3KbSLBctl3qnGnnIhwzr0FvAW+OYA8lCthYv8x34pQ9apE8uIgTRYWZWVLRfBgX53wh4qALwGZWRVgFrAdGJptVwIQlcvXOSsBKc659GzjKuXy1BXJ/cxAhIzMLO6e4lsRauLQTkRrRSiRoAkoAMwsEvgSKA3090/ynrQWiACa5HhYzmv+a8lxrd/M6gLlc4wT+c3fv1nP4i2HGXO1VoQSCbZAbgQrCXwMNAX6Oef25xiyCEgEBmV7TCS++wFmZRs3C+hrZtmn/AcDqcD8fFUvYW1O/D4mzd/EkC71uLazVoQSCbZA5gDeAK4A7gWqmNn52fYtdc6lmdnzwCgzS8D3aX4kvnAZn23sJOAeYJqZvQA0wncPwNhA7gGQ4mXboWRGTl1GmzrR/O1KTRaKFIRAAqCP/9dxuexriO+Gr+fxHfAfBaoCscBlzrnf1o9zziX4v0E0Ad89AkeAV/CFgMhv0tIzGe5fEUpN3kQKzhkDwDnXIIAxDhjj/+904+KBSwItToqnv32+mvg9ifzzlhitCCVSgNQNVELK1NgdfBS7g7subswlLdTkTaQgKQAkZKzefZRRn62ie+OqjLysudfliIQ9BYCEhJMrQlWKLMVrQzpqRSiRQpDnVhAiwZZ9RagPh51PNa0IJVIodAYgnju5ItSjV7QkpoGavIkUFgWAeOrnzf4VodrW5latCCVSqBQA4pn9iWmMmLKUBlXL8/y1bdXkTaSQaQ5APJGRmcWID5aSfDyDybd1JUpN3kQKnQJAPPHS7HX8suUwrwxuT/NaWhFKxAu6BCSFbvbqvby5YDM3da3HNR3V5E3EKwoAKVRbDybzl6nLaXduRf6qJm8inlIASKFJS89k+OQ4SpQwXr+xE2VKqsmbiJc0ByCFZtRnq1izJ5F3bzlPTd5EQoDOAKRQfPTrdj5espO7L2nCxS1qeF2OiKAAkEKwatdRRn2+mgubVOO+3s28LkdE/BQAUqCOpqQzfPISqpYvzbgbOqjJm0gI0RyAFJisLMcDHy9jz5E0PrqjG1XV5E0kpOgMQArMpAWbmLtmP4/3b0nn+pW9LkdEclAASIFYtOkgf5+9jv7tanNL9wZelyMiuVAASNDtS0zjng+W0rBaeV64tp2avImEKM0BSFClZ2YxYkocycczmXL7+VQoo79iIqFK/zolqF78ei2/bk1g3A0daFZTTd5EQpkuAUnQfL1qD2//sIWbz6/PgA51vC5HRM5AASBBseVgMg9+vIL2dSvxxB9ael2OiARAASBnLfVEJsPfX0JEhPH6jR3V5E2kiNAcgJwV5xyPf7aSdfuO8e4t53FuZTV5EykqdAYgZ+WDX3YwLW4X91zSlIuaq8mbSFGiAJB8W7nzKE9+sZoeTatxz6VNvS5HRPJIASD5ciTlBMMnL6FahdKMu6GjmryJFEGaA5A8y8pyjJy6nH2JaUy9oxtVypf2uiQRyQedAUieTZy/ie/W7ueJ/q3oWE9N3kSKKgWA5MmPGw/y8jfruLL9OfyxW32vyxGRs6AAkIDtPepr8taoegWeH9hWTd5EijjNAUhATjZ5S03P5KOhnSivJm8iRZ7+FUtAnp+1lthtCbw2pCNNaqjJm0g40CUgOaOZK/fwzsIt/E+3+lzV/hyvyxGRIAkoAMysiZm9aWbLzSzTzL7PZYyZ2WNmtsPMUs1sgZl1yGVcKzP71sxSzGy3mY02MzWPCVGbDyTx0Ccr6FC3Eo/3b+V1OSISRIGeAbQGrgDW+//LzSPAKOAF4EogCZhrZrVODjCzysBcwAEDgNHAA8BT+SleClbKiQyGvx9H6ZIleOOmTpQuqRNGkXAS6L/oGc65us65QcDqnDvNrCy+AHjOOTfBOTcXGITvQD8i29A7gXLAQOfcHOfcJHwH/5FmFn02P4gEl3OOx6evYv3+Y4y7oQPnVCrndUkiEmQBBYBzLusMQ7oD0cDUbI9JBmYA/bKN6wfMds4lZtv2Ib5Q6BVILVI4Ji/ezvSlu7jv0mb0aFrd63JEpAAE65y+BZAJbMixfY1/X/Zxa7MPcM5tB1JyjBMPLd9xhNEz4unVrDp3X9LE63JEpIAEKwAqA0nOucwc2xOASDMrnW3ckVwen+Df91/MbJiZxZpZ7IEDB4JUruQmK8vx70VbGfL2z1SPKsOrgztQQk3eRMJWMO8DcLlss1z2nWpcbttxzr0FvAUQExOT6xg5e5sOJPHwJyuI3ZZAj6bVeG5gWyqryZtIWAtWACQAUWYWkeMsoBKQ4pxLzzauUi6Pr0juZwZSwNIzs3hrwWbGfbuBcqUi+Pug9lzbqY7aPIgUA8EKgLVABNAEWJdte85r/mvJca3fzOoC5XOMk0KwatdRHvpkBfF7EunXphZPDWhNjaiyXpclIoUkWAGwCEjE99XPZwDMLBLf/QBvZRs3C3jQzKKcc8f82wYDqcD8INUiZ5CWnsm4bzfw1oLNVI4szcSbOtGvbW2vyxKRQhZQAPgP5lf4/1gHiDaz6/x/numcSzGz54FRZpaA79P8SHyTzOOzPdUk4B5gmpm9ADQCngTG5vhqqBSQX7ce5uFPVrD5YDKDOp/L4/1bUilS1/pFiqNAzwBqAB/n2Hbyzw2BrcDz+A74jwJVgVjgMufcvpMPcM4lmNmlwAR89wgcAV7BFwJSgJKOZ/Di12t576dt1KlUjvdu7ULPZvp+v0hxFlAAOOe28v/f6DnVGAeM8f93unHxwCUB1idBMH/9AR6btpLdR1O5pXsDHuzbXO2cRUTtoMNZQvIJnv4qnmlxu2hcvTwf39GNmAZVvC5LREKEAiAMOeeYtWovf/18FQkp6Yy4uAkjLmlC2VJquioi/08BEGb2J6Yx6vNVzF69jzZ1ovn3rV1ofU5Fr8sSkRCkAAgTzjk+XrKTZ76MJy0ji4cvb8HtPRpSMkItnEUkdwqAMLDjcAqPTlvJwo0H6dKgCs9f25ZG1St4XZaIhDgFQBGW6W/e9tLsdZQweHpAa27qWl8N3EQkIAqAImrDvmM8/OkK4rYfoVez6jw7sC11tGiLiOSBAqCISc/MYtL3mxj/3UYiy0Qw9vr2XNNRzdtEJO8UAEXIyp1HefCT5azde4z+7Wrz5JWtqR5VxuuyRKSIUgAUAWnpmbwydz1vL9hMtQplePPmzvRtXcvrskSkiFMAhLjFmw/xyLSVbDmYzOCYujzWvyUVy5XyuiwRCQMKgBB1LC2dF75ey/s/b6dulXJMvq0rFzSp5nVZIhJGFAAhaN7a/Tw2fSV7E9O49YKG/KVvMyJL63+ViASXjioh5HDyCUbPWM1ny3bTtEYFPh3enU71KntdloiEKQVACHDO8eWKPTz5xWqOpqZzz6VNuevixpQpqeZtIlJwFAAe25eYxuPTVzF3zT7anVuR92/rSsva0V6XJSLFgALAI845Pvp1B2NmruFERhaPXdGCWy9Q8zYRKTwKAA9sO5TMo9NWsmjTIbo2rMIL17ajQbXyXpclIsWMAqAQZWY53v1xC3//Zh0lS5RgzDVtGHJePTVvExFPKAAKybq9x3jo0xUs33GES1rUYMw1bahdUc3bRMQ7CoACdiIjize+38jr8zYSVbYU427owFXtz1HzNhHxnAKgAC3fcYSHPlnBun3HuKr9OfztylZUraDmbSISGhQABSD1RCZj56zjnYVbqBFVln/8MYberWp6XZaIyO8oAIJs0aaDPDptJdsOpTCkSz0evaIF0WXVvE1EQo8CIEgS09J5buZaPvhlO/WrRjLl9q50b6zmbSISuhQAQTA3fh+Pf7aSA8eOM6xnI+7v3YxypdXGQURCmwLgLBxKOs5TM+L5YvlumteM4s2bY+hQt5LXZYmIBEQBkA/OOb5Yvpsnv1hN0vEM7u/djOEXNaZ0SbVxEJGiQwGQR3uOpvLE9FV8u3Y/7etW4sVr29G8VpTXZYmI5JkCIEBZWY4Pft3OczPXkpGVxRP9W/KnCxoSoTYOIlJEKQACsOVgMo98uoLFWw7TvXFVnh/YjnpVI70uS0TkrCgATiMjM4t//riFl79ZT+mIEjw/sC2Dz6urNg4iEhYUAKewZk8iD3+6ghU7j9K7ZU2euboNtSqW9bosEZGgUQDkcDwjk9fnbeKNeRupWK4U44d05A/tautTv4iEHQVANnHbE3j4kxVs2J/ENR3rMOoPrahSvrTXZYmIFAgFAJByIoOXv1nPP3/cQq3osrx7y3lc3KKG12WJiBSoYh8AP248yCPTVrDjcCpDz6/Hw5e3IErN20SkGCj0ADCzVsB4oBtwBPgH8JRzLrMw6ziams6zX63ho9gdNKxWno+GnU/XRlULswQREU8VagCYWWVgLhAPDAAaAy8DJYAnCquOb1bv5YnPVnEw6Th39PI1bytbSs3bRKR4KewzgDuBcsBA51wiMMfMooEnzexF/7YCc+DYcZ6csZqvVuyhRa0o/vE/MbQ7t1JBvqSISMgq7ADoB8zOcaD/EHgB6AXMKIgXdc7x2bJdPDUjnpTjmfylTzPu6NWYUhFq3iYixVdhB0AL4LvsG5xz280sxb8v6AGQnpnFsPdimbfuAJ3qVeLF69rRpIaat4mIFHYAVMY38ZtTgn/ffzGzYcAwgHr16uX5BUtFlKBR9Qr0bFadP3ZroOZtIiJ+XnwN1OWyzU6xHefcW8BbADExMbmOOZNRf2iVn4eJiIS1wr4IngBUymV7RXI/MxARkQJS2AGwFt+1/t+YWV2gvH+fiIgUksIOgFlAXzPLPgs7GEgF5hdyLSIixVphB8Ak4Dgwzcx6+yd4nwTGFvQ9ACIi8nuFOgnsnEsws0uBCfi+8nkEeAVfCIiISCEq9G8BOefigUsK+3VFROT3dCusiEgxpQAQESmmzLl83VvlCTM7AGzL58OrAQeDWE640/uVN3q/8kbvV96c7ftV3zlXPefGIhUAZ8PMYp1zMV7XUVTo/cobvV95o/crbwrq/dIlIBGRYkoBICJSTBWnAHjL6wKKGL1feaP3K2/0fuVNgbxfxWYOQEREfq84nQGIiEg2CgARkWIqrAPAzFqZ2bdmlmJmu81stJlFeF1XqDKzJmb2ppktN7NMM/ve65pClZkNMrMvzGyXmSWZ2RIzG+J1XaHMzK4zs0VmdsjM0sxsnZk9YWalva4t1JlZHf/fM2dmFYL1vF6sCFYozKwyMBeIBwYAjYGX8YXeEx6WFspaA1cAPwP6R3l6I4EtwP34btC5AphiZtWcc+M9rSx0VQXmAS/hawTZBV8jyFrACM+qKhpeApLwrZ0SNGE7CWxmjwIP4bsDLtG/7SH8f+HUfvq/mVkJ51yW//efANWccxd5W1Vo8h/oD+bYNgXo5pxr6FFZRY6ZjQHuAiq7cD0YnSUz6wF8DjyLLwiinHNJwXjucL4E1A+YneNA/yFQDujlTUmh7eTBX84s58HfbylQo7BrKeIOobPNU/Jfsh4PjKYAWmeEcwC0IMcyk8657UAKOZalFAmS7vguOcppmFmEmUWa2YXAPcBEffo/pTuBssDrBfHkYTsHAFQm94XmE/z7RILGv9DRAOBWr2spApKBMv7fvwc86GEtIcvMqgJPA0Odc+lmFvTXCOczAIDcPlXYKbaL5IuZNQCmAJ875/7lbTVFQnegB/AAvtCc4G05IWsMsNg5N7OgXiCczwASgEq5bK9I7mcGInlmZlWAWcB2YKjH5RQJzrk4/28XmtlB4N9m9rJzbpOXdYUSM2uN72yyp5lV8m+O9P9a0cwynXOpZ/s64RwAa8lxrd/M6uL7GtXaXB8hkgdmFgl8iW8Ss79zLtnjkoqik2HQEFAA/L+mQCngp1z27QTeAW472xcJ5wCYBTxoZlHOuWP+bYOBVGC+d2VJODCzksDH+P6hXuCc2+9xSUXVBf5ft3haRehZCFycY9vlwMP47jnZHIwXCecAmITvGwbTzOwFoBG+ewDG6h6A3Pk/0V7h/2MdINrMrvP/eaZzLsWbykLSG/jeq3uBKmZ2frZ9S51zx70pK3SZ2df4bs5cDWTiO/g/AHykyz+/5/+a8ffZt/nnmgB+CNZ9AGF7Ixj4WkHgm2Dqhu+6/z+AJ51zmV7WFar8f8FO9UmsoXNua+FVE9rMbCtQ/xS79V7lwsyeBq4BGgAZ+D7FvgtMcs6le1hakWBmt+B7v4J2I1hYB4CIiJxauH8NVERETkEBICJSTCkARESKKQWAiEgxpQAQESmmFAAiIsWUAkBEpJhSAIiIFFP/BzZnWIAUCpYsAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series([0, 100, 300, 200, 400])\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 0\n",
- "1 100\n",
- "20 300\n",
- "21 200\n",
- "22 400\n",
- "dtype: int64"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s = Series([0, 100, 300, 200, 400], index = [0, 1, 20, 21, 22])\n",
- "s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series([0, 100, 300, 200, 400], index = [0, 20, 21, 22, 1])\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.sort_index().plot.line()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Temperature dataset line plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>26</td>\n",
- " <td>11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>31</td>\n",
- " <td>15</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>43</td>\n",
- " <td>25</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>57</td>\n",
- " <td>36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>68</td>\n",
- " <td>46</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>78</td>\n",
- " <td>56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>82</td>\n",
- " <td>61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>79</td>\n",
- " <td>59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>72</td>\n",
- " <td>50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>59</td>\n",
- " <td>39</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>44</td>\n",
- " <td>28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>30</td>\n",
- " <td>16</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " high low\n",
- "0 26 11\n",
- "1 31 15\n",
- "2 43 25\n",
- "3 57 36\n",
- "4 68 46\n",
- "5 78 56\n",
- "6 82 61\n",
- "7 79 59\n",
- "8 72 50\n",
- "9 59 39\n",
- "10 44 28\n",
- "11 30 16"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = DataFrame({\n",
- " \"high\": [26, 31, 43, 57, 68, 78, 82, 79, 72, 59, 44, 30],\n",
- " \"low\": [11, 15, 25, 36, 46, 56, 61, 59, 50, 39, 28, 16]\n",
- "})\n",
- "df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Temp (Fahrenheit)')"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = df.plot.line()\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = df.plot.line()\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = df.plot.line(figsize = (8, 4)) # Option 1: control figsize to space out\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation = 90)\n",
- "None"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = df.plot.line() # Option 2: rotate x-tick labels\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Fahrenheit)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation=90)\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Convert temperature to *celcius*"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>-3.333333</td>\n",
- " <td>-11.666667</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>-0.555556</td>\n",
- " <td>-9.444444</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6.111111</td>\n",
- " <td>-3.888889</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>13.888889</td>\n",
- " <td>2.222222</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>20.000000</td>\n",
- " <td>7.777778</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " high low\n",
- "0 -3.333333 -11.666667\n",
- "1 -0.555556 -9.444444\n",
- "2 6.111111 -3.888889\n",
- "3 13.888889 2.222222\n",
- "4 20.000000 7.777778"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "celcius = (df - 32) / (212 - 32) * 100\n",
- "celcius.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <th></th>\n",
- " <th>high</th>\n",
- " <th>low</th>\n",
- " <th>freezing</th>\n",
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- " </thead>\n",
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- " <th>0</th>\n",
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- " <td>-11.666667</td>\n",
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- " <th>1</th>\n",
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- " <th>3</th>\n",
- " <td>13.888889</td>\n",
- " <td>2.222222</td>\n",
- " <td>0</td>\n",
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- " <td>13.333333</td>\n",
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- " <tr>\n",
- " <th>6</th>\n",
- " <td>27.777778</td>\n",
- " <td>16.111111</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>26.111111</td>\n",
- " <td>15.000000</td>\n",
- " <td>0</td>\n",
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- " <tr>\n",
- " <th>8</th>\n",
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- " <td>0</td>\n",
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- " <tr>\n",
- " <th>9</th>\n",
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- " <td>0</td>\n",
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- " <tr>\n",
- " <th>10</th>\n",
- " <td>6.666667</td>\n",
- " <td>-2.222222</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>-1.111111</td>\n",
- " <td>-8.888889</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
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- "text/plain": [
- " high low freezing\n",
- "0 -3.333333 -11.666667 0\n",
- "1 -0.555556 -9.444444 0\n",
- "2 6.111111 -3.888889 0\n",
- "3 13.888889 2.222222 0\n",
- "4 20.000000 7.777778 0\n",
- "5 25.555556 13.333333 0\n",
- "6 27.777778 16.111111 0\n",
- "7 26.111111 15.000000 0\n",
- "8 22.222222 10.000000 0\n",
- "9 15.000000 3.888889 0\n",
- "10 6.666667 -2.222222 0\n",
- "11 -1.111111 -8.888889 0"
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- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "celcius[\"freezing\"] = 0\n",
- "celcius"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = celcius.plot.line()\n",
- "ax.set_xlabel(\"Month\")\n",
- "ax.set_ylabel(\"Temp (Celcius)\")\n",
- "ax.set_xticks(range(12))\n",
- "ax.set_xticklabels([\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
- " \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"], rotation = 90)\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Stock Market Example"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>year</th>\n",
- " <th>return</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>1970</td>\n",
- " <td>1.0401</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1971</td>\n",
- " <td>1.1431</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1972</td>\n",
- " <td>1.1898</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1973</td>\n",
- " <td>0.8534</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1974</td>\n",
- " <td>0.7353</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " year return\n",
- "0 1970 1.0401\n",
- "1 1971 1.1431\n",
- "2 1972 1.1898\n",
- "3 1973 0.8534\n",
- "4 1974 0.7353"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_csv(\"sp500.csv\")\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How much *current wealth (2021)* would we have given we had invested *1000$ in 1970*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>year</th>\n",
- " <th>return</th>\n",
- " <th>tot</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>2014</td>\n",
- " <td>1.1369</td>\n",
- " <td>88.439335</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>2015</td>\n",
- " <td>1.0138</td>\n",
- " <td>89.659797</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>2016</td>\n",
- " <td>1.1196</td>\n",
- " <td>100.383109</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>2017</td>\n",
- " <td>1.2183</td>\n",
- " <td>122.296742</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>2018</td>\n",
- " <td>0.9557</td>\n",
- " <td>116.878996</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " year return tot\n",
- "44 2014 1.1369 88.439335\n",
- "45 2015 1.0138 89.659797\n",
- "46 2016 1.1196 100.383109\n",
- "47 2017 1.2183 122.296742\n",
- "48 2018 0.9557 116.878996"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df[\"tot\"] = df[\"return\"].cumprod()\n",
- "df.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>return</th>\n",
- " <th>tot</th>\n",
- " <th>wealth</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>year</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>2014</th>\n",
- " <td>1.1369</td>\n",
- " <td>88.439335</td>\n",
- " <td>88439.334579</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2015</th>\n",
- " <td>1.0138</td>\n",
- " <td>89.659797</td>\n",
- " <td>89659.797397</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2016</th>\n",
- " <td>1.1196</td>\n",
- " <td>100.383109</td>\n",
- " <td>100383.109165</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2017</th>\n",
- " <td>1.2183</td>\n",
- " <td>122.296742</td>\n",
- " <td>122296.741896</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2018</th>\n",
- " <td>0.9557</td>\n",
- " <td>116.878996</td>\n",
- " <td>116878.996230</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " return tot wealth\n",
- "year \n",
- "2014 1.1369 88.439335 88439.334579\n",
- "2015 1.0138 89.659797 89659.797397\n",
- "2016 1.1196 100.383109 100383.109165\n",
- "2017 1.2183 122.296742 122296.741896\n",
- "2018 0.9557 116.878996 116878.996230"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "starting = 1000\n",
- "df[\"tot\"] = df[\"return\"].cumprod()\n",
- "df[\"wealth\"] = df[\"tot\"] * starting\n",
- "df.set_index(\"year\").tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Wealth ($)')"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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L5a2zUaDTVHUazoovD7pfn41zy+/Hqnq6qp6OMz2gvO11F5MfOJPsj/U8ICJH4Iy23OoeWgJU4vRG28qk4Mzhm+dx6TzgbBFJ9zh2EVAHvB+CWMYYEzGWby9n8qi+JMbZeh3e/L0F+g7wsLvKyxScxOmZGCbhLIkWLI+577fLfZ+BwC9wkt+bAKpaLyL3AXeISBlOD+xmt26PesW6EZgrIrOBUTgr2jzUNp0hmLGMMSZSVNQ1saW0hguPs9ufvvibAG/EecZ3G7AbuERVPefp3YTXLcsuegRoBK7F7V3irDd6mzs/r819OEnqNqAfkA+cparFbQVUtUxEzgDm4MzrK8cZsHOn13sGM5YxxoTdqkLnv+mJw/qEuSaRSQKc524OUV5enubn54e7GsaYXuRPCzcx+60CPv/FWWSm9MwRoCKyVFXzQhE7UifCG2OM6aKVO8s5rF9Kj01+odbZbhCXBbrTgYiMFpFTulYtY4wxXbVyZwVHDbXbn+3prAd4C7BJRH4lIke3V0hE+onIpSLyBrAcGBzMShpjjAnM3uoGCsvr7PlfBzocBKOqk0TkIuAG4P9EpBpnfcxSoAFnUvhIYATOJPHngGtUtTCUlTbGGNOxlfsHwGSGtyIRrNNRoKr6IvCiiBwOnIkzP28QzuTvYmARsBhY6LFiijHGmDD6YmcFInDkkIxwVyVi+TsNAlXdBGwKYV2MMcYEycqd5Yzqn0p6Uny4qxKxbBSoMcZEoZU7Kzjabn92yBKgMcZEmeLKevZUNXCUDYDpkCVAY4yJMit2lAM2AKYzlgCNMSbKfFFYQWyMMH6wDYDpiCVAY4yJMit2VjBmQBrJCbYDREcsARpjTBRRVb7YWW4DYPzg9zQIEYkHfgjMBIbhbIJ7EFUdELyqGWOMCdTOsjrKaptsAIwf/E6AONv+fB/4N/AeznZFxhhjIsjKnbYFkr8CSYCzgFtV9cFQVcYYY0zXrCwsJyE2hnGD0sNdlYgXyDNAAVaGqiLGGGO6buWOCnIHp5MYZwNgOhNIAvwzcEmoKmKMMaZrWluVVYUVdvvTTx3eAhWR6zxeFgGXish7wHyg3Ku4quqfgls9Y4wx/tqyt4aqhmYmDs0Md1V6hM6eAc7xcWwEcJqP4wpYAjTGmDD5wh0AYyNA/dPZfoA2T9AYY3qIlTsrSIqPYcyAtHBXpUfwO8GJyKki4vO7KiKpInJq8KoFIhInIreKyAYRaRCRnSLysFcZEZHbRWSHiNSJyCIRmeQj1ngReUdEakVkl4jcLSKxoYpljDHhsHJnOUcO6UNcrPVd/BHId+k9YHw753Ld88H0NHAj8Fvgq8CtQJ1XmVuBO4DZwAVANbBARAa1FRCRLGABzi3aGcDdwC3AXSGMZYwx3aq5pZXVuyo5aqjd/vRXIPMApYNzaUBtF+ty4I1EzgEuBo5W1TXtlEnCSVr3quoc99hHwFbgeuDnbtFrgGRgpqpWAvNFJAO4U0TuV9XKYMYK1vfAGGMCsbGkmrqmFo4ebgnQX52NAj0VmOpx6Co3OXlKAs4Dvghiva4E3m0v+bmmABnAS20HVLVGRN4ApnMgaU0H3vZKTi/g9PROA94IcixjjOl2bSvAHGUjQP3WWQ/wROAG92vFWQ2m2atMI1AA/CSI9ToReF1E5gDfcev5FnC9qu5yy+QCLcAGr2vXAhd5vM4F3vUsoKrbRaTWPfdGkGMZY0y3+2JnBWmJcYzqnxruqvQYnY0CfQB4AEBEtgDfUNXPu6Feg4ArgBU4t0LTgfuBV0VksqoqkAVUq2qL17VlQIqIJKhqo1uu3Md7lLnnCHKs/UTkauBqgBEjRnTUXmOM6ZKVO8uZMDSDmJiOnlYZT34/A1TVkaGsiBdxP2ao6l4AEdkNvA+cDrzTVq12rvU+1145f8ocSiynoOoTwBMAeXl5vq4zxpguq6hrYu3uKq44OSfcVelROnsGeG4gwVT1za5VZ78yYHNb8nN9iHO7dTxOAiwD0kUk1qvnlgnUqmqTR6xMH+/RhwO9uWDGMsaYbvXXJVtpbGllxqQh4a5Kj9JZD/DfOD0bf/rUCgRrPtxaINHHcQFa3a8L3PcbDazzKJPrnsOjXO5BQUSGA6ke5YIZyxhjuk1NQzNPLd7C6bkDOHKIjQANRGfzAEcCo9zPnX2MCmK9/g1MFJH+HsdOBeJxngsCLAEqcQbmACAiKThz+OZ5XDcPOFtEPPcGuQhnTuH7IYhljDHd5h+fbKe8tokfTBsd7qr0OJ0NgtnWXRXx8gTOJPg3ROQ3OINgZgMLVPVDt271InIfcIeIlOH0wG7GSeqPesR6zI01V0Rm4yTqO4GH2qYzBDOWMcZ0l/qmFp74YDMnjerHcYd9aRye6UQgE+EBZ4kynAWxk7zPdTJvz2/u5PTTgUdw5tk1Aq8BN3kVvQ8nSd0G9APygbNUtdgjVpmInIGzsPcbOM/qHsZJXKGKZYwxIffy0p2UVDXwu4smhbsqPZI4Mwr8KCgSj5OQLsf38zlU1dbEbEdeXp7m5+eHuxrGmCjR1NLK1AcWMiAjkbnXTkEkOqc/iMhSVc0LRexA1gL9BXA+8D2cwSjXA9/FGZG5Fed5mTHG9Eofb97L8u1l3fZ+r32+i8LyOq6fNjpqk1+oBZIAv4Vzq69tubBPVfVZVf0qzhSFGUGumzHGRLz6phbufH01Fz/xMbe8vKLzC4KgpVX548KNHDE4g9NzB3TLe0ajQBLgcGC9O0+unoNXPvk78M1gVswYYyLdhuIqvv6HxTyzZCujslPZUlpDdYP3apHB99aqIjaX1PCDaYdb768LAkmAuzkwCXwLzrSENocHq0LGGBPpVJW/f7KNC+Z8SElVA09fcTz/d+4RqMLa3aEdEK6qzHlvI6OyU5k+YXBI3yvaBTIKdCFwCs7oxz8DvxWR0UADzly454NeO2OMiTDltY3c+soXvLW6iFPG9OfBbx3NgPQkiivrAVhdWMHxOX1D9v7vrdvD2t2VPHDhRGJt3c8uCSQB/h/QH0BVfydOv/tCnP3xHsXZHNYYY6JWRV0T5z/6IcWV9dx+bi5XfWXU/sWnB6Qn0j8tgVW7QtcDVFXmvLuRoZnJfP2YoSF7n94ikMWwi4Aij9cP48yBM8aYXuGP722ksLyOF/53MieO6nfQORHhyCF9WFVYEfT33VVex4cbSlm4fg/LtpfzqxlHEh8byBMs48uhTIQfDxyHMyjmKVUtcm+FFqtqVbAraIwxkWDHvlqeXryVmccM+1LyazNhaAYfbiylvqmFpPhDnxZd39TCBxtK+XBDCR9sLGVzSQ0A2emJXHriCGblDT/k2OYAvxOgiKQBT+GM9mzmwCa1RcBvgO3Aj0NQR2OMCbv7315HTAz85Oxx7ZaZMKQPLa3K+uIqJg7LPKT3UVUuf+pTPtmyj+T4WE4c1Zf/OWEEp4zJZuzANBv1GUSB9AAfAqYAZwKLcaZCtHkTJ/lZAjTGRJ3l28t4Y8Uubjx9NIP6fGkVyP0mDHV2Y1hVWHnICXDuskI+2bKP28/N5fIpOSTG2QJboRJIApwJ/FBV3xMR75/INuCw4FXLGGMig6ry6/+spX9aIlef1vGMr2FZyWQkxbFq16E9B6yoa+LeeWuZNDzzoAE2JjQCeYqaDOxt51w60NLOOWOM8Wnb3hoefWcDjc2tnRcOk7dWFZG/rYxbvjqWtMSO+wxtA2FWH+JAmIfnr2dvTSP3fH2CJb9uEEgC/Az4TjvnLsTZU88YY/x25+ureXD+en788gpaW/1bmL87NTa3ct9bBYwdmMas44b5dc2EoRmsLaqiqSWwpL5mVyXPfrSVS08csf9WqgmtQBLgz4GZIrIAuApnB/hzReRvOBvJ/jIE9TPGRKk1uyp5b10JE4Zm8PqKXdzzn7X4uztNd/nbx9vYtreW2889gjg/px1MGNqHxuZWNpVU+/0+qsovXltFZkoCP/5q+4NsTHD5nQDdjWjPwNkKaQ7OjhB34WwKe6aqfhaSGhpjotKf3t9EWmIcf79qMldMyeGpxVt4fNHmcFdrv4raJh55ZwOnjOnPaWOz/b7uyCEHBsL4a+6yQvK3lfGzc8aRmZIQcF3NoekwAYrIZHcfQABUdbGqngJkAMOAdFU9WVUXh7iexpgosrW0hv+s3MWlk0fQJzmeX5w/nvMnDua+eQW8snRnuKsHwKPvbqCyvonbzz0ioKkHI/unkhwf6/eEeM+BL7OOs/l93amzUaBLgHoRyXe/XgwsUdW9QF2oK2eMiU6PL9pMXGwM3zt5JAAxMcKD3zqastpGfvrKSvqmJTBtXPi2+Vm5s5y/frSVWccN44jBGQFdGxsjjB+SwWo/R4K2DXx5+ooTbOBLN+vsFujZwH1ALfB94DVgj4gUiMiTInKliOSGupLGmOixp7KeV5bu5MLjhjEg48CcusS4WB779nHkDkrnuueW8fmO8m6v2/a9tdz04ufM+MNiMpLiueUQn8dNGJLBml2VnQ7s8Rz4ctQwG/jS3TpMgKo6X1XvVtVzgL7AROA64GOcnSH+AqwWkVIReT3ktTXG9HhPfriF5tZWvn/qqC+dS0+K5+nvHk//9ASufOYztpTWdEudSqoa+OVrqzjjoYXMW7Wba047nHdvmcrAjPYnvXfkyKF9qGlsYeve9uuvqvzydRv4Ek6BDIJRVV2lqo+r6hXAOOB0YB7O5rjnhaKCIjJURKpFRN3l2NqOi4jcLiI7RKRORBaJyCQf148XkXdEpFZEdonI3d4T+YMZyxjTvoraJp77eBvnTxzCYf1SfZYZkJ7Es1eeSKsq1z63lPqm0E0xrqpv4sH/ruO0B97juU+2MytvOO//ZBo/OyeXPinxnQdox4S2gTAd7Azx0ea9fLa1jJvOGmsDX8LE7wQoIqkicrqI/FxE3sSZFD8fZ1HsPwNXhqiODwC+xhPfCtwBzAYucMssEJFBHnXOAhbgTNmYgbNl0y04o1dDFcsY045nP9pKTWML107teEWVkf1TefiiSRQUVfHL11aHpC6l1Q3M+MNiHn13I6fnDmDBzafxm28cdci9Pk9jBqaREBvT4YT4ZxZvJSsl3u/5hSb4OhwEIyL/g7P+5xSc25/lOLc/lwAPAp+oqv+TXQIkIqcA5+Astv2Ax/EknKR1r6rOcY99BGwFrseZswhwDc4KNjNVtRKYLyIZwJ0icr+qVgYzVoi+DcZEhbrGFp5espXTcwf4NbBk2rgB/GDa4fzhvU0cP7IvFwYxUVTUNfGdJz9lV3kd/7jqRKaM7h+02ADxsTGMG5TO6nZ6gDv21TJ/bTHXnnZ4l3aNMF3TWQ/wOeBy4CPgaFXtr6rnq+o9qvpOiJNfLAc22i31Oj0FZyrGS20HVLUGZ7f66R7lpgNveyWnF3AS2WkhiGWMaccLn21nX00j13XS+/N005ljmTyqLz//1xesKwrObmu1jc1c+cxnbNhTxWPfPi7oya/NhKEZrNpV4XNy/7MfbSVGhMtOsiWUw6mzBPgAsBy4AvjUfTY2W0S+LiIDQ1y3a4Ak4A8+zuXirD26wev4WvecZ7kCzwKquh1nVGuuR5lgxTLG+NDY3MqfF23m+Jws8nL6+n1dXGwMj1x8DGmJ8Vz796XUNDR3qR4NzS18/29LWb69jN9ffAxTQzjV4sghfSivbaKw/OAZYzUNzbzw2Q7OmTCIwX2SQ/b+pnOdjQL9maqeCvQBpgL/xNn14RFgt4hsEpHnROQHInJssColIv2AXwE3q2qTjyJZQLWqej8dLwNSRCTBo1y5j+vL3HPBjuXdjqtFJF9E8ktKSnwVMaZXeO3zQnZV1HPd1NEBXzsgI4lHLpnE1tIabpv7xSEvl9bc0soPn/+cDzaUct83J3LuUYMPKY6/PLdG8jR3eSFV9c1ceXJOSN/fdM6vQTCq2qyqn6nqI6p6saqOwBn8ciuQCfwe+DSI9fo1zvPFNzuqlo9j4uNce+X8KXMosQ4UVn1CVfNUNS872/+llIyJJq2tyuOLNnPE4Aymjju0fwdTDu/PzWeN5fUVu/j7J9sPqQ63zv2Ct1YX8Yvzx/OtbthRPXdQOrExctCE+NZW5ZnFWzhqaB+OHeHz72bTjQLZDxARSQSO58DAmJOAtt/owH8rfb/HkTgjSk8VkUz3cIr7uY+ItOD0utJFJNar55YJ1Hr0GsvcY976cKA3F8xYxhgv728oYeOeah6+6Ogu7WZ+3dTRfLa1jLvfWMOk4ZkB7Zjwq/+s4Z9Ld3LTmWO58isjD7kOgUiKj2V0dtpBA2E+3FjKppIaHvpW174XJjg6Wwt0iIhcKCIPicjHQAWwCLgHGAI8D1wEDFPVnCDVaQwQjzPwpsz9aHsOuBNnYEwBEAt430/xfk5XgNfzOREZDqR6lAtmLGOMl798sJlBGUmcd9SQLsWJiREevmgS/dIS+Ok/V/p9K3Tptn08vXgrV0zJ4cYzAr8F2xVHDs04aE3QpxdvoX9aIudNDO3tV+Ofzm6B7gRexNkHsARnzts0oI+qnqCqP1LVl1V1VxDr9KH7Hp4fs91z5+IMzFkCVOJswwSAiKTgzOGb5xFrHnC2iKR7HLsIZx3T993XwYxljPGwZlclizfu5fIpOSTEBbL7mm99UxO46cyxrNldyaIN3oPDfXv8/c1kpcTz03PGdXuva8KQPuypamBPZT1bSmt4b10Jl544gsQ4m/oQCTq7BXoVzuLX3dbDUdVSYKHnMRHJcb/8oG3qhYjcB9whImU4PbCbcRL6ox6XPgbcCMwVkdk4WzfdCTzUNp1BVeuDFcsYc7AnP9xCSkIs/3PCiKDFnHHMEB6cv47HFm7qdJuiTSXVzF9bzA2njyElIaAnPkHRdpt29a5K3l9fQnyscOnk4H0vTNd0+Buhqk91V0UOwX04Seo2oB+QD5ylqsVtBVS1TETOwNm/8A2cZ3UP4ySuUMUyxgDFlfW8vqKQS088rEvLinlLjIvle18ZyW/eLGDFjnKOHp7Zbtm/fLCZhNgYLg/TfLsjBjs3jD7espeX83dw/sQhDEjv+kozJji6fk+iG6jqM6oqnhPv3bVJf62qw1Q1WVVPUdXlPq5do6qnu2UGq+od3lMeghnLGON49qOtNLcq3w3BcP9LThhBelIcjy/a1G6ZPVX1vLK0kFl5w+iXlhj0OvgjPSmekf1TeXqxswTcFVNywlIP41uPSIDGmJ6ltrGZ5z7eztnjB7W76HVXpCfFc9nkw5i3qqjdHSP+umQrTa2tXPWVL+860Z2OHJJBY3Mrx47I7LC3arqfJUBjTNC9snQnFXVNXHVK6KYcXHFyDvGxMTyxaPOXzlU3NPO3j7YxfcIgcvoHPwEHou054HdP7p7pF8Z/3f9U2BgT1VpalSc/3MLRwzM57rDQTfYekJ7EN48dxivLdnLTWWMOerb24mc7qKxv5upT/V93NFRmHjuUllZl+oRBnRc23cp6gMaYoHpnbTFb99byv6eMDPm0g6tPHUVTSyvPLN66/1hTSytPfrCZE0b2ZVIE3HIckJ7ED6aNJi7W/ruNNPYTMcYE1V8+2MLQzGTOOTL0PZ6R/VOZPmEQf/t4G1X1zqJN/1m5m10V9VxzWnif/ZnIZwnQGBM0K3aU8+nWfXz35Jxu6/F8/9TDqapv5oVPd6CqPPb+JsYMSGPq2NDt9GCigz0DNMYEzV8+3EJaYhwXHR/6xabbHD08k5NG9ePJD7cwKjuVgqIqHrhwIjExttam6Zj1AI0xQbFjXy1vfrGbi48fTnpS8Ca+++OaqYdTVFnPTS9+zsCMRGZMGtqt7296JkuAxpigePTdDcTGCN8L4dSH9pw6pj/jB2dQWd/MlSePDMq6oyb62W+JMabLtpbW8MqyQi49cURYdjkXEX5yzjgmDuvDJSfaWpvGP/YM0BjTZb9/ZwPxscK1U8M3727auAFMG2cDX4z/rAdojOmSjXuqeO3zQr5zUo4t9Gx6FEuAxpgu+d2CDSTFx/L9U23enelZLAEaYw5ZQVEl/165m++enBO2HReMOVSWAI0xh+zh+etJT4zjf0+x3p/peSwBGmMOyarCCt5eXcz3ThlJZkpCuKtjTMAsARpjDsnD89fTJzmeK79i2/yYnskSoDEmYMu3l/FOwR6uPnUUGd286osxwWIJ0BgTsIfmr6dvagJXTMkJd1WMOWSWAI0xAflo014+2FDKNaeNIjXR1tIwPVdEJkARmSUir4tIoYhUi8hSEbnEq4yIyO0iskNE6kRkkYhM8hFrvIi8IyK1IrJLRO4WkdhQxTImmm0preH6fyxjeN9kLpucE+7qGNMlEZkAgZuBauAm4GvAe8A/ROQGjzK3AncAs4EL3PILRGT/LpwikgUsABSYAdwN3ALc5fV+wYxlTFQqrqznsic/QYG/fvcEkhPsbz/Ts0Xq/YsLVLXU4/W7IjIEJzE+KiJJOEnrXlWdAyAiHwFbgeuBn7vXXQMkAzNVtRKYLyIZwJ0icr+qVgYzVki+E8ZEgIq6Ji5/6lPKahp5/urJjMpOC3eVjOmyiOwBeiW/NsuBtpVupwAZwEse19QAbwDTPa6ZDrztlZxewElkp4UgljFRp76phav++hmbS2p44jt5TByWGe4qGRMUEZkA2zEFWON+nQu0ABu8yqx1z+FRrsCzgKpuB2o9ygUzljFRpbmllev/sYz8bWU8fNEkTh7dP9xVMiZoekQCFJEzcJ67/cE9lAVUq2qLV9EyIEVEEjzKlfsIWeaeC3Ys73pfLSL5IpJfUlLiq4gxEUtVuW3uFyxYu4e7Z0zgvImDw10lY4Iq4hOgiOQA/wBeU9VnPE6pr+I+zrVXzp8yhxLrQGHVJ1Q1T1XzsrOzfRUxJmL99r/reHnpTn54xhgum3xYuKtjTNBFdAIUkb7APGA78G2PU2VAuo8pCJlArao2eZTL9BG6Dwd6c8GMZUxUeGvVbv7w3iYuOWE4PzpzTLirY0xIRGwCFJEU4N9AAnCeOzClTQEQC4z2usz7OV0BXs/nRGQ4kOpRLpixjOnxtpTW8JOXV3L08Ezu/NqRiEjnFxnTA0VkAhSROOBlYAwwXVX3eBVZAlQCszyuScGZwzfPo9w84GwRSfc4dhFQB7wfgljG9Gh1jS1c+9xSYmOFP156LIlxNtfPRK9InQf4R+Bc4IdAXxGZ7HFuuarWi8h9wB0iUobTA7sZJ6E/6lH2MeBGYK6IzAZGAXcCD7VNZwhmLGN6ul+8top1xVU8fcXxDM1MDnd1jAmpSE2AX3U//97HuZE4k9Tvw0lStwH9gHzgLFUtbiuoqmXuCNI5OPP6yoGHcRKXp2DGMqZHeumzHby8dCc3nj6aqeMGdH6BMT2cqPocwGiCLC8vT/Pz88NdDWN8Wr2rgpl/XMLxOX3565UnEBtjz/1MZBCRpaqaF4rYEfkM0BjTfSrqmrju78vISkng9xdPsuRneo1IvQVqjAmi1laluKqemoZmahtbqGloobbR+fqVZTspLKvjhasn0y8tMdxVNabbWAI0Jko1Nrfy8ea9vL26iPlritlT1dBu2V9eMJ68nL7dWDtjws8SoDFRpKq+iUXrS/nvmiLeLdhDVX0zyfGxTB2XzZTR/emTHE9qQizJCbGkJsSRkhBLn5R4BqQnhbvqxnQ7S4DG9FCtrcrm0mqWbS9n+fYylm8vZ11xFarQNzWB6RMG8dXxg/jKmP4kxdt8PmO8WQI0pocpKKrkgbfW8dnWfVTWNwOQkRTHpBFZnDNhECeN6kdeTl8bzGJMJywBGtNDNLe08viizfxuwXrSk+I5b+JgjhmRxbEjMhnVP40YS3jGBMQSoDE9wMY9Vdzy8kpW7Cjn3KMG8asZE2zEpjFdZAnQmAjW0qo89eEWHvjvOlISYnnkkmO4YOJgW6DamCCwBGhMGOVv3cdD89dTVFFPZko8mSkJZCbH0yclnszkBD7YUEL+tjLOPGIgv5k5wUZrGhNElgCNCYPNJdXMfquAt1cXMyA9keNz+lJR18SeqnrWF1dRUdtEVUMzGUlxPDjraGYeO9R6fcYEmSVAY7pRaXUDj7yzgX98sp3EuBhuPmssV50ykpSEL/9TbGppBSA+1lYsNCYULAEaE2KqysY91cxbVcQTizZT19TCxccP50dnjiU7vf2BLJb4jAktS4DGBFlrq7JhTzUfb97LJ1v28umWfZRWNwJw5hEDuXV6LqMHpIW5lsYYS4DGBGhfTSOf7yijsLye8ppGymqbKK9tpKzW+Xrb3hrKapsAGJqZzKljsjlxVF8mj+rHYf1Sw1x7Y0wbS4DGdKClVVlXVMWy7WUsc5cb21Jac1CZ1IRYMlMSyEqNJyslgbPGD+T4HCfhDe+bEqaaG2M6YwnQGC+7K+pYuK6E9wr2sGTTXqobnOXG+qUmcMyILGblDePYEVmM6p9Kn5R4EuNsnU1jeiJLgKbXUlXqm1qpamhi054aFq7fw8KCEtYVVwHO7csLjh7CiSP7cuyILIb3TbapCMZEEUuAJiq1tiq7K+vZtKeaTSXux54a9tU0Ut3QTFV9EzWNLbS06v5r4mOF43P6cvtxuUwdN4AxA9Is4RkTxSwBBkhExgOPAicB5cBfgLtUtSWc9YpGDc0t7NhXR2NzK4nxMSTExpAYH0NibCyJ8TE0NLWys7yWwrI6dpbVUVheR2FZHTvKatlcUkNd04EfSXpSHIdnp5HTP4W0xHjSEmNJS4rb//WgPsmcdHg/0hLtn4QxvYX9aw+AiGQBC4A1wAzgcOBBIAb4eRir1uOoKlUNzVTUNlFe20RpTQPbSmvYUlrDlr21bCmtprCsDo8OWqeS42MZmpXMsKxkThzZj8MHpHJ4dhqHZ6fRPy3BenPGmINYAgzMNUAyMFNVK4H5IpIB3Cki97vHgqqxuZXK+ibqGluob2qhrqnF+bq5lbrGZqobWqiqb6KqvtnjczMtrer2cNwP9+v0pDiyUhLom5pAvzTns+cgDlWloq6JvTWN7K1uZG91A/tqGylzh/uX1TZS7n6uqm8mLkZIiIshMS6GhDinl5YQF0OrOiuZNLW00tSsNLpf1zW2UF7XREVd00G3H9ukJcYxsn8qxwzPYuYxwxjZP5Wk+FgamltoaG6lsbmVhuZWGppbiI+J2Z/whmYm0zfVkpwxxn+WAAMzHXjbK9G9AMwGTgPeCPYbvr26iBueX+5X2aT4GNKT4klPiiNWhJqGZqrdj456UmmJcWSlxtPQ1Mq+mkaa2ymckhBLVkoCmSnOcP/BfZJobnGSW2NzK/VNrVTWNdPY3EpMjJAQK8THxhAfG0N6fBwJsTEkJcSSmRy/P06fZGcB6KyUeA7rl2o9NWNMt7EEGJhc4F3PA6q6XURq3XNBT4ATh/XhVzOOJCk+luSEWJLi3M/xsU7CS4wnLcnp2bW3dJaqUtfUQnVDM5V1zZTVOr27fTWN7KtpYG+N08NLio91e4aJ9E9LoF9q4v5eYqYN9zfGRBlLgIHJwhn44q3MPXcQEbkauBpgxIgRh/SGh/VL5bKTurZ6iIiQkhBHSkIcA9K7FMoYY6KGrbYbOF/3B8XXcVV9QlXzVDUvOzs79DUzxhjjN0uAgSkDMn0c74PvnqExxpgIZQkwMAU4z/r2E5HhQKp7zhhjTA9hCTAw84CzRcTzSdpFQB3wfniqZIwx5lBYAgzMY0ADMFdEznQHudwJPBSKOYDGGGNCx0aBBkBVy0TkDGAOzpSHcuBhnCRojDGmB7EEGCBVXQOcHu56GGOM6Rq7BWqMMaZXEtUAVhs2h0xESoBth3h5f6A0iNXpaXpz+3tz26F3t9/a7jhMVUMykdoSYA8gIvmqmhfueoRLb25/b2479O72W9tD33a7BWqMMaZXsgRojDGmV7IE2DM8Ee4KhFlvbn9vbjv07vZb20PMngEaY4zplawHaIwxpleyBGiMMaZXsgQYYiIyWkQeF5EVItIiIgt9lMkUkadEZJ+IVIvIPBEZ7VVmoYhoOx8neZQTEbldRHaISJ2ILBKRSaFvqW/Bar9b7mIRWeaWKRSRZ0VkiFeZiGl/kNv+dRFZKSINIrJFRG72USaS2j5LRF53f07VIrJURC45lPqKyHgReUdEakVkl4jcLSKxhxKrOwSr7f78/vgbqzsFsf2dxvE3VrtU1T5C+AHMAHYALwNrgYU+yrwNFAKXAV8DPsWZNJ/hUWY8MNnr479ACRDnUe42nN0prgfOBN7EmVA6qIe3/2s4mw7PAc4Avg1sBZYBMZHY/iC2/WSgFfgL8FXgDqAJ+JFXrEhq+0fAP4Bv4Swd+Fv353dDIPUFsoBdwALgLOAaoAa4pxe0vdPfn0hre5Db32mcrra/2785ve2Dg/9z/qf3LzFwkvtDPd3j2ECgFvhxB3ETgH3AnzyOJQEVwC88jqXiJMl7utKOcLcfeAFY6nVtW1I8IhLbH8S2vw0s8rr2IffnnxChbe/v49g/gC2B1Nf9z62Mg/8g+Kn7PcqI8rZ3+PsTiW0Pcvs7jBOM9tst0BBT1dZOikwCmvHYT1BVi4GVwHkdXHcOzl/Hz3scmwJkAC95xKrB2blieiD1DpYgtj8e5xfdU7n7WdzPEdX+ILZ9Ek4PyNN/cX7+bbe/I63tvpbwWg4McL/2t77Tgbf14O3GXgCSgdMCjNUtgtV2P35//I7VnYLY/s7i+B2rPZYAwy8JaFbVFq/jDcARHVx3Mc6tsw88juUCLcAGr7Jr8drJPoL42/6ngFNE5DsikiEiY4F7gPfU2aEDel77/W17EtDoowwe5XpC26cAgf6scoECzwKquh2nB5jrUSYa2+6PntB2CF77PeN0NZYlwAiwEUgSkaPaDohIMjAB6OvrAhFJAS4AXlS3z+/KAqp9/IdaBqSISEJQax4cfrVfVf8DXIEzQbYCWAfEAjM9YvW09vv7s98IHO917Qnu57ZyEd12cfbRnAH8wT3kb32zONDT9y6XFWCssOhC2/0R0W2H4LXfR5xDjtXGEmD4vQ1sAR4XkXEiMhhn5/k+OH/Z+HIBkMbBtz/b+FrZQDo4F25+tV9EprnHfw9Mw+kB9wVe9RoR2JPa7+/P/jFghoj8r4hkicjZwC3uOc9yEdl2EcnBeXbzmqo+43HK3/q2V86fMu2d6xZBaLs/IrLtELz2dxAn4FieLAGGmao24vxnPhDnVs8uYBTwLFDczmUXAxtVNd/reBmQ7j1EHMgEalW1KVj1DpYA2v8g8Lqq/kxVF6rqi8DXgak4fxVCD2t/AG1/CicJ/gln4Mtc4G73XFu5iGy7iPQF5gHbcUbutvG3vmXuMW99ONAzjNa2+yMi2w7Ba38HcQKO5c0SYARQ1U+B0Tj3rEer6ik4D3o/9i4rIn1wHu766v0V4NwW9J5H9qXnKJHEz/bnAp97XbcOZ/jz4e6hHtd+f9quqi2qej2QDUzESZht59s+R1zb3Vv1/8YZsXyeOzihjb/1LcDrWY6IDMcZ6VfgUSYa2+6PiGs7BK/9ncQJKJYvlgAjhDrWqeomERmDM5/lSR9FvwEk4jsBLgEqgVltBzyeF84Lfq2Dx4/2bwOO9bxGRI7AGQ241T3UI9vv789eVctU9QtVrQauA5aoats/8ohqu4jE4cxfGwNMV9U9XkX8re884GwRSfc4dhHOHz5to2ejte3+iKi2u+8flPb7EcfvWO2J869J5lC5P4xz3ZdDgQwRudB9/aaq1orIHTh/rZQCR+FMdH5BVef7CHkxsEJV13qfUNV6EbkPuENEytyYN+P8ofNoMNvlryC2/zHgYRHZhfOLPRD4BU7yexMir/3BaruITAa+gtMDzgAuAc52jwGR13bgjzht/yHQ121Dm+UB1Pcx4EZgrojMxrlFfCfwUNvUiGhtuz+/PxHYdgjez76zOA1dbn97EwTtI2iTQnNwHsT6+shxy/wOZ0pDA86Iv5/hsbqLR6z+OCuA3NrB+wnwf8BOnL+SPwCO6entd9t1Lc4cuRq3/IvAqEhtfxDbfhzwGVCN89fuf4CjIvlnj/OHSWdt96u+OKsgveuW2Q38CoiN9rb78/sTaW0Pcvs7jdPV9tt2SMYYY3olewZojDGmV7IEaIwxpleyBGiMMaZXsgRojDGmV7IEaIwxpleyBGiMMaZXsgRojDGmV7IEaIwxpleyBGiM6RJ3D0NjehxLgMb0cCJynoi0ishIr+Mj3eNfc1/PEJF8EakXkSIRuV9E4j3K54rICyKyQ0RqRWS1iPxIRGI8ykwVERWRs0XkdRGpBuZ0W2ONCSJLgMb0fG/h7CV4udfxK4AS4E0R+RbOPoKfAl8D7gKuBu71KD8UWIez08S5wJ/dcj/z8Z5PAivcWL52LTEm4tlaoMZEARG5B7gUZ3FwFRHB2W3+n8BPcBYWfldVv+txzZXAH4BhqrrXK57g7LP2U+AqVR3lHp8KvAf8TlVvCnGzjAkp6wEaEx2eAg4Dprqvp7mvnwbGAiOAl0Qkru0DZ4eFJGACgIgkichdIrIRZ3eKJuDXwEi3vKf/hLg9xoScJUBjooCqbgYWAm09vO8Cn6rqapxttMDZN7HJ42OLe3y4+3k28GPgCZxboMcD97jnkrzesji4LTCm+9mGuMZEj78AfxaR24CZwC3u8X3u56uB5T6ua0uEs4BHVfX+thMicl4772XPTkyPZwnQmOgxF+eZ3gs4d3decI+vw9l0N0dV/9zB9ck4tz4BEJFY4OLQVNWY8LMEaEyUUNV6Efk78APgeVUtd4+3isgtwN9EJAOYBzQCo4CvAxeqai0wH/iB+wxwnxsnsdsbYkw3sWeAxkSXf7mfn/I8qKovAjOAScDLOL3F64BlOMkQ4AbgA5xe5FPAKg6eJmFMVLFpEMZEERG5H7gIGKmqreGujzGRzG6BGhMFRGQcMB64FrjLkp8xnbMeoDFRQEQWAicCrwOXqWpjx1cYYywBGmOM6ZVsEIwxxpheyRKgMcaYXskSoDHGmF7JEqAxxpheyRKgMcaYXun/AVH6kmK/gMtIAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = df.set_index(\"year\")[\"wealth\"].plot.line()\n",
- "ax.set_ylabel(\"Wealth ($)\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-39/lec_38_plotting4.ipynb b/f22/meena_lec_notes/lec-39/lec_38_plotting4.ipynb
deleted file mode 100644
index 22b2351..0000000
--- a/f22/meena_lec_notes/lec-39/lec_38_plotting4.ipynb
+++ /dev/null
@@ -1,2127 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import sqlite3\n",
- "import os\n",
- "\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt\n",
- "matplotlib.rcParams[\"font.size\"] = 16\n",
- "\n",
- "import math\n",
- "\n",
- "import requests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plots using DataFrames"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Bar Plot Example w/ Fire Hydrants\n",
- "\n",
- "- General review of pandas\n",
- "- Some new bar plot options"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>X</th>\n",
- " <th>Y</th>\n",
- " <th>OBJECTID</th>\n",
- " <th>CreatedBy</th>\n",
- " <th>CreatedDate</th>\n",
- " <th>LastEditor</th>\n",
- " <th>LastUpdate</th>\n",
- " <th>FacilityID</th>\n",
- " <th>DataSource</th>\n",
- " <th>ProjectNumber</th>\n",
- " <th>...</th>\n",
- " <th>Elevation</th>\n",
- " <th>Manufacturer</th>\n",
- " <th>Style</th>\n",
- " <th>year_manufactured</th>\n",
- " <th>BarrelDiameter</th>\n",
- " <th>SeatDiameter</th>\n",
- " <th>Comments</th>\n",
- " <th>nozzle_color</th>\n",
- " <th>MaintainedBy</th>\n",
- " <th>InstallType</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>10104</th>\n",
- " <td>-89.439367</td>\n",
- " <td>43.040481</td>\n",
- " <td>286329</td>\n",
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- " <td>1-1830-19</td>\n",
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- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2018.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
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- " <th>10105</th>\n",
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- " <td>43.067854</td>\n",
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- " <td>2018-08-13T18:14:14.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-13T18:17:45.000Z</td>\n",
- " <td>HYDR-4253-4054</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2017.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10106</th>\n",
- " <td>-89.445461</td>\n",
- " <td>43.053305</td>\n",
- " <td>286729</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:37:18.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:39:24.000Z</td>\n",
- " <td>HYDR-3859-4055</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2000.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10107</th>\n",
- " <td>-89.388849</td>\n",
- " <td>43.068576</td>\n",
- " <td>286730</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:51:54.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-15T16:59:15.000Z</td>\n",
- " <td>HYDR-5052-4056</td>\n",
- " <td>TC</td>\n",
- " <td>1-1830-19</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>WATEROUS</td>\n",
- " <td>NaN</td>\n",
- " <td>2017.0</td>\n",
- " <td>5.0</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>MADISON WATER UTILITY</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10108</th>\n",
- " <td>-89.518896</td>\n",
- " <td>43.062062</td>\n",
- " <td>287129</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-20T18:54:01.000Z</td>\n",
- " <td>WUJAG</td>\n",
- " <td>2018-08-20T19:27:16.000Z</td>\n",
- " <td>-2355-4057</td>\n",
- " <td>FASB</td>\n",
- " <td>NaN</td>\n",
- " <td>...</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>PRIVATE</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 25 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " X Y OBJECTID CreatedBy CreatedDate \\\n",
- "10104 -89.439367 43.040481 286329 WUJAG 2018-08-13T18:03:33.000Z \n",
- "10105 -89.426303 43.067854 286330 WUJAG 2018-08-13T18:14:14.000Z \n",
- "10106 -89.445461 43.053305 286729 WUJAG 2018-08-15T16:37:18.000Z \n",
- "10107 -89.388849 43.068576 286730 WUJAG 2018-08-15T16:51:54.000Z \n",
- "10108 -89.518896 43.062062 287129 WUJAG 2018-08-20T18:54:01.000Z \n",
- "\n",
- " LastEditor LastUpdate FacilityID DataSource \\\n",
- "10104 WUJAG 2018-08-13T18:04:02.000Z HYDR-3964-4053 TC \n",
- "10105 WUJAG 2018-08-13T18:17:45.000Z HYDR-4253-4054 TC \n",
- "10106 WUJAG 2018-08-15T16:39:24.000Z HYDR-3859-4055 TC \n",
- "10107 WUJAG 2018-08-15T16:59:15.000Z HYDR-5052-4056 TC \n",
- "10108 WUJAG 2018-08-20T19:27:16.000Z -2355-4057 FASB \n",
- "\n",
- " ProjectNumber ... Elevation Manufacturer Style year_manufactured \\\n",
- "10104 1-1830-19 ... NaN WATEROUS NaN 2018.0 \n",
- "10105 1-1830-19 ... NaN WATEROUS NaN 2017.0 \n",
- "10106 1-1830-19 ... NaN WATEROUS NaN 2000.0 \n",
- "10107 1-1830-19 ... NaN WATEROUS NaN 2017.0 \n",
- "10108 NaN ... NaN NaN NaN NaN \n",
- "\n",
- " BarrelDiameter SeatDiameter Comments nozzle_color \\\n",
- "10104 5.0 NaN NaN NaN \n",
- "10105 5.0 NaN NaN NaN \n",
- "10106 5.0 NaN NaN NaN \n",
- "10107 5.0 NaN NaN NaN \n",
- "10108 NaN NaN NaN NaN \n",
- "\n",
- " MaintainedBy InstallType \n",
- "10104 MADISON WATER UTILITY NaN \n",
- "10105 MADISON WATER UTILITY NaN \n",
- "10106 MADISON WATER UTILITY NaN \n",
- "10107 MADISON WATER UTILITY NaN \n",
- "10108 PRIVATE NaN \n",
- "\n",
- "[5 rows x 25 columns]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# TODO: read \"Fire_Hydrants.csv\" into a DataFrame\n",
- "hdf = pd.read_csv(\"Fire_Hydrants.csv\")\n",
- "hdf.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['X', 'Y', 'OBJECTID', 'CreatedBy', 'CreatedDate', 'LastEditor',\n",
- " 'LastUpdate', 'FacilityID', 'DataSource', 'ProjectNumber',\n",
- " 'InstallDate', 'LifecycleStatus', 'Location', 'SymbolRotation',\n",
- " 'HydrantType', 'Elevation', 'Manufacturer', 'Style',\n",
- " 'year_manufactured', 'BarrelDiameter', 'SeatDiameter', 'Comments',\n",
- " 'nozzle_color', 'MaintainedBy', 'InstallType'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Extract just the column names\n",
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *colors* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "blue 5810\n",
- "Blue 1148\n",
- "Green 320\n",
- "Orange 74\n",
- "BLUE 45\n",
- "green 9\n",
- "Red 9\n",
- "orange 4\n",
- "GREEN 1\n",
- "white 1\n",
- "C 1\n",
- "ORANGE 1\n",
- "Name: nozzle_color, dtype: int64"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Make a series called counts_series which stores the value counts of the \"nozzle_color\"\n",
- "color_counts = hdf[\"nozzle_color\"].value_counts()\n",
- "color_counts # what is wrong with this data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "BLUE 7003\n",
- "GREEN 330\n",
- "ORANGE 79\n",
- "RED 9\n",
- "WHITE 1\n",
- "C 1\n",
- "Name: nozzle_color, dtype: int64"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# TODO: Clean the data ......use str.upper()\n",
- "\n",
- "color_counts = hdf[\"nozzle_color\"].str.upper().value_counts()\n",
- "color_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Fire hydrant count')"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Make a horizontal bar plot of counts of colors and have the colors match\n",
- "# use color list: [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"]\n",
- "ax = color_counts.plot.barh(color = [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"])\n",
- "ax.set_xlabel(\"Fire hydrant count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *style* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "PACER 3620\n",
- "M-3 1251\n",
- "MUELLER 1243\n",
- "WB-59 664\n",
- "K-11 351\n",
- "K-81 162\n",
- "W-59 151\n",
- "CLOW 2500 123\n",
- "CLOW MEDALLION 70\n",
- "CLOW 50\n",
- "CENTURIAN 35\n",
- "EDDY 27\n",
- "MUELLER 90 13\n",
- "MUELLER 86 13\n",
- "MUELLER SUPER CENTURIAN 12\n",
- "MUELLER 92 12\n",
- "MUELLER 93 11\n",
- "MUELLER 91 9\n",
- "MUELLER 89 9\n",
- "MUELLER CENTURIAN 9\n",
- "MUELLER 85 8\n",
- "MUELLER 87 7\n",
- "MUELLER 84 6\n",
- "M-2 6\n",
- "SUPER CENTURIAN 6\n",
- "M-1 5\n",
- "MUELLER 83 4\n",
- "MEDALLION 4\n",
- "PACER 1996 3\n",
- "SUPER CENTURION 3\n",
- "K-10 3\n",
- "PACER 90 3\n",
- "MUELLER 80 2\n",
- "MUELLER 79 2\n",
- "MUELLER CENTENIAL 2\n",
- "MUELLER 82 2\n",
- "PACERR 1\n",
- "2013 1\n",
- "CENTERIAN 1\n",
- "WATEROUS 1\n",
- "MUELLER 88 1\n",
- "WB-67 1\n",
- "KENNEDY 1\n",
- "MUELLER CENTURION 1\n",
- "MUELLER 94 1\n",
- "MUELLER 2004 1\n",
- "MUELLER 2006 1\n",
- "MUELLER 81 1\n",
- "GOLDEN 1\n",
- "Name: Style, dtype: int64"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Do the same thing as we did for the colors but this time for the \"Style\"\n",
- "style_counts = hdf[\"Style\"].str.upper().value_counts()\n",
- "style_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "style_counts.plot.bar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Grab the top 12 \n",
- "top12 = style_counts.iloc[:12]\n",
- "\n",
- "# and them add an index to our Series for the sum of all the \"other\" for \n",
- "top12[\"other\"] = style_counts.iloc[12:].sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Hydrant Type')"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot the results\n",
- "ax = top12.plot.bar(color = \"firebrick\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "ax.set_xlabel(\"Hydrant Type\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### In what *decade* were *pacers manufactured*?\n",
- "### Take a peek at the *Style* column data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 Pacer\n",
- "1 Pacer\n",
- "2 Pacer\n",
- "3 Pacer\n",
- "4 Pacer\n",
- " ... \n",
- "10104 NaN\n",
- "10105 NaN\n",
- "10106 NaN\n",
- "10107 NaN\n",
- "10108 NaN\n",
- "Name: Style, Length: 10109, dtype: object"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "hdf[\"Style\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which *column* gives *year* information?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['X', 'Y', 'OBJECTID', 'CreatedBy', 'CreatedDate', 'LastEditor',\n",
- " 'LastUpdate', 'FacilityID', 'DataSource', 'ProjectNumber',\n",
- " 'InstallDate', 'LifecycleStatus', 'Location', 'SymbolRotation',\n",
- " 'HydrantType', 'Elevation', 'Manufacturer', 'Style',\n",
- " 'year_manufactured', 'BarrelDiameter', 'SeatDiameter', 'Comments',\n",
- " 'nozzle_color', 'MaintainedBy', 'InstallType'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *year_manufactured* for *pacers* and *others*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 1996.0\n",
- "1 1995.0\n",
- "2 1996.0\n",
- "3 1995.0\n",
- "4 1996.0\n",
- " ... \n",
- "10050 2017.0\n",
- "10051 2017.0\n",
- "10052 2017.0\n",
- "10053 2017.0\n",
- "10054 NaN\n",
- "Name: year_manufactured, Length: 3458, dtype: float64"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Let's get the year manufactured for all of the \"Pacer\" hydrants.\n",
- "pacer_years = hdf[hdf[\"Style\"] == \"Pacer\"][\"year_manufactured\"]\n",
- "\n",
- "# Note: We can do this either way\n",
- "# pacer_years = hdf[\"year_manufactured\"][hdf[\"Style\"] == \"Pacer\"]\n",
- "\n",
- "pacer_years"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "18 1987.0\n",
- "22 1996.0\n",
- "23 1996.0\n",
- "71 1987.0\n",
- "72 1987.0\n",
- " ... \n",
- "10104 2018.0\n",
- "10105 2017.0\n",
- "10106 2000.0\n",
- "10107 2017.0\n",
- "10108 NaN\n",
- "Name: year_manufactured, Length: 6651, dtype: float64"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# then do the same for all the other data\n",
- "other_years = hdf[\"year_manufactured\"][hdf[\"Style\"] != \"Pacer\"]\n",
- "other_years"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *decade* for *pacers*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 1990.0\n",
- "1 1990.0\n",
- "2 1990.0\n",
- "3 1990.0\n",
- "4 1990.0\n",
- " ... \n",
- "10050 2010.0\n",
- "10051 2010.0\n",
- "10052 2010.0\n",
- "10053 2010.0\n",
- "10054 NaN\n",
- "Name: year_manufactured, Length: 3458, dtype: float64"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Round each year down to the start of the decade.\n",
- "# e.g. 1987 --> 1980, 2003 --> 2000\n",
- "pacer_decades = (pacer_years // 10 * 10)\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to convert the *decades* back to *int*?\n",
- "- `astype(...)` method\n",
- "- `dropna(...)` method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2000 1730\n",
- "1990 846\n",
- "2010 503\n",
- "1980 21\n",
- "1960 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Drop the NaN values, convert to int, and do value counts\n",
- "pacer_decades = pacer_decades.dropna()\n",
- "pacer_decades = pacer_decades.astype(int).value_counts()\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to *count the decades* for pacers?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1730 1\n",
- "846 1\n",
- "503 1\n",
- "21 1\n",
- "1 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pacer_decades_count = pacer_decades.value_counts()\n",
- "pacer_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Count the *decades* for others."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2010 1196\n",
- "1980 937\n",
- "1970 578\n",
- "1990 431\n",
- "1950 371\n",
- "1960 349\n",
- "2000 215\n",
- "1940 68\n",
- "1930 9\n",
- "1900 1\n",
- "Name: year_manufactured, dtype: int64"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Do the same thing for other_years. Save to a variable called \"other_decades\"\n",
- "other_decades = (other_years // 10 * 10).dropna().astype(int)\n",
- "other_decades_count = other_decades.value_counts()\n",
- "other_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Build a DataFrame from a dictionary of key, Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>pacer</th>\n",
- " <th>other</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>503</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>846</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1730</th>\n",
- " <td>1.0</td>\n",
- " <td>NaN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1900</th>\n",
- " <td>NaN</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1930</th>\n",
- " <td>NaN</td>\n",
- " <td>9.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1940</th>\n",
- " <td>NaN</td>\n",
- " <td>68.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1950</th>\n",
- " <td>NaN</td>\n",
- " <td>371.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1960</th>\n",
- " <td>NaN</td>\n",
- " <td>349.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1970</th>\n",
- " <td>NaN</td>\n",
- " <td>578.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1980</th>\n",
- " <td>NaN</td>\n",
- " <td>937.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1990</th>\n",
- " <td>NaN</td>\n",
- " <td>431.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2000</th>\n",
- " <td>NaN</td>\n",
- " <td>215.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2010</th>\n",
- " <td>NaN</td>\n",
- " <td>1196.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " pacer other\n",
- "1 1.0 NaN\n",
- "21 1.0 NaN\n",
- "503 1.0 NaN\n",
- "846 1.0 NaN\n",
- "1730 1.0 NaN\n",
- "1900 NaN 1.0\n",
- "1930 NaN 9.0\n",
- "1940 NaN 68.0\n",
- "1950 NaN 371.0\n",
- "1960 NaN 349.0\n",
- "1970 NaN 578.0\n",
- "1980 NaN 937.0\n",
- "1990 NaN 431.0\n",
- "2000 NaN 215.0\n",
- "2010 NaN 1196.0"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plot_df = DataFrame({\n",
- " \"pacer\": pacer_decades_count,\n",
- " \"other\": other_decades_count,\n",
- "})\n",
- "plot_df # observe the NaN values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrant Count')"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# make a bar plot\n",
- "\n",
- "ax = plot_df.plot.bar()\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Ignore data from before 1950 using boolean indexing."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Hydrant Count')"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = plot_df[plot_df.index >= 1950].plot.bar()\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Stacked Bar Chart\n",
- "`stacked` parameter accepts boolean value as argument"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = plot_df[plot_df.index >= 1950].plot.bar(stacked=True)\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting 4\n",
- "\n",
- "## Learning objectives\n",
- "- how to use logarithmic axes\n",
- "- how to create multiple plots within same figure"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Logarithmic scale\n",
- "- math.log(y, base)\n",
- "- find an x, such that 10**x == y\n",
- " - math.log10(y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3.0\n",
- "6.0\n"
- ]
- }
- ],
- "source": [
- "print(math.log10(1000))\n",
- "print(math.log10(1000000))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "5.0\n",
- "4.0\n"
- ]
- }
- ],
- "source": [
- "print(math.log(32, 2))\n",
- "print(math.log(256, 4))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "def log_approx(y):\n",
- " assert type(y) == int\n",
- " assert y >= 1\n",
- " return len(str(y))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "9\n",
- "8.09151497716927\n"
- ]
- }
- ],
- "source": [
- "print(log_approx(123456789)) # What will this output?\n",
- "print(math.log10(123456789))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "6\n",
- "5.995590446800246\n"
- ]
- }
- ],
- "source": [
- "print(log_approx(989898))\n",
- "print(math.log10(989898))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1.0"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "errors = []\n",
- "for y in range(1, 1000001):\n",
- " err = abs(log_approx(y) - math.log10(y))\n",
- " errors.append(err)\n",
- "max(errors)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Why does this matter?\n",
- "- Comparing two numbers:\n",
- " - 134234255623423423423432423432432432\n",
- " - 2342343252523\n",
- "\n",
- "- Eventually I don't care what the number is, but only counting the number of digits in the number to know how big the number is!\n",
- "- log base 2: counting how many bits we need\n",
- "- log base 10: 10 digits 0 through 9!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series([1, 10, 100, 1000, 10000, 100000, 1000000])\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.line(logy = True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Population example\n",
- "https://ourworldindata.org/grapher/population"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [],
- "source": [
- "populations = pd.Series({\n",
- " \"China\":1439323776,\n",
- " \"India\": 1380004385,\n",
- " \"Mexico\": 128932753,\n",
- " \"Senegal\":16743927,\n",
- " \"Bahrain\":1701575,\n",
- " \"Grenada\":112523,\n",
- " \"Tuvalu\": 11792\n",
- "})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot populations as a bar chart."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# not that readable\n",
- "populations.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now plot on a logarithmic scale."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "populations.plot.bar(logy = True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Multiple *axessubplots* in the same plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(<Figure size 432x288 with 1 Axes>, <AxesSubplot:>)"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.subplots()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(<Figure size 432x288 with 2 Axes>,\n",
- " array([<AxesSubplot:>, <AxesSubplot:>], dtype=object))"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.subplots(ncols = 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(<Figure size 432x288 with 2 Axes>,\n",
- " array([<AxesSubplot:>, <AxesSubplot:>], dtype=object))"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.subplots(nrows = 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(<Figure size 432x288 with 4 Axes>,\n",
- " array([[<AxesSubplot:>, <AxesSubplot:>],\n",
- " [<AxesSubplot:>, <AxesSubplot:>]], dtype=object))"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.subplots(ncols = 2, nrows = 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "s1 = Series([1, 2, 3, 3, 4])\n",
- "s2 = Series([5, 7, 7, 8])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's create a single plot with two sub figures (line plots) and plot s1 on the left and s2 on the right."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, axes = plt.subplots(ncols = 2)\n",
- "# axes[0] # the area on the left\n",
- "# axes[1] # the area on the right\n",
- "s1.plot.line(ax = axes[0])\n",
- "s2.plot.line(ax = axes[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is wrong with the below plot?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Y-axes are misleading"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How can we fix that?\n",
- "- pass argument to `sharey` parameter while invoking subplots function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, axes = plt.subplots(ncols = 2, sharey = True)\n",
- "# axes[0] # the area on the left\n",
- "# axes[1] # the area on the right\n",
- "pd.Series([1, 2, 3, 3, 4]).plot.line(ax = axes[0])\n",
- "pd.Series([5, 7, 7, 8]).plot.line(ax = axes[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Iris dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>sep-len</th>\n",
- " <th>sep-wid</th>\n",
- " <th>pet-len</th>\n",
- " <th>pet-wid</th>\n",
- " <th>class</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>5.1</td>\n",
- " <td>3.5</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>4.9</td>\n",
- " <td>3.0</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4.7</td>\n",
- " <td>3.2</td>\n",
- " <td>1.3</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>4.6</td>\n",
- " <td>3.1</td>\n",
- " <td>1.5</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>5.0</td>\n",
- " <td>3.6</td>\n",
- " <td>1.4</td>\n",
- " <td>0.2</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " sep-len sep-wid pet-len pet-wid class\n",
- "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
- "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
- "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
- "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
- "4 5.0 3.6 1.4 0.2 Iris-setosa"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Gather the data.\n",
- "resp = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
- "resp.raise_for_status()\n",
- "\n",
- "iris_f = open(\"iris.csv\", \"w\")\n",
- "iris_f.write(resp.text)\n",
- "iris_f.close()\n",
- "\n",
- "iris_df = pd.read_csv(\"iris.csv\",\n",
- " names = [\"sep-len\", \"sep-wid\", \"pet-len\", \"pet-wid\", \"class\"])\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x216 with 3 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot the sepal length vs the sepal width for each of the classes of flowers.\n",
- "colors = [\"r\", \"g\", \"b\"]\n",
- "markers = [\".\", \"^\", \"v\"]\n",
- "\n",
- "varieties = list(set(iris_df[\"class\"]))\n",
- "\n",
- "fig, axes = plt.subplots(ncols = 3, sharex = True, sharey = True, figsize=(12,3))\n",
- "for i in range(len(varieties)):\n",
- " variety = varieties[i]\n",
- " specific_iris_data = iris_df[iris_df[\"class\"] == variety]\n",
- " specific_iris_data.plot.scatter(x = \"sep-len\", y = 'sep-wid', \\\n",
- " ax = axes[i], color = colors[i], marker = markers[i],\n",
- " label = variety)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Self-practice - Student Information Dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CREATE TABLE \"survey\" (\n",
- "\"Lecture\" TEXT,\n",
- " \"Age\" REAL,\n",
- " \"Primary major\" TEXT,\n",
- " \"Other majors\" TEXT,\n",
- " \"Zip Code\" TEXT,\n",
- " \"Pizza topping\" TEXT,\n",
- " \"Pet owner\" TEXT,\n",
- " \"Runner\" TEXT,\n",
- " \"Sleep habit\" TEXT,\n",
- " \"Procrastinator\" TEXT\n",
- ")\n"
- ]
- }
- ],
- "source": [
- "# TODO: establish connection to \"cs220_survey_data.db\"\n",
- "path = \"cs220_survey_data.db\"\n",
- "assert os.path.exists(path)\n",
- "conn = sqlite3.connect(path)\n",
- "\n",
- "# TODO: determine the table name and column types\n",
- "print(pd.read_sql(\"SELECT * FROM sqlite_master WHERE type = 'table'\", conn)[\"sql\"].iloc[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: display all columns of the table\n",
- "survey_data = pd.read_sql(\"SELECT * FROM survey\", conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Using pandas read \"cs220_survey_data.csv\" into a DataFrame called survey\n",
- "\n",
- "survey = pd.read_csv(\"cs220_survey_data.csv\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a bar plot of ages.\n",
- "- x-axis: each unique age\n",
- "- y-axis: count of students with those ages.\n",
- "\n",
- "Things to consider:\n",
- "- Do we really want the ages to be a float value? Make the int conversion.\n",
- " - Remember the survey dataset has a few rows where \"Age\" column has no value - so handle the NA values before the conversion\n",
- "- Think carefully about how to sort the data before you create the plot."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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q2wFszczHR1BzH548bce2zHxod2tq1yJiAfAq4GuZuXWu+7MrEbEf5ZieHcDN4/L+GOVnb5w/IyN9/TJz7CfKgWgfBH5xxHWf1tS9C9gE/Kdm/tnAj4HHm+ly4BkzqH8g8PvArcBjPfUeBv4vcOoMah4EfATY0FNvatrQLFva4WtxAvC9lrfZB3gD8G7g1cBeA9o8F/hky7oTwB8CFwDPb+a9GPgr4B+ay18e8eN/ZvNc/8oMbrs/zQ4cPfOeB/wJcEvzPv8k8Ast654K/Nue63sBF/a9hx8EfncGfR67z964fUa6fP2eqNnVg53NiZKIO5on5JvAvwMWj6Duf2xq/jnwMeBHwHso/1TnfMoptT/QXD+nZe2fB+6kfOl/C/ga5RQbjwKXNF9SPwH+bNAX405qvgDYDNwH/K/mQ/NbwJuavy+lnJdkC/CCjl6L1wCPt2i/BLit5zXcAXwHOKyv3T9vWfdlzevyY2A7cD/wy81zfGvzBbKhef4PG7ZuU/tPdjH9efMYvtRc/1SLuo8DL+17Pbc30xeb98T25vUdOgyAm4C39Vw/r3mfXQT8OiV8P97Me9uwdcfxszemn5HOXr8nanbxQGd7at6IpwG/S/mlt6P5ArgcOAl42gzr3gy8r+f6qym/3H+vr937gO+0rH1l8wIv7Zm3L/AXwFXN9cObL7DfGbLm1ZQ1if130Wb/ps1VLft71JDTeS3f5BdRAvFXgL0pv5a+S/nCO6anXdsg+CLlf1zsS/kFdRHll+XVwMKmzT7A9cCnZ/B+20YJkv7pdsoX2Kbmeptffjt4ahBcCXwPOLhn3jLKAZl/2qLuA8CxPdfvAN47oN3vA9+dwXMxNp+9Mf2MdPb6PXHbmdxovk0DPkAvpyTk1uZD+QPKfz47omXdB/q+jPZr7uuovnbHAj9qWXs78OoB85/T9Pmg5vq7KON/w/b3uCHaHQ88MIPnuH81etC0o+Wb/DbgjL55+wJ/CTwEnNzMaxsEm4DX9Fxf1vTt1/va/RvgH1s+Fx+n/EJ9F7Cgb9kBg94jM3wf3w/81oB2bwXublF3G3BCz/VHB/UP+FXgx7vZ53n92RvTz0hnr9/UtEfuNZSZX8/MtwD/BPgNYBJ4G7AuIm5sUWo78Kye61N/95899VnAD1t2cy/KC9rvUcrR1s9srk9ShpGG8TDli2g6B1B+tbXxI+AK4Lhppve1rHsQ5d+UPiEzH6D8t7rPA5+LiNe1rAnlMd7bc/3u5nJjX7vbgaVtCjfvreOB1wM3RcRRvYvbdXOXfoaydtTvu5TtS8O6Djil5/otlG0l/V5M+eKesTH47I3jZ6T7128m6THfJvp+leykzc9Sxh1vbFH3CuAGyhfxzwL/m7LKeh3NqiXlC/ubwF+27PPfUIYu9uuZ9zTK9oFtPDl88Srg3iFrXkJZbdzpr1HKEMztwCUt+3sNQ6wq03788zbgN3eyLCjnUnmMco6qNnXvpGdje1PrcmB5X7t/BWye4ftuAXAuZUPdpyjbO57J7q0RvA84o5l+QN8aTNPuFOC+FnWntpdcSAm9Eym/2P89Za+TFc1n4wEGDDkM0eex+eyN6Weks9fvifuYyY3m2zTMm3GGdVdQflVOrdL9oHlj/h1ltX2yuXyk7f0DR1B+yWyljGd/vnmD7gDe2tPufODqIWseAHy16etG4K8pG5s/0/x9R7Ps68ABLft7AbBliHYnABta1P1T4IvTtLmQ9qvTfw38jyHafYCyq+fuvE9+njL2vJUyXPT4rr5opnkf908XDWj3B8D1LWv/WvP+fZyysfQhfnq44pP0DXUN2eex+eyN42eky9dvatojzjUUEZdSknBDB7X/KXAysBC4PDPvjYgDKR/4FwD3AB/LzOtnUPtQ4L/QjH9T9mb5w8z8Rk+bpcCjmXnv4CoD665q+nwYZVU6KF9S6ymn+l6dLV/4iNgXeFZm3t7mdkPUPZYy5n1WZt63i3bvooyTvnLIui+mfJC/Mk27j1G+VC9t0e2d1Xo9JbSWUMa3r215++cMmP1IZt7T1+4CYH3bPjev4amUcfyDKMOT91HeF/8nM9e3qdfUHNfP3irKnkcrmOefkb76I339nqi9JwSBNF9ExM9Qxu/vycxH5ro/0jD2yI3FOxMRR0XELn8hzqe6M60dEcdExOkR8aKdLF8aEefNsD9TtQdtrJpx7XGru7PamflgZt6emY/Mtz77vnjiNs+OiP8QEb/drGEQEcsi4qKIuKq5PLRtX/tqv22UtbvsM7BnbCNoMc7WaiPNXNdtW5uy2+U36NlFjbJR+qC+dq12xeyy9rjVHcc++1w8pf0vUrYtTG2DuZNy9PZGyk4a36Ls/XMfsKzlc9FJ7S77PDXtEWsETTJOO1HGbue8boe1z6W8ad5AGfv8beBFwPURsaJtH2ep9rjV7bL2uNXtsnZXdX+P8kX6fMreSNdTtgncQ9mj7EjKeYzupWy/mw+1u+xzMZP0mG8T3R3I0UndrmpT9i9/R9+8pZQ9LLYAR+YMfkV1WXvc6o5jn30unlJjI3B6z/VfaD5jp/W1ewtwS8vnopPaXfZ5atpTzj76MHAt5fQMuzIBnDkP6nZVexnw7d4ZmXlXRBwNfAH42yj/I/rhln3tsva41R3HPvtcPGkJZRfRKd9vLr/X1+5W4NnzpHaXfQb2nNNQ30T5VfCJXTWKiPtp94XdVd2uat8LHNw/MzMfjIgTKQfp/BVlf+e2uqo9bnW7rD1udbus3VXdbTx1uPVxYB0/fXTy/pSTPs6H2l32Gdhz9hpaB7xkyLYxD+p2VXuScmqGn5JlV8ZVlHP4vGfIerNRe9zqdll73Op2Wburun9PGU6aqrUjM4/MzFv72v0S8P/mSe0u+/xE0bGfKGOHR49L3a5qU/YwWks5qGVnbQL4Y9of2dhJ7XGrO4599rl4ym2OA/71EO2uAM5u+Vx0UrvLPk9NHlAmSZXbU4aGJEkzZBBIUuUMAkmqnEEgSZUzCCSpcv8f2nm+kwis8eUAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "age_counts = survey[\"Age\"].dropna().astype(int).value_counts()\n",
- "age_counts = age_counts.sort_index()\n",
- "age_counts.plot.bar(color = \"k\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Write equivalent SQL query to retrive the age column values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "ages = pd.read_sql(\"\"\"\n",
- "SELECT Age\n",
- "FROM survey\n",
- "\"\"\", conn)\n",
- "\n",
- "# If you repeat the bar plot with SQL version of the data, you will see what are called as \"MultiIndex\" - that is beyond the scope of this course"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a bar plot of primary majors.\n",
- "- x-axis: each major\n",
- "- y-axis: count of students with those majors."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "major_counts = survey[\"Primary major\"].value_counts()\n",
- "major_counts.plot.bar(color = \"k\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Write equivalent SQL query to retrive the Primary major column values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {},
- "outputs": [],
- "source": [
- "ages = pd.read_sql(\"\"\"\n",
- "SELECT `Primary major`\n",
- "FROM survey\n",
- "\"\"\", conn)\n",
- "\n",
- "# If you repeat the bar plot with SQL version of the data, you will see what are called as \"MultiIndex\" - that is beyond the scope of this course"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a scatter plot to visualize the corelation between \"Age\" and \"Procrastinator\" columns\n",
- "\n",
- "- \"Procrastinator\" column has 3 values\n",
- "- Create a single scatter plot\n",
- "- You will find that there is absolutely no correlation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot the sepal length vs the sepal width for each of the classes of flowers.\n",
- "colors = [\"r\", \"g\", \"b\"]\n",
- "markers = [\".\", \"^\", \"v\"]\n",
- "\n",
- "procrastination = list(set(survey[\"Procrastinator\"]))\n",
- "\n",
- "plot_area = None\n",
- "for i in range(len(procrastination)):\n",
- " curr_procrastination = procrastination[i]\n",
- " specific_procrastination_data = survey[survey[\"Procrastinator\"] == curr_procrastination]\n",
- " plot_area = specific_procrastination_data.plot.scatter(x = \"Age\", y = 'Procrastinator', \\\n",
- " ax = plot_area, color = colors[i], marker = markers[i],\n",
- " label = curr_procrastination)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-39/lec_38_plotting4_template.ipynb b/f22/meena_lec_notes/lec-39/lec_38_plotting4_template.ipynb
deleted file mode 100644
index 4c0ea88..0000000
--- a/f22/meena_lec_notes/lec-39/lec_38_plotting4_template.ipynb
+++ /dev/null
@@ -1,898 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import sqlite3\n",
- "import os\n",
- "\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "\n",
- "import matplotlib\n",
- "from matplotlib import pyplot as plt\n",
- "matplotlib.rcParams[\"font.size\"] = 16\n",
- "\n",
- "import math\n",
- "\n",
- "import requests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Bar plots using DataFrames"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Bar Plot Example w/ Fire Hydrants\n",
- "\n",
- "- General review of pandas\n",
- "- Some new bar plot options"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: read \"Fire_Hydrants.csv\" into a DataFrame\n",
- "hdf = ???\n",
- "hdf.tail()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Extract just the column names\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *colors* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Make a series called counts_series which stores the value counts of the \"nozzle_color\"\n",
- "color_counts = ???\n",
- "color_counts # what is wrong with this data?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Clean the data ......use str.upper()\n",
- "\n",
- "color_counts = ???\n",
- "color_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Make a horizontal bar plot of counts of colors and have the colors match\n",
- "# use color list: [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"]\n",
- "ax = ???(color = [\"b\", \"g\", \"darkorange\", \"r\", \"c\", \"0.5\"])\n",
- "ax.set_xlabel(\"Fire hydrant count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Let's create a *bar plot* to visualize *style* of fire hydrants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Do the same thing as we did for the colors but this time for the \"Style\"\n",
- "style_counts = ???\n",
- "style_counts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "style_counts.plot.bar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Grab the top 12 \n",
- "top12 = ???\n",
- "\n",
- "# and them add an index to our Series for the sum of all the \"other\" for \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot the results\n",
- "ax = top12.plot.bar(color = \"firebrick\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "ax.set_xlabel(\"Hydrant Type\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### In what *decade* were *pacers manufactured*?\n",
- "### Take a peek at the *Style* column data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "hdf[\"Style\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which *column* gives *year* information?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "hdf.columns"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *year_manufactured* for *pacers* and *others*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Let's get the year manufactured for all of the \"Pacer\" hydrants.\n",
- "pacer_years = ???\n",
- "\n",
- "# Note: We can do this either way\n",
- "# pacer_years = hdf[\"year_manufactured\"][hdf[\"Style\"] == \"Pacer\"]\n",
- "\n",
- "pacer_years"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# then do the same for all the other data\n",
- "other_years = ???\n",
- "other_years"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to get the *decade* for *pacers*?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Round each year down to the start of the decade.\n",
- "# e.g. 1987 --> 1980, 2003 --> 2000\n",
- "pacer_decades = ???\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to convert the *decades* back to *int*?\n",
- "- `astype(...)` method\n",
- "- `dropna(...)` method"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Drop the NaN values, convert to int, and do value counts\n",
- "pacer_decades = ???\n",
- "pacer_decades"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How to *count the decades* for pacers?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pacer_decades_count = ???\n",
- "pacer_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Count the *decades* for others."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Do the same thing for other_years. Save to a variable called \"other_decades\"\n",
- "other_decades = (other_years // 10 * 10).dropna().astype(int)\n",
- "other_decades_count = other_decades.value_counts()\n",
- "other_decades_count"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Build a DataFrame from a dictionary of key, Series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plot_df = DataFrame({\n",
- " \"pacer\": pacer_decades_count,\n",
- " \"other\": other_decades_count,\n",
- "})\n",
- "plot_df # observe the NaN values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# make a bar plot\n",
- "\n",
- "ax = ???\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Ignore data from before 1950 using boolean indexing."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = ???.plot.bar()\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Stacked Bar Chart\n",
- "`stacked` parameter accepts boolean value as argument"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ax = plot_df[plot_df.index >= 1950].plot.bar(???)\n",
- "ax.set_xlabel(\"Decade\")\n",
- "ax.set_ylabel(\"Hydrant Count\")\n",
- "None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plotting 4\n",
- "\n",
- "## Learning objectives\n",
- "- how to use logarithmic axes\n",
- "- how to create multiple plots within same figure"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Logarithmic scale\n",
- "- math.log(y, base)\n",
- "- find an x, such that 10**x == y\n",
- " - math.log10(y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(math.log10(1000))\n",
- "print(math.log10(1000000))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(math.log(32, 2))\n",
- "print(math.log(256, 4))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def log_approx(y):\n",
- " assert type(y) == int\n",
- " assert y >= 1\n",
- " return len(str(y))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(log_approx(123456789)) # What will this output?\n",
- "print(math.log10(123456789))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(log_approx(989898))\n",
- "print(math.log10(989898))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "errors = []\n",
- "for y in range(1, 1000001):\n",
- " err = abs(log_approx(y) - math.log10(y))\n",
- " errors.append(err)\n",
- "max(errors)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Why does this matter?\n",
- "- Comparing two numbers:\n",
- " - 134234255623423423423432423432432432\n",
- " - 2342343252523\n",
- "\n",
- "- Eventually I don't care what the number is, but only counting the number of digits in the number to know how big the number is!\n",
- "- log base 2: counting how many bits we need\n",
- "- log base 10: 10 digits 0 through 9!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s = Series([1, 10, 100, 1000, 10000, 100000, 1000000])\n",
- "s.plot.line()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s.plot.line(???)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Population example\n",
- "https://ourworldindata.org/grapher/population"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "populations = pd.Series({\n",
- " \"China\":1439323776,\n",
- " \"India\": 1380004385,\n",
- " \"Mexico\": 128932753,\n",
- " \"Senegal\":16743927,\n",
- " \"Bahrain\":1701575,\n",
- " \"Grenada\":112523,\n",
- " \"Tuvalu\": 11792\n",
- "})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot populations as a bar chart."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# not that readable\n",
- "???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now plot on a logarithmic scale."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "???"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Multiple *axessubplots* in the same plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.subplots(ncols = 2, nrows = 2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "s1 = Series([1, 2, 3, 3, 4])\n",
- "s2 = Series([5, 7, 7, 8])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's create a single plot with two sub figures (line plots) and plot s1 on the left and s2 on the right."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, axes = plt.subplots(ncols = 2)\n",
- "# axes[0] # the area on the left\n",
- "# axes[1] # the area on the right\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What is wrong with the below plot?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Y-axes are misleading"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# How can we fix that?\n",
- "- pass argument to `sharey` parameter while invoking subplots function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fig, axes = plt.subplots(ncols = 2, ??)\n",
- "# axes[0] # the area on the left\n",
- "# axes[1] # the area on the right\n",
- "pd.Series([1, 2, 3, 3, 4]).plot.line(ax = axes[0])\n",
- "pd.Series([5, 7, 7, 8]).plot.line(ax = axes[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Iris dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Gather the data.\n",
- "resp = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\n",
- "resp.raise_for_status()\n",
- "\n",
- "iris_f = open(\"iris.csv\", \"w\")\n",
- "iris_f.write(resp.text)\n",
- "iris_f.close()\n",
- "\n",
- "iris_df = pd.read_csv(\"iris.csv\",\n",
- " names = [\"sep-len\", \"sep-wid\", \"pet-len\", \"pet-wid\", \"class\"])\n",
- "iris_df.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot the sepal length vs the sepal width for each of the classes of flowers.\n",
- "colors = [\"r\", \"g\", \"b\"]\n",
- "markers = [\".\", \"^\", \"v\"]\n",
- "\n",
- "varieties = list(set(iris_df[\"class\"]))\n",
- "\n",
- "fig, axes = plt.subplots(ncols = 3, sharex = True, sharey = True, figsize=(12,3))\n",
- "for i in range(len(varieties)):\n",
- " variety = varieties[i]\n",
- " specific_iris_data = iris_df[iris_df[\"class\"] == variety]\n",
- " specific_iris_data.plot.scatter(x = \"sep-len\", y = 'sep-wid', \\\n",
- " ax = axes[i], color = colors[i], marker = markers[i],\n",
- " label = variety)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Self-practice - Student Information Dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: establish connection to \"cs220_survey_data.db\"\n",
- "path = \"cs220_survey_data.db\"\n",
- "assert os.path.exists(path)\n",
- "conn = sqlite3.connect(path)\n",
- "\n",
- "# TODO: determine the table name and column types\n",
- "print(pd.read_sql(\"SELECT * FROM sqlite_master WHERE type = 'table'\", conn)[\"sql\"].iloc[0])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: display all columns of the table\n",
- "survey_data = pd.read_sql(\"SELECT * FROM survey\", conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Using pandas read \"cs220_survey_data.csv\" into a DataFrame called survey\n",
- "\n",
- "survey = pd.read_csv(\"cs220_survey_data.csv\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a bar plot of ages.\n",
- "- x-axis: each unique age\n",
- "- y-axis: count of students with those ages.\n",
- "\n",
- "Things to consider:\n",
- "- Do we really want the ages to be a float value? Make the int conversion.\n",
- " - Remember the survey dataset has a few rows where \"Age\" column has no value - so handle the NA values before the conversion\n",
- "- Think carefully about how to sort the data before you create the plot."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "age_counts = survey[\"Age\"].dropna().astype(int).value_counts()\n",
- "age_counts = age_counts.sort_index()\n",
- "age_counts.plot.bar(color = \"k\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Write equivalent SQL query to retrive the age column values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ages = pd.read_sql(\"\"\"\n",
- "SELECT Age\n",
- "FROM survey\n",
- "\"\"\", conn)\n",
- "\n",
- "# If you repeat the bar plot with SQL version of the data, you will see what are called as \"MultiIndex\" - that is beyond the scope of this course"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a bar plot of primary majors.\n",
- "- x-axis: each major\n",
- "- y-axis: count of students with those majors."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "major_counts = survey[\"Primary major\"].value_counts()\n",
- "major_counts.plot.bar(color = \"k\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Write equivalent SQL query to retrive the Primary major column values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ages = pd.read_sql(\"\"\"\n",
- "SELECT `Primary major`\n",
- "FROM survey\n",
- "\"\"\", conn)\n",
- "\n",
- "# If you repeat the bar plot with SQL version of the data, you will see what are called as \"MultiIndex\" - that is beyond the scope of this course"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create a scatter plot to visualize the corelation between \"Age\" and \"Procrastinator\" columns\n",
- "\n",
- "- \"Procrastinator\" column has 3 values\n",
- "- Create a single scatter plot\n",
- "- You will find that there is absolutely no correlation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot the sepal length vs the sepal width for each of the classes of flowers.\n",
- "colors = [\"r\", \"g\", \"b\"]\n",
- "markers = [\".\", \"^\", \"v\"]\n",
- "\n",
- "procrastination = list(set(survey[\"Procrastinator\"]))\n",
- "\n",
- "plot_area = None\n",
- "for i in range(len(procrastination)):\n",
- " curr_procrastination = procrastination[i]\n",
- " specific_procrastination_data = survey[survey[\"Procrastinator\"] == curr_procrastination]\n",
- " plot_area = specific_procrastination_data.plot.scatter(x = \"Age\", y = 'Procrastinator', \\\n",
- " ax = plot_area, color = colors[i], marker = markers[i],\n",
- " label = curr_procrastination)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-39/.ipynb_checkpoints/lec_38_plotting4-checkpoint.ipynb b/f22/meena_lec_notes/lec-39/lec_39_plotting4.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-39/.ipynb_checkpoints/lec_38_plotting4-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-39/lec_39_plotting4.ipynb
diff --git a/f22/meena_lec_notes/lec-39/.ipynb_checkpoints/lec_38_plotting4_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-39/lec_39_plotting4_template.ipynb
similarity index 100%
rename from f22/meena_lec_notes/lec-39/.ipynb_checkpoints/lec_38_plotting4_template-checkpoint.ipynb
rename to f22/meena_lec_notes/lec-39/lec_39_plotting4_template.ipynb
--
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