From 67c90c91d385e0461a89c6266133397ff1b1ecc6 Mon Sep 17 00:00:00 2001
From: Louis Oliphant <ltoliphant@wisc.edu>
Date: Wed, 1 May 2024 00:54:42 -0500
Subject: [PATCH] Louis Lecture 38 Plotting 3
---
.../Lec38_Plotting3_Solution_Oliphant.ipynb | 238 +++++++++++++-----
.../Lec38_Plotting3_Template_Oliphant.ipynb | 35 ++-
2 files changed, 209 insertions(+), 64 deletions(-)
diff --git a/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Solution_Oliphant.ipynb b/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Solution_Oliphant.ipynb
index 60acaee..f53ec88 100644
--- a/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Solution_Oliphant.ipynb
+++ b/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Solution_Oliphant.ipynb
@@ -1448,7 +1448,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1576,7 +1576,7 @@
"total 0.967913 1.000000 "
]
},
- "execution_count": 12,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -1595,7 +1595,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -1637,7 +1637,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1657,7 +1657,7 @@
"dtype: int64"
]
},
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
@@ -1687,7 +1687,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -1707,7 +1707,7 @@
"dtype: int64"
]
},
- "execution_count": 15,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
@@ -1738,7 +1738,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1747,7 +1747,7 @@
"Text(0, 0.5, '# Craft Breweries')"
]
},
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
@@ -1780,7 +1780,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1800,7 +1800,7 @@
"dtype: int64"
]
},
- "execution_count": 17,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
@@ -1831,7 +1831,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -1851,7 +1851,7 @@
"dtype: int64"
]
},
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
@@ -1887,7 +1887,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -1996,7 +1996,7 @@
"11 30 16"
]
},
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -2021,7 +2021,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -2056,7 +2056,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -2065,7 +2065,7 @@
"Text(0, 0.5, 'Temp (Fahrenheit)')"
]
},
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
@@ -2105,7 +2105,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -2214,7 +2214,7 @@
"11 -1.111111 -8.888889"
]
},
- "execution_count": 22,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -2227,6 +2227,128 @@
"celcius_df"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "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": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# or do it using the apply() function\n",
+ "celcius_df = temp_df.apply(f_to_c)\n",
+ "celcius_df"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -2236,7 +2358,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -2345,13 +2467,13 @@
"11 -1.111111 -8.888889"
]
},
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "celcius_df = (lambda f: (f - 32)*5/9)(temp_df)\n",
+ "celcius_df = temp_df.apply(lambda f: (f - 32)*5/9)\n",
"celcius_df"
]
},
@@ -2364,7 +2486,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -2486,7 +2608,7 @@
"11 -1.111111 -8.888889 0"
]
},
- "execution_count": 21,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -2505,7 +2627,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -2541,7 +2663,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -2753,13 +2875,13 @@
"[5 rows x 25 columns]"
]
},
- "execution_count": 23,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "hdf = pd.read_csv(\"Fire_Hydrants.csv\")\n",
+ "hdf = pd.read_csv(\"fire_hydrants.csv\")\n",
"hdf.tail()"
]
},
@@ -2772,7 +2894,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -2787,7 +2909,7 @@
" dtype='object')"
]
},
- "execution_count": 24,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -2813,7 +2935,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -2835,7 +2957,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 25,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -2862,7 +2984,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -2878,7 +3000,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -2898,7 +3020,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -2907,7 +3029,7 @@
"Text(0, 0.5, 'Fire hydrant count')"
]
},
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
@@ -2951,7 +3073,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -2960,7 +3082,7 @@
"Text(0.5, 0, 'Hydrant Type')"
]
},
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
@@ -3005,7 +3127,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -3025,7 +3147,7 @@
"Name: year_manufactured, Length: 3458, dtype: float64"
]
},
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -3040,7 +3162,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -3060,7 +3182,7 @@
"Name: year_manufactured, Length: 6651, dtype: float64"
]
},
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -3073,7 +3195,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -3093,7 +3215,7 @@
"Name: year_manufactured, Length: 3458, dtype: float64"
]
},
- "execution_count": 31,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -3107,7 +3229,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -3127,7 +3249,7 @@
"Name: year_manufactured, Length: 6651, dtype: float64"
]
},
- "execution_count": 32,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -3140,7 +3262,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -3155,7 +3277,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 33,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -3169,7 +3291,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -3189,7 +3311,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -3203,7 +3325,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -3306,7 +3428,7 @@
"2010 503.0 1196"
]
},
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -3322,7 +3444,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
@@ -3331,7 +3453,7 @@
"Text(0, 0.5, 'Hydrant Count')"
]
},
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
@@ -3355,7 +3477,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -3364,7 +3486,7 @@
"Text(0, 0.5, 'Hydrant Count')"
]
},
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
@@ -3388,7 +3510,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
diff --git a/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Template_Oliphant.ipynb b/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Template_Oliphant.ipynb
index be4d1df..1240865 100644
--- a/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Template_Oliphant.ipynb
+++ b/s24/Louis_Lecture_Notes/38_Plotting3/Lec38_Plotting3_Template_Oliphant.ipynb
@@ -392,11 +392,22 @@
"celcius_df"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# or do it using the apply() function\n",
+ "celcius_df = temp_df.apply(f_to_c)\n",
+ "celcius_df"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### We can also do that using a lambda function"
+ "#### Try writing a lambda function and using the apply() function to do the same thing"
]
},
{
@@ -405,8 +416,7 @@
"metadata": {},
"outputs": [],
"source": [
- "celcius_df = (lambda f: (f - 32)*5/9)(temp_df)\n",
- "celcius_df"
+ "#TODO -- write your solution here"
]
},
{
@@ -464,7 +474,7 @@
"metadata": {},
"outputs": [],
"source": [
- "hdf = pd.read_csv(\"Fire_Hydrants.csv\")\n",
+ "hdf = pd.read_csv(\"fire_hydrants.csv\")\n",
"hdf.tail()"
]
},
@@ -481,7 +491,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# write your code here"
+ "# write your code here -- try typing hdf. (don't forget the dot) and pressing tab -- Anything pop up that looks like it might work?\n"
]
},
{
@@ -590,7 +600,20 @@
"metadata": {},
"outputs": [],
"source": [
- "# write your code here"
+ "# write your code here\n",
+ "pacer_years=hdf[hdf['Style']=='Pacer']['year_manufactured']\n",
+ "other_years=hdf[hdf['Style']!='Pacer']['year_manufactured']\n",
+ "pacer_year=pacer_year.dropna()\n",
+ "other_year=other_year.dropna()\n",
+ "pacer_decades = pacer_year//10 * 10\n",
+ "pacer_decades = pacer_decades.value_counts()\n",
+ "other_decades = (other_year//10 * 10).value_counts()\n",
+ "plot_df = DataFrame({'Pacer':pacer_decades,'Other':other_decades})\n",
+ "plot_df.index=[int(x) for x in plot_df.index]\n",
+ "ax=plot_df.plot.bar()\n",
+ "ax.set_xlabel(\"Manufacture Year\")\n",
+ "ax=plot_df.plot.bar(stacked=True)\n",
+ "ax.set_xlabel(\"Manufacture Year\")"
]
}
],
--
GitLab