diff --git a/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec36_plotting2_850.ipynb b/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec36_plotting2_850.ipynb
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-{
- "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": [
- "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",
- "matplotlib.rcParams[\"font.size\"] = 12"
- ]
- },
- {
- "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": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 1: Requests and file writing\n",
- "\n",
- "# use requests to get this file \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
- "response = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\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 to avoid spamming their server\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": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 2: Making a DataFrame\n",
- "\n",
- "# read the \"iris.csv\" file into a Pandas dataframe\n",
- "\n",
- "# display the head of the data frame\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "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\n",
- "\n",
- "# 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 [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 4: Connect to our database version of this data\n",
- "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
- "\n",
- "# find out the name of the table\n",
- "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "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.\n",
- "\n",
- "pd.read_sql(\"\"\"\n",
- "\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\", color=\"peachpuff\")`\n",
- "\n",
- "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": []
- },
- {
- "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"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Add a title to your plot.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Correlation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# What is the correlation between our DataFrame columns?\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# What is the correlation between age and height (don't use .iloc)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### The Size can be based on a DataFrame value"
- ]
- },
- {
- "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",
- "trees_df.plot.scatter(x=\"age\", y=\"height\", marker = \"H\", s=trees_df[\"diameter\"] * 50) # this way allows you to make it bigger"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Use subplots to group scatterplot data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Re-visit the Iris Data"
- ]
- },
- {
- "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",
- "varieties = ???\n",
- "varieties"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# In SQL\n",
- "varietes = pd.read_sql(\"\"\"\n",
- "\n",
- "\"\"\", iris_conn)\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",
- "iris_df.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
- ]
- },
- {
- "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 i in range(len(varietes)):\n",
- " variety = varietes[i]\n",
- " pass"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# copy/paste the code above, but this time make each plot a different color\n",
- "colors = [\"blue\", \"green\", \"red\"]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# copy/paste the code above, but this time make each plot a different color AND marker\n",
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Did you notice that it made 3 plots?!?! What's deceiving about this?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Have to be VERY careful to not crop out data.\n",
- "# We'll talk about this next lecture."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Better yet, we could combine these."
- ]
- },
- {
- "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"
- ]
- },
- {
- "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": [
- "# Plot!\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# What are the correlations?\n"
- ]
- },
- {
- "attachments": {
- "image.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "https://www.researchgate.net/publication/247907373_Stupid_Data_Miner_Tricks_Overfitting_the_SP_500"
- ]
- }
- ],
- "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": "3.9.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec36_plotting2_complete.ipynb b/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec_37_plotting2_scatter_plots.ipynb
similarity index 55%
rename from f22/andy_lec_notes/lec37_Dec07_Plotting2/lec36_plotting2_complete.ipynb
rename to f22/andy_lec_notes/lec37_Dec07_Plotting2/lec_37_plotting2_scatter_plots.ipynb
index c64abac6d84e9380d4fac3ca026912c4dac9ed40..b23934b2075ac053d328aca9019556dc0037ca7f 100644
--- a/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec36_plotting2_complete.ipynb
+++ b/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec_37_plotting2_scatter_plots.ipynb
@@ -5,6 +5,14 @@
"execution_count": 1,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ps/g2y03gdx37l3dqhct_j0rcdm0000gn/T/ipykernel_88160/234030902.py:2: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
+ " from IPython.core.display import display, HTML\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -29,6 +37,15 @@
"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",
@@ -37,6 +54,7 @@
"import os\n",
"\n",
"import matplotlib\n",
+ "# new import statement\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import requests\n",
@@ -47,41 +65,33 @@
"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"
+ "#### 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": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
- "# Warmup 1: Requests and file writing\n",
- "\n",
- "# use requests to get this file \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
- "response = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\")\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 to avoid spamming their server\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",
+ "path = \"bus.db\"\n",
+ "# assert existence of path\n",
+ "assert os.path.exists(path)\n",
"\n",
- "# Look at the file you downloaded. What's wrong with it?"
+ "# establish connection to bus.db\n",
+ "conn = sqlite3.connect(path)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -105,85 +115,486 @@
" <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",
+ " <th>Route</th>\n",
+ " <th>daily</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",
+ " <td>80</td>\n",
+ " <td>10211.79</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",
+ " <td>2</td>\n",
+ " <td>4808.03</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",
+ " <td>6</td>\n",
+ " <td>4537.02</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",
+ " <td>10</td>\n",
+ " <td>4425.23</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",
+ " <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": [
- " 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"
+ " 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": 4,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# Warmup 2: Making a DataFrame\n",
+ "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",
- "# read the \"iris.csv\" file into a Pandas dataframe\n",
- "iris_df = pd.read_csv(\"iris.csv\")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# let's extract daily column from df\n",
+ "df[\"daily\"]"
+ ]
+ },
+ {
+ "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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\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",
- "# display the head of the data frame\n",
- "iris_df.head()"
+ "# Oops wrong x-axis labels!"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -207,29 +618,669 @@
" <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",
+ " <th>Route</th>\n",
+ " <th>daily</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",
+ " <td>80</td>\n",
+ " <td>10211.79</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",
+ " <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": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='Route'>"
+ ]
+ },
+ "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 = 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": 12,
+ "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": 12,
+ "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": 13,
+ "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": 14,
+ "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": 16,
+ "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": 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>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": 17,
+ "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": 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>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",
@@ -268,15 +1319,12 @@
"4 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
- "execution_count": 5,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"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\n",
- "\n",
"# Attribute Information:\n",
"# 1. sepal length in cm\n",
"# 2. sepal width in cm\n",
@@ -284,17 +1332,24 @@
"# 4. petal width in cm\n",
"# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
"\n",
- "# These should be our headers [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\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",
+ " 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": 6,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -346,20 +1401,27 @@
"0 CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"... "
]
},
- "execution_count": 6,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# Warmup 4: Connect to our database version of this data!\n",
"iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
- "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+ "pd.read_sql(\"SELECT * FROM sqlite_master \" , iris_conn) # WHERE type='table'"
+ ]
+ },
+ {
+ "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": 7,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -489,15 +1551,12 @@
"9 5.4 3.9 1.3 0.4 Iris-setosa"
]
},
- "execution_count": 7,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"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.\n",
- "\n",
"pd.read_sql(\"\"\"\n",
" SELECT *\n",
" FROM iris\n",
@@ -530,7 +1589,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -579,118 +1638,300 @@
" <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",
+ " <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": 27,
+ "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>4</th>\n",
- " <td>2</td>\n",
- " <td>2.5</td>\n",
- " <td>1.5</td>\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",
- "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"
+ " 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": 8,
+ "execution_count": 22,
"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\", color=\"peachpuff\")`\n",
- "\n",
- "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`?"
+ "# What is the correlation between our DataFrame columns?\n",
+ "corr_df = trees_df.corr()\n",
+ "corr_df"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
+ "0.7974683544303798"
]
},
- "execution_count": 9,
+ "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": [
- "trees_df.plot.scatter(x=\"age\", y=\"height\", color = \"g\") # TODO: change y to diameter"
+ "# 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": [
- "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)"
+ "### Variating Stylistic Parameters"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -699,13 +1940,13 @@
"<AxesSubplot:xlabel='age', ylabel='height'>"
]
},
- "execution_count": 10,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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dQSwEDgBXAW8GtgBflbS8TfvHI2Jx0/JoOWVaX7WbVc4hYfZGJX1WSgmIiDgZEbdHxHMR8VpE7AKeBa4s4/1tAHSactQhYZYp8bPSl2sQkpYB7wT2tmlyhaQjkp6WtEVSclhySZslTUmamp6enrN6rQQbN84+HzVk2zduLKces6oq8bNSekBIOhf4CvBgRPww0eQx4HJgFNgAXAd8MvVaEbEtIuoRUa/VanNVspVhYqL9fNQNIyNZO+sP31lWDSV+VkoNCEnnAF8GfgbcmGoTEc9ExLP5qaingDuB95dYpvVDu/moGzz1aH/5zrLqKPGzUlpASBKwHVgGbIiIn3f5qwFozgqz6mj3P77Dob98Z1n1lPRZKfMI4j7gXcD7IuLldo0krcuvUSBpJdkdTw+XU6L1Xev/+A6H/vKdZdVVwmelrO9BXAx8GFgNHGz6fsMHJI3lj8fy5lcDT0o6CTwCPATcXUadVhGN//FrNYdDP/nOsuqb48+KIqLQF+yXer0eU1NT/S7DbP4YHc2uOXRSq8Hhw3Nfj80JSbsjop7a5qE2zCzNd5YNPQeEmaX5zrKh54Aws/Z8Z9lQc0CY2ex8Z9nQckCYWWe+s2woJcc4MjP7JePjvltpyPgIwszMkhwQZmaW5IAwM7MkB4SZmSU5IMzMLMkBYWZmSQ4IMzNLckCYmVmSA8LMzJIcEGZmluSAMDOzpLKmHF0kabukfZKOS3pC0rpZ2t8k6aCkY5Lul7SojDrNzOx1ZR1BLAQOAFcBbwa2AF+VtLy1oaS1wC1kc1MvBy4B7iipTjMzy5USEBFxMiJuj4jnIuK1iNgFPAtcmWh+PbA9IvZGxFFgK7CpjDrNzOx1fbkGIWkZ8E5gb2LzZcCepvU9wDJJFyVeZ7OkKUlT091Mrm5mZl0rPSAknQt8BXgwIn6YaLIYONa03nh8QWvDiNgWEfWIqNdqtTMraHISRkezn2Zm9gulBoSkc4AvAz8DbmzT7ASwpGm98fh44QVNTsL69TA9nf10SJiZ/UJpASFJwHZgGbAhIn7epuleYFXT+irgUES8UGhBjXCYmcnWZ2YcEmZmTco8grgPeBfwvoh4eZZ2O4AbJF0q6ULgVuCBQitpDYcGh4SZ2S+U9T2Ii4EPA6uBg5JO5MsHJI3lj8cAIuIbwL3AJLAvX24rrJh24dDgkDAzA0AR0e8aClGv12Nqaqpzw9HR7JpDJ7WaJ2jvp8lJ2LgRJiZgfLzf1ZjNW5J2R0Q9tW34htqYmICRkdnbjIxk7aw/fPOAWSUMX0CMj8OuXbCozegdixZl2/2v1v7wzQNmlTF8AWHV5ZsHzCpl+AKi8Ufo1Kn09lOn/MeoH3zzgFnl+CJ1O75IXS73i1lf+CJ1M1+krib3i1nlDF9ANC5St/tjNDLii9T94H4xq5zhCwho/8fIf4T6y/1iVinDGRDwy3+M/EeoGtwvZpUxvAEBr/8xqtX8R6hK3C9mlbCw3wX03fi474qpIveLWd8N9xGEmZm15YAwM7MkB4SZmSU5IMzMLMkBMTmZDfPgMX7MzN5guAPC8w6YmbVVWkBIulHSlKRTkh6Ypd0mSaebpiU9IWlN4QV53gEzs1mVeQTxE+Au4P4u2j4eEYublkcLrcTzDpiZdVRaQETEQxHxN8ALZb1nkucdMDPrSlWvQVwh6YikpyVtkZT8xrekzflpq6npbuYSANi4sX04NMzMZO3MzIZYFQPiMeByYBTYAFwHfDLVMCK2RUQ9Iuq1Wq27V/e8A2ZmXalcQETEMxHxbES8FhFPAXcC7y/sDTzvgJlZVyoXEAkBqNBX9LwDZmYdlXmb60JJ5wELgAWSzktdW5C0TtKy/PFKYAvwcOEFed4BM7NZlXkEcSvwMnAL8MH88a2SxvLvOozl7a4GnpR0EngEeAi4e04q8rwDZmZtKSL6XUMh6vV6TE1N9bsMM7OBIml3RNRT2wbhGoSZmfWBA8LMzJIcEGZmljRvrkFImgb2neGvLwWOFFhOP3lfqmm+7Mt82Q/wvjRcHBHJbxrPm4A4G5Km2l2kGTTel2qaL/syX/YDvC/d8CkmMzNLckCYmVmSAyKzrd8FFMj7Uk3zZV/my36A96UjX4MwM7MkH0GYmVmSA8LMzJIcEGZmljQUASHpxnxq0lOSHujQ9iZJByUdk3S/pEUlldmVbvdF0iZJp/ORchvLmtIK7UDSIknbJe2TdFzSE5LWzdK+sv3Sy75UvV8AJO2U9Lykl/Jpf/94lrZV7peu9mMQ+qRB0jskvSJp5yxtCuuToQgI4CfAXcD9szWStJZsOPKrgeXAJcAdc11cj7ral9zjEbG4aXl0bkvryULgAHAV8GayeT++Kml5a8MB6Jeu9yVX5X4BuAdYHhFLgH8N3CXpytZGA9AvXe1Hrup90vA54HvtNhbdJ0MREBHxUET8DfBCh6bXA9sjYm9EHAW2ApvmuLye9LAvlRYRJyPi9oh4Lp9edhfwLJD6AFe6X3rcl8rL/zufaqzmy4pE06r3S7f7MRAkXQu8CHxrlmaF9slQBEQPLgP2NK3vAZZJuqhP9ZytKyQdyQ+vt6Rm8KuKfBbBdwJ7E5sHql867AsMQL9I+rykGeCHwPNkk3e1qny/dLkfUPE+kbQEuBP4RIemhfaJA+KNFgPHmtYbjy/oQy1n6zHgcmAU2ABcB3yyrxW1Ielc4CvAgxHxw0STgemXLvZlIPolIj5K9t/33WSzOp5KNKt8v3S5H4PQJ1vJjgwOdGhXaJ84IN7oBLCkab3x+HgfajkrEfFMRDybn/J4iuxfH+/vd12tJJ0DfBn4GXBjm2YD0S/d7Mug9AtARJyOiO8AbwM+kmgyEP3SaT+q3ieSVgPXAJ/ponmhfeKAeKO9wKqm9VXAoYgY6PP9uQDU7yKaSRKwHVgGbIiIn7dpWvl+6WFfWlWuXxIWkj53X/l+adFuP1pVrU/WkF1w3i/pIHAzsEHS9xNtC+2ToQgISQslnQcsABZIOq/NOcYdwA2SLpV0IXAr8ECJpXbU7b5IWpefC0fSSrI7ax4ut9qO7gPeBbwvIl6epV3l+4Uu96Xq/SJpVNK1khZLWpDfFXMd8LeJ5pXtl172o+p9QjbO0gpgdb58Afg6sDbRttg+iYh5vwC38/pdDI3ldmCM7JBsrKntfwIOAS8B/x1Y1O/6z2RfgE/l+3ESeIbssPncftfftB8X57W/ktfdWD4waP3Sy74MQL/UgG+T3S3zEvAU8O/zbQPTL73sR9X7JLFvtwM7y+gTD9ZnZmZJQ3GKyczMeueAMDOzJAeEmZklOSDMzCzJAWFmZkkOCDMzS3JAmJlZkgPCzMySHBBmZpbkgDA7C5JukfTjfKrRH0j6g/z5BZI+nc8x8KyyqWKjMW6WpDcrm6b0eUn/T9Jdkhb0d2/M3qhSk2KYDaAfk801cBD4Q2CnpN8A/g2wjmxwtZPAX7T83oNk4+X8BnA+sIts2tIvllK1WRc8FpNZgST9X+A24GPARER8MX/+GuB/A+cCFwH7gbdEPvKrpOuAzREx3o+6zVJ8BGF2FiR9iGz0zOX5U4uBpcCvkR0RNDQ/vpgsKJ7PppEAstO9nWYLMyuVA8LsDEm6GPgScDXweESczo8gRDb/8duamv960+MDZFNfLo2IV0sq16xnvkhtdubOJ5sHYhpA0r8jm9sY4KvAxyS9VdJbgD9t/FJEPA98E/i0pCWSzpG0QtJVpVZv1oEDwuwMRcQPgE8Dj5NdcP5N4Lv55i+RhcCTwBPAI8CrwOl8+4eAXwF+ABwF/hL41bJqN+uGL1KblUDSOuALEXFxv2sx65aPIMzmgKQ3SfrdfA7xt5Ld2fTX/a7LrBc+gjCbA5JGyOZEXgm8TDbJ/Mci4qW+FmbWAweEmZkl+RSTmZklOSDMzCzJAWFmZkkOCDMzS3JAmJlZ0v8Hz2GGSnsWjvEAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -717,28 +1958,28 @@
}
],
"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"
+ "# Option 1:\n",
+ "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = \"diameter\")"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "Text(0.5, 1.0, 'Tree Age vs Height')"
+ "<AxesSubplot:xlabel='age', ylabel='height'>"
]
},
- "execution_count": 11,
+ "execution_count": 25,
"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>"
]
@@ -750,21 +1991,28 @@
}
],
"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\")"
+ "# 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": [
- "#### Correlation"
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Iris Data\n"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -788,98 +2036,223 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>age</th>\n",
- " <th>height</th>\n",
- " <th>diameter</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>age</th>\n",
- " <td>1.000000</td>\n",
- " <td>0.797468</td>\n",
- " <td>0.854578</td>\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>height</th>\n",
- " <td>0.797468</td>\n",
- " <td>1.000000</td>\n",
- " <td>0.839345</td>\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>diameter</th>\n",
- " <td>0.854578</td>\n",
- " <td>0.839345</td>\n",
- " <td>1.000000</td>\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": [
- " 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"
+ " 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": 12,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# What is the correlation between our DataFrame columns?\n",
- "corr_df = trees_df.corr()\n",
- "corr_df"
+ "# In Pandas\n",
+ "varietes = list(set(iris_df[\"class\"]))\n",
+ "varietes"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.7974683544303798"
+ "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
]
},
- "execution_count": 13,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# What is the correlation between age and height (don't use .iloc)\n",
- "corr_df['age']['height']"
+ "# 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": [
- "### The Size can be based on a DataFrame value"
+ "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": 14,
+ "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='age', ylabel='height'>"
+ "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
]
},
- "execution_count": 14,
+ "execution_count": 30,
"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>"
]
@@ -891,28 +2264,42 @@
}
],
"source": [
- "# Option 1:\n",
- "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=\"diameter\")"
+ "# 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": 15,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
+ "image/png": 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\n",
"text/plain": [
- "<AxesSubplot:xlabel='age', ylabel='height'>"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -924,27 +2311,87 @@
}
],
"source": [
- "# Option 2:\n",
- "trees_df.plot.scatter(x=\"age\", y=\"height\", marker = \"H\", s=trees_df[\"diameter\"] * 50) # this way allows you to make it bigger"
+ "# 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": [
- "## Use subplots to group scatterplot data"
+ "Did you notice that it made 3 plots?!?! What's decieving about this?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Re-visit the Iris Data\n"
+ "### 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": 16,
+ "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": [
{
@@ -977,199 +2424,114 @@
" </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",
+ " <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>146</th>\n",
- " <td>6.3</td>\n",
- " <td>2.5</td>\n",
- " <td>5.0</td>\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>147</th>\n",
- " <td>6.5</td>\n",
+ " <th>102</th>\n",
+ " <td>7.1</td>\n",
" <td>3.0</td>\n",
- " <td>5.2</td>\n",
- " <td>2.0</td>\n",
+ " <td>5.9</td>\n",
+ " <td>2.1</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",
+ " <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>149</th>\n",
- " <td>5.9</td>\n",
+ " <th>104</th>\n",
+ " <td>6.5</td>\n",
" <td>3.0</td>\n",
- " <td>5.1</td>\n",
- " <td>1.8</td>\n",
+ " <td>5.8</td>\n",
+ " <td>2.2</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": 16,
- "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": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
+ "</table>\n",
+ "</div>"
+ ],
"text/plain": [
- "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+ " 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": 17,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# In Pandas\n",
- "varietes = list(set(iris_df[\"class\"]))\n",
- "varietes"
+ "iris_virginica = iris_df[iris_df[\"class\"] == \"Iris-virginica\"]\n",
+ "assert(len(iris_virginica) == 50)\n",
+ "iris_virginica.head()"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+ "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
]
},
- "execution_count": 18,
+ "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": [
- "# In SQL\n",
- "varietes = list(pd.read_sql(\"\"\"\n",
- " SELECT DISTINCT class\n",
- " FROM iris\n",
- "\"\"\", iris_conn)[\"class\"])\n",
- "varietes"
+ "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
]
},
{
"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": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "# If you want to continue using SQL instead, don't close the connection!\n",
- "iris_conn.close()"
+ "### 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": 20,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -1178,13 +2540,13 @@
"<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
]
},
- "execution_count": 20,
+ "execution_count": 35,
"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>"
]
@@ -1196,44 +2558,19 @@
}
],
"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\")"
+ "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = 0, ylim = 0)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 36,
"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",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAANkAAADbCAYAAADgdjR9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUtUlEQVR4nO3dfZRdVXnH8e/v3pnMayADxCziJECMGmMhEUcBkddGLaCojdVgFbXSKAqrLqWgSCjyIoVVrVVQjA0aXqrShRUMLpdSDRVoxUkh0USMEQIzBDAJE5rJTOb16R/n3OTOnXtn7hnuOfdlns9ad2XuvufluUme2fvss8/eMjOcc/FJlTsA52qdJ5lzMfMkcy5mnmTOxcyTzLmYeZI5FzNPMudilmiSSVoh6XeS9kn6o6RTkjy/c+VQl9SJJL0FuAF4H/AIcGRS53aunJTUiA9JDwNrzGxNIid0rkIk0lyUlAY6gNmStknqlnSTpKYkzu9cOSVSk0maCzwDbADeAQwB9wDrzezzOduuBFYCtLS0vH7RokWxxxeH/sERnti1j9Gsv9+UxNxZjcxsrKcuJfb0DdG9pw8hDKN9VjOzmuuD8p4+JGFmtLc101CXynu8BUe00DQjXY6vOK1t2LBhl5nNLmbbpJKsDXgB+LCZrQ3LlgNXmNnrCu3X0dFhnZ2dsccXh929A5x8w8/ZPzR6oKyxPsVDl53J4a0NBT9fd9GbeftNDxZdnjmeS5akDWbWUcy2iTQXzawH6AZqdsj/7t4BNnbtYXfvAACHtzZw4/LjaKhL0TwjTUNdihuXH3cgIbp7+qlPjf3rT0vcu3EHdSmNKa9Ppdg3OMKNy4+jsT7FzIY6GuvHHs9VrsR6F4FvAxdL+glBc/FTwLoEzx+bex57hsvu3kR9KsXQ6Cg3Lj+Oc5e+PPyNYmAi9/dLe1sTQ6OjY8r2DY6w5sEn2Dc4tnxodJT2tiaWzJvFyQuPoLunn/a2Jk+wKpHkfbJrgF8DW4HfAY8C1yV4/ljs7h3gsrs3sX9olL0Dw+wfGuXSuzex7fm9XHb3JgaGjb6hEQaGjUvv3jSupmusT9GSdU2VnWAtDelxNdbhrQ0smTfLE6yKJFaTmdkQ8InwVTMyzb79HEyO+lSKx7r25C3v7uk/kCDnLn05i488hHs37hhXgzXXp/joycdw7pK5LJwz80D57t4Br8mqTJLNxZqUr9k3NDrK0nmz8pa3tx28a5FpZtalNK6J2Dc0yq0PPcnqXz5xoPlZqFnqKpuPXXyJspt92R0SC+fMnLCjIruZ2TswcuB4zfUH/0l6B0bGNT9zm6WZ5qerXF6TTUFuk+3cpS/P2yGRaQ4+1rWHpfNmjWn25WtmNs9I8Y7j5rJu044xNVsK+NYvnyCt8b2O2c1PV5k8ySIq1GQ7vLVh3H/2iZp37W1N9A8Nj9m+b3CUdZuezdt0/H5n97hYcpufrjJ5czGCQj2J+ZpsxWyrnJoJgm78jKa68Z8DNM8Y3+voKpcnWQT5biBnmmz5th0azun4GB49sG13Tz/pVP4kgqD7vlCnxgVvPoaHLjvTOz2qhCdZBIV6EvM12YaGRxjJGd8yYkE5QMuM9JghUrlGRo2/en173s/OXTLXa7Aq4kkWQaGexHz/4bfv7st7jEz5vsERGtLja7JMU3DVOYupr0vz3o6xtdX5J80f04HiKp93fERUqCcx19J5syYsb29rYiRncHZacMsHjqfrhX6uuW/LgQ6Ty89axGEtM8b1ULrq4DXZFBQztGnhnJmcf9L8MWW5tVBux0cqJeYe2sQ1920Z02Hy5fu3csail3mCVSmvyWJ09TuP5fwTjz5wn6ytZQYbu/bQ3tZEd08/jXVphkYOduM31qWLGo7lqosnWcwWzpnJwjkzx90zW3XO4oLDsfYPj4wp3z884vfDqpg3FxOQ757Z1es285GTj6ahTmM6UdpaZpD7IK2vvFPdvCZLQL4hVAPDxtqHnwLEylMX8P4T5nN4awMbu/aQlhjOev4sLXlzsYp5TZaAfPfXAPoGRxgYHuXm9dsOlLXMSDOQc4NtYMTGPHPmqosn2UuUO+1Avs+7e/pZdc5iZqRFfZ57Y9mjRvYNjtBYP/afpaEueD7NR9xXJ28uvgSTPd+V/Xnf0Agjo/mvrbI7NtrbmsZtNzA8yj/cu5kr7vmtP0NWhRKrySStl7RfUm/4+n1S547DZAOAcz8vlGAwvmMjX0fHvsERf4asSiXdXLzIzFrD16sTPndJTTZYON/nhTTV143Zr6m+cAOj0IBkV7n8mmyK2tuaJryfVaizI5/+oeGi9/NnyKpP0kl2vaRdkh6SdHrC5y65iZp5Y2ajapi4ZzB7eFXuIOT6tKhL4XMtVrEkOz4uA7YAg8AK4EeSlprZH7M3yp6me/78+eMOUikyzbq9AweHRWWafdnTD8w9tJF/39DNjzbuoK/Aoy2Ndelx+2UPQs6cz2eoqk5JTgn3q6y3ayWdB5wNfC1nu9XAagim6U4qvqiKebbsyh/+htv+5+lJj5XdXMzInc7Ak6t6lfOazIDCjwZXuMmeLdv2/N68CdZUP77pmG8aAlc7EqnJJM0CTgAeAIYJFgI8lWCq7qo10bNlj3Xtyb/PkiPDyXIOdprkNhddbUmquVgPXAssAkaAx4F3mVlV3yuD8c26jLbm+rzbn7TgMO7ZuGNMmfcY1rakVnXZaWZvMLOZZjbLzE40s58lce5y6ekbyls+PIqvzjLN+LCqmEw0/cDCOTN9dZZpxG9Gx2Sy6Qd8dZbpw2uyGOVOP+BzdExPnmQxy0w/4KYvby7GYLJnzNz04jVZifkaYi6X12QlFGVBCjd9eJKVUJQFKdz04UlWQlEWpHDThydZCUVZkMJNH97xUWLFLkjhpg9PshgUGjTspidvLjoXM08y52LmSeZczDzJnIuZJ5lzMfMkcy5miSeZpFeGc+LfkfS5nSuHctRkNwO/LsN5nSuLRJNM0gpgD/CfSZ7XuXKKNOJD0luBpUBrdrmZXVnEvocAVwN/Dnx0gu2qYppu54pVdJJJugl4L/ALoC/ro2Kn0r4GWGNmXRPNmFst03Q7V6woNdl5wFIz64p6EklLgWXA66Lu61y1i5Jkuwmup6bidOBo4OmwFmsF0pIWm9nxUzymc1VhwiSTtCDr7ZeAOyVdDzyfvZ2ZPTHJeVYD38t6fwlB0l1YdKTOVanJarJtjF995e052xgw4Sp3ZtZH1nWcpF5gv5ntLD5U56rThElmZrF08ZvZVXEc17lKVHQSSfpqgfKvlCwa52pQlJrqwwXKP1iCOJyrWZP2Lkr6m8y2WT9nLAB2lTwq52pIMV34mZpqBmNrLSPoZfxQqYNyrpZMmmRmdgaApGvN7Ir4Q3KutkS5GX2lpHHXcGY2mm9j51wgSsfHMDCU+5I0IOlJSV+S1DrhEZybhqIk2cXAz4G3Aq8B3kbwyMqlBCM33gR8pcTxOVf1ojQXPw0cb2Yvhu+3SuoENpjZKyT9BthQ8gidq3JRarJDgOacsmbg0PDn5wBfWcG5HFFqstuAn0n6F6ALaAf+Dlgbfv5W4PelDc+56hclyf4e+AOwApgLPEswX8e3ws9/AawvZXDO1YKikyzsqr8lfOX7fH+pgnKuliQ2x4dz01WSc3w4Ny0lMseHc9NZlC78lzLHh3PTVpQky8zxcZKkBdmvYnaWdIekZyX9n6Stki6YWsjOVZcozcVvhH9GnuMjdD3wUTMbkLQIWC/pUTPzUSKuphVdk5lZqsCrmATDzDab2UDmbfh6xRRidq6qRJ4oR9I8SSdO5WSSvi6pD3ic4Gb2j/Nss1JSp6TOnTt9MitX/aJMpDNf0kMECXJ/WPYeSf9a7DHM7BPATOAU4AfAQJ5tVptZh5l1zJ49u9hDO1exotRk3wTuI0iSobDsZ8BbopzQzEbM7EGCsY8+uamreVE6Pt4InGNmo5IMwMxelHToJPtNdG6/JnM1L0pN9jywMLtA0mLg6cl2lPQySSsktUpKS3obwc3tn0eK1rkqFCXJ/glYJ+kjBNPDnQd8H7ihiH2NoGnYDfSEx/qUmd0TMV7nqk6UUfi3SnqBYIG+LoKp4FaZ2Q+L2HcncNpUg3SumkUahR8m1A9jicS5GjXZ0km5MwbnZWa3liYc52rPZDVZMfPcG+BJ5lwBky2ddEZSgThXq6a0/pikz5Y6EOdq1VQX+bu8pFE4V8OmmmSafBPnHEw9ye4oaRTO1bAoo/AvyfxsZhdmlX+61EE5V0ui1GSFpn3zNcucm0Axy9meGf6YlnQGY6/HFgB74wjMuVpRzLCqNeGfjYy96ZxZzvbiUgflXC0pZjnbYwAk3WZm58cfknO1JcpEOudLqpd0iqT3AUhqkdQSX3jOVb8ovYvHAlsJVnHJNCFPw8ctOjehKL2L3wCuNLNFHJzj4wHgzSWPyrkaEiXJXsvBm9CZOT724atrOjehKEm2HXh9doGkNwLbJttRUoOkNZKekrRX0qOSzooWqnPVKcqT0auA+yTdAjRI+hzBvB3FzGlfRzBlwWkEE++cDdwl6Vgz2x4tZOeqS5TexXXAXwCzCdYomw+828x+WsS++8zsKjPbbmaj4bGeJKdmdK4WRVkEcAbwboIF2OcCzwC7JG2OupStpDnAq4DNeT5bSTBZD/Pnz49yWOcqUtTexTMJRnh0hH+eBnw9ygkl1QN3AmvN7PHcz32abldrolyTvQt4hZntCd9vkfQIQcdHURPuSEoBtwODwEURzu1c1YpSkz0HNOeUNRGszjIpSSK4iT0HWG5mQ5Ps4lxNiFKT3Q78RNLXCGYCngd8Ergta6Q+ZlZo6u1vAK8BlplZ/xTjda7qyMyK21B6sojNzMzGLW8r6SiC+2wDwHDWRx8zszsLHayjo8M6OzuLis+5JEnaYGYdxWwbZZruY6YakJk9hc8L4qapqc7x4ZwrkieZczHzJHMuZp5kzsXMk8y5mHmSORczTzLnYuZJ5lzMPMmci5knmXMx8yRzLmaeZM7FzJPMuZh5kjkXM08y52LmSeZczDzJnItZYkkm6SJJnZIGJH0nqfM6V25RJtJ5qXYA1wJvwxepcNNIYklmZj8AkNQBtCd1XufKza/JnItZxSWZpJXhtVvnzp07yx2Ocy9ZxSWZz4Xvak3FJZlztSaxjg9JdeH50kBaUiMwbGbDE+/pXHVLsia7AugHPgt8IPz5igTP71xZJNmFfxVwVVLnc65S+DWZczHzJHMuZp5kzsXMk8y5mHmSORczTzLnYuZJ5lzMPMmci5knmXMx8yRzLmaeZM7FzJPMuZh5kjkXM08y52LmSeZczDzJnIuZJ5lzMUtymu7DJP2HpH2SnpL0/qTO7Vw5JTlN983AIDAHWArcJ2mjmW1OMAbnEpdITSapBVgOrDKzXjN7ELgX+GAS53eunJJqLr4KGDGzrVllG4HXJnR+58omqeZiK/BiTtmLwMzcDSWtBFaGbwck/Tbm2MrpCGBXuYOISS1/N4BXF7thUknWCxySU3YIsDd3QzNbDawGkNRpZh3xh1cetfz9avm7QfD9it02qebiVqBO0iuzypYA3unhal4iSWZm+4AfAFdLapF0MvBO4PYkzu9cOSV5M/oTBCts/gn4LnBhEd33q2OPqrxq+fvV8neDCN9PZhZnIM5Nez6syrmYeZI5F7OKTLJaHucoqUHSmvB77ZX0qKSzyh1XqUl6paT9ku4odyylJmmFpN+F/z//KOmUibZPcuxiFLU8zrEO6AJOA54GzgbuknSsmW0vZ2AldjPw63IHUWqS3gLcALwPeAQ4ctJ9Kq3jIxzn2AP8WWYYlqTbgWfM7LNlDS4mkjYBXzCzu8sdSylIWgH8JbAFWGhmHyhzSCUj6WFgjZmtKXafSmwuTqtxjpLmEHznWqilkXQIcDXwmXLHUmqS0kAHMFvSNkndkm6S1DTRfpWYZEWPc6x2kuqBO4G1ZvZ4ueMpkWsIftN3lTuQGMwB6oH3AKcQXMq8jkmWZa7EJCt6nGM1k5QiGPEyCFxU5nBKQtJSYBnwz2UOJS794Z9fM7NnzWwX8GWC6+qCKrHj48A4RzP7Q1hWU+McJQlYQ/Cb8WwzGypzSKVyOnA08HTwFWkF0pIWm9nxZYyrJMysR1I3EKkjo+I6PgAkfY/gi1xAUCX/GHhTjfQuIukWgu+1zMx6yxxOyUhqZmwr5BKCpLvQzHaWJagSk3Q1cBZwDjBE8PDxejNbVWifSqzJIBjneCvBOMfdFDfOsSpIOgr4GDAAPBf+xgf4mJndWbbASsDM+oC+zHtJvcD+Wkmw0DUEz8ptBfYDdwHXTbRDRdZkztWSSuz4cK6meJI5FzNPMudi5knmXMw8yZyLmSeZczHzJJvmJG2WdHqBz04PRzgU2vdoSSapUu+3VgRPsiokabukZaU4lpm91szWJ33e6cSTzLmYeZKVUVgzfE7SFkk9kr4tqTH87O2SHpO0R9LDko4Ly28H5gM/ktQr6dI8xz1D0m+y3t8v6ZGs9w9KeldWDMvCn5skfSeMZQvwhqx9JjrvX0t6WtIuSZ8v4V9RbTAzf5XpBWwHfgvMAw4DHgKuBY4nGLd5ApAGPhRu25C137IJjttI8FjGEQTjU58DdhA8k9cUfnZ47rGAfwR+GcYyL4ytOyfeZVnvjyYYyP2t8LhLCMZkvqbcf7eV9PKarPxuMrMuM3uBYKDpecDfAt80s1+Z2YiZrSX4z3tiMQc0s/1AJ3AqwZO8m4AHgZPDY/zBzHbn2fW9wHVm9oIFD11+tcjv8AUz6zezjQRPsS8pcr9pwZOs/LKfIH4KmAscBXwmbCrukbSHoGaZm+8Akm4Jm3C9ki4Pix8geL7r1PDn9QST95wWvs9nbp54ivFc1s99BM+RuZAnWfnNy/p5PkGzrougRpmV9Wo2s++G2415dMLMPm5mreHri2FxbpI9wORJ9myeeMacKtpXc+BJVgk+Kald0mHA5cD3Ca5xPi7pBAVaJJ0jKTPPyfPAgkmO+zDBGlpvBB6x4Hm8owiu8/6rwD53AZ+T1CapHbg45/NizutyeJKV378BPwWeCF/XmlknwXXZTQTT420DPpy1z/XAFWFT8pJ8B7VgJZ3/BTab2WBY/N/AU2b2pwKxfIGgifhkGFPuqjuTnteN5w9tlpGk7cAFZnZ/uWNx8fGazLmYeZI5FzNvLjoXM6/JnIuZJ5lzMfMkcy5mnmTOxcyTzLmYeZI5F7P/BzWNyz/YIImEAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 216x216 with 1 Axes>"
]
},
"metadata": {
@@ -1243,173 +2580,88 @@
}
],
"source": [
- "# Write a for loop that iterates through each variety in classes\n",
- "# and makes a plot for only that class\n",
+ "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+ " xlim = (0, 6), ylim = (0, 6),\n",
+ " figsize = (3, 3))\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\", label=variety)"
+ "# What is wrong with this plot?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What is the maximum pet-len?"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 37,
"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>"
+ "6.9"
]
},
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "# copy/paste the code above, but this time make each plot a different color\n",
- "colors = [\"blue\", \"green\", \"red\"]\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\", label=variety, color=colors[i])\n"
+ "iris_virginica[\"pet-length\"].max()"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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gzjgjnFZqVwxzEIMHh9NK7YppDqK0NIRDMcxBXHRRxxGD5iCkK93NQaS05Gg+6+2So0uWhNNKEyfCpX1mBuXILFgQfmlOmgRf/3quq8mOWCycVqqry9wC7/lm+fIQEhMmwNln57oayVdpCwitSS0iUli0JrWIiKRMa1KLiEgkrUktIiKRUhlBaE1qEZEikkpAtH/eVmtSi4gUgaQDQutTi4gUl5R/6ZvZMDM7KxPFiIhI/kg6IMxsuJktB/4ELI23XWFm/ytTxYmISO6kMoL4L8KlvQcQ1oIAeBEYn+6iREQk91KZpB4LfMHdD5qZA7h7q5kN7OF5IiLSB6UygtgCnJzYYGanAevTWpGIiOSFVALiXmCJmX0N6G9mk4BFwD0ZqUxERHIqlbe5PmJm7xIu+b0BuBa43d0XZ6g2ERHJoVTmIIiHweKMVCIiInml24Aws+uS+Sbu/khP+5jZDGAqMBpodPepSTznZeACoNTd9ydTS6qKcZ2AuXNh0SK4+mr4zndyXU12aG0EkdR1ux6Emf0qie/h7n5hjy9k9vfAQeAioKKngDCzycD/BM4liYDozXoQ7SuNlZWFZSmLYaWxsrKwqlri/Q8/zF092TBhArz4Ysf7Wl1NJOj1ehDufkG6inD3p+PF1AM13e0bf+vsHcA1wO/SVUOiWCyEQ1tbuEG4P25c4Y4k5s7tGA4QgnHu3MIdSSxf3jEcIIwkli/XSEKkJ726vpKZ3ZruQjqZT7g44Ds91HGDmTWbWXMsFkvpBVpawl/PiUpLQ3uhWrQotfZCkLguczLtInJYby/ANzutVSSIjzDOBh7oaV93f8jd6929vjrFP/vr6sJfz4n27Qvtherqq1NrLwQTJqTWLiKH9TYgLK1VtH9Ts37AfwKzMjUp3a66Osw5VFRAVVXYNjQU7uklCKeROo+aysoK9/QShNNIncNAE9UiyeltQDye1ioOqwLqgUVm9g7QFG/faGbnpvvFJk2Cdetg6dKwLfQJaggT0rffDqecEraFPkENYUJ62bIw17JsmSaoRZLV7buYOuxodou73xvRfrO7/yCJ5/cnTIrfQZikvh7YnzhSMDMDhiQ8bRiwIr5/zN07nRQ6rDfvYhIRKXbdvYsplRHE3C7a5yT5/DlAG3ArMCX+9Zz4ZcR3m9lwD95pvwHtM89bugsHERFJvx4/SW1m7Z9xKDGzC+g4/zAS2JXMC7n7PGBeFw9XdvGcFjI03yEiIt1L5lIbDfFtOZD4iWknXOF1ZrqLEhGR3OsxINx9BICZ/djdr8l8SSIikg+SnoNw92vMrNTMzjWzqwHM7BgzOyZz5YmISK6ksib1aGAt8DCHTzv9HR1PO4mISIFI5V1MC4C57v5JDq9J/QpwTtqrEhGRnEslIE7n8Afk2tek3gNUpLsoERHJvVQCogX428QGMxsLvJnOgkREJD+ksqLc7cAvzOxHwFFmdhvwdWB6RioTEZGcSuVdTEuAi4Fq4FfAcOByd9eFk0VEClDSIwgzKwMuByYAQ4FNwDYzW+3uH2SoPhERyZFUTjEtAE4lfHJ6HWEEMRs4EUhq7WoREek7UgmIicBJ7r4jfv+PZraCMEmtgBARKTCpvIvpHeDoTm0VwNvpK0eyYckSmD49bIvFmjXw6KNhWyxiMWhqCluR3khlBPEY8JyZPQBsJKzVcCPw44QrvuLuL6e3REmn0aNh1arwdUNDuP/667mtKdNmzoQHHzx8f8YMeKDHBW37tsZGmDYtrBi4d2841sWwIJakVyoLBv01id3c3UceWUm9owWDerZkCVx22Ufbn3kGLr00+/Vkw5o1cNppH23/4x9h1Kjs15MNsRjU1kJb2+G2ioqwamIhL6krvdPdgkFJjyDar+oqfdfixV23F2pArFjRdXuhBkRLSxg5JAZEaWloV0BIKnq7JrX0QRMnptZeCMaOTa29ENTVhdNKifbtC+0iqVBAFJFLLw1zDolGjy7c0QOEUcKMGR3bZswo3NEDhFFCQ0M4rVRVFbYNDRo9SOqSnoPId5qDSN6SJeG00sSJhR0OidasCaeVxo4t7HBIFIuF00p1dQoH6Vp3cxAKCBGRItZdQOgUk4iIRFJAiIhIJAWEiIhEUkCIiEgkBYSIiERSQIiISCQFhIiIRFJAiIhIJAWEiIhEUkCIiEgkBYSIiERSQIiISCQFhIiIRFJAiIhIpKwFhJnNMLNmM/vQzBZ2s9+1ZrbSzHaa2UYz+56ZJb00qvRszRp49NGwFRHpSjZHEJuBu4BHetjvaOCfgOOAzwCfB27JaGVFZOZMOO00mDo1bGfOzHVFIpKvshYQ7v60uy8Gtvew3wJ3/62773X3TcATwNnZqLHQrVkDDz7Yse3BBzWSEJFofWEO4jxgddQDZnZD/LRVcywWy3JZfc+KFam1i0hxy+uAMLOvAfXAvVGPu/tD7l7v7vXVWnS3R2PHptYuIsUtbwPCzCYCdwOXuPu2HJdTEEaNghkzOrbNmBHaRUQ6y8t3B5nZxcDDwBfc/Q+5rqeQPPAAfOMb4bTS2LEKBxHpWtYCIv5W1f5ACVBiZuXAfnff32m/CwkT05e7u86OZ8CoUQoGEelZNk8xzQHagFuBKfGv55jZcDPbbWbD4/vdDgwEfhlv321mz2axThERIYsjCHefB8zr4uHKhP0uyEY9IiLSvbydpBYRkdxSQIiISCQFhIiIRFJAiIhIJAWEiIhEUkCIiEgkBYSIiERSQIiISCQFhIiIRFJAiIhIJAWEiIhEUkCIiEikog+IWAyamsJWREQOK+qAaGyE2loYPz5sGxtzXZGISP4o2oCIxWDaNGhrg9bWsJ02TSMJEZF2RRsQLS1QVtaxrbQ0tIuISBEHRF0d7N3bsW3fvtAuIiJFHBDV1dDQABUVUFUVtg0NoV1ERLK45Gg+mjQJxo0Lp5Xq6hQOIiKJijogIISCgkFE5KOK9hSTiIh0TwEhIiKRFBAiIhJJASEiIpEUECIiEkkBISIikRQQIiISSQEhIiKRFBAiIhJJASEiIpEUECIiEkkBISIikRQQIiISSQEhIiKRshYQZjbDzJrN7EMzW9jDvjeZ2Ttm1mpmj5jZUVkqsyjEYtDUpPW3RaR72RxBbAbuAh7pbiczuwi4Ffg8UAeMBO7MdHHForERamth/PiwbWzMdUUikq+yFhDu/rS7Lwa297DrtUCDu6929/eAfwOmZri8ohCLwbRp0NYGra1hO22aRhIiEi0f5yBOB15LuP8aMMTMBnfe0cxuiJ+2ao7pt1yPWlqgrKxjW2lpaBcR6SwfA6ISaE243/71gM47uvtD7l7v7vXVWje0R3V1sHdvx7Z9+0K7iEhn+RgQu4GqhPvtX+/KQS0FpboaGhqgogKqqsK2oUFrcotItP65LiDCamAM8FT8/hhgi7v3NHchSZg0CcaNC6eV6uoUDiLStawFhJn1j79eCVBiZuXAfnff32nXHwMLzewJ4G1gDrAwW3UWg+pqBYOI9Cybp5jmAG2Et7BOiX89x8yGm9luMxsO4O7PAd8DfgWsi9/uyGKdIiICmLvnuoa0qK+v9+bm5lyXISLSp5jZSnevj3osHyepRUQkDyggREQkkgJCREQiFcwchJnFCBPavXEcsC2N5fQF6nNxUJ+Lw5H0udbdI9/XWDABcSTMrLmrSZpCpT4XB/W5OGSqzzrFJCIikRQQIiISSQERPJTrAnJAfS4O6nNxyEifNQchIiKRNIIQEZFICggREYmkgBARkUhFExBmNsjM/o+Z7TGzdWb2D13s9zdm9ryZbTOzPj1Bk0KfrzWzlWa208w2mtn34pdn71NS6O9XzOzPZtZqZlvN7FEzq4raN98l2+dOz3nZzLwvHmNI6ThPNbMD8atFt9/Oz2616ZHKcTazkWa2xMx2xX+Pfa+3r1s0AQH8B7AXGAJMBhaY2ekR++0jLFY0LYu1ZUqyfT4a+CfCpzE/A3weuCVLNaZTsv1dDpzt7gOBkYR1Su7KWpXplWyfATCzyeTnQmGpSKXPv3P3yoTbr7NVZJol1WczKwNeBF4GTgBqgMd7/aruXvA34Jj4D/eUhLbHgLu7ec7J4ceT+/qz1eeE/W4Gnsl1H7LRX8Ia6D8GfpnrPmS6z8BAYC1wFuBA/1z3IZN9BqYCy3Jdc5b7fAPw23S9drGMIE4BDrj72oS214Au/9IqAEfS5/MIS7/2JSn118zOMbNWwlrnXwZ+mPEK0y/VYzwfWAC8k+nCMijVPv+P+GmWtWZ2ex89rZZKn88CWszs2Xi/f21mo3v7wsUSEJVAa6e2VmBADmrJll712cy+BtQD92aorkxJqb/uvszDKaYa4PtAS0ary4yk+2xm9cDZwANZqCuTUjnOvwH+Bjie8EfAJOBbGa0uM1Lpcw3wFeB+YCjwC+Bn8VNPKSuWgNgNdJ6ErCL89VioUu6zmU0E7gYucfe+djXMXh1jd98EPAc8maG6MimpPptZP+A/gVn+0TXg+5qkj7O7v+Xuf3X3g+7+B+A7wBVZqDHdUvm33UY4rfasu+8l/KE3GBjVmxculoBYC/Q3s08ktI2h751GSUVKfTazi4GHgcvi/5n6miM5xv2BkzJSVWYl2+cqwqhwkZm9AzTF2zea2bmZLzOtjuQ4O2AZqSqzUunz64R+pkeuJ2CyONHzJNBImPA5mzBEOz1iPwPKgdPiP+hy4Khc15/hPl8IbAfOy3XNWervZGB4/FjXAq8AT+e6/kz1Od7PExJun47/2z4RKMt1HzJ4nC8BhsS//iSwCrgj1/VnuM+nAu8D44AS4CbgL709zjnveBZ/wIOAxcAeYD3wD/H24YQh3PD4/br4f57EW0uu689wn38F7I+3td+ezXX9Gezvd4GN8f02Ei50NjjX9Weyz52e0/5vvM+9iynF43wvsCW+31uEU0ylua4/08cZ+HvgTWAn8OuoIEn2pov1iYhIpGKZgxARkRQpIEREJJICQkREIikgREQkkgJCREQiKSBERCSSAkIkR8xsdVfrE5jZ+Wa2sZvn1vXlNR2kb1BAiKTAzFrMbFw6vpe7n+5Jrk+QztcVSZYCQkREIikgpCjF/yK/zcz+aGbvmdn/NrPy+GOXmtnvzWyHmf23mZ0Rb3+McGmDZ+LLV/5LxPe9wMz+kHB/qZmtSLi/LH7V3A6jAjOrMLOF8Vr+SLheUvtzunvdyWa2Pn7t/2+n8Uck0ueXHhQ5EpOBiwjXt3kGmGNmTwOPAJcBzcAU4Odmdqq7fzV+9dPp7r60i+/5O+BkMzsO2EFYj+CgmQ0gXO/qb4HfRjzvDsIVZU8iXJDt2fYHol7XzOriD59DuEDbKcAKM3va3df05och0plGEFLMHnT3De7+LuECfpOA64H/cvdX3f2Auz8KfEhYqatH7v4BIVjOI1xi+3VgGeEKnGcBb7j79oinXgV8193fdfcNhAVfknGnu7e5+2uEVcbGJPk8kR4pIKSYbUj4eh1hBa5a4J/jp5d2mNkOYFj8sY8wsx/FT/vsNrPZ8eZXgPMJIfEK4Yqafxe/vdJFLUMj6klG4vKh7xNWHxNJCwWEFLNhCV8PBzYTfkl/192PTbgd7e6N8f06XP7Y3f/R3Svjt/nx5s4B8Qo9B8TbEfV0eKnUuiZy5BQQUsxuNLMaMxsEzAYWEVbV+0cz+4wFx5jZF+JzCBDWFxjZw/f9b8K8wFhghbuvJoxMPkNYJznKU8BtZvYxM6sBZnZ6PJnXFUkrBYQUs58ALxAWk3kLuMvdmwnzEA8C7xEWXpma8Jx/J0xm7zCzW6K+qbvvAf4vsNrDusAQJq/XufvWLmq5k3Ba6a/xmh7r9HiPryuSblowSIqSmbXQ/buRRIqeRhAiIhJJASEiIpF0iklERCJpBCEiIpEUECIiEkkBISIikRQQIiISSQEhIiKR/j/uKO2qW5iXjAAAAABJRU5ErkJggg==\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>"
+ "(0.0, 6.0)"
]
},
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "# copy/paste the code above, but this time make each plot a different color AND marker\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",
- " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", label=variety, color=colors[i], marker=markers[i])"
+ "ax.get_ylim()"
]
},
{
- "cell_type": "code",
- "execution_count": 24,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "# Did you notice that it made 3 plots?!?! What's deceiving about this?"
+ "Let's include assert statements to make sure we don't crop the plot!"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 39,
"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": {
- "image/png": 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eaiLZKTFEkhHalBjAD80sr/GWbMEimdLW7qOd8jsB0Cm/U1zndbSV2EQSGfl8lEj1UYCZHQV2AguAO939YIpiE0mLtnYfHfH4CI4cPUKn/E4svWZpq+d1tJXYRBJJDJOAS4D7gG3AQOAW4DngbeBO4L+BG1IaoUiKJTtlRJ7lsaV6C/tr9gOwv2Y/lR9UtnqthiuxiXQEibQxbAbOcfcPGuzrDqxy91PMrH/064+lKji1MUi6hTV9dluFtUSnZIcw2xhOBE5otO8EoCT69S6gS7KBiIQtrOmz20pTW0jYEkkMc4E/m9m3zOxiM7sBeAGYE/18BJEqJZEOIdFxDLG2ghMKI78fnVB4AjNHz0x7W0FYS3SKxCTSxvA/iUyYdyXQD3gXeAB4JPr5S8DSVAYnki7JTp/dtVNXPqz9EIAPaz+kW1G3VqfPDjtOkbaK+43B3Y+5+0PufpG7D3b34dHv66KfH3H3w61dR6QlYU0TneyUEbHRyzHxjmJOlqa2kExI6FedsNd8ltyyetdqhj48lDfGv5HW34pj4xG6depGnuVxzI/Vj0co617W7HkttTGcP/D8dhOnSFvFnRiiaz5fTqTK6MMGH8XXrUmkFWFNE53sOIZYG8N3/vQdPqz9kOLCYn5+8c/T1sagqS0kU0Jf89nMTgPWAL9z96vaci3JHmHWpTccx5BIN9DGbQyHag+ltY1BS3RKpiTSKylVaz4/ALyegutIFslEXXoy3UDDbmMQyYREEkOb13w2syuJJJf/m2CcksXauoRlshLtBqo5jyRXJJIY2rTms5mdCPwI+H4rx403swozq9i7d28C4UlHFZvOOj8vH4D8vHweu+SxtNalJ7PC2bABw7j0jEsD+y4dfKmqeyTrJNJdNa+ZLT/OS9wNzGqtjcLdH3b3cncv7927d7zhSQeWZ3k8vuZxjh47CsDRY0d5fM3jcS1hGZNoN9dkqq4K8gpYuHFhYN/Ctxcm1MYQVndckbZIeNpsMys1s/MSPOfTwJeA/51oeZL92rriWKJtBbHyOhd0BiKL38RT3u/X/z7QQwig9lgtv1//+7TEKZIpiXRXHQg8RWQcgwNdzeybwMXu3tqMqhcCg4CtZgaRcRD5Znamu5+TeNiSTdraLTPRbq6x8iYtmsTrO19nSO8hzBg1o9XyRp8+mlsvuPW4FdVGnz46LXGKZEoi/ex+RWSK7c8T6aEE8GcijdKteRj4TYPvbyaSKL6dQPmSpdrSLTOZbq55lkdRQRFr96wFYM2eNXQu6Nxq1VWn/E7ce1HL6y2nMk6RTEmkKukzwH3ufozooLboFNwlLZ4VOe5Dd98V24CDwBF3V+uytEmy3VzD7h6rqS2kI0kkMewGTm24w8zOBLYmWqi7T9PgNmmr+raC/EhbQef8zgkt0Zlsm0aycYZVnkhbJVKV9BPgWTObDhSY2RjgViIruomErmFbQcXOCs7qexYzLm69rSDsqSY0tYV0NHEnBnf/tZm9B4wnsrTnNcAd7v50mmKTHJTIFBUN2wocZ83u+NoKwp5qQlNbSEeTUHdVd3/a3b/i7kPc/WIlBUmlZLpzqu5eJPVafGMws+viuYi7/zo14UguS7Q7p6alFkmP1qqSro7jGg4oMUibJNOdU3X3IunRYmJw9y+GFYjktqaqhFp7a8hk3X0ibSEiHU3CU2IAmNkPUh2I5K6O1p1TU1tItkt2hRF1U5WU6WhVQpraQrJdsonBUhqF5LSO1J1TU1tILkiqKgl4PKVRiESt3b020yG0SN1jJRfEnRjM7ObY1+7+7Qb7/zPVQUlumrd2Hp986JPMXzc/06E0qaO1hYgky9w9vgPN9rv7iU3sf8/de6Q8MqC8vNwrKirScWlph3rd34uqw1X07NKTfbfsy3Q4xznmx3h9x+vHtYWc2//chBYVEkk3M1vl7uXJnt9qG4OZDY9+mW9mXyTYvnAycCDZwkVi5q2dV7+ectXhKuavm8/lQy7PcFRBHaktRKQt4ml8nhX9szPBgWxOZMbVSakOSnLPxOcnBr6f8NyEdpcYRHJFq4nB3T8BYGZz3f3f0x+S5JpXtr5S/7YQU3W4ile2vsL5A8/PUFQiuSuR2VX/3cwKgfOAfu4+z8yKo58dSleAkv2GDRjGzNEz63v7ABTlF6naRiRDElnz+ZPAQqAGGADMA75AZPrtK9ISneSEgrwCrj/neiDSXfWsvmdlOCKR3JZIV4oHgR+6+xlArFvGMuCClEclOam9d1cVyRWJJIYh/HNgW2zN50NAl1QHJbkp1gA94bkJGY5EJLclkhi2AEMb7jCzzwCbUhmQ5KamuquKSGYkkhjuAJ4zs7uAIjObCvwOuD0tkUlOaaq7qohkRtyJwd2fBS4GegMvAQOBb7j74jTFJjmipe6qIhK+RHoldQK+AYwA+gE7gH1mts7dj6QpPskB6q4q0r4kMu32g8DpREY6VxJ5Y7gV6A/EtTa0SFMadlcVkcxLJDFcApzi7tXR79eb2V+JND4rMYiIZIlEGp93ASc02tcFeDd14YiISKYl8sbwGPAnM/sFsB0oBSYCcxvMwIq7v5jaEEVEJEyJJIb/iP55a6P9N0Y3iAx8O7mpk83sceAioJjI28f97j4zgfJFmrX74G76du2b6TBEskIik+h9oo1lTQeud/caMzsDWGpmf3P3VW28ruS41btWM/Thobwx/g2tvyySAqEtO+Xu69w91h/Ro9spYZUv2WvKkim4u9ZfFkmRUNcjNLP/Y2YfAm8RabR+voljxptZhZlV7N27N8zwpANavWs1L1e+jOMsr1zOm7vezHRIIh1eqInB3ScA3YDPAwuITOHd+JiH3b3c3ct79+4dZnjSAU1ZMqV+YFxNXY3eGkRSIPQVzN29zt3/QmRNh2+HXb5kj8rqShZvXkxxYTElRSUUFxbzwuYXqKyuzHRoIh1aIr2S0lG22hgkaaUlpay4fgW1x2rr9xXmFVJaUprBqEQ6vlASg5n1AYYDzwKHgS8BY4CxYZQv2SnP8jSfkkgahPXG4ESqjR4iUn1VCXzP3f8YUvkiIhKnUBKDu+8lsj60iIi0c6E3PouISPumxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEhBKYjCzIjObZWaVZnbAzP5mZqPCKFtERBIT1htDAbAN+AJQAtwBzDezQSGVLyIicSoIoxB3PwRMa7DrWTP7BzAU2BJGDCIiEp+MtDGYWV/gX4B1TXw23swqzKxi79694QcnIpLjQk8MZlYIPAHMcfe3Gn/u7g+7e7m7l/fu3Tvs8EREcl6oicHM8oDHgI+Am8IsW0RE4hNKGwOAmRkwC+gLfMXda8MqW0RE4hdaYgAeBAYDX3L3wyGWKyIiCQhrHEMZ8B/Ap4FdZnYwuo0Lo3wREYlfWN1VKwELoywREWkbTYkhIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgFKDCIiEqDEICIiAUoMIiISoMQgIiIBSgwiIhKgxCAiIgGhJQYzu8nMKsysxsxmh1WuiIgkpiDEsnYC9wAjgS4hlisiIgkILTG4+wIAMysHBoRVroiIJCbMN4a4mNl4YHz024Nm9nYm40mzXsC+TAfRzuieNE33pWm6L007vS0nt7vE4O4PAw9nOo4wmFmFu5dnOo72RPekabovTdN9aZqZVbTlfPVKEhGRACUGEREJCK0qycwKouXlA/lm1hk46u5Hw4qhHcqJKrME6Z40TfelabovTWvTfTF3T1UgLRdkNg24s9Huu9x9WigBiIhIXEJLDCIi0jGojUFERAKUGEREJECJIY3MrIeZ/cHMDplZpZmNbea4a82szswONtguDDfacCQyZ5aZTTazXWb2gZn92syKQgozdPHelxx7VorMbFb0384BM/ubmY1q4ficeF4SuS/JPi9KDOn1APAR0BcYBzxoZkOaOfY1d+/aYFsaVpAhi82Z9euWDjKzkcAPgIuAQcDJwF3pDi6D4rovUbnyrBQA24AvACXAHcB8MxvU+MAce17ivi9RCT8vSgxpYmbFwKXAHe5+0N3/AiwErs5sZJnl7gvc/WmgqpVDrwFmufs6d38fuBu4Ns3hZUwC9yVnuPshd5/m7lvc/Zi7Pwv8AxjaxOE587wkeF+SosSQPv8C1Ln7xgb73gSae2P4VzPbZ2YbzeyO6LiPXDaEyP2KeRPoa2Y9MxRPe5KTz4qZ9SXy72pdEx/n7PPSyn2BJJ6XnHigMqQr8EGjfR8A3Zo4djlwFlBJ5AGfBxwFpqczwHau8f2Lfd2N3P6tOiefFTMrBJ4A5rj7W00ckpPPSxz3JannRW8M6XMQOLHRvhOBA40PdPd33P0f0dfCNcCPgG+GEGN71vj+xb4+7v7lklx8VswsD3iMSHvdTc0clnPPSzz3JdnnRYkhfTYCBWZ2WoN9n6L5172GHLC0RNVxrCNyv2I+Bex296z97S9JWf2smJkBs4h04LjU3WubOTSnnpcE7ktjcT0vSgxp4u6HgAXAj8ys2MzOB75OJMMHmNmoaD0hZnYGkV4Gfwwz3rCYWUF0nqz6ObOaqfOcC1xvZmea2UnA7cDsEEMNVbz3JZeelagHgcHAaHc/3MJxOfW8EOd9Sfp5cXdtadqAHsDTwCFgKzA2un8gkVffgdHvfwLsjh73DpHXvcJMx5+mezKNyG8tDbdpje9J9Nj/jN6X/cCjQFGm48/0fcmxZ6Useh+ORO9BbBuXy89LIvcl2edFcyWJiEiAqpJERCRAiUFERAKUGEREJECJQUREApQYREQkQIlBREQClBhEUsjM1jU3372ZXWhm21s4d5CZea5MiiftlxKD5Dwz22JmX0rFtdx9iMe5PkIqyxVJJSUGEREJUGKQrBH9DXyqma03s/fN7NHo/EOY2VfNbLWZVZvZq2Z2dnT/Y0SmEXgmuuzhLU1c94tmtqbB90vM7K8Nvv+LmV3SIIYvRb/uYmazo7GsB85tcE5L5Y4zs63ROfRvS+EtEomL6jIl24wDRhKZG+YZ4HYzW0BkyczRQAVwFbDQzE5396vN7PPADe6+pJlrvgacama9gGoi89sfM7NuROa2Hwq83MR5dwKnRLdiYFHsg6bKbbA04wXA6UQWX/mrmS1w9w3J3AyRZOiNQbLNL919m7u/B9wLjAG+BfzK3Ve6e527zwFqgPPiuaC7HyGSUP4NKAf+H/AX4PzoNf7uTU/vfDlwr7u/5+7bgBlx/gx3ufthd3+TyEpkn2rtBJFUUmKQbLOtwdeVQD8is1F+P1qNVG1m1UBp9LPjmNlD0eqdg2Z2a3T3MuBCIslhGbCUyGLsX4h+35R+TcQTj10Nvv6QyOpkIqFRYpBsU9rg64HATiL/Od/r7t0bbCe4+1PR4wJTDLv7je7eNbr9V3R348SwjNYTw7tNxBMoKrEfTSQcSgySbSaa2QAz6wHcSmSN20eAG81smEUUm9n/iLYRQGS++pNbue6rROr9PwP81d3XEXkTGUZkXd2mzAemmtlJZjYAmNTo83jKFQmdEoNkmyeBxUQWJXkHuMfdK4i0M/wSeB/YBFzb4JzpRBqpq83s5qYu6pEV+d4A1rn7R9HdrwGV7r6nmVjuIlJ99I9oTI1X72u1XJFM0EI9kjXMbAst9y4SkTjojUFERAKUGEREJEBVSSIiEqA3BhERCVBiEBGRACUGEREJUGIQEZEAJQYREQn4/0OOy++8ClKFAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "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",
+ "image/png": "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\n",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 216x216 with 1 Axes>"
]
},
"metadata": {
@@ -1419,61 +2671,36 @@
}
],
"source": [
- "colors = [\"blue\", \"green\", \"red\"]\n",
- "markers = [\"o\", \"^\", \"v\"]\n",
- "min_x = iris_df[\"pet-width\"].min()\n",
- "max_x = iris_df[\"pet-width\"].max()\n",
- "min_y = iris_df[\"pet-length\"].min()\n",
- "max_y = iris_df[\"pet-length\"].max()\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\", label=variety, color=colors[i], marker=markers[i], xlim=(min_x, max_x), ylim=(min_y, max_y))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Have to be VERY careful to not crop out data.\n",
- "# We'll talk about this next lecture."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Better yet, we could combine these."
+ "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": [
- "### 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"
+ "### 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": 28,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 216x216 with 1 Axes>"
]
},
"metadata": {
@@ -1483,19 +2710,13 @@
}
],
"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\", label=variety, color=colors[i], marker=markers[i], ax=plot_area)"
+ "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]"
]
},
{
@@ -1508,7 +2729,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -1584,7 +2805,7 @@
"3 Seth BC 2.5 6 72"
]
},
- "execution_count": 29,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -1627,7 +2848,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -1640,7 +2861,7 @@
"Name: height, dtype: string"
]
},
- "execution_count": 30,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -1665,7 +2886,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -1674,7 +2895,7 @@
"<AxesSubplot:xlabel='attendance', ylabel='gpa'>"
]
},
- "execution_count": 31,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
@@ -1697,7 +2918,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -1756,7 +2977,7 @@
"height -0.464758 -0.635586 1.000000"
]
},
- "execution_count": 32,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -1764,30 +2985,11 @@
"source": [
"students.corr()"
]
- },
- {
- "attachments": {
- "image.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "https://www.researchgate.net/publication/247907373_Stupid_Data_Miner_Tricks_Overfitting_the_SP_500"
- ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1801,7 +3003,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.1"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec_37_plotting2_scatter_plots_template.ipynb b/f22/andy_lec_notes/lec37_Dec07_Plotting2/lec_37_plotting2_scatter_plots_template.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a27852bcfb55a4ba9d76eaa776d8d99a1d23383d
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@@ -0,0 +1,833 @@
+{
+ "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": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}