diff --git a/sum23/lecture_materials/22_Plotting1/bus.db b/sum23/lecture_materials/22_Plotting1/bus.db
new file mode 100644
index 0000000000000000000000000000000000000000..3303edbe663ab1bcff61a5c3977edc23fbd1f0c7
Binary files /dev/null and b/sum23/lecture_materials/22_Plotting1/bus.db differ
diff --git a/sum23/lecture_materials/22_Plotting1/iris-flowers.db b/sum23/lecture_materials/22_Plotting1/iris-flowers.db
new file mode 100644
index 0000000000000000000000000000000000000000..4b3fca4ebaed7a7ba9f8a1dc97d521bf57636e24
Binary files /dev/null and b/sum23/lecture_materials/22_Plotting1/iris-flowers.db differ
diff --git a/sum23/lecture_materials/22_Plotting1/iris.csv b/sum23/lecture_materials/22_Plotting1/iris.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5c4316cd695e7c72f1db7ef496ffd2d2ef705b25
--- /dev/null
+++ b/sum23/lecture_materials/22_Plotting1/iris.csv
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+
diff --git a/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_notes.ipynb b/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_notes.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..587cefcb3cab8ba2295c822da01e4a7ec075cad8
--- /dev/null
+++ b/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_notes.ipynb
@@ -0,0 +1,4100 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 22: Bar Plots and Scatter Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import statements\n",
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "import sqlite3\n",
+ "import os\n",
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 1: Create a database called student_grades.db with a single table called grades"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# establish connection to a new database\n",
+ "grades_conn = sqlite3.connect(\"student_grades.db\")\n",
+ "\n",
+ "# Q: When outer data structure is a dictionary, are inner data structures\n",
+ "# rows or columns of the DataFrame table?\n",
+ "# A: columns\n",
+ "\n",
+ "df = 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",
+ "})\n",
+ "\n",
+ "# convert the DataFrame to sql database\n",
+ "df.to_sql(\"grades\", con = grades_conn, if_exists = \"replace\", index = False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 2: What are the columns of our table? What are their datatypes?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CREATE TABLE \"grades\" (\n",
+ "\"name\" TEXT,\n",
+ " \"grade\" TEXT,\n",
+ " \"gpa\" REAL,\n",
+ " \"attendance\" INTEGER\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "query = \"SELECT * FROM sqlite_master\"\n",
+ "\n",
+ "df = pd.read_sql(query, grades_conn)\n",
+ "print(df['sql'].iloc[0])\n",
+ "\n",
+ "# name is TEXT (in Python, str)\n",
+ "# grade is TEXT (in Python, str)\n",
+ "# gpa is REAL (in Python, float)\n",
+ "# attendance is INTEGER (in Python, int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 4: What is the data in \"grades\" table? \n",
+ "Save this to a variable called \"student_grades\" and display it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>name</th>\n",
+ " <th>grade</th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Cole</td>\n",
+ " <td>C</td>\n",
+ " <td>2.0</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Cynthia</td>\n",
+ " <td>AB</td>\n",
+ " <td>3.5</td>\n",
+ " <td>11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Alice</td>\n",
+ " <td>B</td>\n",
+ " <td>3.0</td>\n",
+ " <td>10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Seth</td>\n",
+ " <td>BC</td>\n",
+ " <td>2.5</td>\n",
+ " <td>6</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name grade gpa attendance\n",
+ "0 Cole C 2.0 4\n",
+ "1 Cynthia AB 3.5 11\n",
+ "2 Alice B 3.0 10\n",
+ "3 Seth BC 2.5 6"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "student_grades = pd.read_sql(\"SELECT * FROM grades\", grades_conn)\n",
+ "student_grades"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 5: Make a scatter plot where the attendance of a student is on the x-axis and their gpa on the y-axis\n",
+ "Preview to upcoming topic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='attendance', ylabel='gpa'>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "student_grades.plot.scatter(x = \"attendance\", y = \"gpa\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 6: What is the correlation between gpa and attendance?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/07/v8h5vw9j6v71xlj9rgbrh5h40000gn/T/ipykernel_74543/3765178056.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " student_grades.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>gpa</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.976831</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>attendance</th>\n",
+ " <td>0.976831</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " gpa attendance\n",
+ "gpa 1.000000 0.976831\n",
+ "attendance 0.976831 1.000000"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "student_grades.corr()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 7: Close the connection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grades_conn.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bar plots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Learning Objectives:\n",
+ "- Make a bar plot from a Pandas Series\n",
+ "- Add features: x-label, y-label, title, gridlines, color to plot\n",
+ "- Set the index of a DataFrame certain column\n",
+ "- Create an 'other' column in a DataFrame\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Without this Jupyter notebook cannot display the \"first\" plot in older versions \n",
+ "# of Python / mathplotlib / jupyter\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Helpful documentation and an overview of how matplotlib works\n",
+ "https://matplotlib.org/stable/tutorials/introductory/usage.html\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "***Just for today's lecture, let's have import statements inside the notebook file. You should never do this when you write project code***"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# matplotlib is a plotting module similar to MATLAB\n",
+ "import matplotlib\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# matplotlib is highly configurable, acts like a style sheet for plots\n",
+ "# rc stands for runtime config, syntax is like a dictionary\n",
+ "\n",
+ "# matplotlib.rcParams # show all parameters\n",
+ "# matplotlib.rcParams[\"font.size\"] # show current font size setting\n",
+ "matplotlib.rcParams[\"font.size\"] = 12 # change current font size setting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plots from pandas Series\n",
+ "\n",
+ "- matplotlib integrates with pandas, just like sqlite3 integrates with pandas\n",
+ "- Syntax: ```Series.plot.<PLOT_FUNCTION>(...)```\n",
+ "\n",
+ "## Bar plots: From a Series\n",
+ "- Series indices are the x-axis labels\n",
+ "- Series values are the height of each bar\n",
+ "\n",
+ "https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.bar.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s = Series({\"Police\": 5000000, \"Fire\": 3000000, \"Schools\": 2000000})\n",
+ "\n",
+ "# What are the three terminologies associated with pandas Series?\n",
+ "# A: Index and Integer Position and Value\n",
+ "\n",
+ "# make a bar plot...notice the type\n",
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# if we store the returned object in a variable, we can configure the AxesSubplot\n",
+ "# typically the variable name used is 'ax'\n",
+ "ax = s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we set the x-axis, y-axis labels, and title?\n",
+ "- plotting functions return what is called as AxesSubplot\n",
+ "- store into a variable and use the AxesSubplot object\n",
+ "- Syntax:\n",
+ "```\n",
+ "ax.set_ylabel(\"...\")\n",
+ "ax.set_xlabel(\"...\")\n",
+ "ax.set_title(\"...\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# What is this 1e6? Can we get rid of it?\n",
+ "# Instead of 1e6, divide all values in s by 1000000 (1 million)\n",
+ "# better plot:\n",
+ "ax = (s / 1000000).plot.bar()\n",
+ "\n",
+ "# set the y label to \"Dollars (Millions)\"\n",
+ "ax.set_ylabel(\"Dollars (Millions)\")\n",
+ "\n",
+ "# set the x label to \"City Agency\"\n",
+ "ax.set_xlabel(\"City Agency\")\n",
+ "\n",
+ "# this is self-explanatory, so we will skip this for other example plots\n",
+ "\n",
+ "# set the title to \"Annual City Spending\"\n",
+ "ax.set_title(\"Annual City Spending\") \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we rotate the x-axis labels so that they are more readable?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Police 5000000\n",
+ "Fire 3000000\n",
+ "Schools 2000000\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Which aspect of the Series is the x-axis label coming from?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer: Index"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "How can we extract the indices from a Series?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Police', 'Fire', 'Schools'], dtype='object')"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.index"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's use that to set x-axis tick label formatting.\n",
+ "- Syntax:\n",
+ "```\n",
+ "ax.set_xticklabels(<list of x-axis labels>, rotation = ???)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = (s / 1000000).plot.bar()\n",
+ "ax.set_ylabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")\n",
+ "\n",
+ "# give the x ticklabels a rotation of 45 degrees\n",
+ "ax.set_xticklabels(list(s.index), rotation = 45)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we change the figure size?\n",
+ "- figsize keyword argument\n",
+ "- should be a tuple with two values: width and height (in inches)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = (s / 1000000).plot.bar(figsize=(12,6))\n",
+ "ax.set_ylabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we make the bars horizontal?\n",
+ "https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.barh.html\n",
+ "- switch figsize arguments\n",
+ "- change y-label to x-label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x150 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# paste the previous code cell here and modify\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5))\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Change bar color by using the argument color = ' ' \n",
+ "- Syntax: ``` plot.bar(figsize = (width, height), color = ???)```\n",
+ "- 8 standard colors: r, g, b, c, m, y, k, w (for example: ```color = 'k'```, which is black)\n",
+ " - you could also specify the entire color as a string (for example: ```color = 'red'```)\n",
+ "- can use value of grey between 0 and 1 (for example: ```color = '0.6'```)\n",
+ "- can use a tuple (r, g, b) between 0 and 1 (for example: ```color = (0, .3, .4)```)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x150 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# color as a single char \n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'k') # black color\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x150 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# color as a str\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'red') # red color\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Annual City Spending')"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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5PZdEQqo9fPhQ5VmEa9euxeHDh+Hn5/dSJBKAx0yIapVvvvkG169fx7p166CtrY05c+ZoOqSX2rVr19C2bVv4+vqiWbNmKCkpwalTp5Camgpzc3PExMRoOsTnhsmEqBb5/PPPcf36dTRr1gyLFy9WebU2PT82NjYIDAxESkoKkpOTUVhYCFtbW7zzzjuYMWOG/LjJy4DHTIiISDIeMyEiIsmYTIiISDIeM1GjsrIy3LhxA8bGxs/tLrJERDVJCIH79+/Dzs6u0gswmUzU6MaNG/JbVRMR1SUZGRmVPreHyUSNym+Ol5GRIb+jKBFRbZafnw8HBwf591tFmEzUqHxqy8TEhMmEiOqUp03d8wA8ERFJxmRCRESSMZkQEZFkTCZERCQZkwkREUnGZEJERJIxmRARkWS8zqQGeEWaQlv/6fWo9vg1nDfXJqoMRyZERCQZkwkREUnGZEJERJJVO5kcO3YMAwcOhKOjI/T09GBjY4POnTtjypQp1WrH29sbrq6u1e1eLZo0aYLg4GCN9E1EVBdVK5nEx8fD09MT+fn5iI6Oxt69e7F48WJ06dIFW7durakYiYjoBVets7mio6PxyiuvYM+ePdDR+f9V3377bURHR6s9OCIiqh2qNTLJycmBpaWlQiKRN/TEE7g2b96Mzp07w8jICEZGRnB3d8eqVauU1jtx4gS6deuG+vXro2nTpoiKikJZWZlCnWvXrmHkyJGwtraGnp4eXFxcEBMTo1QvNzcX48ePh729PXR1ddG0aVPMmDEDRUVFlW5XWVkZ5s2bh5YtW8LAwABmZmZo3bo1Fi9eXNVdQ0T0UqvWyKRz585YuXIlPvzwQ4wYMQKvvfYa6tWrp1Rv1qxZmDt3LgYNGoQpU6bA1NQU58+fx9WrVxXq3bp1CyNGjMCUKVMQHh6On376CWFhYbCzs8OoUaMAANnZ2fD09MQ///yDuXPnokmTJti1axdCQ0ORlpaG2NhYAMCjR4/g4+ODtLQ0zJ49G61bt0ZKSgoiIyNx+vRpxMfHV7hd0dHRiIiIwMyZM9G9e3cUFxfjzz//xL179yrdH0VFRQqJKj8/v6q7koioTpEJIap8NVZOTg7eeustpKamAgDq1auHDh06oF+/fpg4cSKMjIxw5coVNG/eHG+//TY2btxYYVve3t5ITk7GsWPH0LFjR3n5q6++CgcHB+zevRsAEBYWhqioKKV648ePx7Jly/Dnn3+iRYsWWL58Of7zn//g+++/x5AhQ+T1oqOjMX36dOzduxe+vr4A/j0A7+3tjbVr1wIA+vXrh+vXr+PUqVNV3RUAgIiICMyePVup3P1j8KLFOoYXLdLLKj8/H6ampsjLy6v0oX/VmuaysLBASkoKTpw4gaioKAwYMAB//fUXwsLC4Obmhjt37uC///0vSktLMWHChKe2Z2trq5AgAKB169YKI5gDBw6gVatWSvWCg4MhhMCBAwfk9QwNDeHv769UDwD2799fYRwdO3bEmTNnMH78eOzZs6fKI4ywsDDk5eXJXxkZGVVaj4iornmm26m0b98e7du3BwAUFxdj+vTpWLRoEaKjo2FqagoAlT54vpyFhYVSmZ6eHgoLC+Xvc3Jy0KRJE6V6dnZ28uXl/9ra2io9WtLa2ho6OjryeqqEhYXB0NAQGzduxLJly6CtrY3u3btjwYIF8u1URU9PD3p6epVuIxHRy0DyRYv16tVDeHg4AOD8+fOwsrICAFy/fl1q0wD+TTg3b95UKr9x4wYAwNLSUl4vKysLT87a3b59GyUlJfJ6qujo6GDy5Mk4efIkcnNzsWXLFmRkZKBXr154+PChWraDiKguq1YyUfWlDgB//PEHgH9HC2+88Qa0tbWxdOlS6dEB6NGjB37//XecPHlSoXz9+vWQyWTw8fGR13vw4AG2b9+uVK98eVWYmZnB398fEyZMQG5uLtLT0yVvAxFRXVetaa5evXqhUaNG6NevH5ydnVFWVobTp08jJiYGRkZGmDRpEpo0aYJPPvkEc+fORWFhIYYPHw5TU1P8/vvvuHPnjsoD1pX56KOPsH79evTp0wdz5sxB48aNER8fj9jYWIwbNw4tWrQAAIwaNQpLlixBUFAQ0tPT4ebmhtTUVHz22Wd488030bNnzwr76NevH1xdXdG+fXtYWVnh6tWr+PLLL9G4cWM0b968WvESEb2MqpVMZs6ciR07dmDRokW4efMmioqK0LBhQ/Ts2RNhYWFwcXEBAMyZMwfNmzfH119/jREjRkBHRwfNmzfHhx9+WO0ArayscPjwYYSFhSEsLAz5+flo2rQpoqOjMXnyZHk9fX19JCYmYsaMGVi4cCGys7Nhb2+P0NBQ+TRcRXx8fBAXF4eVK1ciPz8ftra28PX1xaeffqry1GciIlJUrVODqXLlp9Dx1OC6h6cG08uqRk4NJiIiUoXJhIiIJONje2tAcljlw0EiorqGIxMiIpKMyYSIiCRjMiEiIsmYTIiISDImEyIikozJhIiIJGMyISIiyZhMiIhIMiYTIiKSjMmEiIgkYzIhIiLJmEyIiEgyJhMiIpKMyYSIiCRjMiEiIsmYTIiISDImEyIikozJhIiIJONje2uAV6QptPU1HQXRs/k1XGg6BKqFODIhIiLJmEyIiEgyJhMiIpKs1iaTtWvXQiaTqXz5+/tDJpNh7dq1mg6TiOilUOsPwK9ZswbOzs4KZQ0bNkRoaCicnJw0FBUR0cul1icTV1dXtG/fXqm8cePGT1334cOHqF+/fk2ERUT0Uqm101yVSU9PV5rmioiIgEwmw8mTJ+Hv7w9zc3P5yEUIgdjYWLi7u8PAwADm5ubw9/fH5cuXNbQFRES1S61PJqWlpSgpKVF4VWbQoEFo1qwZtm3bhmXLlgEA3n//fYSEhKBnz57Yvn07YmNjceHCBXh6eiIrK+t5bAYRUa1W66e5PDw8lMr+/vvvCusHBQVh9uzZ8vdHjx7Ft99+i5iYGEyePFle3q1bN7Ro0QJffPEFFixYoLKtoqIiFBUVyd/n5+c/yyYQEdV6tT6ZrF+/Hi4uLgplOjoVb9bgwYMV3u/atQsymQwjR45UGNXY2tqiTZs2SEpKqrCtyMhIhcRERPSyqvXJxMXFRekAfHp6eoX1GzZsqPA+KysLQgjY2NiorN+0adMK2woLC1MYzeTn58PBwaEKURMR1S21PplUl0wmU3hvaWkJmUyGlJQU6OnpKdVXVfb4ssqWExG9LF66ZPKkvn37IioqCpmZmRg6dKimwyEiqpVe+mTSpUsXjB07FqNHj8avv/6K7t27w9DQEDdv3kRqairc3Nwwbtw4TYdJRPRCe+mTCQAsX74cHh4eWL58OWJjY1FWVgY7Ozt06dIFHTt21HR4REQvPJkQgg8vUJP8/HyYmprC/WPweSZUa/F5JvS48u+1vLw8mJiYVFiv1l+0SEREmsdkQkREkjGZEBGRZDwAXwOSwyqfWyQiqms4MiEiIsmYTIiISDImEyIikozJhIiIJGMyISIiyZhMiIhIMiYTIiKSjMmEiIgkYzIhIiLJmEyIiEgyJhMiIpKMyYSIiCRjMiEiIsmYTIiISDImEyIikozJhIiIJGMyISIiyZhMiIhIMj62twZ4RZpCW1/TURAR/b9fw0WNts+RCRERScZkQkREkjGZEBGRZM81maxduxYymUz+0tHRQaNGjTB69GhkZmZWqy1vb294e3srlMlkMkRERKgvYCIiqhKNHIBfs2YNnJ2dUVhYiIMHDyIyMhLJyck4d+4cDA0Nn7ndI0eOoFGjRmqMlIiIqkIjycTV1RXt27cHAPj4+KC0tBRz587F9u3bMWLEiGdu18PDQ10hEhFRNbwQx0zKk8DVq1fx6NEjhIWF4ZVXXoGuri7s7e0xYcIE3Lt376ntqJrmyszMxNixY+Hg4ABdXV3Y2dnB398fWVlZ8jr5+fkIDQ1V6DMkJAQFBQXq3EwiojrrhbjO5NKlSwAAKysrvPXWW9i/fz/CwsLQrVs3nD17FuHh4Thy5AiOHDkCPT29KrebmZmJDh06oLi4GJ988glat26NnJwc7NmzB3fv3oWNjQ0ePnwILy8vXL9+XV7nwoULmDVrFs6dO4d9+/ZBJpOpbL+oqAhFRUXy9/n5+dJ2BBFRLaWRZFJaWoqSkhI8evQIycnJmDdvHoyNjWFiYoI9e/YgOjoaU6dOBQD4+vrCwcEBw4YNw/r16/Hee+9VuZ9Zs2bhzp07OHPmDFxcXOTlQ4cOlf/3V199hbNnz+LYsWPyqbcePXrA3t4e/v7+2L17N3r37q2y/cjISMyePftZdgERUZ2ikWkuDw8P1KtXD8bGxujbty9sbW2RkJCAkydPAgCCg4MV6g8ZMgSGhobYv39/tfpJSEiAj4+PQiJ50q5du+Dq6gp3d3eUlJTIX7169YJMJkNSUlKF64aFhSEvL0/+ysjIqFZ8RER1hUZGJuvXr4eLiwt0dHRgY2ODhg0bAvj3LC8dHR1YWVkp1JfJZLC1tUVOTk61+snOzn7q2V1ZWVm4dOkS6tWrp3L5nTt3KlxXT0+vWtNuRER1lUaSiYuLi3xK6XEWFhYoKSlBdna2QkIRQuDWrVvo0KFDtfqxsrLC9evXK61jaWkJAwMDrF69usLlRERUuRfibK5yPXr0AABs3LhRoTwuLg4FBQXy5VXVu3dvJCYm4uLFixXW6du3L9LS0mBhYYH27dsrvZo0aVLt7SAietm8EGdzlfP19UWvXr0wffp05Ofno0uXLvKzudq2bYvAwMBqtTdnzhwkJCSge/fu+OSTT+Dm5oZ79+5h9+7dmDx5MpydnRESEoK4uDh0794dH330EVq3bo2ysjJcu3YNe/fuxZQpU9CpU6ca2mIiorrhhUomMpkM27dvR0REBNasWYP58+fD0tISgYGB+Oyzz6p9fMLe3h7Hjx9HeHg4oqKikJOTAysrK3Tt2hUNGjQAABgaGiIlJQVRUVFYsWIFrly5AgMDAzg6OqJnz54cmRARVYFMCFGzN7l/ieTn58PU1BTuH4PPMyGiF8qzPs+k/HstLy8PJiYmFdZ7oY6ZEBFR7cRkQkREkr1Qx0zqiuSwyoeDRER1DUcmREQkGZMJERFJxmRCRESSMZkQEZFkTCZERCQZkwkREUnGZEJERJIxmRARkWS8aFGNym9zxmfBE1FdUf599rTbODKZqFH5kyAdHBw0HAkRkXrdv38fpqamFS5nMlGj8tvaX7t2rdKdXhfk5+fDwcEBGRkZdf7WMdzWuonbWjVCCNy/fx92dnaV1mMyUSMtrX8PQZmamtb5D2c5ExMTbmsdxG2tm551W6vy45gH4ImISDImEyIikozJRI309PQQHh5e7ccL10bc1rqJ21o3PY9t5WN7iYhIMo5MiIhIMiYTIiKSjMmEiIgkYzJRgwcPHiAkJAR2dnbQ19eHu7s7vvvuO02HpXb379/HtGnT8MYbb8DKygoymQwRERGaDqtGHDhwAO+88w6cnZ1haGgIe3t7DBgwAL/99pumQ1O706dPo0+fPnB0dISBgQEaNGiAzp07Y+PGjZoOrcatXLkSMpkMRkZGmg5F7ZKSkiCTyVS+jh49qvb+eNGiGgwaNAgnTpxAVFQUWrRogc2bN2P48OEoKytDQECApsNTm5ycHKxYsQJt2rTBW2+9hZUrV2o6pBqzdOlS5OTkYNKkSWjVqhWys7MRExMDDw8P7NmzB6+//rqmQ1Sbe/fuwcHBAcOHD4e9vT0KCgqwadMmBAYGIj09HTNnztR0iDUiMzMToaGhsLOzQ15enqbDqTGfffYZfHx8FMpcXV3V35EgSeLj4wUAsXnzZoVyX19fYWdnJ0pKSjQUmfqVlZWJsrIyIYQQ2dnZAoAIDw/XbFA1JCsrS6ns/v37wsbGRvTo0UMDET1/nTp1Eg4ODpoOo8b07dtX9OvXTwQFBQlDQ0NNh6N2iYmJAoDYtm3bc+mP01wS/fTTTzAyMsKQIUMUykePHo0bN27g2LFjGopM/cqHyC8Da2trpTIjIyO0atUKGRkZGojo+bO0tISOTt2cvNi4cSOSk5MRGxur6VDqDCYTic6fPw8XFxelP7rWrVvLl1PdkJeXh5MnT+LVV1/VdCg1oqysDCUlJcjOzkZsbCz27NmD6dOnazostbt9+zZCQkIQFRWFRo0aaTqcGjdhwgTo6OjAxMQEvXr1Qmpqao30Uzd/djxHOTk5aNq0qVJ5+R2Ey29LT7XfhAkTUFBQgBkzZmg6lBoxfvx4LF++HACgq6uLr776Cu+//76Go1K/8ePHo2XLlhg3bpymQ6lRpqammDRpEry9vWFhYYFLly5h4cKF8Pb2Rnx8PHr16qXW/phM1KCyqZ+XZVqorvv000+xadMmfP3112jXrp2mw6kRn3zyCcaMGYPbt29j586dmDhxIgoKChAaGqrp0NQmLi4OO3fuxKlTp+r832bbtm3Rtm1b+ftu3bph4MCBcHNzw7Rp05hMXjQWFhYqRx+5ubkA/n+EQrXX7NmzMW/ePMyfPx8TJ07UdDg1xtHREY6OjgCAN998EwAQFhaGoKAgWFlZaTI0tXjw4AEmTJiADz74AHZ2drh37x4A4J9//gHw71lt9erVg6GhoQajrFlmZmbo27cvli1bhsLCQhgYGKitbR4zkcjNzQ1//PEHSkpKFMrPnTsHoIZOwaPnZvbs2YiIiEBERAQ++eQTTYfzXHXs2BElJSW4fPmypkNRizt37iArKwsxMTEwNzeXv7Zs2YKCggKYm5tjxIgRmg6zxon/3Y5R3SMzjkwkGjhwIL799lvExcVh2LBh8vJ169bBzs4OnTp10mB0JMXcuXMRERGBmTNnIjw8XNPhPHeJiYnQ0tJSeUywNrK1tUViYqJSeVRUFJKTk5GQkABLS0sNRPb83L17F7t27YK7uzv09fXV2jaTiUS9e/eGr68vxo0bh/z8fDRr1gxbtmzB7t27sXHjRmhra2s6RLVKSEhAQUEB7t+/DwD4/fff8cMPPwD4d2qkfv36mgxPbWJiYjBr1iz4+fmhT58+SlcMe3h4aCgy9Rs7dixMTEzQsWNH2NjY4M6dO9i2bRu2bt2KqVOn1okpLgDQ19eHt7e3UvnatWuhra2tclltFhAQAEdHR7Rv3x6Wlpb4+++/ERMTg6ysLKxdu1b9HT6Xq1nquPv374sPP/xQ2NraCl1dXdG6dWuxZcsWTYdVIxo3biwAqHxduXJF0+GpjZeXV4XbWdf+bFavXi26desmLC0thY6OjjAzMxNeXl5iw4YNmg7tuairFy1GRkYKd3d3YWpqKrS1tYWVlZUYOHCgOH78eI30x+eZEBGRZDwAT0REkjGZEBGRZEwmREQkGZMJERFJxmRCRESSMZkQEZFkTCZERCQZkwkREUnGZEK1wtq1a+VPepTJZNDX14etrS18fHwQGRmJ27dvS247PT1dXhYcHIwmTZpID1yNiouL4ezsjKioKHnZ4/slKSlJaR0hBJo1awaZTKZ0uxCZTIaIiAj5+6SkJKV2IiIilG4I6O3t/ULceiQwMBBvvfWWpsOg/+G9uahWWbNmDZydnVFcXIzbt28jNTUVCxYswOeff46tW7eiZ8+emg6xxsTGxuLu3bv44IMPlJYZGxtj1apVSl/yycnJSEtLg7GxsdI6R44ceaYnDb4oj7qNiIiAs7MzDhw4gNdff13T4bz0ODKhWsXV1RUeHh7o1q0bBg8ejEWLFuHs2bMwNDTEoEGDkJWVpekQVXr48KGk9UtKSrBw4UK88847Kp+3MWzYMMTFxSE/P1+hfNWqVejcubP8OSWP8/DweKZk0qpVK7Rq1ara66mbk5MT/Pz8FEZqpDlMJlTrOTo6IiYmBvfv35c/drbczz//jM6dO6N+/fowNjaGr68vjhw58kz9LFmyBN27d4e1tTUMDQ3h5uaG6OhoFBcXK9Tz9vaGq6srDh48CE9PT9SvXx/vvPMOAODAgQPyx6gaGBjA0dERgwcPfmqy+fnnn5GZmYnAwECVy4cPHw4A2LJli7wsLy8PcXFx8r6f9OQ0V1WpmubKzc3F+PHjYW9vD11dXTRt2hQzZsxAUVGRUp8TJ07Ehg0b4OLigvr166NNmzbYtWuXQr3s7GyMHTsWDg4O0NPTg5WVFbp06YJ9+/Yp1AsMDMS+ffuQlpZW7e0g9WIyoTrhzTffhLa2Ng4ePCgv27x5MwYMGAATExNs2bIFq1atwt27d+Ht7Y3U1NRq95GWloaAgABs2LABu3btwrvvvouFCxeqfE76zZs3MXLkSAQEBOCXX37B+PHjkZ6ejj59+kBXVxerV6/G7t27ERUVBUNDQ/nT/ioSHx8Pa2vrCkcEJiYm8Pf3x+rVq+VlW7ZsgZaWlsJzdmrCo0eP4OPjg/Xr12Py5MmIj4/HyJEjER0djUGDBinVj4+PxzfffIM5c+YgLi4ODRo0wMCBAxUewhUYGIjt27dj1qxZ2Lt3L1auXImePXsqPdXU29sbQgj88ssvNbqNVAU1ci9iIjVbs2aNACBOnDhRYR0bGxvh4uIihBCitLRU2NnZCTc3N1FaWiqvc//+fWFtbS08PT2V2n78FvpBQUGicePGFfZVWloqiouLxfr164W2trbIzc2VLyu/ff3+/fsV1vnhhx8EAHH69Omqbraci4uL8PPzUyp/fL8kJiYKAOL8+fNCCCE6dOgggoODhRBCvPrqq8LLy0thXQAiPDxc/r58/cTERHlZeHi40i33vby8FNpatmyZACC+//57hXoLFiwQAMTevXsV+rSxsRH5+fnyslu3bgktLS0RGRkpLzMyMhIhISGV75T/sbe3F8OGDatSXao5HJlQnSEee5rCxYsXcePGDQQGBkJL6/8/5kZGRhg8eDCOHj1a7eMYp06dQv/+/WFhYQFtbW3Uq1cPo0aNQmlpKf766y+Fuubm5koHhd3d3aGrq4uxY8di3bp11Xoc7o0bN2BtbV1pHS8vLzg5OWH16tU4d+4cTpw4UeEUlzodOHAAhoaG8Pf3VygPDg4GAOzfv1+h3MfHR+GEABsbG1hbW+Pq1avyso4dO2Lt2rWYN28ejh49qjSV+Dhra2tkZmaqYUtICiYTqhMKCgqQk5MDOzs7AJBPhzRs2FCprp2dHcrKynD37t0qt3/t2jV069YNmZmZWLx4MVJSUnDixAksWbIEAFBYWKhQX1W/Tk5O2LdvH6ytrTFhwgQ4OTnByckJixcvfmr/hYWFT33Mqkwmw+jRo7Fx40YsW7YMLVq0QLdu3aq8jc8qJycHtra2SqcQW1tbQ0dHR2lqysLCQqkNPT09hX24detWBAUFYeXKlejcuTMaNGiAUaNG4datW0rr6uvrK+1/ev6YTKhOiI+PR2lpqfzAcPkX1s2bN5Xq3rhxA1paWjA3N69y+9u3b0dBQQF+/PFHjBw5El27dkX79u2hq6ursv6TX6zlunXrhp07dyIvLw9Hjx5F586dERISgu+++67S/i0tLZGbm/vUOIODg3Hnzh0sW7YMo0ePfvqGqYGFhQWysrIURoYAcPv2bZSUlDzTc9UtLS3x5ZdfIj09HVevXkVkZCR+/PFH+Wjncbm5uXX+2e21AZMJ1XrXrl1DaGgoTE1N5QfDW7ZsCXt7e2zevFnhS66goABxcXHyM7yqqjw56OnpycuEEPj222+fKWZtbW106tRJPrI5efJkpfWdnZ2rdMaSvb09pk6din79+iEoKOiZYquuHj164MGDB9i+fbtC+fr16+XLpXB0dMTEiRPh6+urtJ9KSkqQkZHxQpyq/LLjRYtUq5w/fx4lJSUoKSnB7du3kZKSgjVr1kBbWxs//fQTrKysAABaWlqIjo7GiBEj0LdvX7z//vsoKirCwoULce/evWpfm+Dr6wtdXV0MHz4c06ZNw6NHj7B06dJqTZUtW7YMBw4cQJ8+feDo6IhHjx7Jz7562sWW3t7emDNnDh4+fPjUJPi8r7sYNWoUlixZgqCgIKSnp8PNzQ2pqan47LPP8Oabb1b7QtK8vDz4+PggICAAzs7OMDY2xokTJ7B7926ls8POnj2Lhw8fwsfHR52bRM+AyYRqlfKpG11dXZiZmcHFxQXTp0/HmDFj5ImkXEBAAAwNDREZGYlhw4ZBW1sbHh4eSExMhKenZ7X6dXZ2RlxcHGbOnIlBgwbBwsICAQEBmDx5Mnr37l2lNtzd3bF3716Eh4fj1q1bMDIygqurK37++We88cYbla4bEBCA8PBwxMfHY8iQIdWKvabp6+sjMTERM2bMwMKFC5GdnQ17e3uEhoYiPDz8mdrr1KkTNmzYgPT0dBQXF8PR0RHTp0/HtGnTFOpu374dlpaWT91/VPNk4smJTiJ6IfXr1w8lJSVISEjQdCgvhNLSUjRr1gwBAQGYP3++psN56fGYCVEtERkZiX379uHEiROaDuWFsHHjRjx48ABTp07VdCgEJhOiWsPV1RVr1qxReXrsy6isrAybNm2CmZmZpkMhcJqLiIjUgCMTIiKSjMmEiIgkYzIhIiLJmEyIiEgyJhMiIpKMyYSIiCRjMiEiIsmYTIiISLL/A3TIFz8uuJ+bAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 400x150 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# color as tuple of (r, g, b)\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = (.2, .5, 0))\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we mark gridlines?\n",
+ "- use ax.grid()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x150 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# copy the previous code and add grid lines\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'b')\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")\n",
+ "ax.grid()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Examples with the Bus Route Database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = \"bus.db\"\n",
+ "\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": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find the tables in `bus.db`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>type</th>\n",
+ " <th>name</th>\n",
+ " <th>tbl_name</th>\n",
+ " <th>rootpage</th>\n",
+ " <th>sql</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>table</td>\n",
+ " <td>boarding</td>\n",
+ " <td>boarding</td>\n",
+ " <td>2</td>\n",
+ " <td>CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>table</td>\n",
+ " <td>routes</td>\n",
+ " <td>routes</td>\n",
+ " <td>55</td>\n",
+ " <td>CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " type name tbl_name rootpage \\\n",
+ "0 table boarding boarding 2 \n",
+ "1 table routes routes 55 \n",
+ "\n",
+ " sql \n",
+ "0 CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ... \n",
+ "1 CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"... "
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.read_sql(\"\"\"\n",
+ "SELECT *\n",
+ "FROM sqlite_master\n",
+ "WHERE type = 'table'\n",
+ "\"\"\", conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>index</th>\n",
+ " <th>StopID</th>\n",
+ " <th>Route</th>\n",
+ " <th>Lat</th>\n",
+ " <th>Lon</th>\n",
+ " <th>DailyBoardings</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>1163</td>\n",
+ " <td>27</td>\n",
+ " <td>43.073655</td>\n",
+ " <td>-89.385427</td>\n",
+ " <td>1.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1163</td>\n",
+ " <td>47</td>\n",
+ " <td>43.073655</td>\n",
+ " <td>-89.385427</td>\n",
+ " <td>0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2</td>\n",
+ " <td>1163</td>\n",
+ " <td>75</td>\n",
+ " <td>43.073655</td>\n",
+ " <td>-89.385427</td>\n",
+ " <td>0.34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>3</td>\n",
+ " <td>1164</td>\n",
+ " <td>6</td>\n",
+ " <td>43.106465</td>\n",
+ " <td>-89.340021</td>\n",
+ " <td>10.59</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>4</td>\n",
+ " <td>1167</td>\n",
+ " <td>3</td>\n",
+ " <td>43.077867</td>\n",
+ " <td>-89.369993</td>\n",
+ " <td>3.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3967</th>\n",
+ " <td>3967</td>\n",
+ " <td>6533</td>\n",
+ " <td>67</td>\n",
+ " <td>43.057329</td>\n",
+ " <td>-89.510756</td>\n",
+ " <td>16.88</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3968</th>\n",
+ " <td>3968</td>\n",
+ " <td>6539</td>\n",
+ " <td>15</td>\n",
+ " <td>43.064361</td>\n",
+ " <td>-89.517233</td>\n",
+ " <td>15.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3969</th>\n",
+ " <td>3969</td>\n",
+ " <td>6541</td>\n",
+ " <td>3</td>\n",
+ " <td>43.049934</td>\n",
+ " <td>-89.478167</td>\n",
+ " <td>2.56</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3970</th>\n",
+ " <td>3970</td>\n",
+ " <td>6543</td>\n",
+ " <td>70</td>\n",
+ " <td>43.093289</td>\n",
+ " <td>-89.501726</td>\n",
+ " <td>0.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3971</th>\n",
+ " <td>3971</td>\n",
+ " <td>6543</td>\n",
+ " <td>71</td>\n",
+ " <td>43.093289</td>\n",
+ " <td>-89.501726</td>\n",
+ " <td>6.73</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>3972 rows × 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index StopID Route Lat Lon DailyBoardings\n",
+ "0 0 1163 27 43.073655 -89.385427 1.03\n",
+ "1 1 1163 47 43.073655 -89.385427 0.11\n",
+ "2 2 1163 75 43.073655 -89.385427 0.34\n",
+ "3 3 1164 6 43.106465 -89.340021 10.59\n",
+ "4 4 1167 3 43.077867 -89.369993 3.11\n",
+ "... ... ... ... ... ... ...\n",
+ "3967 3967 6533 67 43.057329 -89.510756 16.88\n",
+ "3968 3968 6539 15 43.064361 -89.517233 15.53\n",
+ "3969 3969 6541 3 43.049934 -89.478167 2.56\n",
+ "3970 3970 6543 70 43.093289 -89.501726 0.11\n",
+ "3971 3971 6543 71 43.093289 -89.501726 6.73\n",
+ "\n",
+ "[3972 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.read_sql(\"\"\"\n",
+ "SELECT * from\n",
+ "boarding\n",
+ "\"\"\", conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What are the top routes, and how many people ride them daily?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Route</th>\n",
+ " <th>daily</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>80</td>\n",
+ " <td>10211.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>4808.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6</td>\n",
+ " <td>4537.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>10</td>\n",
+ " <td>4425.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3</td>\n",
+ " <td>2708.55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>4</td>\n",
+ " <td>2656.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>15</td>\n",
+ " <td>2179.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>38</td>\n",
+ " <td>1955.85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>28</td>\n",
+ " <td>1868.31</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>5</td>\n",
+ " <td>1634.69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>14</td>\n",
+ " <td>1373.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>16</td>\n",
+ " <td>1258.93</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>18</td>\n",
+ " <td>1039.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>22</td>\n",
+ " <td>995.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>19</td>\n",
+ " <td>827.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>50</td>\n",
+ " <td>748.75</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>67</td>\n",
+ " <td>729.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>70</td>\n",
+ " <td>710.80</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>30</td>\n",
+ " <td>687.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>72</td>\n",
+ " <td>636.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>13</td>\n",
+ " <td>615.20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>40</td>\n",
+ " <td>602.92</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>21</td>\n",
+ " <td>590.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>20</td>\n",
+ " <td>545.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>71</td>\n",
+ " <td>497.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>56</td>\n",
+ " <td>477.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>57</td>\n",
+ " <td>464.86</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>73</td>\n",
+ " <td>448.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>75</td>\n",
+ " <td>435.35</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>44</td>\n",
+ " <td>416.90</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>11</td>\n",
+ " <td>392.43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>47</td>\n",
+ " <td>379.89</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>81</td>\n",
+ " <td>371.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>58</td>\n",
+ " <td>362.59</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>12</td>\n",
+ " <td>329.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>37</td>\n",
+ " <td>319.82</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>27</td>\n",
+ " <td>298.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>17</td>\n",
+ " <td>294.55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>82</td>\n",
+ " <td>219.48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td>33</td>\n",
+ " <td>206.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>1</td>\n",
+ " <td>181.44</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>52</td>\n",
+ " <td>176.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td>39</td>\n",
+ " <td>140.89</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td>35</td>\n",
+ " <td>140.42</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>31</td>\n",
+ " <td>139.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>51</td>\n",
+ " <td>137.57</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>55</td>\n",
+ " <td>129.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td>84</td>\n",
+ " <td>114.21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td>29</td>\n",
+ " <td>111.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49</th>\n",
+ " <td>26</td>\n",
+ " <td>107.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>32</td>\n",
+ " <td>86.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51</th>\n",
+ " <td>34</td>\n",
+ " <td>81.97</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>49</td>\n",
+ " <td>61.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td>36</td>\n",
+ " <td>59.13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td>48</td>\n",
+ " <td>30.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>25</td>\n",
+ " <td>24.19</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Route daily\n",
+ "0 80 10211.79\n",
+ "1 2 4808.03\n",
+ "2 6 4537.02\n",
+ "3 10 4425.23\n",
+ "4 3 2708.55\n",
+ "5 4 2656.99\n",
+ "6 15 2179.98\n",
+ "7 38 1955.85\n",
+ "8 28 1868.31\n",
+ "9 5 1634.69\n",
+ "10 14 1373.81\n",
+ "11 16 1258.93\n",
+ "12 18 1039.57\n",
+ "13 22 995.21\n",
+ "14 19 827.53\n",
+ "15 50 748.75\n",
+ "16 67 729.54\n",
+ "17 70 710.80\n",
+ "18 30 687.13\n",
+ "19 72 636.95\n",
+ "20 13 615.20\n",
+ "21 40 602.92\n",
+ "22 21 590.86\n",
+ "23 20 545.91\n",
+ "24 71 497.09\n",
+ "25 56 477.44\n",
+ "26 57 464.86\n",
+ "27 73 448.87\n",
+ "28 75 435.35\n",
+ "29 44 416.90\n",
+ "30 11 392.43\n",
+ "31 47 379.89\n",
+ "32 81 371.76\n",
+ "33 58 362.59\n",
+ "34 12 329.51\n",
+ "35 37 319.82\n",
+ "36 27 298.07\n",
+ "37 17 294.55\n",
+ "38 82 219.48\n",
+ "39 33 206.53\n",
+ "40 1 181.44\n",
+ "41 52 176.24\n",
+ "42 39 140.89\n",
+ "43 35 140.42\n",
+ "44 31 139.87\n",
+ "45 51 137.57\n",
+ "46 55 129.23\n",
+ "47 84 114.21\n",
+ "48 29 111.28\n",
+ "49 26 107.10\n",
+ "50 32 86.47\n",
+ "51 34 81.97\n",
+ "52 49 61.83\n",
+ "53 36 59.13\n",
+ "54 48 30.65\n",
+ "55 25 24.19"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_sql(\"\"\"\n",
+ " SELECT route, SUM(DailyBoardings) as daily\n",
+ " FROM boarding\n",
+ " GROUP BY route\n",
+ " ORDER BY daily DESC\n",
+ "\"\"\", conn)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's take the daily column out as a Series ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s = df['daily']\n",
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Oops, too much data. Let's filter down to top 5 routes. How can we do that in SQL?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Route</th>\n",
+ " <th>daily</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>80</td>\n",
+ " <td>10211.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>4808.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6</td>\n",
+ " <td>4537.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>10</td>\n",
+ " <td>4425.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3</td>\n",
+ " <td>2708.55</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Route daily\n",
+ "0 80 10211.79\n",
+ "1 2 4808.03\n",
+ "2 6 4537.02\n",
+ "3 10 4425.23\n",
+ "4 3 2708.55"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# TODO: add the appropriate SQL clause\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",
+ "LIMIT 5\n",
+ "\"\"\", conn)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, plot this!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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CAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMYihAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYy+sh9Oc//1lPPfWUevTooa5du2rw4MFatmyZx8zhw4cVHx+vbt26KSQkRMnJyTp16lSL661bt06RkZGy2WwaMGCAsrOzdfPmzWZzVVVVmjFjhhwOhwICAhQTE6Pi4mKvXCMAAOiYvBpCH330kcaNG6fg4GD9/ve/144dO7RgwQK5XC73TEVFhWJjY3Xjxg1t2rRJGzZs0PHjxzV27FhduHDBY70VK1Zo7ty5Sk5O1u7duzVnzhzl5OQoLS3NY66hoUETJkxQcXGx1q5dq+3bt6tXr15KTExUaWmpNy8ZAAB0IBbX7VXyAFVWVmrIkCF68cUXlZeX1+rcP//zP6ukpEQnT55UUFCQJOnrr7/W4MGD9frrr2v16tWSpOrqavXt21cvvvii3nvvPff35+TkKDMzU+Xl5Ro6dKgkKS8vT2lpadq3b59iYmIkSY2NjRo+fLi6deumAwcO3PN11NbWKjg4WDU1Ne79eUP/jE+8tnZbOb3qaV9vAQAASff+77fX7gitX79e165d04IFC1qdaWxsVFFRkaZNm+axyX79+ikuLk6FhYXuY7t27ZLT6VRqaqrHGqmpqXK5XNq2bZv7WGFhoYYMGeKOIEmyWq2aPn26Dh48qMrKygdwhQAAoKPzWgj96U9/kt1uV0VFhUaMGCGr1aqwsDD94he/UG1trSTp5MmTun79uqKjo5t9f3R0tE6cOCGn0ylJKi8vlyRFRUV5zPXp00cOh8N9vmm2tTUl6ciRI63uu6GhQbW1tR4PAADQOXkthCorK1VfX6+UlBT99Kc/1Z49e/TGG2/o97//vZ566im5XC5VV1dLkux2e7Pvt9vtcrlcunz5sqTvXxqz2WwKDAxscbZprabZ1tZsOt+alStXKjg42P2IiIi4vwsHAAAdhtVbC9+6dUtOp1NLlixRRkaGJCk2NlYPP/yw5s2bp+LiYgUEBEiSLBZLq+vcfu5e5+539nYLFy7Ur371K/fXtbW1xBAAAJ2U1+4IhYaGSpJ+8pOfeByfNGmSpO9/Zb5ppqU7NJcuXZLFYlFISIh7PafTqfr6+hZnb78DFBoa2uqaUst3oJrYbDYFBQV5PAAAQOfktRBq6T06kty/Ou/n56eBAweqa9euKisrazZXVlamQYMGyd/fX9Lf3hv0w9lz587p4sWLGjZsmPtYVFRUq2tK8pgFAADm8loITZs2TZK0c+dOj+M7duyQJD3xxBOyWq1KSkrS1q1bVVdX5545c+aMSkpKlJyc7D6WmJgof39/5efne6yXn58vi8WiKVOmuI9NnTpVFRUVHr8m39jYqIKCAo0ePVrh4eEP6jIBAEAH5rX3CCUkJCgpKUlvvvmmbt26pSeeeEL/9V//pezsbE2ePFn/+I//KEnKzs7WqFGjNHnyZGVkZMjpdCorK0sOh0Pz5893r2e325WZmanFixfLbrcrISFBhw4d0tKlSzVr1iz3ZwhJ0syZM5Wbm6uUlBStWrVKYWFhysvL07Fjx7Rnzx5vXTIAAOhgvPaBipJ0/fp1ZWdn66OPPtK3336r8PBwvfDCC1qyZIlsNpt77osvvtCCBQu0f/9+Wa1WjR8/XmvWrNHAgQObrfnOO+8oNzdXp0+fVu/evZWamqpFixapS5cuHnPnz59Xenq6ioqKVF9frxEjRmjZsmWKj4+/r2vgAxXvHR+oCABoL+7132+vhlBnQAjdO0IIANBe+PyTpQEAANo7QggAABiLEAIAAMYihAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMYihAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMYihAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLKuvNwC0N/0zPvH1Fv6/nV71tK+3AAAdAneEAACAsQghAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMbiAxUBtFud4cMtJT7gEmjPuCMEAACMRQgBAABjEUIAAMBYvEcIAHBXvF8LnRV3hAAAgLEIIQAAYCxCCAAAGIsQAgAAxiKEAACAsQghAABgLEIIAAAYixACAADGIoQAAICx2jSE1q9fL4vFom7dujU7d/jwYcXHx6tbt24KCQlRcnKyTp061eI669atU2RkpGw2mwYMGKDs7GzdvHmz2VxVVZVmzJghh8OhgIAAxcTEqLi4+IFfFwAA6JjaLIQqKyv1L//yLwoPD292rqKiQrGxsbpx44Y2bdqkDRs26Pjx4xo7dqwuXLjgMbtixQrNnTtXycnJ2r17t+bMmaOcnBylpaV5zDU0NGjChAkqLi7W2rVrtX37dvXq1UuJiYkqLS316rUCAICOoc3+1tgvfvELPfnkk7Lb7dq8ebPHuaysLNlsNhUVFSkoKEiSNHLkSA0ePFhr1qzR6tWrJUnV1dVavny5Xn75ZeXk5EiSYmNjdfPmTWVmZmrevHkaOnSoJOn9999XeXm59u3bp5iYGElSXFychg8frvT0dB04cKCtLh0AALRTbXJHqKCgQKWlpcrLy2t2rrGxUUVFRZo2bZo7giSpX79+iouLU2FhofvYrl275HQ6lZqa6rFGamqqXC6Xtm3b5j5WWFioIUOGuCNIkqxWq6ZPn66DBw+qsrLyAV4hAADoiLweQlVVVZo3b55WrVqlvn37Njt/8uRJXb9+XdHR0c3ORUdH68SJE3I6nZKk8vJySVJUVJTHXJ8+feRwONznm2ZbW1OSjhw50uJ+GxoaVFtb6/EAAACdk9dDaM6cORoyZIhmz57d4vnq6mpJkt1ub3bObrfL5XLp8uXL7lmbzabAwMAWZ5vWapptbc3bf+4PrVy5UsHBwe5HRETEXa4QAAB0VF4NoS1btujjjz/W7373O1ksljvO3un87efude5+Z5ssXLhQNTU17sfZs2dbXQMAAHRsXnuz9NWrV5WWlqZXX31V4eHhunLliiTpxo0bkqQrV66oS5cuCg0NldTyHZpLly7JYrEoJCREkhQaGiqn06n6+noFBAQ0mx05cqT769DQ0FbXlFq+AyVJNptNNpvt/i4WAAB0SF67I3Tx4kWdP39eb731lnr06OF+bNy4UdeuXVOPHj30wgsvaODAgeratavKysqarVFWVqZBgwbJ399f0t/eG/TD2XPnzunixYsaNmyY+1hUVFSra0rymAUAAGbyWgj17t1bJSUlzR4/+clP5O/vr5KSEi1fvlxWq1VJSUnaunWr6urq3N9/5swZlZSUKDk52X0sMTFR/v7+ys/P9/hZ+fn5slgsmjJlivvY1KlTVVFR4fFr8o2NjSooKNDo0aNb/DwjAABgFq+9NObv76/Y2Nhmx/Pz8/XQQw95nMvOztaoUaM0efJkZWRkyOl0KisrSw6HQ/Pnz3fP2e12ZWZmavHixbLb7UpISNChQ4e0dOlSzZo1y/0ZQpI0c+ZM5ebmKiUlRatWrVJYWJjy8vJ07Ngx7dmzx1uXDQAAOpB28bfGIiMjtXfvXnXp0kXPPPOMZsyYoUGDBulPf/qTevbs6TG7aNEivf3229q8ebMSEhK0bt06ZWRkKDc312POZrOpuLhYcXFxevXVV5WUlKRvv/1WO3fu1Lhx49ry8gAAQDvVZp8s3SQ/P7/ZS1vS958kfa93al577TW99tprd53r1auXPvjgg/vdIgAAMES7uCMEAADgC4QQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAY1l9vQEAAHDv+md84ustPBCnVz3t6y1I4o4QAAAwGCEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGN5LYQ+++wzzZw5U5GRkQoMDNQjjzyif/qnf9IXX3zRbPbw4cOKj49Xt27dFBISouTkZJ06darFddetW6fIyEjZbDYNGDBA2dnZunnzZrO5qqoqzZgxQw6HQwEBAYqJiVFxcfEDv04AANBxeS2E3n33XZ0+fVpz587Vjh07tHbtWlVVVemJJ57QZ5995p6rqKhQbGysbty4oU2bNmnDhg06fvy4xo4dqwsXLnisuWLFCs2dO1fJycnavXu35syZo5ycHKWlpXnMNTQ0aMKECSouLtbatWu1fft29erVS4mJiSotLfXWJQMAgA7G6q2Fc3NzFRYW5nEsMTFRgwYNUk5OjsaPHy9JysrKks1mU1FRkYKCgiRJI0eO1ODBg7VmzRqtXr1aklRdXa3ly5fr5ZdfVk5OjiQpNjZWN2/eVGZmpubNm6ehQ4dKkt5//32Vl5dr3759iomJkSTFxcVp+PDhSk9P14EDB7x12QAAoAPx2h2hH0aQJHXr1k1Dhw7V2bNnJUmNjY0qKirStGnT3BEkSf369VNcXJwKCwvdx3bt2iWn06nU1FSPNVNTU+VyubRt2zb3scLCQg0ZMsQdQZJktVo1ffp0HTx4UJWVlQ/qMgEAQAfWpm+Wrqmp0eHDh/X3f//3kqSTJ0/q+vXrio6ObjYbHR2tEydOyOl0SpLKy8slSVFRUR5zffr0kcPhcJ9vmm1tTUk6cuRIq3tsaGhQbW2txwMAAHRObRpCaWlpunbtmhYtWiTp+5e7JMlutzebtdvtcrlcunz5snvWZrMpMDCwxdmmtZpmW1vz9p/bkpUrVyo4ONj9iIiIuI8rBAAAHUmbhdDixYv14Ycf6t/+7d80cuRIj3MWi6XV77v93L3O3e/s7RYuXKiamhr3o+llPAAA0Pl47c3St8vOztby5cu1YsUK/fKXv3QfDw0NldTyHZpLly7JYrEoJCTEPet0OlVfX6+AgIBms7fHVWhoaKtrSi3fgWpis9lks9nu/eIAAECH5fU7QtnZ2Vq6dKmWLl2qf/3Xf/U4N3DgQHXt2lVlZWXNvq+srEyDBg2Sv7+/pL+9N+iHs+fOndPFixc1bNgw97GoqKhW15TkMQsAAMzl1RBatmyZli5dqszMTC1ZsqTZeavVqqSkJG3dulV1dXXu42fOnFFJSYmSk5PdxxITE+Xv76/8/HyPNfLz82WxWDRlyhT3salTp6qiosLj1+QbGxtVUFCg0aNHKzw8/MFdJAAA6LC89tLYW2+9paysLCUmJurpp5/W559/7nH+iSeekPT9HaNRo0Zp8uTJysjIkNPpVFZWlhwOh+bPn++et9vtyszM1OLFi2W325WQkKBDhw5p6dKlmjVrlvszhCRp5syZys3NVUpKilatWqWwsDDl5eXp2LFj2rNnj7cuGQAAdDBeC6GPP/5Y0vef/7Nr165m510ulyQpMjJSe/fu1YIFC/TMM8/IarVq/PjxWrNmjXr27OnxPYsWLVL37t2Vm5urNWvWqHfv3srIyHD/FloTm82m4uJipaen69VXX1V9fb1GjBihnTt3aty4cV66YgAA0NF4LYT27t17z7MjR4685zs1r732ml577bW7zvXq1UsffPDBPe8BAACYh78+DwAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADAWIQQAAIxFCAEAAGMRQgAAwFiEEAAAMBYhBAAAjEUIAQAAYxFCAADAWIQQAAAwFiEEAACMRQgBAABjEUIAAMBYhBAAADBWpw6hq1evat68eQoPD5e/v79GjBih//iP//D1tgAAQDth9fUGvCk5OVmHDh3SqlWr9Oijj+qjjz7Sc889p1u3bun555/39fYAAICPddoQ2rFjhz799FN3/EhSXFycvv76a73xxhv66U9/qoceesjHuwQAAL7UaV8aKywsVLdu3ZSSkuJxPDU1Vd98840OHDjgo50BAID2otPeESovL9djjz0mq9XzEqOjo93nx4wZ0+z7Ghoa1NDQ4P66pqZGklRbW+vF3Uq3Guq9un5b8PZ/R22F56L96AzPhdQ5ng+ei/aD5+L+1ne5XHec67QhVF1drR/96EfNjtvtdvf5lqxcuVLZ2dnNjkdERDzYDXZCwW/7egdownPRvvB8tB88F+1HWz0XdXV1Cg4ObvV8pw0hSbJYLPd9buHChfrVr37l/vrWrVu6dOmSQkND77hee1ZbW6uIiAidPXtWQUFBvt6O8Xg+2g+ei/aD56L96CzPhcvlUl1dncLDw+8412lDKDQ0tMW7PpcuXZL0tztDP2Sz2WSz2TyOhYSEPPD9+UJQUFCH/h91Z8Pz0X7wXLQfPBftR2d4Lu50J6hJp32zdFRUlP7617+qsbHR43hZWZkkadiwYb7YFgAAaEc6bQhNnTpVV69e1ZYtWzyOf/DBBwoPD9fo0aN9tDMAANBedNqXxiZNmqSJEydq9uzZqq2t1aBBg7Rx40bt2rVLBQUFRn2GkM1m05IlS5q95Aff4PloP3gu2g+ei/bDtOfC4rrb75V1YFevXtWiRYu0adMmXbp0SZGRkVq4cKGeffZZX28NAAC0A506hAAAAO6k075HCAAA4G4IIQAAYCxCCAAAGIsQAgAAxiKEAACAh/r6eiUnJ+vo0aO+3orX8VtjnZDL5dKBAwdUXl6u6upqWSwW2e12DRs2TKNHj+6wfzOts3I6naqqqtLf/d3f+XorgE9VV1fr1KlT+tGPfqTQ0FBfb8doNTU16tGjh0pLSzV27Fhfb8e7XOhUNm7c6Orbt6/Lz8/PZbFYPB5+fn6uvn37ujZu3OjrbeI2mzdvdvn5+fl6G51eSUmJa+LEia7IyEjXM8884/ryyy+bzXz++ec8F21gxYoVrn79+rkGDhzo+t3vfudyuVyut956y/Xwww+7/Pz8XFar1ZWRkeHjXXZ+3bt3v+PDYrG4AgICXN27d3cFBQX5erte02k/WdpE//mf/6nnn39eEydO1G9+8xtFR0e7/7jspUuX9Je//EUffPCBXnjhBT300ENKSUnx8Y6BtnH48GElJCQoNDRUQ4cO1Z49e/THP/5Rb7/9tmbPnu3r7Rnlww8/VGZmpkaPHi2Hw6G0tDR99913Sk9P189//nM9/vjj+uyzz/TrX/9a0dHReu6553y95U7r6tWreuSRRxQfH9/s3I0bN7Rx40Y9+eST6t27tw9213Z4aawT+fGPf6zHH39c77333h3nfv7zn+vQoUP68ssv22hnZnrzzTfvae7o0aP6wx/+oO+++87LOzLXlClTVFVVpU8//VSBgYGqra3VnDlztHHjRuXk5GjBggWSpAMHDmjMmDE8F140ZswYDRgwQB9++KEkKS8vT/Pnz9fMmTOVm5vrnnv22WdVVVWlzz77zFdb7fTWr1+vN954Q+PGjVNeXp7Cw8Pd565cuSK73a69e/fqySef9OEu24Cvb0nhwfH393ft3bv3rnMlJSUuf3//NtiR2ZpejvzhS5QtPXg5xrseeeQR15YtW5odz8zMdPn5+bmWLVvmcrl4aawt2O121yeffOL++sKFCy6LxeLauXOnx9yWLVtcdru9rbdnnMrKStfkyZNdwcHBrn//9393H79y5YrLYrG4SktLfbi7tsFLY52I3W7X//zP/2jcuHF3nDtx4oT7JTN4j8Ph0NSpU5WTk3PHuT/+8Y+aNWtWG+3KTFeuXFHPnj2bHV+2bJmsVquysrLU2NioSZMm+WB3Zrl+/boCAwPdX/fo0UOSFBYW5jHncDh09erVNt2bicLDw/Xxxx+roKBAr7/+ugoKCrR+/fpO/3LY7QihTiQlJUULFixQUFCQnnnmGfn5eX46wq1bt7RlyxZlZGToZz/7mY92aY4f//jHOn78+F1/+yUoKKiNdmSuiIgIHT16tMXfflmyZIkkKTs7W4cOHWrrrRmnZ8+eqqysdH/t5+enV155pVkInT9/XiEhIW28O3NNnz5dCQkJmjNnjkaMGKFXX33VmN8wJoQ6kRUrVujo0aN69tln1b17dz322GOy2+2yWCyqrq7WX//6V129elXx8fFasWKFr7fb6Q0fPlzr16+/61xgYCC/Ou9lY8aM0R/+8Ae98sorLZ5fsmSJ/Pz8tGTJEmP+z99XoqKi9Oc//1nPP/+8JMlisejdd99tNnfw4EE99thjbb09o4WFhWnz5s3asmWLfvnLX8plyFuIebN0J7Rjxw4VFhbqyJEjqq6uliSFhoYqKipKU6dOVWJioo93aIarV6+qurpa/fr18/VWjFdSUqJ3331XeXl5cjgcrc699dZbKioqUklJSRvuziz//d//rStXrtz1JfxXXnlFY8aM0UsvvdRGO8Ptrl27posXL6p3796y2Wy+3o5XEUIAAMBY/IkNAABgLEIIAAAYixACAADGIoQAAICxCCEAAGAsQggAABiLEAIAAMb6fwGJ1bkG3OGUAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s = df['daily']\n",
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Huh, what exactly is route 0? Where is that coming from?\n",
+ "Q: Can you guess where it is coming from? \n",
+ "\n",
+ "A: It is coming from dataframe row index!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Route</th>\n",
+ " <th>daily</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>80</td>\n",
+ " <td>10211.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>4808.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>6</td>\n",
+ " <td>4537.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>10</td>\n",
+ " <td>4425.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3</td>\n",
+ " <td>2708.55</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Route daily\n",
+ "0 80 10211.79\n",
+ "1 2 4808.03\n",
+ "2 6 4537.02\n",
+ "3 10 4425.23\n",
+ "4 3 2708.55"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's fix that: we can use df.set_index(...)\n",
+ "- set_index returns a brand new DataFrame object instance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>daily</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Route</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>80</th>\n",
+ " <td>10211.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>4808.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>4537.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>4425.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2708.55</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " daily\n",
+ "Route \n",
+ "80 10211.79\n",
+ "2 4808.03\n",
+ "6 4537.02\n",
+ "10 4425.23\n",
+ "3 2708.55"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = df.set_index('Route')\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now plot this..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Route'>"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s = df[\"daily\"]\n",
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Wouldn't it be nice to have an \"other\" bar to represent other routes?\n",
+ "- we have to now get rid of LIMIT clause\n",
+ "- we have to deal with other routes using pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>daily</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Route</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>80</th>\n",
+ " <td>10211.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>4808.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>4537.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>4425.23</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2708.55</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " daily\n",
+ "Route \n",
+ "80 10211.79\n",
+ "2 4808.03\n",
+ "6 4537.02\n",
+ "10 4425.23\n",
+ "3 2708.55"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_sql(\"\"\"\n",
+ "SELECT Route, SUM(DailyBoardings) AS daily\n",
+ "FROM boarding\n",
+ "GROUP BY Route\n",
+ "ORDER BY daily DESC\n",
+ "\"\"\", conn)\n",
+ "\n",
+ "df = df.set_index(\"Route\")\n",
+ "s = df[\"daily\"]\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We are back to plotting all route bars ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Route'>"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we slice a pandas dataframe?\n",
+ "- Recall that .iloc allows us to do slicing.\n",
+ "- For reproducing previous 5-route plot, we just need to take first 5 route details and populate into a series s.\n",
+ "- For the \"other\" part, we want all the rows in dataframe after row 5 summed up together."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s = df['daily'].iloc[:5]\n",
+ "other_boardings = df[\"daily\"].iloc[5:].sum()\n",
+ "s[\"other\"] = other_boardings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Route'>"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "s.plot.bar()\n",
+ "# Q: Where did the xlabel come from? \n",
+ "# A: the index of s (from the set_index call on df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's fix the plot aesthetics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Rides / Day (Thousands)')"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = (s / 1000).plot.bar(color = \"k\")\n",
+ "ax.set_ylabel(\"Rides / Day (Thousands)\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Close the bus.db database connection here\n",
+ "conn.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's try a different dataset."
+ ]
+ },
+ {
+ "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": 51,
+ "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": 52,
+ "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": 52,
+ "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": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-width</th>\n",
+ " <th>class</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5.1</td>\n",
+ " <td>3.5</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>4.9</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>4.7</td>\n",
+ " <td>3.2</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4.6</td>\n",
+ " <td>3.1</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.0</td>\n",
+ " <td>3.6</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-width class\n",
+ "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
+ "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
+ "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
+ "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
+ "4 5.0 3.6 1.4 0.2 Iris-setosa"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Attribute Information:\n",
+ "# 1. sepal length in cm\n",
+ "# 2. sepal width in cm\n",
+ "# 3. petal length in cm\n",
+ "# 4. petal width in cm\n",
+ "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+ "\n",
+ "# These should be our headers \n",
+ "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
+ "\n",
+ "iris_df = pd.read_csv(\"iris.csv\",\n",
+ " names = [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"])\n",
+ "iris_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 4: Connect to our database version of this data!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>type</th>\n",
+ " <th>name</th>\n",
+ " <th>tbl_name</th>\n",
+ " <th>rootpage</th>\n",
+ " <th>sql</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>table</td>\n",
+ " <td>iris</td>\n",
+ " <td>iris</td>\n",
+ " <td>2</td>\n",
+ " <td>CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " type name tbl_name rootpage \\\n",
+ "0 table iris iris 2 \n",
+ "\n",
+ " sql \n",
+ "0 CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n \"... "
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = \"iris-flowers.db\"\n",
+ "assert os.path.exists(path)\n",
+ "\n",
+ "iris_conn = sqlite3.connect(path)\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": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-width</th>\n",
+ " <th>class</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5.8</td>\n",
+ " <td>4.0</td>\n",
+ " <td>1.2</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5.7</td>\n",
+ " <td>3.8</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.3</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>5.7</td>\n",
+ " <td>4.4</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5.5</td>\n",
+ " <td>3.5</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.5</td>\n",
+ " <td>4.2</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.4</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.4</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.7</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.9</td>\n",
+ " <td>1.7</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>5.4</td>\n",
+ " <td>3.9</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.4</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-width class\n",
+ "0 5.8 4.0 1.2 0.2 Iris-setosa\n",
+ "1 5.7 3.8 1.7 0.3 Iris-setosa\n",
+ "2 5.7 4.4 1.5 0.4 Iris-setosa\n",
+ "3 5.5 3.5 1.3 0.2 Iris-setosa\n",
+ "4 5.5 4.2 1.4 0.2 Iris-setosa\n",
+ "5 5.4 3.4 1.7 0.2 Iris-setosa\n",
+ "6 5.4 3.4 1.5 0.4 Iris-setosa\n",
+ "7 5.4 3.7 1.5 0.2 Iris-setosa\n",
+ "8 5.4 3.9 1.7 0.4 Iris-setosa\n",
+ "9 5.4 3.9 1.3 0.4 Iris-setosa"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.read_sql(\"\"\"\n",
+ " SELECT *\n",
+ " FROM iris\n",
+ " WHERE class = 'Iris-setosa'\n",
+ " ORDER BY `sep-length` DESC, `sep-width` ASC\n",
+ " LIMIT 10\n",
+ "\"\"\", iris_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 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": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1.9</td>\n",
+ " <td>1.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>1.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2</td>\n",
+ " <td>1.8</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2</td>\n",
+ " <td>2.5</td>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "0 1 1.5 0.8\n",
+ "1 1 1.9 1.2\n",
+ "2 1 1.8 1.4\n",
+ "3 2 1.8 0.9\n",
+ "4 2 2.5 1.5"
+ ]
+ },
+ "execution_count": 61,
+ "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": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# TODO: change y to diameter\n",
+ "trees_df.plot.scatter(x = 'age', y='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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot with some more beautification options.\n",
+ "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"g\", marker = \"D\", s = 50) \n",
+ "# D for diamond"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Tree Age vs Height')"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Add a title to your plot.\n",
+ "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"g\", 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": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>age</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.797468</td>\n",
+ " <td>0.854578</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>height</th>\n",
+ " <td>0.797468</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.839345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>diameter</th>\n",
+ " <td>0.854578</td>\n",
+ " <td>0.839345</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "age 1.000000 0.797468 0.854578\n",
+ "height 0.797468 1.000000 0.839345\n",
+ "diameter 0.854578 0.839345 1.000000"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# What is the correlation between our DataFrame columns?\n",
+ "corr_df = trees_df.corr()\n",
+ "corr_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7974683544303798"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# What is the correlation between age and height (don't use .iloc)\n",
+ "# Using index in this case isn't considered as hardcoding\n",
+ "corr_df['age']['height']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variating Stylistic Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Option 1:\n",
+ "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = \"diameter\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Option 2:\n",
+ "# this way allows you to make it bigger\n",
+ "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Iris Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-width</th>\n",
+ " <th>class</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5.1</td>\n",
+ " <td>3.5</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>4.9</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>4.7</td>\n",
+ " <td>3.2</td>\n",
+ " <td>1.3</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4.6</td>\n",
+ " <td>3.1</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5.0</td>\n",
+ " <td>3.6</td>\n",
+ " <td>1.4</td>\n",
+ " <td>0.2</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>145</th>\n",
+ " <td>6.7</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.2</td>\n",
+ " <td>2.3</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>146</th>\n",
+ " <td>6.3</td>\n",
+ " <td>2.5</td>\n",
+ " <td>5.0</td>\n",
+ " <td>1.9</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>147</th>\n",
+ " <td>6.5</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.2</td>\n",
+ " <td>2.0</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>148</th>\n",
+ " <td>6.2</td>\n",
+ " <td>3.4</td>\n",
+ " <td>5.4</td>\n",
+ " <td>2.3</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>149</th>\n",
+ " <td>5.9</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 5 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-width class\n",
+ "0 5.1 3.5 1.4 0.2 Iris-setosa\n",
+ "1 4.9 3.0 1.4 0.2 Iris-setosa\n",
+ "2 4.7 3.2 1.3 0.2 Iris-setosa\n",
+ "3 4.6 3.1 1.5 0.2 Iris-setosa\n",
+ "4 5.0 3.6 1.4 0.2 Iris-setosa\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 Iris-virginica\n",
+ "146 6.3 2.5 5.0 1.9 Iris-virginica\n",
+ "147 6.5 3.0 5.2 2.0 Iris-virginica\n",
+ "148 6.2 3.4 5.4 2.3 Iris-virginica\n",
+ "149 5.9 3.0 5.1 1.8 Iris-virginica\n",
+ "\n",
+ "[150 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 71,
+ "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": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Iris-virginica', 'Iris-versicolor', 'Iris-setosa']"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# In Pandas\n",
+ "varietes = list(set(iris_df[\"class\"]))\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# In SQL\n",
+ "varietes = list(pd.read_sql(\"\"\"\n",
+ " SELECT DISTINCT class\n",
+ " FROM iris\n",
+ "\"\"\", iris_conn)[\"class\"])\n",
+ "varietes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "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": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='pet-width', ylabel='pet-length'>"
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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ocixr+bYD2lrgUL9L4nT7zzsFuhwg6Lk9w3PgwIFz9oWEhCg6OjqoFi57e4bnX/uKNWbRtrPal993ra655Oyv4YD7Vv/7oKas+M9Z7X++s6dG9rooABVZU9mxE0rP3qUtecXOtv4p8ZqX2kvREeEBrMxaPvu6TLcu+Jdq6v7vV3NYiKE3JvVT14uiA1gZ0Dw19vPb7cBz6msmGpKSkqJp06bpnnvucedH+JW3A0/So2+ds69w9k0e7x//h7H2j9/8fZty99lVe9qvjFDDUL8ucXolrU8AK7OWLv+93iXsnBIWYmjfrBEBqAho3hr7+e32ZekLFy5UYmKiLr/8cs2YMUMLFizQE088oZ/97GdKTEzU7NmzdfHFF2v8+PHKzs5298cEpZdy8hrsX7B5n58qsb5Hlu9qsP/3K+q/OSaaJr/4qLbkFbuEHUmqNU1tyStWgb0qQJVZy/JtB+oNO5JUU2dqxScH/VwRYB1uB54DBw6oW7du+uyzzzR9+nTdf//9ysjI0O7du9W1a1eVlJTo7bff1qhRo/SnP/3JmzU3e7n77Q32f3DaKQF45uNCR4P9W/Mb/rtA4xSVHGuwv9BB4PGGrQUNH88/9bsFwLm5HXgyMzP129/+9qzTWoZh6P7779crr7wiSRo7dqz27NnjWZVBpt8lcQ32X58S76dKrO+apIbXQ/Xt3PDfBRonsW1Eg/1JsSxe9oa+yQ0fzz/1uwXAubkdeOx2u44fP15v3/fff6/S0lJJUmxsrNxcJhS0HhyU0mA/V2t5z//c2bPBfq7W8o7O8a3UPyVeoWf8AyfUMNQ/JZ6rtbzkzj4XKyyk/rWRYSEGV2sBHnA78PTs2VOzZs1yBptTSkpK9Mwzz6hnz56SpIMHD6pDhw4eFRmMlt93bZPa4b4/nyP0nKsd7pmX2kv9urjOMPTrEqd5qb0CVJE1vTGp31mh59RVWgDc5/ZVWh9++KGGDh2qsLAw3XjjjWrfvr2OHDmid999VzU1Ndq0aZP+67/+S4888ohqa2s1d+5cL5fuXb668eCCzfv0QV4x9+Hxg9+v+FRb8+3ch8fHCuxVKnRUcR8eH1vxyUHl7rdzHx7gJ/j8snRJ+s9//qOnn35aW7ZskcPhUGxsrAYMGKDHHntMPXr0cHe3AcGdlgEACD5+CTxWQuABACD4+Pw+PAAAAMHC7e/Skn5Yx7Ns2TIVFRWddcWWYRjKycnxqDgAAABvcDvwZGZmKi0tTW3bttWll14qm83m0s+ZMgAA0Fy4HXiee+453XHHHcrKyjor7AAAADQnbq/hKSoq0oQJEwg7AACg2XM78Fx++eU6cuSIN2sBAADwCbcDz6xZszR79mx988033qwHAADA69xewzN//nyVl5fr0ksvVc+ePRUb6/qld4ZhaO3atR4XCAAA4Cm3A89//vMfhYaGql27djp06JAOHTrk0n/mt6gDAAAEituBp7Cw0ItlAAAA+A53WgYAAJbnUeCprq7Wyy+/rNTUVA0ZMkR5eXmSpLVr1yo/P98rBQIAAHjK7VNadrtdAwcO1Oeff64OHTroyJEjqqyslCStWbNGGzdu1IIFC7xWKAAAgLvcnuGZOnWqysrK9Mknn+jAgQMuXyUxcOBAvf/++14pEAAAwFNuz/C8+eabmjNnjq666irV1ta69HXs2FFff/21x8UBAAB4g9szPBUVFUpMTKy37+TJk6qpqXG7KAAAAG9yO/AkJydr69at9fZt27ZNl112mdtFAQAAeJPbgWfs2LGaM2eO1q5d61y/YxiGtm/frhdffFG//vWvvVYkAACAJwzz9NXGTXDy5EmNHDlSGzduVExMjEpLSxUXFyeHw6Hhw4dr3bp1CgkJntv8VFRUKDo6WuXl5YqKigp0OQAAoBEa+/nt9qLl8PBwrV+/XsuXL9dbb72lI0eOKC4uTjfffLPuuuuuoAo7AADA2tye4bEaZngAAAg+jf38ZhoGAABYXpNOad14442Nfq1hGMrJyWlyQQAAAN7WpMBTV1cnwzAa9VrOlAEAgOaiSYHnvffe81EZAAAAvhPwNTyVlZWaOnWqhg4dqvj4eBmGoYyMDLf29fjjj8swDF1xxRXeLRIAAAQ1rwSeuro63XjjjcrLy2vytg6HQwsXLlR1dbVGjRrldg27du3SCy+8oPbt27u9DwAAYE1u34fndKZp6r333lNlZWWTt01MTFRpaakMw5DdbteiRYuavI+amhqNGzdO999/vz799FPZ7fYm78NX8ouPqqjkmJJiI5UcFxnocixt7MKt+s+hcvXs2EavTrg20OVY1vJtB7S1wKF+l8Tp9p93CnQ5ANAoXgk8nmjsIuiGzJ49WyUlJXrmmWd08803e6Eqz5UdO6H07F3aklfsbOufEq95qb0UHREewMqsZ+H7+zRrw17n8w/2OZT06FuaftPlGn995wBWZi2ffV2mWxf8SzV1P1yQsGbnIU1b9ZnemNRPXS+KDnB1ANCwgK/h8dSePXv09NNP6y9/+YtatWoV6HKc0rN3KXef60xT7j67JmfvDFBF1nV62Dndk2994edKrO30sHNKTZ2pkfNzA1QRADSeVwJPSEiI7rnnHsXFxXljd41WV1en8ePHa/To0RoxYkSTtq2urlZFRYXLw1vyi49qS16xas+4NL/WNLUlr1gF9iqv/azz3diFWxvs//Wij/xUibUt33bgrLBzSk2dqRWfHPRzRQDQNG4HngMHDujkyZOSfjgtlZmZqYsvvljSD2tqDhw44J0KG/DHP/5ReXl5mjt3bpO3ffbZZxUdHe18dOrkvbUIRSXHGuwvdBB4vOU/h8ob7N/1dZl/CrG4rQWOBvtz9zefdXMAUB+3A09ycrJ27qz/9Mynn36q5ORkt4tqjAMHDmj69OmaMWOGWrRoobKyMpWVlammpkZ1dXUqKyvT8ePHz7n9tGnTVF5e7nwcPOi9f6Emto1osD8plsXL3tIjoeG1Iz07tvFPIRbXNzm2wf5+l/h3dhcAmsrtwNPQnZRra2u9shi5Ifn5+Tp+/LgeeughxcTEOB+5ubn64osvFBMTo2nTpp1ze5vNpqioKJeHt3SOb6X+KfEKPWMMQg1D/VPiuVrLi5b+v74N9nO1lnfc2edihYXU//90WIjB1VoAmj2PrtKqL9RUV1drw4YNPl/P07NnT23evPms9ocffljl5eXKzMxUx44dfVpDQ+al9tLk7J0uV2n16xKneam9AlaTVU2/6fJ6FyhPv+nyAFRjXW9M6qeR83Nd1vKEhRh6Y1K/AFYFAI1jmE340quZM2fqySefbNRrJ0yYoJdffrlRr92wYYOqqqpUWVmp8ePH6/bbb9cdd9whSRoxYoQiIiKUlpamrKws7d+/X4mJiefc1w033CC73a7du3c36mef0tivl2+qAnuVCh1V3IfHD3696CPt+rqM+/D42IpPDip3v5378ABoFhr7+d2kGZ4+ffpo4sSJMk1TCxYs0K9+9auz7mxss9nUvXt3jRkzptH7feCBB1RUVOR8vmLFCq1YsUKSVFBQoKSkJNXW1qq2tjbovpQ0OY6g4y+EHP+4/eedCDoAgk6TZnhON27cOE2fPt3ni5P9xVczPAAAwHd8MsNzuszMTHc3BQAA8CuPbjz45ZdfKjU1VRdeeKFatGihHTt2SPphrU99C4oBAAACwe3As2vXLvXu3Vvvv/++brjhBtXW1jr7jh49qr/+9a9eKRAAAMBTbgeeRx99VD169NC+ffv06quvuiwm7tOnj7Zv3+6VAgEAADzl9hqe3NxcLVmyRBERES6zO5LUvn17HT582OPiAAAAvMGjOy23aNGi3r7S0lLZbDa3iwIAAPAmtwNPjx49tHr16nr7/vnPf+rqq692uygAAABvcvuU1kMPPaQxY8YoMjJSv/71ryX98IWe7777rv73f/9XK1eu9FqRAAAAnnD7xoOSNGvWLGVkZLjcATksLExPPvmkHn30Ua8V6Q/ceBAAgODT2M9vjwKPJH399dfauHGjjhw5ori4OA0bNqzB77pqrgg8AAAEH5/faVmSamtr9cEHH+jjjz+Ww+FQbGysWrdurYsuukhhYR7tGgAAwGvcnuGx2+0aPny4duzYobCwMMXGxsrhcKimpka9evXSxo0bFRcX5+16fYYZHgAAgk9jP7/dvkprypQp2rt3r5YuXarjx4/r22+/1fHjx7VkyRLl5eVpypQp7u4aAADAq9w+77Ru3To9/fTTSk1NdbaFhoZqzJgx+u6775SRkeGN+gAAADzm0Y0Hu3XrVm/fFVdcIQ/XQgMAAHiN24Fn8ODB2rRpU71977zzjm644QZ3dw0AAOBVbp/SeuKJJzR69GjV1tZqzJgx6tChgw4fPqylS5dq1apVWrVqlUpKSpyvb9u2rVcKBgAAaCq3r9IKCfm/ySHDMJx/PrW709sknfUFo80NV2kBABB8fH4fnunTp58VagAAAJojj++0bBXM8AAAEHx8fh8eAACAYEHgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAluf2fXiA5ualnDzl7rfr+pR4TRzYJdDlWNb7e7/Trq/LdNXFMbo+JT7Q5VhWfvFRFZUcU1JspJLjIgNdDhD0CDwIev/aV6wxi7Y5n2/NL9FzG/dq+X3X6ppLYgNYmbUUOao0an6uSo+ddLbFRITrjUnXqVNsRAArs5ayYyeUnr1LW/KKnW39U+I1L7WXoiPCA1gZENw4pYWgd3rYOd2df/vIz5VY25lhR5JKj53UyPkfBqgia0rP3qXcfXaXttx9dk3O3hmgigBrIPAgqL2Uk9dg/4LN+/xUibW9v/e7s8LOKaXHTuqD02Yj4L784qPakles2jNugF9rmtqSV6wCe1WAKgOCH4EHQS13v73Bfj6IvWPX12UN9u84UOqfQiyuqORYg/2FDgIP4C4CD4Jav0viGuxnUa139OzYpsH+qy6O8U8hFpfYtuG1UEmxLF4G3EXgQVB7cFBKg/1creUdAy5rp5hzLJiNiQgnWHpJ5/hW6p8Sr1DDcGkPNQz1T4nnai3AAwQeBL3l913bpHa4541J150Vek5dpQXvmZfaS/26uM5c9usSp3mpvQJUEWANhmmesTruPNXYr5dH87Vg8z59kFfMfXh87IO8Yu04UMp9eHyswF6lQkcV9+EBfkJjP78JPD8i8AAAEHwa+/nNKS0AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5AQ88lZWVmjp1qoYOHar4+HgZhqGMjIxGbbtq1SqlpqaqS5cuatmypZKSkjR27Fjl5eX5tmgAABBUAh54HA6HFi5cqOrqao0aNapJ286ZM0fHjh3TY489pn/+8596+umntXPnTl111VX6/PPPfVMwmq33936nF3O+0gd5xYEuBQDQzIQFuoDExESVlpbKMAzZ7XYtWrSo0duuW7dO7dq1c2m78cYblZSUpD/96U9N2heCV5GjSqPm56r02ElnW0xEuN6YdJ06xUYEsDIAQHMR8BkewzBkGIZb254ZdiQpISFBHTt21MGDBz0tDUHizLAjSaXHTmrk/A8DVBEAoLkJeODxtvz8fBUVFalbt24Nvq66uloVFRUuDwSf9/d+d1bYOaX02ElObwEAJFks8NTU1CgtLU2tWrXSlClTGnzts88+q+joaOejU6dOfqoS3rTr67IG+3ccKPVPIQCAZs0ygcc0TaWlpemDDz7QK6+88pMBZtq0aSovL3c+OAUWnHp2bNNg/1UXx/inEABAsxbwRcveYJqmJkyYoCVLligrK0u//OUvf3Ibm80mm83mh+rgSwMua6eYiPB6T2vFRITr+pT4AFQFAGhugn6G51TYyczM1KJFi3T33XcHuiT42RuTrlNMRLhL26mrtAAAkIJ8hsc0Td13333KzMzUyy+/rHHjxgW6JARAp9gI7Zw+VB/kFWvHgVJddXEMMzsAABfNIvBs2LBBVVVVqqyslCTt2bNHK1eulCSNGDFCERERSktLU1ZWlvbv36/ExERJUnp6uv7+979r/Pjx6t69uz766CPnPm02m3r16uX/N4OAuT4lnqADAKiXYZqmGegikpKSVFRUVG9fQUGBkpKSdO+99yorK8v5/Ke2S0xMVGFhYaNrqKioUHR0tMrLyxUVFdXUtwAAAAKgsZ/fzSLwNAcEHgAAgk9jP7+DftEyAADATyHwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAyyPwAAAAywsLdAFWl198VEUlx5QUG6nkuMhAlwMAwHmJwOMjZcdOKD17l7bkFTvb+qfEa15qL0VHhAewMgAAzj+c0vKR9Oxdyt1nd2nL3WfX5OydAaoIAIDzF4HHB/KLj2pLXrFqTdOlvdY0tSWvWAX2qgBVBgDA+YnA4wNFJcca7C90EHgAAPAnAo8PJLaNaLA/KZbFywAA+BOBxwc6x7dS/5R4hRqGS3uoYah/SjxXawEA4GcEHh+Zl9pL/brEubT16xKneam9AlQRAADnLy5L95HoiHC9ktZHBfYqFTqquA8PAAABRODxseQ4gg4AAIHGKS0AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5BB4AAGB5AQ88lZWVmjp1qoYOHar4+HgZhqGMjIxGb//dd9/p3nvvVVxcnCIiItS3b1/l5OT4rmAAABB0Ah54HA6HFi5cqOrqao0aNapJ21ZXV2vQoEHKycnRiy++qLVr16p9+/YaPny43n//fd8UjGYrv/ioNu/9TgX2qkCXAgBoZsICXUBiYqJKS0tlGIbsdrsWLVrU6G3//ve/a/fu3frXv/6lvn37SpIGDhyoK6+8UlOnTtXHH3/sq7LRjJQdO6H07F3aklfsbOufEq95qb0UHREewMoAAM1FwGd4DMOQYRhubbt69WpddtllzrAjSWFhYbr77ru1bds2ffPNN94qE81YevYu5e6zu7Tl7rNrcvbOAFUEAGhuAh54PLF792716NHjrPZTbZ9//vk5t62urlZFRYXLA8Env/iotuQVq9Y0XdprTVNb8oo5vQUAkBTkgcfhcKht27ZntZ9qczgc59z22WefVXR0tPPRqVMnn9UJ3ykqOdZgf6GDwAMACPLAI6nB02EN9U2bNk3l5eXOx8GDB31RHnwssW1Eg/1JsZF+qgQA0JwFfNGyJ2JjY+udxSkpKZGkemd/TrHZbLLZbD6rDf7ROb6V+qfEK3ef3eW0VqhhqF+XOCXHEXgAAEE+w9O9e3d99tlnZ7Wfarviiiv8XRICYF5qL/XrEufS1q9LnOal9gpQRQCA5iaoZ3huvfVWTZw4UR9//LGuueYaSVJNTY2WLFmia665RgkJCQGuEP4QHRGuV9L6qMBepUJHlZJiI5nZAQC4aBaBZ8OGDaqqqlJlZaUkac+ePVq5cqUkacSIEYqIiFBaWpqysrK0f/9+JSYmSpLGjx+v+fPn6/bbb9fs2bPVrl07LViwQHv37tWmTZsC9n4QGMlxBB0AQP2aReB54IEHVFRU5Hy+YsUKrVixQpJUUFCgpKQk1dbWqra2VuZp6zRsNptycnI0depUTZ48WceOHVPPnj21YcMGDRgwwO/vAwAANE+GaZ5xA5PzVEVFhaKjo1VeXq6oqKhAlwMAABqhsZ/fQb1oGQAAoDEIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPKaxZ2Wm4NT91+sqKgIcCUAAKCxTn1u/9R9lAk8Pzr1PV6dOnUKcCUAAKCpKisrFR0dfc5+vlriR3V1dTp06JBat24twzC8tt+Kigp16tRJBw8e5CsrfIyx9g/G2T8YZ/9gnP3Dl+NsmqYqKyuVkJCgkJBzr9RhhudHISEh6tixo8/2HxUVxf9MfsJY+wfj7B+Ms38wzv7hq3FuaGbnFBYtAwAAyyPwAAAAyyPw+JjNZtOMGTNks9kCXYrlMdb+wTj7B+PsH4yzfzSHcWbRMgAAsDxmeAAAgOUReAAAgOUReAAAgOUReNx09OhRPfzww0pISNAFF1ygnj176h//+MdPbvf111/r4Ycf1oABA9SmTRsZhqHFixf7vuAg5e44r1q1SqmpqerSpYtatmyppKQkjR07Vnl5eX6oOji5O9abNm3SkCFDlJCQIJvNpnbt2unGG2/U+vXr/VB18HF3nM/0+OOPyzAMXXHFFT6oMvi5O86LFy+WYRj1Pg4fPuyHyoOLp8fz2rVrNWDAAEVFRSkyMlLdunXTwoULfVIrNx500+jRo7V9+3bNnj1bl156qZYtW6bU1FTV1dVpzJgx59xu3759Wrp0qXr27KkRI0YoOzvbj1UHH3fHec6cOerQoYMee+wxde7cWQcPHtSsWbN01VVX6aOPPlK3bt38+C6Cg7tj7XA41K1bN02YMEEdOnRQSUmJ/vrXv+qmm27Sq6++qrvvvtuP76L5c3ecT7dr1y698MILat++vY+rDV6ejnNmZqZ+9rOfubTFxsb6qtyg5ck4z549W4899ph++9vfatq0aQoPD9eXX36pEydO+KZYE0321ltvmZLMZcuWubQPGTLETEhIMGtqas65bW1trfPP27dvNyWZmZmZvio1qHkyzkeOHDmr7ZtvvjHDw8PNtLQ0r9ca7DwZ6/qcOHHCvOiii8zrr7/em2UGPW+M88mTJ82ePXua6enp5oABA8xu3br5qtyg5ck4Z2ZmmpLM7du3+7rMoOfJOH/yySdmSEiIOWfOHF+X6cQpLTesXr1arVq10u233+7SPm7cOB06dEgff/zxObdt6Hs+4MqTcW7Xrt1ZbQkJCerYsaMOHjzo9VqDnSdjXZ/w8HC1adNGYWFMIp/OG+M8e/ZslZSU6JlnnvFVmUHP28cz6ufJOL/00kuy2WyaPHmyr8t04tPXDbt379bll19+1i/zHj16OPvhOW+Pc35+voqKijidVQ9vjHVdXZ1qamp06NAhzZgxQ1999ZUeeeQRn9QbrDwd5z179ujpp5/WX/7yF7Vq1cpndQY7bxzPN998s0JDQ9W2bVuNHj2a3+v18GSct2zZossvv1yvv/66LrvsMoWGhqpjx4569NFHfXZKi39+ucHhcKhz585ntbdt29bZD895c5xramqUlpamVq1aacqUKV6r0Sq8MdYjRozQxo0bJf3wBYHLly/XTTfd5N1Cg5wn41xXV6fx48dr9OjRGjFihM9qtAJPxvnU2r9rr71WUVFR+uyzzzR79mxde+21ys3N1ZVXXumzuoONJ+P8zTffqLi4WOnp6XrqqafUtWtX5eTkaPbs2Tp48KCWLl3q9XoJPG4yDMOtPjSNN8bZNE2lpaXpgw8+0Ouvv65OnTp5qzxL8XSs582bp7KyMn377bdasmSJ7rzzTmVlZSk1NdWbZQY9d8f5j3/8o/Ly8vTGG2/4oizLcXechw8fruHDhzuf9+/fXzfddJO6d++u6dOna+3atV6tM9i5O851dXWqrKxUdna27rrrLknSwIEDVVVVpblz52rmzJnq0qWLV2vllJYbYmNj602uJSUlkv4v3cIz3hhn0zQ1YcIELVmyRIsXL9Yvf/lLr9dpBd4Y65SUFPXu3VsjR47Ua6+9pkGDBmnSpEmqq6vzer3Byt1xPnDggKZPn64ZM2aoRYsWKisrU1lZmWpqalRXV6eysjIdP37cp7UHE2//jk5KStJ1112njz76yCv1WYUn43zqirdhw4a5tP/iF7+QJO3YscNbZToReNzQvXt3ffHFF6qpqXFp/+yzzySJ+2J4iafjfCrsZGZmatGiRVwe3QBfHNN9+vRRaWmpiouLvVKjFbg7zvn5+Tp+/LgeeughxcTEOB+5ubn64osvFBMTo2nTpvm8/mDhi+PZNE0uOjmDJ+N8ap3Pmcwfv97TF2PN354bbr31Vh09elSvv/66S3tWVpYSEhJ0zTXXBKgya/FknE3T1H333afMzEy9/PLLGjdunK/LDWrePqZN09T777+vNm3acO+S07g7zj179tTmzZvPelx55ZVKSkrS5s2b9eCDD/rjLQQFbx/PBQUFys3N1bXXXuvNMoOeJ+N82223SZI2bNjg0r5+/XqFhISod+/e3i/YbxfAW8yQIUPMmJgYc+HChea7775r3nfffaYkc8mSJc7XjB8/3gwNDTULCwtdtl2xYoW5YsUKc86cOaYkc9KkSc42uHJ3nB988EFTkjl+/Hhz69atLo8dO3YE4q00e+6O9ciRI80nnnjCfP3118333nvPXLZsmTl06FBTkjl//vxAvJVmzZPfHWfiPjzn5u44Dxo0yJw5c6a5evVqMycnx5w7d66ZkJBgtm7d2vzss88C8VaaNXfH+cSJE+ZVV11lRkdHmy+++KL5zjvvmH/4wx/M0NBQ88EHH/RJrQQeN1VWVprp6elmhw4dzBYtWpg9evQws7OzXV5zzz33mJLMgoICl3ZJ53zAlbvjnJiYeM4xTkxM9O+bCBLujvWcOXPM3r17mzExMWZoaKgZGxtrDhs2zHzzzTf9/A6Cgye/O85E4Dk3d8f54YcfNrt27Wq2bt3aDAsLMxMSEsy7777b3Lt3r5/fQXDw5Hh2OBzm/fffb7Zv394MDw83L730UvP55593uUGvNxmm+eMJMwAAAItiDQ8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag8AALA8Ag+AZmv9+vXKyMjw+c8pLCyUYRhavHjxT742IyNDhmG4tM2aNUtr1qw567WLFy+WYRj65JNPvFQpAHcReAA0W+vXr9fMmTN9/nMuvPBCbd26VTfddJNb258r8ABoPsICXQAABJrNZuObsAGLY4YHgNecOt2zc+dOjR49WlFRUYqOjtbdd9+t4uJil9cuX75cffv2VWRkpFq1aqVhw4Zp586dzv57771X8+fPlyQZhuF8FBYWnvPn//73v1d0dLRqa2udbZMnT5ZhGHr++eedbQ6HQyEhIZo3b56kc5/Seuutt9SzZ0/ZbDYlJyfrhRdeOOtnGoahqqoqZWVlOWu84YYbXF5TWVmpBx54QHFxcYqNjdXo0aN16NChBscSgHcReAB43a233qouXbpo5cqVysjI0Jo1azRs2DCdPHlS0g+ngFJTU9W1a1e99tprevXVV1VZWanrr79ee/bskSQ98cQT+tWvfiVJ2rp1q/Nx4YUXnvPnDh48WBUVFdq2bZuzbdOmTWrZsqXeeecdZ1tOTo5M09TgwYPPua+cnBz98pe/VOvWrfWPf/xDzz//vF577TVlZma6vG7r1q1q2bKlRowY4axxwYIFLq+ZMGGCwsPDtWzZMj333HN67733dPfddzdyNAF4hU++gx3AeWnGjBmmJHPKlCku7UuXLjUlmUuWLDEPHDhghoWFmZMnT3Z5TWVlpdmhQwfzjjvucLZNmjTJbMqvqaqqKrNFixbmk08+aZqmaX799demJPMPf/iD2bJlS/P77783TdM077vvPjMhIcG5XUFBgSnJzMzMdLZdc801ZkJCgnn8+HFnW0VFhdm2bduzaoqMjDTvueees+rJzMw0JZkTJ050aX/uuedMSea3337b6PcGwDPM8ADwurFjx7o8v+OOOxQWFqbNmzdr48aNqqmp0W9+8xvV1NQ4HxdccIEGDBig99577yf3X1dX57LtqVNYERER6tu3rzZt2iRJeuedd9SmTRv9/ve/14kTJ/Thhx9K+mHWp6HZnaqqKm3fvl2jR4/WBRdc4Gxv3bq1brnllqYOh0aOHOnyvEePHpKkoqKiJu8LgHsIPAC8rkOHDi7Pw8LCFBsbK4fDoSNHjkiSevfurfDwcJfH8uXLZbfbf3L/48ePd9lu0KBBzr7Bgwfro48+UlVVlTZt2qQbb7xRsbGxuvrqq7Vp0yYVFBSooKCgwcBTWlqqurq6s95Hfe+tMWJjY12e22w2SdLx48ebvC8A7uEqLQBed/jwYV100UXO5zU1NXI4HIqNjVVcXJwkaeXKlUpMTHRr/xkZGXrwwQedz1u3bu3886BBg/TEE09oy5YtysnJ0YwZM5ztb7/9tpKTk53PzyUmJkaGYejw4cP1vjcAwYfAA8Drli5dqquvvtr5/LXXXlNNTY1uuOEGXXfddQoLC9P+/ft12223Nbif02dCWrZs6WxPSkpSUlJSvdv06dNHUVFRmjt3rg4fPqwhQ4ZI+mHmZ86cOXrttdfUtWtXJSQknPPnRkZGqk+fPlq1apWef/5552mtyspKrVu3rt46ma0BmjcCDwCvW7VqlcLCwjRkyBB9/vnneuKJJ3TllVfqjjvuUIsWLfTkk0/qscceU35+voYPH66YmBgdOXJE27ZtU2RkpPNmg927d5ckzZkzR7/4xS8UGhqqHj16qEWLFuf82aGhoRowYIDWrVun5ORkXXLJJZKkfv36yWazKScnR+np6T/5Hp566ikNHz5cQ4YM0SOPPKLa2lrNmTNHkZGRKikpcXlt9+7d9d5772ndunW68MIL1bp1a1122WXuDh8AH2ANDwCvW7Vqlb788kuNHj1a06dP1y233KK3337bGVSmTZumlStX6quvvtI999yjYcOGaerUqSoqKlL//v2d+xkzZowmTJigBQsWqG/fvurdu3ej7l9zan3O6et0bDabrrvuurPaz2XIkCFas2aNKioqdOedd+p3v/udbrvtNo0fP/6s17744otKSUnRXXfdpd69e+v+++//yf0D8C/DNE0z0EUAsIaMjAzNnDlTxcXFzrU6ANAcMMMDAAAsj8ADAAAsj1NaAADA8pjhAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlvf/ARGUTu9x0n8QAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot petal width vs petal length for flowers that are of class iris-setosa\n",
+ "iris_df[iris_df[\"class\"] == 'Iris-setosa'].plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Write a for loop that iterates through each variety in classes\n",
+ "# and makes a plot for only that class\n",
+ "\n",
+ "# For each class add a color and a marker style\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "\n",
+ "for i in range(len(varietes)):\n",
+ " variety = varietes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+ " \n",
+ " #make a scatter plot for this variety\n",
+ " variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
+ " label = variety, color = colors[i], marker = markers[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Did you notice that it made 3 plots?!?! What's decieving about this?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We can make Subplots in plots, called an AxesSubplot, keyword ax\n",
+ "1. if AxesSuplot ax passed, then plot in that subplot\n",
+ "2. if ax is None, create a new AxesSubplot\n",
+ "3. return AxesSubplot that was used"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "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": 82,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sep-length</th>\n",
+ " <th>sep-width</th>\n",
+ " <th>pet-length</th>\n",
+ " <th>pet-width</th>\n",
+ " <th>class</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>100</th>\n",
+ " <td>6.3</td>\n",
+ " <td>3.3</td>\n",
+ " <td>6.0</td>\n",
+ " <td>2.5</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101</th>\n",
+ " <td>5.8</td>\n",
+ " <td>2.7</td>\n",
+ " <td>5.1</td>\n",
+ " <td>1.9</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>102</th>\n",
+ " <td>7.1</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.9</td>\n",
+ " <td>2.1</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td>6.3</td>\n",
+ " <td>2.9</td>\n",
+ " <td>5.6</td>\n",
+ " <td>1.8</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td>6.5</td>\n",
+ " <td>3.0</td>\n",
+ " <td>5.8</td>\n",
+ " <td>2.2</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sep-length sep-width pet-length pet-width class\n",
+ "100 6.3 3.3 6.0 2.5 Iris-virginica\n",
+ "101 5.8 2.7 5.1 1.9 Iris-virginica\n",
+ "102 7.1 3.0 5.9 2.1 Iris-virginica\n",
+ "103 6.3 2.9 5.6 1.8 Iris-virginica\n",
+ "104 6.5 3.0 5.8 2.2 Iris-virginica"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iris_virginica = iris_df[iris_df['class'] == 'Iris-virginica']\n",
+ "assert(len(iris_virginica) == 50)\n",
+ "iris_virginica.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='pet-width', ylabel='pet-length'>"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's learn about *xlim* and *ylim*\n",
+ "- Allows us to set x-axis and y-axis limits\n",
+ "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
+ "- You need to be careful about setting the UPPER-BOUND"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='pet-width', ylabel='pet-length'>"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = 0, ylim = 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+ " xlim = (0, 6), ylim = (0, 6),\n",
+ " figsize = (3, 3))\n",
+ "\n",
+ "# What is wrong with this plot?\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What is the maximum pet-len?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6.9"
+ ]
+ },
+ "execution_count": 87,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iris_virginica['pet-length'].max()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 6.0)"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ax.get_ylim()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's include assert statements to make sure we don't crop the plot!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[89], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m ax \u001b[39m=\u001b[39m iris_virginica\u001b[39m.\u001b[39mplot\u001b[39m.\u001b[39mscatter(x \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpet-width\u001b[39m\u001b[39m\"\u001b[39m, y \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpet-length\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 2\u001b[0m xlim \u001b[39m=\u001b[39m (\u001b[39m0\u001b[39m, \u001b[39m6\u001b[39m), ylim \u001b[39m=\u001b[39m (\u001b[39m0\u001b[39m, \u001b[39m6\u001b[39m),\n\u001b[1;32m 3\u001b[0m figsize \u001b[39m=\u001b[39m (\u001b[39m3\u001b[39m, \u001b[39m3\u001b[39m))\n\u001b[0;32m----> 4\u001b[0m \u001b[39massert\u001b[39;00m iris_virginica[\u001b[39m\"\u001b[39m\u001b[39mpet-length\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mmax() \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39mget_ylim()[\u001b[39m1\u001b[39m]\n",
+ "\u001b[0;31mAssertionError\u001b[0m: "
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+ " xlim = (0, 6), ylim = (0, 6),\n",
+ " figsize = (3, 3))\n",
+ "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Now let's try all 4 assert statements\n",
+ "\n",
+ "```\n",
+ "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+ "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+ "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+ "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+ " xlim = (0, 7), ylim = (0, 7),\n",
+ " figsize = (3, 3))\n",
+ "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+ "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+ "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+ "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Time-Permitting\n",
+ "Plot this data in an interesting/meaningful way & identify any correlations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>name</th>\n",
+ " <th>grade</th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " <th>height</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Cole</td>\n",
+ " <td>C</td>\n",
+ " <td>2.0</td>\n",
+ " <td>4</td>\n",
+ " <td>68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Cynthia</td>\n",
+ " <td>AB</td>\n",
+ " <td>3.5</td>\n",
+ " <td>11</td>\n",
+ " <td>66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Alice</td>\n",
+ " <td>B</td>\n",
+ " <td>3.0</td>\n",
+ " <td>10</td>\n",
+ " <td>60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Seth</td>\n",
+ " <td>BC</td>\n",
+ " <td>2.5</td>\n",
+ " <td>6</td>\n",
+ " <td>72</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name grade gpa attendance height\n",
+ "0 Cole C 2.0 4 68\n",
+ "1 Cynthia AB 3.5 11 66\n",
+ "2 Alice B 3.0 10 60\n",
+ "3 Seth BC 2.5 6 72"
+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "students = pd.DataFrame({\n",
+ " \"name\": [\n",
+ " \"Cole\",\n",
+ " \"Cynthia\",\n",
+ " \"Alice\",\n",
+ " \"Seth\"\n",
+ " ],\n",
+ " \"grade\": [\n",
+ " \"C\",\n",
+ " \"AB\",\n",
+ " \"B\",\n",
+ " \"BC\"\n",
+ " ],\n",
+ " \"gpa\": [\n",
+ " 2.0,\n",
+ " 3.5,\n",
+ " 3.0,\n",
+ " 2.5\n",
+ " ],\n",
+ " \"attendance\": [\n",
+ " 4,\n",
+ " 11,\n",
+ " 10,\n",
+ " 6\n",
+ " ],\n",
+ " \"height\": [\n",
+ " 68,\n",
+ " 66,\n",
+ " 60,\n",
+ " 72\n",
+ " ]\n",
+ "})\n",
+ "students"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 0.3333333333333333\n",
+ "1 0.25\n",
+ "2 0.0\n",
+ "3 0.5\n",
+ "Name: height, dtype: string"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Min, Max, and Overall Difference in Student Height\n",
+ "min_height = students[\"height\"].min()\n",
+ "max_height = students[\"height\"].max()\n",
+ "diff_height = max_height - min_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+ "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+ "\n",
+ "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+ "height_colors = height_colors / 2 \n",
+ "\n",
+ "# Color must be a string (e.g. c='0.34')\n",
+ "height_colors = height_colors.astype(\"string\")\n",
+ "\n",
+ "height_colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='attendance', ylabel='gpa'>"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/07/v8h5vw9j6v71xlj9rgbrh5h40000gn/T/ipykernel_74543/882796491.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+ " students.corr()\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>gpa</th>\n",
+ " <th>attendance</th>\n",
+ " <th>height</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>gpa</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.976831</td>\n",
+ " <td>-0.464758</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>attendance</th>\n",
+ " <td>0.976831</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>-0.635586</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>height</th>\n",
+ " <td>-0.464758</td>\n",
+ " <td>-0.635586</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " gpa attendance height\n",
+ "gpa 1.000000 0.976831 -0.464758\n",
+ "attendance 0.976831 1.000000 -0.635586\n",
+ "height -0.464758 -0.635586 1.000000"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "students.corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_template.ipynb b/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_template.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e29b6248f7c0628cd8635c6c929974c29193f4e9
--- /dev/null
+++ b/sum23/lecture_materials/22_Plotting1/lec_22_plotting1_bar_plots_template.ipynb
@@ -0,0 +1,1428 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 22: Bar Plots and Scatter Plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import statements\n",
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "import sqlite3\n",
+ "import os\n",
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 1: Create a database called student_grades.db with a single table called grades"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# establish connection to a new database\n",
+ "grades_conn = sqlite3.connect(\"student_grades.db\")\n",
+ "\n",
+ "# Q: When outer data structure is a dictionary, are inner data structures\n",
+ "# rows or columns of the DataFrame table?\n",
+ "# A: \n",
+ "\n",
+ "df = 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",
+ "})\n",
+ "\n",
+ "# convert the DataFrame to sql database\n",
+ "df.to_sql(\"grades\", con = grades_conn, if_exists = \"replace\", index = False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 2: What are the columns of our table? What are their datatypes?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_sql(???, grades_conn)\n",
+ "print(df['sql'].iloc[0])\n",
+ "\n",
+ "# name is TEXT (in Python, str)\n",
+ "# grade is TEXT (in Python, str)\n",
+ "# gpa is REAL (in Python, float)\n",
+ "# attendance is INTEGER (in Python, int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 4: What is the data in \"grades\" table? \n",
+ "Save this to a variable called \"student_grades\" and display it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "student_grades = pd.read_sql(\"???\", grades_conn)\n",
+ "student_grades"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 5: Make a scatter plot where the attendance of a student is on the x-axis and their gpa on the y-axis\n",
+ "Preview to upcoming topic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "student_grades.plot.scatter(x = \"attendance\", y = \"gpa\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 6: What is the correlation between gpa and attendance?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 7: Close the connection."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bar plots"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Learning Objectives:\n",
+ "- Make a bar plot from a Pandas Series\n",
+ "- Add features: x-label, y-label, title, gridlines, color to plot\n",
+ "- Set the index of a DataFrame certain column\n",
+ "- Create an 'other' column in a DataFrame\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Without this Jupyter notebook cannot display the \"first\" plot in older versions \n",
+ "# of Python / mathplotlib / jupyter\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Helpful documentation and an overview of how matplotlib works\n",
+ "https://matplotlib.org/stable/tutorials/introductory/usage.html\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "***Just for today's lecture, let's have import statements inside the notebook file. You should never do this when you write project code***"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# matplotlib is a plotting module similar to MATLAB\n",
+ "import matplotlib\n",
+ "\n",
+ "# matplotlib is highly configurable, acts like a style sheet for plots\n",
+ "# rc stands for runtime config, syntax is like a dictionary\n",
+ "\n",
+ "matplotlib.rcParams # show all parameters\n",
+ "#matplotlib.rcParams[\"???\"] # show current font size setting\n",
+ "#matplotlib.rcParams[\"???\"] = ??? # change current font size setting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plots from pandas Series\n",
+ "\n",
+ "- matplotlib integrates with pandas, just like sqlite3 integrates with pandas\n",
+ "- Syntax: ```Series.plot.<PLOT_FUNCTION>(...)```\n",
+ "\n",
+ "## Bar plots: From a Series\n",
+ "- Series indices are the x-axis labels\n",
+ "- Series values are the height of each bar\n",
+ "\n",
+ "https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.bar.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s = Series({\"Police\": 5000000, \"Fire\": 3000000, \"Schools\": 2000000})\n",
+ "\n",
+ "# What are the three terminologies associated with pandas Series?\n",
+ "# A: \n",
+ "\n",
+ "# make a bar plot...notice the type\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# if we store the returned object in a variable, we can configure the AxesSubplot\n",
+ "# typically the variable name used is 'ax'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we set the x-axis, y-axis labels, and title?\n",
+ "- plotting functions return what is called as AxesSubplot\n",
+ "- store into a variable and use the AxesSubplot object\n",
+ "- Syntax:\n",
+ "```\n",
+ "ax.set_ylabel(\"...\")\n",
+ "ax.set_xlabel(\"...\")\n",
+ "ax.set_title(\"...\")\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# What is this 1e6? Can we get rid of it?\n",
+ "# Instead of 1e6, divide all values in s by 1000000 (1 million)\n",
+ "# better plot:\n",
+ "\n",
+ "# set the y label to \"Dollars (Millions)\"\n",
+ "\n",
+ "# set the x label to \"City Agency\"\n",
+ "\n",
+ "# this is self-explanatory, so we will skip this for other example plots\n",
+ "\n",
+ "# set the title to \"Annual City Spending\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we rotate the x-axis labels so that they are more readable?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Which aspect of the Series is the x-axis label coming from?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer: "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "How can we extract the indices from a Series?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's use that to set x-axis tick label formatting.\n",
+ "- Syntax:\n",
+ "```\n",
+ "ax.set_xticklabels(<list of x-axis labels>, rotation = ???)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ax = (s / 1000000).plot.bar()\n",
+ "ax.set_ylabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")\n",
+ "\n",
+ "# give the x ticklabels a rotation of 45 degrees\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we change the figure size?\n",
+ "- figsize keyword argument\n",
+ "- should be a tuple with two values: width and height (in inches)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ax = (s / 1000000).plot.bar(???)\n",
+ "ax.set_ylabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we make the bars horizontal?\n",
+ "https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.barh.html\n",
+ "- switch figsize arguments\n",
+ "- change y-label to x-label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# paste the previous code cell here and modify\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Change bar color by using the argument color = ' ' \n",
+ "- Syntax: ``` plot.bar(figsize = (width, height), color = ???)```\n",
+ "- 8 standard colors: r, g, b, c, m, y, k, w (for example: ```color = 'k'```, which is black)\n",
+ " - you could also specify the entire color as a string (for example: ```color = 'red'```)\n",
+ "- can use value of grey between 0 and 1 (for example: ```color = '0.6'```)\n",
+ "- can use a tuple (r, g, b) between 0 and 1 (for example: ```color = (0, .3, .4)```)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# color as a single char \n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = ???) # black color\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# color as a str\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = ???) # red color\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# color as tuple of (r, g, b)\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = (.2, .5, 0))\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we mark gridlines?\n",
+ "- use ax.grid()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# copy the previous code and add grid lines\n",
+ "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'b')\n",
+ "ax.set_xlabel(\"Dollars (Millions)\")\n",
+ "ax.set_title(\"Annual City Spending\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Examples with the Bus Route Database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = \"bus.db\"\n",
+ "\n",
+ "# assert existence of path\n",
+ "\n",
+ "# establish connection to bus.db\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find the tables in `bus.db`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.read_sql(\"\"\"\n",
+ "SELECT *\n",
+ "FROM sqlite_master\n",
+ "WHERE type = 'table'\n",
+ "\"\"\", conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.read_sql(\"\"\"\n",
+ "SELECT * from\n",
+ "boarding\n",
+ "\"\"\", conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### What are the top routes, and how many people ride them daily?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_sql(\"\"\"\n",
+ "\n",
+ "\"\"\", conn)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's take the daily column out as a Series ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Oops, too much data. Let's filter down to top 5 routes. How can we do that in SQL?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO: add the appropriate SQL clause\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",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, plot this!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Huh, what exactly is route 0? Where is that coming from?\n",
+ "Q: Can you guess where it is coming from? \n",
+ "\n",
+ "A: It is coming from dataframe row index!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's fix that: we can use df.set_index(...)\n",
+ "- set_index returns a brand new DataFrame object instance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now plot this..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s = df[\"daily\"]\n",
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Wouldn't it be nice to have an \"other\" bar to represent other routes?\n",
+ "- we have to now get rid of LIMIT clause\n",
+ "- we have to deal with other routes using pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_sql(\"\"\"\n",
+ "SELECT Route, SUM(DailyBoardings) AS daily\n",
+ "FROM boarding\n",
+ "GROUP BY Route\n",
+ "ORDER BY daily DESC\n",
+ "\"\"\", conn)\n",
+ "\n",
+ "df = df.set_index(\"Route\")\n",
+ "s = df[\"daily\"]\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We are back to plotting all route bars ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s.plot.bar()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How can we slice a pandas dataframe?\n",
+ "- Recall that .iloc allows us to do slicing.\n",
+ "- For reproducing previous 5-route plot, we just need to take first 5 route details and populate into a series s.\n",
+ "- For the \"other\" part, we want all the rows in dataframe after row 5 summed up together."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s.plot.bar()\n",
+ "# Q: Where did the xlabel come from? \n",
+ "# A: "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's fix the plot aesthetics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Close the bus.db database connection here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's try a different dataset."
+ ]
+ },
+ {
+ "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": [
+ "path = \"iris-flowers.db\"\n",
+ "assert os.path.exists(path)\n",
+ "\n",
+ "iris_conn = sqlite3.connect(path)\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": [
+ "## 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
+}
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