From ad34363985af4bdfcc48cdee291ecf36379cf1d4 Mon Sep 17 00:00:00 2001
From: msyamkumar <msyamkumar@wisc.edu>
Date: Sun, 4 Dec 2022 17:09:52 -0600
Subject: [PATCH] Removing checkpoints
---
...ec_35_plotting1_bar_plots-checkpoint.ipynb | 2214 -----------------
...tting1_bar_plots_template-checkpoint.ipynb | 754 ------
2 files changed, 2968 deletions(-)
delete mode 100644 f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots_template-checkpoint.ipynb
diff --git a/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots-checkpoint.ipynb b/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots-checkpoint.ipynb
deleted file mode 100644
index e9efdba..0000000
--- a/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots-checkpoint.ipynb
+++ /dev/null
@@ -1,2214 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# this allows the full screen to be used\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import sqlite3\n",
- "import os"
- ]
- },
- {
- "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": 3,
- "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: columns (when outer data structure is a list, inner data structures represent rows)\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": 4,
- "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": [
- "df = pd.read_sql(\"SELECT * FROM sqlite_master\", 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": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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": 5,
- "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": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='attendance', ylabel='gpa'>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "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": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gpa</th>\n",
- " <th>attendance</th>\n",
- " </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": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "student_grades.corr()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Warmup 7: Close the connection."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "grades_conn.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Lecture 35: Bar Plots\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": 9,
- "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": 10,
- "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\"] = 18 # 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": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = Series({\"Police\": 5000000, \"Fire\": 3000000, \"Schools\": 2000000})\n",
- "\n",
- "# What are the two terminologies associated with pandas Series?\n",
- "# A: index and integer position\n",
- "\n",
- "# make a bar plot...notice the type\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 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": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 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",
- "# 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\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we rotate the x-axis labels so that they are more readable?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Police 5000000\n",
- "Fire 3000000\n",
- "Schools 2000000\n",
- "dtype: int64"
- ]
- },
- "execution_count": 14,
- "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": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Answer: indices"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "How can we extract the indices from a Series?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Police', 'Fire', 'Schools'], dtype='object')"
- ]
- },
- "execution_count": 16,
- "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",
- "\n",
- "- Syntax:\n",
- "```\n",
- "ax.set_xticklabels(<list of x-axis labels>, rotation = ???)\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Text(0, 0, 'Police'), Text(1, 0, 'Fire'), Text(2, 0, 'Schools')]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar()\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")\n",
- "\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": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 108x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "ax = (s / 1000000).plot.bar(figsize = (1.5, 4))\n",
- "ax.set_ylabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we make the bars horizontal?\n",
- "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": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 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": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# color as a single char\n",
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'k')\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Annual City Spending')"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# color as a str\n",
- "ax = (s / 1000000).plot.barh(figsize = (4, 1.5), color = 'red')\n",
- "ax.set_xlabel(\"Dollars (Millions)\")\n",
- "ax.set_title(\"Annual City Spending\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 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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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "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": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x108 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# 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()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Examples with the Bus Route Database"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "path = \"bus.db\"\n",
- "# assert existence of path\n",
- "assert os.path.exists(path)\n",
- "\n",
- "# establish connection to bus.db\n",
- "conn = sqlite3.connect(path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Find the tables in `bus.db`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "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": 25,
- "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": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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": 26,
- "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": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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": 27,
- "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": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = 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": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>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": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "LIMIT 5\n",
- "\"\"\", conn)\n",
- "df"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, plot this!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = df[\"daily\"]\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 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": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Route</th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>80</td>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>2</td>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>6</td>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>10</td>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>3</td>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route daily\n",
- "0 80 10211.79\n",
- "1 2 4808.03\n",
- "2 6 4537.02\n",
- "3 10 4425.23\n",
- "4 3 2708.55"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df"
- ]
- },
- {
- "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": 32,
- "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": 32,
- "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": 33,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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6mWLd3cDh0+znKbrL6Yy7pM6z6luSNFne2kCS1IyhI0lqxtCRJDVj6EiSmjF0JEnNGDqSpGYMHUlSM4aOJKkZQ0eS1IyhI0lqxtCRJDVj6EiSmjF0JEnNGDqSpGYMHUlSM4aOJKkZQ0eS1Mys365aktRZeOonZnsIAKw4+9CJ9e2WjiSpGUNHktSMoSNJasbQkSQ1Y+hIkpoxdCRJzRg6kqRmDB1JUjMbPXSS7JzkjCQ3J1mZ5NEkdyR5Z5Jtx9TvkuSqJI8keTzJDUkOmqLvLZKckuSeJGuSPJhk8bh+Z9q3JGnyJrGlcyxwCnA/cAbwNuALwJ8ANyZ5waAwyU7AjcD+wDl97XbAJ5McMqbvc4H3AXcBJwFXAG8Frk6y3r9lA/qWJE3YJC6D8zHgrKr61lDbXyW5F3gn8GbgL/r2s4Dtgb2r6g6AJJcAdwIXJtm1qqpv350uaJZW1RGDjpM8AJwPHAlcNvSe0+5bktTGRt/SqaplI4EzcHm//CmAfpfYYcB1g1DoX/8Y8EFgZ2CfodcfBQQ4b6Tfi4DVwNGDhg3oW5LUQMuJBC/vl//eL/cA5gE3jam9uV8OB8M+wFrgluHCqloD3DFSO9O+JUkNNAmdJFsCpwPfY90usB375UNjXjJoWzDUtiOwqqqemKJ+hyRbbWDfo+M9IcmyJMtWrlw5VZkkaYZabemcB+wHnF5VX+jbtumX40JkzUjN4PG42nH1M+17PVW1pKoWVdWi+fPnT1UmSZqhiYdOkjOBE4ElVXXW0KrV/XLemJdtPVIzeDyudlz9TPuWJDUw0dBJ8m7gj4GLgd8ZWf1wvxy3m2vQNrx77GG6XWjjgmQB3a63Jzewb0lSAxMLnSTvAt4FXAIcN2Z68nK63V/7j3n5fv1y2VDbrXTj3XfkfbYG9hqpnWnfkqQGJhI6SU4H3g18GPitqlo7WtNPX74aODDJnkOv3Q44DriX9WeqXQ4UcPJIV8fTHZ+59Fn0LUlqYKOfHJrkLcB7gK8A1wKvTzJc8u9V9en+8WnAwcCnkpwLfJsuRBYAhw5vHVXV8iQXAicmWQpcA+xGd0WC61n/xNAZ9S1JamMSVyQYnP/yo8D/GrP+euDTAFV1X5IDgLOBU4GtgNuB11bVtWNeezKwAjgBOBRYBVxANytuva2pDehbkjRhGz10quoY4JgZ1N8NHD7N2qeAxf3PRu1bkjR53tpAktSMoSNJasbQkSQ1Y+hIkpoxdCRJzRg6kqRmDB1JUjOGjiSpGUNHktSMoSNJasbQkSQ1Y+hIkpoxdCRJzRg6kqRmDB1JUjOGjiSpGUNHktSMoSNJasbQkSQ1Y+hIkpoxdCRJzRg6kqRmDB1JUjOGjiSpGUNHktSMoSNJasbQkSQ1s0mHTpItkpyS5J4ka5I8mGRxkm1ne2yStDnapEMHOBd4H3AXcBJwBfBW4Ookm/q/XZLmnOfN9gAmJcnudEGztKqOGGp/ADgfOBK4bJaGJ0mbpU35r/2jgADnjbRfBKwGjm49IEna3G3KobMPsBa4ZbixqtYAd/TrJUkNpapmewwTkWQ58JKqeumYdX8H/AYwr6qeHLP+BOCE/ukuwBcmOdZp2AFYNctjmCv8LNbxs1jHz2KdufJZvKKq5o82brLHdIBtgCemWLdmqOYHQqeqlgBLJjSuGUuyrKoWzfY45gI/i3X8LNbxs1hnrn8Wm/LutdXAvCnWbT1UI0lqZFMOnYeBHZKMC54FwKpxu9YkSZOzKYfOrXT/vn2HG5NsDewFLJuFMW2oObOrbw7ws1jHz2IdP4t15vRnsSlPJHgl8K/AlSPn6ZxEd57Ob1bVR2ZrfJK0OdpkQwcgyQXAicCVwDXAbnRXJPgscFBVrZ3F4UnSZmdTD50tgZPppj8vpJtGeDlwelU9Nnsjk6TN0yYdOpKkuWVTnkggSZpjNuWTQ6VNVpJdgB8Gvl5VX5rt8UjT5ZaO5qQk85L8XpIPJHlXkp+cou6QJP/YenwtJDkgyX8faXtTkofobtfxWeDeJHcnOXhWBqlZkWSHJH+Y5E+THDDU/kdJvpzk0SSf6mfxzike05ljkiyiuxjpArrL9KwGHgKWVdWtszm2VpJsQ/cLdQ+6K4UDfBf4n1V1zkjtG4BLqmrLtqOcvD5MH6iqN/fP3wB8GPgm8HHgq8B/Ag4Hng8cWFU3z85o29qcvydJXkZ3nuGOfVPRXTV/PnAO8C/AC4CfAr4F7FlV/zYLQx2vqvyZAz/AQcDdwFN0V8ce/XmqX3/IbI+1wWfxjv7ffCbdF+eXgJv6z+ADI7VvAJ6a7TFP6HP4OvD7Q8+/AHwO2H6k7keAFcDfz/aYG3wmm/33BFgMPAr8Ot3J77cD9wO3Aa8cqvt5uutPnjfbYx7+8ZjOHJDk54F/oLt0z7uAm+n+altDd524BcD+wHHANUl+sao+M0vDbeE3gMur6n/2zz+f5JPA+4G3JHl+VR0/e8Nr5oV0f6mS5AXATwLHVtU3h4uq6qtJ/go4rfkIG/J78n2/DHyoqj4GkORUus/lPVW1fFBUVZ9J8rfAL8zOMMczdOaG9wDLgddU1eNj1t8NXJvkXOAG4N3ApvhlGtgJ+MvhhupO5D0pyTeBdybZsqqOnY3BNfRvwM794+/S/RW/ZoraJ9j0j9H6Pen8KN3nMHBnv/yXMbW30W0RzRmb+v+kzxU/A1w0xRfp+6o7oXUJsHeTUc2eNXTHKH5Av/VzBnBMkovZtP8f/t/Am5O8pKq+R/fX7FuSrPfHYn8M7FjW/0W0KfJ70vku628wDP4QGXfC+xrWHRedE9zSmRueBF40zdoXMeYeQJuY+4H9gAvHrayqdycpul0sBzYcV2t/ChwB3Jrkz+hutX4RcHeSjwJfA15OdxB5QV+7KfN70nmY7r/7wGPAScA9Y2pfAaxsMajpcvbaHJDkKuBVwC8M75MdU/dK4NPATVX1q42G11ySM4DfB15eVY8+Td3pdLtQqjbB2WsASX4UuBQ4gG6W0uCv1sEXN3QHlf+wqi5qP8J2/J50knwEeFlVHTKN2pvobuPyusmPbHoMnTkgyc50s7NeRLcPenCA9Am6G9ENDpAeSHdg+YCqmu1baE9Mf07OscAVVXX7M9S+BVhUVb/VZHCzJMlr6Gbx7UI3weA7dMd8bgGuGp1csCnye9LpP4eXV9XTnp+W5KV0x0YvG0w6mAsMnTkiyY8DZwOvY/wdT58ArgZOq6r7W45Nmiv8njz3GTpzTH9QeA+6E78GJ709DCx/pgOo0ubC78lzl6EzxyUJ3W0ZngfcX94DSNJz2KY83fQ5JclZSb6R5MEkx/ZtB9PN5LqPbmbK15P89myOU5KeDUNnDkjyJuCPgEfodhH8dZLD6K6vVXRTh/+abgroB5IcPltjlZ4Lkhy9qV4I9rnO3WtzQJLP0v0B8Oqq+l6Ss4DfpdvCeXVVfaev257uOktfqaoDZ2m40pyX5J3AGZvqVPrnMrd05oadgY/2Z50DXEw3LfTCQeAA9NNiPwjs1XqAkrQxeEWCuWEe3eybgUHQfGNM7X/QXbZc2qwkmcnN6n5oYgPRs+KWztywgu5M64HB458dU3sA8O+THpA0By2kC5PHp/Hz3dkZop6JWzpzw98C70nyLbobc70d+CKwU5LjgY8BWwLHAK+nu5GXtLl5ALivqn7xmQqT/DHdVak1xxg6c8P76S5x8j/6598E3kz3F9uNwF/17aHb5XZG4/FJc8FtdDcmmw5nSM1Rhs4cUFWP99fWehXdBIJbBtfSSvIq4BS6M6/vorsL4Fdma6zSLPoX4NeTLKyqFc9Q+2XgnyY/JM2UU6YlSc04kUCS1IyhI0lqxtCRJDVj6EgTkuTAJDXy81iS25OckmTiE3mSLEzy7iR7Tfq9pOlw9po0eR8FrqGb8v4y4I3A+4DdgBMm/N4LgXfRnYB8x4TfS3pGho40ebdX1UcGT5J8gO5WFccleWdVrZy9oUltuXtNaqy/s+XNdFs+Ow3ak+yR5Mok/5FkTZK7krw9yXpXSk5yXZIVo/32u9Iqybv758cAn+lXXzy0i++6odckye8muS3J6iSPJvlMkumehCnNiFs60uwYhM03AJIsAq6nu2bYhcDXgNcBfwbsCbxhA97jn4D3Au8AlgA39O3D1+77MHAU3aWWLqa7+OwbgE8n+bWq+vgGvK80JUNHmrxtkuzAumM6vwP8NHBrVX2xr3k/3S/8/avqcwBJ/gK4HHh9kg9V1f+dyZtW1ZeSfJoudG4a3sXX9/+rdAHz21W1ZKj9/XRbYu9PcnV5Brk2InevSZP3HmAl8HXgc8DvAUuBwwCSvITuiuIfHwQOQP/L/r3901+dwLiOBh4Frkqyw+AH2B64mm4Swk9O4H21GXNLR5q8JcAVwPOBV9LdmvzlwJp+/Y/1yzvHvPYuYC3w4xMY127AC3n6W2W8lO6K59JGYehIk3dvVV3bP/77JP8M/DPd1cOPpNvtNhNT7e6a6fc5dFtgr3+ams/PsE/paRk6UmNVdWOSDwNvTHI+cF+/avcx5bvS7QYfvmvmN4C9x9SO2xp6uuMx99LdKv3mqnrsGQcubQQe05Fmx5nAU8AZVfV1uvsmvS7JTw0KkgQ4rX965dBrvwi8MMm+Q7Vb0N0CY9QgTF48Zt0ldL8Dzho3wCQvnd4/RZo+t3SkWVBV9yX5W+ANSV4N/D7dlOkbkgymTP834BeBy0Zmri0B/gC4sp9p9iTw64z/Pt9FN1ng95KsprtB4Ner6h+r6mNJLgZOTPIzwP8BVtEdb9of+AkmcyxJmzG3dKTZ86d0kwTOqKpldDPYrqeb3bYYeAXdpIM3Dr+oqh4AfoXueMyZdLc3/yzwptE3qKrv0B03+jZwHt0leU4fWn9s3/9auq2qC/p+HmPdVpa00XgTN0lSM27pSJKaMXQkSc0YOpKkZgwdSVIzho4kqRlDR5LUjKEjSWrG0JEkNWPoSJKa+f+kp7rrjEOrkQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s = df[\"daily\"]\n",
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 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": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>daily</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Route</th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>80</th>\n",
- " <td>10211.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>4808.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>4537.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>4425.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>2708.55</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " daily\n",
- "Route \n",
- "80 10211.79\n",
- "2 4808.03\n",
- "6 4537.02\n",
- "10 4425.23\n",
- "3 2708.55"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.read_sql(\"\"\"\n",
- "SELECT Route, SUM(DailyBoardings) AS daily\n",
- "FROM boarding\n",
- "GROUP BY Route\n",
- "ORDER BY daily DESC\n",
- "\"\"\", conn)\n",
- "\n",
- "df = df.set_index(\"Route\")\n",
- "s = df[\"daily\"]\n",
- "df.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### We are back to plotting all route bars ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How can we slice a pandas dataframe?\n",
- "- Recall that .iloc allows us to do slicing.\n",
- "- For reproducing previous 5-route plot, we just need to take first 5 route details and populate into a series s.\n",
- "- For the \"other\" part, we want all the rows in dataframe after row 5 summed up together."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Route\n",
- "80 10211.79\n",
- "2 4808.03\n",
- "6 4537.02\n",
- "10 4425.23\n",
- "3 2708.55\n",
- "other 29296.56\n",
- "Name: daily, dtype: float64"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s = df[\"daily\"].iloc[:5]\n",
- "other_boardings = df[\"daily\"].iloc[5:].sum()\n",
- "s[\"other\"] = other_boardings\n",
- "s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Route'>"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "s.plot.bar()\n",
- "# Q: Where did the xlabel come from? \n",
- "# A: It comes from \"set_index call on the dataframe\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's fix the plot aesthetics."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "(s / 1000).plot.bar(color = \"k\")\n",
- "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
- "None"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots_template-checkpoint.ipynb
deleted file mode 100644
index 2d9a33b..0000000
--- a/f22/meena_lec_notes/lec-36/.ipynb_checkpoints/lec_35_plotting1_bar_plots_template-checkpoint.ipynb
+++ /dev/null
@@ -1,754 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# this allows the full screen to be used\n",
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import statements\n",
- "import pandas as pd\n",
- "from pandas import DataFrame, Series\n",
- "import sqlite3\n",
- "import os"
- ]
- },
- {
- "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": [
- "# Lecture 35: Bar Plots\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 two 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": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
--
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