matplot_autograder_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2c1a02c-8971-4760-9d60-1b4add1cc94d",
   "metadata": {},
   "source": [
    "# Autograding `matplotlib` plots with `matplotcheck` and `otter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65023f72-ff81-4b3d-9d1c-34571944d939",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install pandas --q\n",
    "! pip install matplotlib --q\n",
    "! pip install otter-grader --q\n",
    "! pip install matplotcheck -- q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2298764b-50a2-40ed-90c1-4ae3e04ebad2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotcheck.base as mpc\n",
    "import matplotcheck.notebook as nb\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f84bd202-f84d-433e-b8f2-ccd218e347e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tips = pd.read_csv(\"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b82ded5-84df-4f21-aafd-1b7c2e4f03cf",
   "metadata": {
    "tags": []
   },
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8562430-af54-434a-8907-c514a4a2299d",
   "metadata": {},
   "source": [
    "## Quick Intro to `matplotcheck`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea53a4eb-115c-4051-b05c-b5376653d53f",
   "metadata": {},
   "source": [
    "We'll start by looking at a quick example using the `tips` data set. We will create a simple `matplotlib` plot and use it to explore `matplotcheck`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "03f3c140-e935-4985-b0e1-8fd3ad259e5c",
   "metadata": {
    "tags": []
   },
   "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>sex</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  count\n",
       "0    Male    157\n",
       "1  Female     87"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_df=tips[\"sex\"].value_counts().to_frame().reset_index()\n",
    "example_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "38b09f2a-d9c5-4ae3-86b7-e1cd49b1aed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(example_df[\"sex\"], example_df[\"count\"])\n",
    "plt.title(\"Male and Female Patrons\")\n",
    "plt.xlabel(\"sex\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada2f86d-8d1b-410b-879e-696dbd9aa21e",
   "metadata": {},
   "source": [
    "Pretty simple. Now let's take a look at `matplotcheck`.\n",
    "\n",
    "\n",
    "By adding in the following line:\n",
    "\n",
    "```python\n",
    "plot_1_hold = nb.convert_axes(plt, which_axes=\"current\") \n",
    "```\n",
    "*Note that `plot_1_hold` is just a variable and can be replaced with any other name.*\n",
    "\n",
    "`matplotcheck` \"saves\" a copy of the plot so it can be autograded. *Make sure this is before the call to `plt.show()`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "568d1679-12e2-4462-93e3-38501926678a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(example_df[\"sex\"], example_df[\"count\"])\n",
    "plt.title(\"Male and Female Patrons\")\n",
    "plt.xlabel(\"sex\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plot_1_hold = nb.convert_axes(plt, which_axes=\"current\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18eeb9d-16d9-489a-b053-e84c4613a972",
   "metadata": {},
   "source": [
    "We can now begin to test the plot above. We start by creating a `PlotTester` object using our saved plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0731ce07-9b7f-4177-bc79-0280445f5d05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_tester_1 = mpc.PlotTester(plot_1_hold)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098c5cf2-503e-409b-9345-15f4cdf28fc2",
   "metadata": {},
   "source": [
    "`matplotcheck` allows for many different types of tests to be run. For example, we can test whether the plot is of the correct type. We can do this by using the `assert_plot_type` method. Currently, only bar, line, and scatter plots work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "078b682c-0d7d-4e6f-a902-5177e14f81bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_tester_1.assert_plot_type(\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a149c9c0-1fff-488e-a807-947a62ba4dae",
   "metadata": {},
   "source": [
    "We can see that by not getting an error, this plot is of the correct type. Let's see what would happen if the plot type was incorrect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ae67d937-46c1-4995-ac3a-68070cfdfccd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Plot is not of type line",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplot_tester_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_plot_type\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mline\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\engin7\\lib\\site-packages\\matplotcheck\\base.py:173\u001b[0m, in \u001b[0;36mPlotTester.assert_plot_type\u001b[1;34m(self, plot_type, message)\u001b[0m\n\u001b[0;32m    171\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39max\u001b[38;5;241m.\u001b[39mpatches, message\u001b[38;5;241m.\u001b[39mformat(plot_type)\n\u001b[0;32m    172\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m plot_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mline\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 173\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_line(), message\u001b[38;5;241m.\u001b[39mformat(plot_type)\n\u001b[0;32m    174\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    175\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    176\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlot_type to test must be either: scatter, bar or line\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    177\u001b[0m     )\n",
      "\u001b[1;31mAssertionError\u001b[0m: Plot is not of type line"
     ]
    }
   ],
   "source": [
    "plot_tester_1.assert_plot_type(\"line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a67692-2706-4664-a96a-9ddaec5522cb",
   "metadata": {},
   "source": [
    "Other features of `matplotcheck` include:\n",
    " * Checking title\n",
    " * Checking axis labels\n",
    " \n",
    "Because the workflow of these is similar, we'll combine them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f8c99ad-2a1d-4440-9e63-c2317a5a5da5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_tester_1.assert_title_contains([\"Male\", \"and\", \"Female\", \"Patrons\"])\n",
    "plot_tester_1.assert_axis_label_contains(axis=\"x\", strings_expected=[\"sex\"])\n",
    "plot_tester_1.assert_axis_label_contains(axis=\"y\", strings_expected=[\"Count\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d402e408-c590-4ec6-9a04-8587784a1156",
   "metadata": {},
   "source": [
    "The tests are not case sensitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "574b0f22-7e17-42ff-8ff3-d747d828f8e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_tester_1.assert_axis_label_contains(axis=\"x\", strings_expected=[\"SEX\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673968cd-1ff5-4dcd-bb79-0c8b8bd1301b",
   "metadata": {},
   "source": [
    "The title does not need to contain all of the expected string but will flag if the title does not contain the expected words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f640314d-4f56-407c-9bf3-6169a5a8c5b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Title does not contain expected string: pineapple",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[48], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplot_tester_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_title_contains\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrings_expected\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpineapple\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\engin7\\lib\\site-packages\\matplotcheck\\base.py:262\u001b[0m, in \u001b[0;36mPlotTester.assert_title_contains\u001b[1;34m(self, strings_expected, title_type, message_default, message_or, message_no_title)\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    256\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtitle_type must be one of the following \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    257\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfigure\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meither\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    258\u001b[0m     )\n\u001b[0;32m    260\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m title, message_no_title\n\u001b[1;32m--> 262\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_string_contains\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstrings_expected\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage_default\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage_default\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage_or\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmessage_or\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    267\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\engin7\\lib\\site-packages\\matplotcheck\\base.py:132\u001b[0m, in \u001b[0;36mPlotTester.assert_string_contains\u001b[1;34m(self, string, strings_expected, message_default, message_or)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(check, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m check\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m string:\n\u001b[1;32m--> 132\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(message_default\u001b[38;5;241m.\u001b[39mformat(check))\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(check, \u001b[38;5;28mlist\u001b[39m):\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28many\u001b[39m(\n\u001b[0;32m    135\u001b[0m         [c\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m string \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m check]\n\u001b[0;32m    136\u001b[0m     ):\n",
      "\u001b[1;31mAssertionError\u001b[0m: Title does not contain expected string: pineapple"
     ]
    }
   ],
   "source": [
    "plot_tester_1.assert_title_contains(strings_expected=\"pineapple\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26813b55-fae8-425c-84de-5412ffba2051",
   "metadata": {},
   "source": [
    "There is also an option to check that the data in the plot is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dc994092-628b-4f11-919c-7af43875fc73",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "expected_data = pd.DataFrame(\n",
    "    {\"sex\": [\"Male\", \"Female\"], \"count\": [157,87]}\n",
    ")\n",
    "\n",
    "plot_tester_1.assert_xydata(\n",
    "    expected_data, xcol=\"sex\", ycol=\"count\", xlabels=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35493fbb-9004-41a0-959e-c504ac541983",
   "metadata": {},
   "source": [
    "## `matplotcheck` and `otter`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b7be05-0764-4ffe-a239-18d1146da39c",
   "metadata": {},
   "source": [
    "The workflow with `otter` will look something like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d29f23-70d3-4bd5-9f77-9b1b7d2951d8",
   "metadata": {},
   "source": [
    "```raw\n",
    "# BEGIN QUESTION\n",
    "name: q2.4\n",
    "```\n",
    "\n",
    "\n",
    "Note that this *does not* have the \n",
    "\n",
    "```raw\n",
    "manual: true\n",
    "```\n",
    "\n",
    "tag in the question config."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afa2966e-b708-4d6e-b83b-32996ada542a",
   "metadata": {},
   "source": [
    "**Question 2.4:** Plot a bar chart of Male and Female patrons. \n",
    "\n",
    "Your plot should have:\n",
    " * An x-axis labeled as `sex`\n",
    " * A y-axis labeled as `Count`\n",
    " * A title `Male and Female Patrons`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac70b49-706c-4ca5-a5b3-571591564a95",
   "metadata": {},
   "source": [
    "```raw\n",
    "# BEGIN SOLUTION\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "80686db7-536a-4460-bc3e-a66cc727618b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BEGIN SOLUTION\n",
    "plt.bar(example_df[\"sex\"], example_df[\"count\"])\n",
    "plt.title(\"Male and Female Patrons\")\n",
    "plt.xlabel(\"sex\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "# END SOLUTION\n",
    "plot_1_hold = nb.convert_axes(plt, which_axes=\"current\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d85e3a2-3f67-422d-a188-774f620a6627",
   "metadata": {},
   "source": [
    "```raw\n",
    "# END SOLUTION\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f13b301-4369-44f0-8c73-0c71f9662218",
   "metadata": {},
   "source": [
    "```raw\n",
    "# BEGIN TESTS\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45a0a501-e74f-45c4-aac9-4ce840551b0a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_tester_1 = mpc.PlotTester(plot_1_hold)\n",
    "plot_tester_1.assert_plot_type(\"bar\")\n",
    "plot_tester_1.assert_title_contains([\"Male\", \"and\", \"Female\", \"Patrons\"])\n",
    "plot_tester_1.assert_axis_label_contains(axis=\"x\", strings_expected=[\"sex\"])\n",
    "plot_tester_1.assert_axis_label_contains(axis=\"y\", strings_expected=[\"Count\"])\n",
    "expected_data = pd.DataFrame(\n",
    "    {\"sex\": [\"Male\", \"Female\"], \"count\": [157,87]}\n",
    ")\n",
    "\n",
    "plot_tester_1.assert_xydata(\n",
    "    expected_data, xcol=\"sex\", ycol=\"count\", xlabels=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8546a7-451f-4f29-b325-25460fb79e81",
   "metadata": {},
   "source": [
    "```raw\n",
    "# END TESTS\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d748e2ee-e58c-4149-a608-0cf92408fe9e",
   "metadata": {},
   "source": [
    "```raw\n",
    "# END QUESTION\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b17d058-9bd2-454e-b0eb-a6f6e3a3c223",
   "metadata": {},
   "source": [
    "That's pretty much it. \n",
    "\n",
    "Notes:\n",
    "* `matplotcheck` has been tested to work on the student facing notebook autograder but has not been tested on the Gradescope side. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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