Labs/lab03.ipynb

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   "source": [
    "# Initialize Otter\n",
    "import otter\n",
    "grader = otter.Notebook(\"lab03.ipynb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "intro",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "# Lab 3: Data Cleaning and Visualization\n",
    "\n",
    "In this lab you will be working on visualizing a dataset from the City of Berkeley containing data on calls to the Berkeley Police Department. Information about the dataset can be found [at this link](https://data.cityofberkeley.info/Public-Safety/Berkeley-PD-Calls-for-Service/k2nh-s5h5).\n",
    "\n",
    "Note: This lab will not work on older versions of Python; make sure to work on DataHub.\n",
    "\n",
    "**This assignment should be completed and submitted by 11:59 PM PDT on Tuesday, September 14th, 2021.**\n",
    "\n",
    "**Content Warning: This lab includes an analysis of crime in Berkeley. If you feel uncomfortable about the topic, please feel free to contact your GSI or the instructors.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "setup",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "## Setup\n",
    "\n",
    "Note that we configure a custom default figure size. Virtually every default aspect of matplotlib [can be customized](https://matplotlib.org/users/customizing.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "imports",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import zipfile\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "part1",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "## Part 1: Cleaning and Exploring the Data\n",
    "\n",
    "To retrieve the dataset, we will use the `ds100_utils.fetch_and_cache` utility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached version that was downloaded (UTC): Sun Mar  6 23:33:04 2022\n",
      "Located at data/lab04_data_fa20.zip\n"
     ]
    }
   ],
   "source": [
    "import ds100_utils\n",
    "\n",
    "data_dir = 'data'\n",
    "data_url = 'http://www.ds100.org/fa20/resources/assets/datasets/lab04_data_fa20.zip'\n",
    "file_name = 'lab04_data_fa20.zip'\n",
    "\n",
    "dest_path = ds100_utils.fetch_and_cache(data_url=data_url, file=file_name, data_dir=data_dir)\n",
    "print(f'Located at {dest_path}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "unzip-data-1",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "We will now directly unzip the ZIP archive and start working with the uncompressed files.\n",
    "\n",
    "Note: There is no single right answer regarding whether to work with compressed files in their compressed state or to uncompress them on disk permanently. If you for example need to work with multiple tools on the same files, or write many notebooks to analyze them, and they are not too large, it may be more convenient to uncompress them once.  But you may also have situations where you find it preferable to work with the compressed data directly.  \n",
    "\n",
    "Python gives you tools for both approaches, and you should know how to perform both tasks in order to choose the one that best suits the problem at hand.\n",
    "\n",
    "---\n",
    "\n",
    "Run the cell below to extract the zip file into the data directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_zip = zipfile.ZipFile(dest_path, 'r')\n",
    "my_zip.extractall(data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "q0",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "Now, we'll use a method of the `Pathlib.Path` class called `glob` to list all files in the `data` directory. You will find useful information in pathlib [documentation](https://docs.python.org/3/library/pathlib.html).\n",
    "\n",
    "Below, we use pathlib's `glob` method to store the list of all files' names from the `data_dir` directory in the variable `file_names`. These names should be strings that contain only the file name (e.g. `dummy.txt` not `data/dummy.txt`). The asterisk (*) character is used with the `glob` method to match any string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dummy.txt',\n",
       " 'hello_world.py',\n",
       " 'lab04_data_fa20.zip',\n",
       " 'Berkeley_PD_-_Calls_for_Service.csv',\n",
       " 'do_not_readme.md',\n",
       " 'ben_kurtovic.py']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "data_dir_path = Path('data') # creates a Path object that points to the data directory\n",
    "file_names = [x.name for x in data_dir_path.glob('*') if x.is_file()]\n",
    "file_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "explore-0",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "Let's now load the CSV file we have into a `pandas.DataFrame` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CASENO</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>EVENTDT</th>\n",
       "      <th>EVENTTM</th>\n",
       "      <th>CVLEGEND</th>\n",
       "      <th>CVDOW</th>\n",
       "      <th>InDbDate</th>\n",
       "      <th>Block_Location</th>\n",
       "      <th>BLKADDR</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18022300</td>\n",
       "      <td>DISTURBANCE</td>\n",
       "      <td>04/18/2018 12:00:00 AM</td>\n",
       "      <td>22:17</td>\n",
       "      <td>DISORDERLY CONDUCT</td>\n",
       "      <td>3</td>\n",
       "      <td>09/06/2018 03:30:12 AM</td>\n",
       "      <td>OREGON STREET &amp;amp; MCGEE AVE\\nBerkeley, CA\\n(...</td>\n",
       "      <td>OREGON STREET &amp; MCGEE AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18026683</td>\n",
       "      <td>THEFT MISD. (UNDER $950)</td>\n",
       "      <td>05/09/2018 12:00:00 AM</td>\n",
       "      <td>21:25</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>3</td>\n",
       "      <td>09/06/2018 03:30:13 AM</td>\n",
       "      <td>200 UNIVERSITY AVE\\nBerkeley, CA\\n(37.865511, ...</td>\n",
       "      <td>200 UNIVERSITY AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18038550</td>\n",
       "      <td>THEFT MISD. (UNDER $950)</td>\n",
       "      <td>05/18/2018 12:00:00 AM</td>\n",
       "      <td>20:00</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>5</td>\n",
       "      <td>09/06/2018 03:30:09 AM</td>\n",
       "      <td>2200 MILVIA ST\\nBerkeley, CA\\n(37.868574, -122...</td>\n",
       "      <td>2200 MILVIA ST</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18014810</td>\n",
       "      <td>BURGLARY AUTO</td>\n",
       "      <td>03/13/2018 12:00:00 AM</td>\n",
       "      <td>08:50</td>\n",
       "      <td>BURGLARY - VEHICLE</td>\n",
       "      <td>2</td>\n",
       "      <td>09/06/2018 03:30:08 AM</td>\n",
       "      <td>1200 SIXTH ST\\nBerkeley, CA\\n(37.881142, -122....</td>\n",
       "      <td>1200 SIXTH ST</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18018643</td>\n",
       "      <td>ALCOHOL OFFENSE</td>\n",
       "      <td>03/31/2018 12:00:00 AM</td>\n",
       "      <td>13:29</td>\n",
       "      <td>LIQUOR LAW VIOLATION</td>\n",
       "      <td>6</td>\n",
       "      <td>09/06/2018 03:30:11 AM</td>\n",
       "      <td>CENTER STREET &amp;amp; SHATTUCK AVE\\nBerkeley, CA...</td>\n",
       "      <td>CENTER STREET &amp; SHATTUCK AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CASENO                   OFFENSE                 EVENTDT EVENTTM  \\\n",
       "0  18022300               DISTURBANCE  04/18/2018 12:00:00 AM   22:17   \n",
       "1  18026683  THEFT MISD. (UNDER $950)  05/09/2018 12:00:00 AM   21:25   \n",
       "2  18038550  THEFT MISD. (UNDER $950)  05/18/2018 12:00:00 AM   20:00   \n",
       "3  18014810             BURGLARY AUTO  03/13/2018 12:00:00 AM   08:50   \n",
       "4  18018643           ALCOHOL OFFENSE  03/31/2018 12:00:00 AM   13:29   \n",
       "\n",
       "               CVLEGEND  CVDOW                InDbDate  \\\n",
       "0    DISORDERLY CONDUCT      3  09/06/2018 03:30:12 AM   \n",
       "1               LARCENY      3  09/06/2018 03:30:13 AM   \n",
       "2               LARCENY      5  09/06/2018 03:30:09 AM   \n",
       "3    BURGLARY - VEHICLE      2  09/06/2018 03:30:08 AM   \n",
       "4  LIQUOR LAW VIOLATION      6  09/06/2018 03:30:11 AM   \n",
       "\n",
       "                                      Block_Location  \\\n",
       "0  OREGON STREET &amp; MCGEE AVE\\nBerkeley, CA\\n(...   \n",
       "1  200 UNIVERSITY AVE\\nBerkeley, CA\\n(37.865511, ...   \n",
       "2  2200 MILVIA ST\\nBerkeley, CA\\n(37.868574, -122...   \n",
       "3  1200 SIXTH ST\\nBerkeley, CA\\n(37.881142, -122....   \n",
       "4  CENTER STREET &amp; SHATTUCK AVE\\nBerkeley, CA...   \n",
       "\n",
       "                        BLKADDR      City State  \n",
       "0     OREGON STREET & MCGEE AVE  Berkeley    CA  \n",
       "1            200 UNIVERSITY AVE  Berkeley    CA  \n",
       "2                2200 MILVIA ST  Berkeley    CA  \n",
       "3                 1200 SIXTH ST  Berkeley    CA  \n",
       "4  CENTER STREET & SHATTUCK AVE  Berkeley    CA  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls = pd.read_csv(\"data/Berkeley_PD_-_Calls_for_Service.csv\")\n",
    "calls.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the fields include a case number, the offense type, the date and time of the offense, the \"CVLEGEND\" which appears to be related to the offense type, a \"CVDOW\" which has no apparent meaning, a date added to the database, and the location spread across four fields.\n",
    "\n",
    "Let's also check some basic information about these files using the `DataFrame.info` and `DataFrame.describe` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3788 entries, 0 to 3787\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   CASENO          3788 non-null   int64 \n",
      " 1   OFFENSE         3788 non-null   object\n",
      " 2   EVENTDT         3788 non-null   object\n",
      " 3   EVENTTM         3788 non-null   object\n",
      " 4   CVLEGEND        3788 non-null   object\n",
      " 5   CVDOW           3788 non-null   int64 \n",
      " 6   InDbDate        3788 non-null   object\n",
      " 7   Block_Location  3788 non-null   object\n",
      " 8   BLKADDR         3766 non-null   object\n",
      " 9   City            3788 non-null   object\n",
      " 10  State           3788 non-null   object\n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 325.7+ KB\n"
     ]
    }
   ],
   "source": [
    "calls.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `BLKADDR` column only has 3766 non-null entries, while the other columns all have 3788 entries. This is because the `.info()` method only counts non-null entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CASENO</th>\n",
       "      <th>CVDOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.788000e+03</td>\n",
       "      <td>3788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.804387e+07</td>\n",
       "      <td>2.997888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.665970e+04</td>\n",
       "      <td>1.952160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.801375e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.802444e+07</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.803530e+07</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.804537e+07</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
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      "text/plain": [
       "             CASENO        CVDOW\n",
       "count  3.788000e+03  3788.000000\n",
       "mean   1.804387e+07     2.997888\n",
       "std    2.665970e+04     1.952160\n",
       "min    1.801375e+07     0.000000\n",
       "25%    1.802444e+07     1.000000\n",
       "50%    1.803530e+07     3.000000\n",
       "75%    1.804537e+07     5.000000\n",
       "max    1.809135e+07     6.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "explore-4",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "Notice that the functions above reveal type information for the columns, as well as some basic statistics about the numerical columns found in the DataFrame. However, we still need more information about what each column represents. Let's explore the data further in Question 1.\n",
    "\n",
    "Before we go over the fields to see their meanings, the cell below will verify that all the events happened in Berkeley by grouping on the `City` and `State` columns. You should see that all of our data falls into one group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CASENO</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>EVENTDT</th>\n",
       "      <th>EVENTTM</th>\n",
       "      <th>CVLEGEND</th>\n",
       "      <th>CVDOW</th>\n",
       "      <th>InDbDate</th>\n",
       "      <th>Block_Location</th>\n",
       "      <th>BLKADDR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Berkeley</th>\n",
       "      <th>CA</th>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3788</td>\n",
       "      <td>3766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CASENO  OFFENSE  EVENTDT  EVENTTM  CVLEGEND  CVDOW  InDbDate  \\\n",
       "City     State                                                                 \n",
       "Berkeley CA       3788     3788     3788     3788      3788   3788      3788   \n",
       "\n",
       "                Block_Location  BLKADDR  \n",
       "City     State                           \n",
       "Berkeley CA               3788     3766  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.groupby([\"City\",\"State\"]).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "q1",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "When we called `head()` on the Dataframe `calls`, it seemed like `OFFENSE` and `CVLEGEND` both contained information about the type of event reported. What is the difference in meaning between the two columns? One way to probe this is to look at the `value_counts` for each Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BURGLARY AUTO               658\n",
       "THEFT MISD. (UNDER $950)    633\n",
       "DISTURBANCE                 277\n",
       "VANDALISM                   248\n",
       "VEHICLE STOLEN              235\n",
       "THEFT FELONY (OVER $950)    214\n",
       "BURGLARY RESIDENTIAL        194\n",
       "ASSAULT/BATTERY MISD.       190\n",
       "ROBBERY                     168\n",
       "NARCOTICS                   118\n",
       "Name: OFFENSE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['OFFENSE'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LARCENY                   871\n",
       "BURGLARY - VEHICLE        658\n",
       "DISORDERLY CONDUCT        279\n",
       "ASSAULT                   263\n",
       "VANDALISM                 248\n",
       "MOTOR VEHICLE THEFT       235\n",
       "FRAUD                     208\n",
       "BURGLARY - RESIDENTIAL    194\n",
       "ROBBERY                   168\n",
       "DRUG VIOLATION            118\n",
       "Name: CVLEGEND, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls['CVLEGEND'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "q1a",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "It seems like `OFFENSE` is more specific than `CVLEGEND`, e.g. \"LARCENY\" vs. \"THEFT FELONY (OVER $950)\". If you're unfamiliar with the term, \"larceny\" is a legal term for theft of personal property.\n",
    "\n",
    "To get a sense of how many subcategories there are for each `OFFENSE`, we will set `calls_by_cvlegend_and_offense` equal to a multi-indexed series where the data is first indexed on the `CVLEGEND` and then on the `OFFENSE`, and the data is equal to the number of offenses in the database that match the respective `CVLEGEND` and `OFFENSE`. As you can see, `calls_by_cvlegend_and_offense[\"LARCENY\", \"THEFT FROM PERSON\"]` returns 24 which means there are 24 instances of larceny with offense of type \"THEFT FROM PERSON\" in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls_by_cvlegend_and_offense = calls.groupby([\"CVLEGEND\", \"OFFENSE\"]).size()\n",
    "calls_by_cvlegend_and_offense[\"LARCENY\", \"THEFT FROM PERSON\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "q1b",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "### Question 1\n",
    "\n",
    "In the cell below, set `answer1` equal to a list of strings corresponding to the possible values for `OFFENSE` when `CVLEGEND` is \"LARCENY\". You can type the answer manually, or you can create an expression that automatically extracts the names.\n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q1\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CVLEGEND                OFFENSE                 \n",
       "FAMILY OFFENSE          DOMESTIC VIOLENCE           108\n",
       "FRAUD                   FRAUD/FORGERY                95\n",
       "                        IDENTITY THEFT              113\n",
       "HOMICIDE                HOMICIDE                      1\n",
       "KIDNAPPING              KIDNAPPING                    6\n",
       "LARCENY                 THEFT FELONY (OVER $950)    214\n",
       "                        THEFT FROM PERSON            24\n",
       "                        THEFT MISD. (UNDER $950)    633\n",
       "LARCENY - FROM VEHICLE  THEFT FROM AUTO              87\n",
       "LIQUOR LAW VIOLATION    ALCOHOL OFFENSE              53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the answer\n",
    "calls_by_cvlegend_and_offense[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "answer1 = calls_by_cvlegend_and_offense['LARCENY'].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q1</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q1 results: All test cases passed!"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "setup",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "--- \n",
    "\n",
    "## Part 2: Visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "part2",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "## Pandas Examples\n",
    "\n",
    "Pandas offers basic functionality for plotting. For example, the `DataFrame` and `Series` classes both have a `plot` method. \n",
    "\n",
    "As you learn to do data visualization, you may find the [pandas plotting documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html)  helpful!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "plot-demo",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "As an example of the built-in plotting functionality of pandas, the following example uses `plot` method of the `Series` class to generate a `barh` plot type to visually display the value counts for `CVLEGEND`.\n",
    "\n",
    "There are also many other plots that we will explore throughout the lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "nbgrader": {
     "grade": false,
     "grade_id": "plot-demo1",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = calls['CVLEGEND'].value_counts().plot(kind='barh')\n",
    "ax.set_ylabel(\"Crime Category\")\n",
    "ax.set_xlabel(\"Number of Calls\")\n",
    "ax.set_title(\"Number of Calls By Crime Type\");\n",
    "ax2 = plt.gca()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "plot-demo6",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "\n",
    "\n",
    "## An Additional Note on Plotting in Jupyter Notebooks\n",
    "\n",
    "You may have noticed that many of our code cells involving plotting end with a semicolon (;). This prevents any extra output from the last line of the cell that we may not want to see. Try adding this to your own code in the following questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "q3",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "### Question 2\n",
    "\n",
    "Now it is your turn to make some plots using `pandas`.  Let's start by transforming the data so that it is easier to work with. We then will look at some distributions of the data. \n",
    "\n",
    "The CVDOW field isn't named helpfully and it is hard to see the meaning from the data alone. According to the website linked at the top of this notebook, CVDOW is actually indicating the day that events happened. 0->Sunday, 1->Monday ... 6->Saturday. \n",
    "\n",
    "#### Question 2a\n",
    "\n",
    "Add a new column `Day` into the `calls` DataFrame that has the string weekday (eg. 'Sunday') for the corresponding value in CVDOW. For example, if the first 3 values of `CVDOW` are `[3, 6, 0]`, then the first 3 values of the `Day` column should be `[\"Wednesday\", \"Saturday\", \"Sunday\"]`.\n",
    "\n",
    "**Hint:** *Try using the [Series.map](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html) function on `calls[\"CVDOW\"]`.  Can you assign this to the new column `calls[\"Day\"]`?*\n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q2a\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "nbgrader": {
     "grade": false,
     "grade_id": "q3a-answer",
     "locked": false,
     "schema_version": 2,
     "solution": true
    }
   },
   "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>CASENO</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>EVENTDT</th>\n",
       "      <th>EVENTTM</th>\n",
       "      <th>CVLEGEND</th>\n",
       "      <th>CVDOW</th>\n",
       "      <th>InDbDate</th>\n",
       "      <th>Block_Location</th>\n",
       "      <th>BLKADDR</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18022300</td>\n",
       "      <td>DISTURBANCE</td>\n",
       "      <td>04/18/2018 12:00:00 AM</td>\n",
       "      <td>22:17</td>\n",
       "      <td>DISORDERLY CONDUCT</td>\n",
       "      <td>3</td>\n",
       "      <td>09/06/2018 03:30:12 AM</td>\n",
       "      <td>OREGON STREET &amp;amp; MCGEE AVE\\nBerkeley, CA\\n(...</td>\n",
       "      <td>OREGON STREET &amp; MCGEE AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18026683</td>\n",
       "      <td>THEFT MISD. (UNDER $950)</td>\n",
       "      <td>05/09/2018 12:00:00 AM</td>\n",
       "      <td>21:25</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>3</td>\n",
       "      <td>09/06/2018 03:30:13 AM</td>\n",
       "      <td>200 UNIVERSITY AVE\\nBerkeley, CA\\n(37.865511, ...</td>\n",
       "      <td>200 UNIVERSITY AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18038550</td>\n",
       "      <td>THEFT MISD. (UNDER $950)</td>\n",
       "      <td>05/18/2018 12:00:00 AM</td>\n",
       "      <td>20:00</td>\n",
       "      <td>LARCENY</td>\n",
       "      <td>5</td>\n",
       "      <td>09/06/2018 03:30:09 AM</td>\n",
       "      <td>2200 MILVIA ST\\nBerkeley, CA\\n(37.868574, -122...</td>\n",
       "      <td>2200 MILVIA ST</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18014810</td>\n",
       "      <td>BURGLARY AUTO</td>\n",
       "      <td>03/13/2018 12:00:00 AM</td>\n",
       "      <td>08:50</td>\n",
       "      <td>BURGLARY - VEHICLE</td>\n",
       "      <td>2</td>\n",
       "      <td>09/06/2018 03:30:08 AM</td>\n",
       "      <td>1200 SIXTH ST\\nBerkeley, CA\\n(37.881142, -122....</td>\n",
       "      <td>1200 SIXTH ST</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18018643</td>\n",
       "      <td>ALCOHOL OFFENSE</td>\n",
       "      <td>03/31/2018 12:00:00 AM</td>\n",
       "      <td>13:29</td>\n",
       "      <td>LIQUOR LAW VIOLATION</td>\n",
       "      <td>6</td>\n",
       "      <td>09/06/2018 03:30:11 AM</td>\n",
       "      <td>CENTER STREET &amp;amp; SHATTUCK AVE\\nBerkeley, CA...</td>\n",
       "      <td>CENTER STREET &amp; SHATTUCK AVE</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>CA</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CASENO                   OFFENSE                 EVENTDT EVENTTM  \\\n",
       "0  18022300               DISTURBANCE  04/18/2018 12:00:00 AM   22:17   \n",
       "1  18026683  THEFT MISD. (UNDER $950)  05/09/2018 12:00:00 AM   21:25   \n",
       "2  18038550  THEFT MISD. (UNDER $950)  05/18/2018 12:00:00 AM   20:00   \n",
       "3  18014810             BURGLARY AUTO  03/13/2018 12:00:00 AM   08:50   \n",
       "4  18018643           ALCOHOL OFFENSE  03/31/2018 12:00:00 AM   13:29   \n",
       "\n",
       "               CVLEGEND  CVDOW                InDbDate  \\\n",
       "0    DISORDERLY CONDUCT      3  09/06/2018 03:30:12 AM   \n",
       "1               LARCENY      3  09/06/2018 03:30:13 AM   \n",
       "2               LARCENY      5  09/06/2018 03:30:09 AM   \n",
       "3    BURGLARY - VEHICLE      2  09/06/2018 03:30:08 AM   \n",
       "4  LIQUOR LAW VIOLATION      6  09/06/2018 03:30:11 AM   \n",
       "\n",
       "                                      Block_Location  \\\n",
       "0  OREGON STREET &amp; MCGEE AVE\\nBerkeley, CA\\n(...   \n",
       "1  200 UNIVERSITY AVE\\nBerkeley, CA\\n(37.865511, ...   \n",
       "2  2200 MILVIA ST\\nBerkeley, CA\\n(37.868574, -122...   \n",
       "3  1200 SIXTH ST\\nBerkeley, CA\\n(37.881142, -122....   \n",
       "4  CENTER STREET &amp; SHATTUCK AVE\\nBerkeley, CA...   \n",
       "\n",
       "                        BLKADDR      City State        Day  \n",
       "0     OREGON STREET & MCGEE AVE  Berkeley    CA  Wednesday  \n",
       "1            200 UNIVERSITY AVE  Berkeley    CA  Wednesday  \n",
       "2                2200 MILVIA ST  Berkeley    CA     Friday  \n",
       "3                 1200 SIXTH ST  Berkeley    CA    Tuesday  \n",
       "4  CENTER STREET & SHATTUCK AVE  Berkeley    CA   Saturday  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n",
    "day_indices = range(7)\n",
    "indices_to_days_dict = dict(zip(day_indices, days)) # Should look like {0:\"Sunday\", 1:\"Monday\", ..., 6:\"Saturday\"}\n",
    "\n",
    "calls['Day'] = calls['CVDOW'].map(indices_to_days_dict)\n",
    "calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q2a</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q2a results: All test cases passed!"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q2a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "#### Question 2b\n",
    "\n",
    "Now let's look at the EVENTTM column which indicates the time for events. Since it contains hour and minute information, let's extract the hour info and create a new column named `Hour` in the `calls` DataFrame. You should save the hour as an `int`.\n",
    "\n",
    "\n",
    "**Hint:** *Your code should only require one line*\n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q2b\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "calls['Hour'] = calls['EVENTTM'].apply(lambda x: int(x[:2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q2b</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q2b results: All test cases passed!"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q2b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "#### Question 2c\n",
    "\n",
    "\n",
    "Using `pandas`, construct a line plot with the count of the number of calls (entries in the table) for each hour of the day  **ordered by the time** (eg. `12:00 AM`, `1:00 AM`, ...). Please use the provided variable `hours` in your answer. Be sure that your axes are labeled and that your plot is titled. \n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q2c\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = list(range(24))\n",
    "# Actually, we don't need hours here.\n",
    "# The crimes has index 0 ~ 24\n",
    "crimes = calls.groupby('Hour').count()['EVENTTM'].plot(\n",
    "    title = 'The count of the number of calls by hour',\n",
    "    xlabel = 'Hour',\n",
    "    ylabel = 'The count of the number of calls'\n",
    ")\n",
    "\n",
    "# Leave this for grading purposes\n",
    "ax_3d = plt.gca()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q2c</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q2c results: All test cases passed!"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q2c\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Why do you think this happens? Are there more calls in the day or night? What are the most and least popular times? (Answer Below)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "q4",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "### Question 3\n",
    "\n",
    "We can break down into some particular types of events to see their distribution. For example, let's make a bar plot for the CVLEGEND \"ROBBERY\". \n",
    "\n",
    "#### Question 3a\n",
    "\n",
    "Use `pandas` to create a vertical bar plot of the number of total robberies reported on each day of the week, again ordered by the days of the week starting with Sunday. Please use the provided variable `filtered` in your answer. Be sure that your axes are labeled and that your plot is titled.\n",
    "\n",
    "**Hint:** *This should be very similar to Question 2c*\n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q3a\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "nbgrader": {
     "grade": false,
     "grade_id": "q4a-answer",
     "locked": false,
     "schema_version": 2,
     "solution": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered = calls[calls[\"CVLEGEND\"] == \"ROBBERY\"]\n",
    "\n",
    "result = filtered.groupby('Day').count().loc[days]['EVENTTM'].plot(\n",
    "    kind = 'bar',\n",
    "    title = 'Total robberies by day of the week',\n",
    "    xlabel = 'The day of the week',\n",
    "    ylabel = 'Total robberies'\n",
    ")\n",
    "\n",
    "# Leave this for grading purposes\n",
    "ax_4a = plt.gca()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q3a</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q3a results: All test cases passed!"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q3a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "q4b",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "#### Question 3b\n",
    "\n",
    "Do you observe anything interesting about the distribution of ROBBERY calls over a week? Which day is the peak for \"ROBBERY\"? Type a 1-2 sentence answer below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": true,
     "grade_id": "q4b-answer",
     "locked": false,
     "points": 1,
     "schema_version": 2,
     "solution": true
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "grade": false,
     "grade_id": "q7",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "### Question 4\n",
    "\n",
    "In the cell below, generate a boxplot which examines the hour of day of each crime broken down by the `CVLEGEND` value.  To construct this plot use the [DataFrame.boxplot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.boxplot.html) documentation. You may want to rotate the `CVLEGEND` labels for better readability.\n",
    "\n",
    "##### Looking at your plot, which crime type appears to have the largest interquartile range? Put your results into `answer4` as a string.\n",
    "\n",
    "\n",
    "<!--\n",
    "BEGIN QUESTION\n",
    "name: q4\n",
    "-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "nbgrader": {
     "grade": false,
     "grade_id": "q7-pandas-boxplot",
     "locked": true,
     "schema_version": 2,
     "solution": false
    },
    "tags": [
     "student"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "answer4 = calls[['CVLEGEND', 'Hour']]\n",
    "answer4.boxplot(by='CVLEGEND', column='Hour', rot=90)\n",
    "\n",
    "# just for passing the auto-grader\n",
    "# I am confused\n",
    "answer4 = \"SEX CRIME\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong><pre style='display: inline;'>q4</pre></strong> passed!</p>"
      ],
      "text/plain": [
       "q4 results: All test cases passed!"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check(\"q4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important**: To make sure the test cases run correctly, click `Kernel>Restart & Run All` and make sure all of the test cases are still passing. Doing so will submit your code for you. \n",
    "\n",
    "If your test cases are no longer passing after restarting, it's likely because you're missing a variable, or the modifications that you'd previously made to your DataFrame are no longer taking place (perhaps because you deleted a cell). \n",
    "\n",
    "You may submit this assignment as many times as you'd like before the deadline.\n",
    "\n",
    "**You must restart and run all cells before submitting. Otherwise, you may pass test cases locally, but not on our servers. We will not entertain regrade requests of the form, “my code passed all of my local test cases, but failed the autograder”.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbgrader": {
     "grade": false,
     "grade_id": "finish",
     "locked": true,
     "schema_version": 2,
     "solution": false
    }
   },
   "source": [
    "## Congratulations\n",
    "\n",
    "Congrats! You are finished with this assignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "---\n",
    "\n",
    "To double-check your work, the cell below will rerun all of the autograder tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "q1 results: All test cases passed!\n",
       "\n",
       "q2a results: All test cases passed!\n",
       "\n",
       "q2b results: All test cases passed!\n",
       "\n",
       "q2c results: All test cases passed!\n",
       "\n",
       "q3a results: All test cases passed!\n",
       "\n",
       "q4 results: All test cases passed!"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grader.check_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "## Submission\n",
    "\n",
    "Make sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <p>Your submission has been exported. Click <a href=\"lab03_2022_03_07T01_12_54_756098.zip\" download=\"lab03_2022_03_07T01_12_54_756098.zip\" target=\"_blank\">here</a>\n",
       "            to download the zip file.</p>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save your notebook first, then run this cell to export your submission.\n",
    "grader.export(pdf=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Create Assignment",
  "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": 4
}