{
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    "# GGR274 Lab 4: Introduction to Data Wrangling, Part 1\n",
    "\n",
    "## Logistics\n",
    "\n",
    "Like last week, our lab grade will be based on attendance and submission of a few small tasks to MarkUs during the lab session (or by 23:59 on Thursday).\n",
    "\n",
    "Complete the tasks in this Jupyter notebook and submit your completed file to [MarkUs](https://markus-ds.teach.cs.toronto.edu).\n",
    "Here are the instructions for submitting to MarkUs (same as last week):\n",
    "\n",
    "1. Download this file (`Lab_4.ipynb`) from JupyterHub. (See [our JupyterHub Guide](../../../guides/jupyterhub_guide.ipynb) for detailed instructions.)\n",
    "2. Submit this file to MarkUs under the **lab4** assignment. (See [our MarkUs Guide](../../../guides/markus_guide.ipynb) for detailed instructions.)\n",
    "\n",
    "Note: there's no autograding set up for this week's lab, but your TA will be checking that your submitted lab file is complete as part of your \"lab attendance\" grade."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535ac824",
   "metadata": {},
   "source": [
    "## Task 1: Read the csv file into a `DataFrame`\n",
    "\n",
    "Read the csv file `ArrestsStripSearches.csv` into a pandas Dataframe called `police_df` related to Toronto Police Race and Identity Based Data - Arrests and Strip Searches.\n",
    "\n",
    "_The file is located in the same folder as the notebook._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da33b4b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0j/ybsv4ncn5w50v40vdh5jjlww0000gn/T/ipykernel_79053/1181357276.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>_id</th>\n",
       "      <th>Arrest_Year</th>\n",
       "      <th>Arrest_Month</th>\n",
       "      <th>EventID</th>\n",
       "      <th>ArrestID</th>\n",
       "      <th>PersonID</th>\n",
       "      <th>Perceived_Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age_group__at_arrest_</th>\n",
       "      <th>Youth_at_arrest__under_18_years</th>\n",
       "      <th>...</th>\n",
       "      <th>Actions_at_arrest___Resisted__d</th>\n",
       "      <th>Actions_at_arrest___Mental_inst</th>\n",
       "      <th>Actions_at_arrest___Assaulted_o</th>\n",
       "      <th>Actions_at_arrest___Cooperative</th>\n",
       "      <th>SearchReason_CauseInjury</th>\n",
       "      <th>SearchReason_AssistEscape</th>\n",
       "      <th>SearchReason_PossessWeapons</th>\n",
       "      <th>SearchReason_PossessEvidence</th>\n",
       "      <th>ItemsFound</th>\n",
       "      <th>ObjectId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>July-Sept</td>\n",
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       "      <td>6017884.0</td>\n",
       "      <td>326622</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 35 to 44 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>July-Sept</td>\n",
       "      <td>1014562</td>\n",
       "      <td>6056669.0</td>\n",
       "      <td>326622</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 35 to 44 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>Oct-Dec</td>\n",
       "      <td>1029922</td>\n",
       "      <td>6057065.0</td>\n",
       "      <td>326622</td>\n",
       "      <td>Unknown or Legacy</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 35 to 44 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan-Mar</td>\n",
       "      <td>1052190</td>\n",
       "      <td>6029059.0</td>\n",
       "      <td>327535</td>\n",
       "      <td>Black</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 25 to 34 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan-Mar</td>\n",
       "      <td>1015512</td>\n",
       "      <td>6040372.0</td>\n",
       "      <td>327535</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 25 to 34 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
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       "   _id  Arrest_Year Arrest_Month  EventID   ArrestID  PersonID  \\\n",
       "0    1         2020    July-Sept  1005907  6017884.0    326622   \n",
       "1    2         2020    July-Sept  1014562  6056669.0    326622   \n",
       "2    3         2020      Oct-Dec  1029922  6057065.0    326622   \n",
       "3    4         2021      Jan-Mar  1052190  6029059.0    327535   \n",
       "4    5         2021      Jan-Mar  1015512  6040372.0    327535   \n",
       "\n",
       "      Perceived_Race Sex Age_group__at_arrest_  \\\n",
       "0              White   M   Aged 35 to 44 years   \n",
       "1              White   M   Aged 35 to 44 years   \n",
       "2  Unknown or Legacy   M   Aged 35 to 44 years   \n",
       "3              Black   M   Aged 25 to 34 years   \n",
       "4        South Asian   M   Aged 25 to 34 years   \n",
       "\n",
       "  Youth_at_arrest__under_18_years  ... Actions_at_arrest___Resisted__d  \\\n",
       "0                     Not a youth  ...                             NaN   \n",
       "1                     Not a youth  ...                             NaN   \n",
       "2                     Not a youth  ...                             NaN   \n",
       "3                     Not a youth  ...                             NaN   \n",
       "4                     Not a youth  ...                             NaN   \n",
       "\n",
       "   Actions_at_arrest___Mental_inst  Actions_at_arrest___Assaulted_o  \\\n",
       "0                              NaN                              NaN   \n",
       "1                              NaN                              NaN   \n",
       "2                              NaN                              NaN   \n",
       "3                              NaN                              NaN   \n",
       "4                              NaN                              NaN   \n",
       "\n",
       "  Actions_at_arrest___Cooperative  SearchReason_CauseInjury  \\\n",
       "0                             1.0                       NaN   \n",
       "1                             NaN                       NaN   \n",
       "2                             NaN                       NaN   \n",
       "3                             NaN                       NaN   \n",
       "4                             NaN                       NaN   \n",
       "\n",
       "   SearchReason_AssistEscape  SearchReason_PossessWeapons  \\\n",
       "0                        NaN                          NaN   \n",
       "1                        NaN                          NaN   \n",
       "2                        NaN                          NaN   \n",
       "3                        NaN                          NaN   \n",
       "4                        NaN                          NaN   \n",
       "\n",
       "   SearchReason_PossessEvidence  ItemsFound  ObjectId  \n",
       "0                           NaN         NaN         1  \n",
       "1                           NaN         NaN         2  \n",
       "2                           NaN         NaN         3  \n",
       "3                           NaN         NaN         4  \n",
       "4                           NaN         NaN         5  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "police_df = pd.read_csv('ArrestsStripSearches.csv')\n",
    "\n",
    "police_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4903d4",
   "metadata": {},
   "source": [
    "## Task 2: Create a `DataFrame` of arrests in 2021\n",
    "\n",
    "### a. Subset the tabular data by rows\n",
    "\n",
    "- Create a boolean variable named `Arrests_2021` computed from `police_df` that is `True` if `Arrest_Year` is `2021` and `False` otherwise.\n",
    "\n",
    "- Use `Arrests_2021` to select rows of `police_df` that correspond to arrests in 2021.  Save this new DataFrame in a variable called `police_2021_df`.  Examine the `head()` of this `DataFrame`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "840760c8",
   "metadata": {},
   "outputs": [
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       "      <td>2021</td>\n",
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       "      <td>1015512</td>\n",
       "      <td>6040372.0</td>\n",
       "      <td>327535</td>\n",
       "      <td>South Asian</td>\n",
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       "      <td>6060688.0</td>\n",
       "      <td>327535</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 25 to 34 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan-Mar</td>\n",
       "      <td>1035445</td>\n",
       "      <td>6053833.0</td>\n",
       "      <td>330778</td>\n",
       "      <td>Black</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 25 to 34 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan-Mar</td>\n",
       "      <td>1050464</td>\n",
       "      <td>6063477.0</td>\n",
       "      <td>330778</td>\n",
       "      <td>Black</td>\n",
       "      <td>M</td>\n",
       "      <td>Aged 25 to 34 years</td>\n",
       "      <td>Not a youth</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "   _id  Arrest_Year Arrest_Month  EventID   ArrestID  PersonID Perceived_Race  \\\n",
       "3    4         2021      Jan-Mar  1052190  6029059.0    327535          Black   \n",
       "4    5         2021      Jan-Mar  1015512  6040372.0    327535    South Asian   \n",
       "5    6         2021     Apr-June  1019145  6060688.0    327535    South Asian   \n",
       "6    7         2021      Jan-Mar  1035445  6053833.0    330778          Black   \n",
       "7    8         2021      Jan-Mar  1050464  6063477.0    330778          Black   \n",
       "\n",
       "  Sex Age_group__at_arrest_ Youth_at_arrest__under_18_years  ...  \\\n",
       "3   M   Aged 25 to 34 years                     Not a youth  ...   \n",
       "4   M   Aged 25 to 34 years                     Not a youth  ...   \n",
       "5   M   Aged 25 to 34 years                     Not a youth  ...   \n",
       "6   M   Aged 25 to 34 years                     Not a youth  ...   \n",
       "7   M   Aged 25 to 34 years                     Not a youth  ...   \n",
       "\n",
       "  Actions_at_arrest___Resisted__d  Actions_at_arrest___Mental_inst  \\\n",
       "3                             NaN                              NaN   \n",
       "4                             NaN                              NaN   \n",
       "5                             NaN                              NaN   \n",
       "6                             NaN                              NaN   \n",
       "7                             NaN                              NaN   \n",
       "\n",
       "   Actions_at_arrest___Assaulted_o Actions_at_arrest___Cooperative  \\\n",
       "3                              NaN                             NaN   \n",
       "4                              NaN                             NaN   \n",
       "5                              NaN                             1.0   \n",
       "6                              NaN                             NaN   \n",
       "7                              NaN                             1.0   \n",
       "\n",
       "   SearchReason_CauseInjury  SearchReason_AssistEscape  \\\n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "5                       NaN                        NaN   \n",
       "6                       NaN                        NaN   \n",
       "7                       NaN                        NaN   \n",
       "\n",
       "   SearchReason_PossessWeapons  SearchReason_PossessEvidence  ItemsFound  \\\n",
       "3                          NaN                           NaN         NaN   \n",
       "4                          NaN                           NaN         NaN   \n",
       "5                          NaN                           NaN         NaN   \n",
       "6                          NaN                           NaN         NaN   \n",
       "7                          NaN                           NaN         NaN   \n",
       "\n",
       "   ObjectId  \n",
       "3         4  \n",
       "4         5  \n",
       "5         6  \n",
       "6         7  \n",
       "7         8  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arrests_2021 = police_df['Arrest_Year'] == 2021\n",
    "\n",
    "police_2021_df = police_df[Arrests_2021]\n",
    "\n",
    "police_2021_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97e5e1e",
   "metadata": {},
   "source": [
    "### b. Inspect columns of the tabular data\n",
    "\n",
    "- Create a variable called `Arrest_column_names` that stores a list of the column names of `police_2021_df` and print the list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f89b19a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_id',\n",
       " 'Arrest_Year',\n",
       " 'Arrest_Month',\n",
       " 'EventID',\n",
       " 'ArrestID',\n",
       " 'PersonID',\n",
       " 'Perceived_Race',\n",
       " 'Sex',\n",
       " 'Age_group__at_arrest_',\n",
       " 'Youth_at_arrest__under_18_years',\n",
       " 'ArrestLocDiv',\n",
       " 'StripSearch',\n",
       " 'Booked',\n",
       " 'Occurrence_Category',\n",
       " 'Actions_at_arrest___Concealed_i',\n",
       " 'Actions_at_arrest___Combative__',\n",
       " 'Actions_at_arrest___Resisted__d',\n",
       " 'Actions_at_arrest___Mental_inst',\n",
       " 'Actions_at_arrest___Assaulted_o',\n",
       " 'Actions_at_arrest___Cooperative',\n",
       " 'SearchReason_CauseInjury',\n",
       " 'SearchReason_AssistEscape',\n",
       " 'SearchReason_PossessWeapons',\n",
       " 'SearchReason_PossessEvidence',\n",
       " 'ItemsFound',\n",
       " 'ObjectId']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arrest_column_names = list(police_2021_df)\n",
    "\n",
    "Arrest_column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac06b58c",
   "metadata": {},
   "source": [
    "## Task 3: Select columns from `police_2021_df` and examine the distributions of each column\n",
    "\n",
    "- Create a new DataFrame from `police_2021_df` with the following columns: `_id`, `Sex`,` Perceived_Race`, `SearchReason_PossessWeapons` and assign it to the variable `police_2021_raceweapons`.  Examine the `head()` of this `DataFrame`.\n",
    "\n",
    "- Use `.value_counts()` to compute the distributions of `Sex`, `Perceived_Race`, `SearchReason_PossessWeapons`. You do not need to save these values in variables, though you may do so if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f5e7c72",
   "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>_id</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Perceived_Race</th>\n",
       "      <th>SearchReason_PossessWeapons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>M</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>M</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _id Sex Perceived_Race  SearchReason_PossessWeapons\n",
       "3    4   M          Black                          NaN\n",
       "4    5   M    South Asian                          NaN\n",
       "5    6   M    South Asian                          NaN\n",
       "6    7   M          Black                          NaN\n",
       "7    8   M          Black                          NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "police_2021_raceweapons = police_2021_df[['_id', 'Sex', 'Perceived_Race', 'SearchReason_PossessWeapons']]\n",
    "\n",
    "police_2021_raceweapons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fee1817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M    26815\n",
       "F     6479\n",
       "U        3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "police_2021_df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3c49996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perceived_Race\n",
       "White                   14116\n",
       "Black                    8878\n",
       "Unknown or Legacy        2444\n",
       "East/Southeast Asian     2361\n",
       "South Asian              1871\n",
       "Middle-Eastern           1730\n",
       "Latino                    960\n",
       "Indigenous                935\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "police_2021_df['Perceived_Race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a71d1796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SearchReason_PossessWeapons\n",
       "0.0    478\n",
       "1.0    208\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "police_2021_df['SearchReason_PossessWeapons'].value_counts()"
   ]
  }
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