03.file.ipynb

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   "source": [
    "<h1><p style=\"text-align: center;\">Data Analysis with Python <br>Project - 1</p><h1> - Traffic Police Stops <img src=\"https://docs.google.com/uc?id=17CPCwi3_VvzcS87TOsh4_U8eExOhL6Ki\" class=\"img-fluid\" alt=\"CLRSWY\" width=\"200\" height=\"100\"> "
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   "source": [
    "Are you more likely to get arrested at a certain time of day? Are drug-related stops on the rise? In this chapter, you will answer these and other questions by analyzing the dataset visually, since plots can help you to understand trends in a way that examining the raw data cannot."
   ]
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   "source": [
    "## Calculating the hourly arrest rate"
   ]
  },
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   "source": [
    "When a police officer stops a driver, a small percentage of those stops ends in an arrest. This is known as the **arrest rate**. In this exercise, you'll find out whether the arrest rate varies by time of day.\n",
    "\n",
    "First, you'll calculate the arrest rate across all stops. Then, you'll calculate the **hourly arrest rate** by using the ``hour`` attribute of the ``index``. The hour ranges from ``0`` to ``23``, in which:\n",
    "\n",
    "``0 = midnight``<br>\n",
    "``12 = noon`` <br>\n",
    "``23 = 11 PM``"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "> Before starting your work in this section **repeat the steps which you did in the first chapter for preparing the data.** Continue to this chapter based on where you were in the end of the first chapter."
   ]
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   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Take the mean of the ``is_arrested`` column to calculate the overall arrest rate.\n",
    "*   Group by the ``hour`` attribute of the ``DataFrame`` index to calculate the hourly arrest rate.\n",
    "*   Save the **hourly arrest rate** ``Series`` as a new object, ``hourly_arrest_rate``."
   ]
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      "C:\\Users\\C\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
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       "                                id   stop_date stop_time location_raw  \\\n",
       "stop_datetime                                                           \n",
       "2005-01-02 01:55:00  RI-2005-00001  2005-01-02     01:55      Zone K1   \n",
       "2005-01-02 20:30:00  RI-2005-00002  2005-01-02     20:30      Zone X4   \n",
       "2005-01-04 12:55:00  RI-2005-00004  2005-01-04     12:55      Zone X4   \n",
       "2005-01-06 01:30:00  RI-2005-00005  2005-01-06     01:30      Zone X4   \n",
       "2005-01-12 08:05:00  RI-2005-00006  2005-01-12     08:05      Zone X1   \n",
       "\n",
       "                    police_department driver_gender  driver_age_raw  \\\n",
       "stop_datetime                                                         \n",
       "2005-01-02 01:55:00               600             M          1985.0   \n",
       "2005-01-02 20:30:00               500             M          1987.0   \n",
       "2005-01-04 12:55:00               500             M          1986.0   \n",
       "2005-01-06 01:30:00               500             M          1978.0   \n",
       "2005-01-12 08:05:00                 0             M          1973.0   \n",
       "\n",
       "                     driver_age driver_race_raw driver_race  ...  \\\n",
       "stop_datetime                                                ...   \n",
       "2005-01-02 01:55:00        20.0               W       White  ...   \n",
       "2005-01-02 20:30:00        18.0               W       White  ...   \n",
       "2005-01-04 12:55:00        19.0               W       White  ...   \n",
       "2005-01-06 01:30:00        27.0               B       Black  ...   \n",
       "2005-01-12 08:05:00        32.0               B       Black  ...   \n",
       "\n",
       "                    search_type_raw search_type  contraband_found  \\\n",
       "stop_datetime                                                       \n",
       "2005-01-02 01:55:00             NaN         NaN             False   \n",
       "2005-01-02 20:30:00             NaN         NaN             False   \n",
       "2005-01-04 12:55:00             NaN         NaN             False   \n",
       "2005-01-06 01:30:00             NaN         NaN             False   \n",
       "2005-01-12 08:05:00             NaN         NaN             False   \n",
       "\n",
       "                    stop_outcome is_arrested  stop_duration out_of_state  \\\n",
       "stop_datetime                                                              \n",
       "2005-01-02 01:55:00     Citation       False       0-15 Min        False   \n",
       "2005-01-02 20:30:00     Citation       False      16-30 Min        False   \n",
       "2005-01-04 12:55:00     Citation       False       0-15 Min        False   \n",
       "2005-01-06 01:30:00     Citation       False       0-15 Min        False   \n",
       "2005-01-12 08:05:00     Citation       False        30+ Min         True   \n",
       "\n",
       "                     drugs_related_stop district  frisk  \n",
       "stop_datetime                                            \n",
       "2005-01-02 01:55:00               False  Zone K1  False  \n",
       "2005-01-02 20:30:00               False  Zone X4  False  \n",
       "2005-01-04 12:55:00               False  Zone X4  False  \n",
       "2005-01-06 01:30:00               False  Zone X4  False  \n",
       "2005-01-12 08:05:00               False  Zone X1  False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "def prc(x):return round(x*100,1)\n",
    "\n",
    "df=pd.read_csv(\"police_2.csv\",parse_dates=True, index_col=[\"stop_datetime\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    463981\n",
       "True      16603\n",
       "Name: is_arrested, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.is_arrested.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(df.is_arrested.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_datetime\n",
       "0     5.2\n",
       "1     6.7\n",
       "2     6.1\n",
       "3     5.3\n",
       "4     5.4\n",
       "5     3.3\n",
       "6     1.3\n",
       "7     1.4\n",
       "8     2.0\n",
       "9     2.5\n",
       "10    2.6\n",
       "11    2.7\n",
       "12    3.1\n",
       "13    3.0\n",
       "14    3.2\n",
       "15    3.2\n",
       "16    3.4\n",
       "17    3.9\n",
       "18    4.0\n",
       "19    3.1\n",
       "20    3.9\n",
       "21    6.0\n",
       "22    4.4\n",
       "23    4.5\n",
       "Name: is_arrested, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(df.groupby(df.index.hour).is_arrested.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourly_arrest_rate=df.groupby(df.index.hour).is_arrested.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the hourly arrest rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll create a line plot from the ``hourly_arrest_rate`` object. A line plot is appropriate in this case because you're showing how a quantity changes over time.\n",
    "\n",
    "This plot should help you to spot some trends that may not have been obvious when examining the raw numbers!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Import ``matplotlib.pyplot`` using the alias ``plt``.\n",
    "*   Create a **line plot** of ``hourly_arrest_rate`` using the ``.plot()`` method.\n",
    "*   Label the ``x-axis`` as ``'Hour'``, label the ``y-axis`` as ``'Arrest Rate'``, and title the plot ``'Arrest Rate by Time of Day'``.\n",
    "*   Display the plot using the ``.show()`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hourly_arrest_rate)\n",
    "plt.xlabel(\"Hour\")\n",
    "plt.ylabel(\"Arrest Rate\")\n",
    "plt.title(\"Arrest Rate by Time of Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting drug-related stops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a small portion of traffic stops, drugs are found in the vehicle during a search. You'll assess whether these **drug-related stops** are becoming more common over time.\n",
    "\n",
    "The Boolean column ``drugs_related_stop`` indicates whether drugs were found during a given stop. You'll calculate the **annual drug rate** by **resampling** this column, and then you'll use a line plot to visualize how the rate has changed over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Calculate the **annual rate** of drug-related stops by **resampling** the ``drugs_related_stop`` column (on the ``'A'`` frequency) and taking the mean.\n",
    "*   Save the annual drug rate ``Series`` as a new object, ``annual_drug_rate``.\n",
    "*   Create a line plot of ``annual_drug_rate`` using the ``.plot()`` method.\n",
    "*   Display the plot using the ``.show()`` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_datetime\n",
       "2005-12-31    0.8\n",
       "2006-12-31    0.7\n",
       "2007-12-31    0.8\n",
       "2008-12-31    0.8\n",
       "2009-12-31    1.0\n",
       "2010-12-31    1.0\n",
       "2011-12-31    1.1\n",
       "2012-12-31    1.0\n",
       "2013-12-31    1.2\n",
       "2014-12-31    1.3\n",
       "2015-12-31    1.1\n",
       "Freq: A-DEC, Name: drugs_related_stop, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(df.drugs_related_stop.resample(\"A\").mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample(\"A\") (:Tarih_saat formatında hazırlanmış olan INDEX sütununu Y=year ölçeğinde süzerek gruplandırarak sonucu getiriyor)\n",
    "# Year için y,Y,a,A harfleri  kullanılabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_drug_rate = prc( df.drugs_related_stop.resample(\"A\").mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(annual_drug_rate)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Drug Related Stop Rate\")\n",
    "plt.title(\"Annual Drug Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing drug and search rates (to be deleted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you saw in the last exercise, the rate of **drug-related stops** increased significantly between ``2005`` and ``2015``. You might hypothesize that the rate of vehicle searches was also increasing, which would have led to an increase in drug-related stops even if more drivers were not carrying drugs.\n",
    "\n",
    "You can test this hypothesis by calculating the annual search rate, and then plotting it against the annual drug rate. If the hypothesis is true, then you'll see both rates increasing over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Calculate the annual search rate by **resampling** the ``search_conducted`` column, and save the result as ``annual_search_rate``.\n",
    "*   Concatenate ``annual_drug_rate`` and ``annual_search_rate`` along the ``columns axis``, and save the result as ``annual``.\n",
    "*   Create subplots of the drug and search rates from the ``annual`` ``DataFrame``.\n",
    "*   Display the subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stop_datetime\n",
       "2005-12-31    5.1\n",
       "2006-12-31    3.8\n",
       "2007-12-31    4.2\n",
       "2008-12-31    4.0\n",
       "2009-12-31    5.0\n",
       "2010-12-31    4.2\n",
       "2011-12-31    3.8\n",
       "2012-12-31    3.2\n",
       "2013-12-31    2.9\n",
       "2014-12-31    3.0\n",
       "2015-12-31    2.8\n",
       "Freq: A-DEC, Name: search_conducted, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_searh_rate=prc(df.search_conducted.resample(\"y\").mean())\n",
    "annual_searh_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>search_conducted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>0.8</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>0.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>0.8</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>0.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>1.1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>1.2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>1.3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>1.1</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               drugs_related_stop  search_conducted\n",
       "stop_datetime                                      \n",
       "2005-12-31                    0.8               5.1\n",
       "2006-12-31                    0.7               3.8\n",
       "2007-12-31                    0.8               4.2\n",
       "2008-12-31                    0.8               4.0\n",
       "2009-12-31                    1.0               5.0\n",
       "2010-12-31                    1.0               4.2\n",
       "2011-12-31                    1.1               3.8\n",
       "2012-12-31                    1.0               3.2\n",
       "2013-12-31                    1.2               2.9\n",
       "2014-12-31                    1.3               3.0\n",
       "2015-12-31                    1.1               2.8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual=pd.concat([annual_drug_rate,annual_searh_rate],axis=1)\n",
    "annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ap=annual.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tallying violations by district"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The state of **Rhode Island** is broken into six police districts, also known as zones. How do the zones compare in terms of what violations are caught by police?\n",
    "\n",
    "In this exercise, you'll create a frequency table to determine how many violations of each type took place in each of the six zones. Then, you'll filter the table to focus on the ``\"K\"`` zones, which you'll examine further in the next exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a ``frequency table`` from the ``district`` and ``violation`` columns using the ``pd.crosstab()`` function.\n",
    "*   Save the ``frequency table`` as a new object, ``all_zones``.\n",
    "*   Select rows ``'Zone K1'`` through ``'Zone K3'`` from ``all_zones`` using the ``.loc[]`` accessor.\n",
    "*   Save the smaller table as a new object, ``k_zones``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zone K1</th>\n",
       "      <td>3786</td>\n",
       "      <td>7127</td>\n",
       "      <td>1501</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>33067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>11285</td>\n",
       "      <td>16440</td>\n",
       "      <td>5103</td>\n",
       "      <td>4056</td>\n",
       "      <td>2897</td>\n",
       "      <td>57500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>12959</td>\n",
       "      <td>16218</td>\n",
       "      <td>3926</td>\n",
       "      <td>3871</td>\n",
       "      <td>3660</td>\n",
       "      <td>68234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X1</th>\n",
       "      <td>1725</td>\n",
       "      <td>3711</td>\n",
       "      <td>752</td>\n",
       "      <td>192</td>\n",
       "      <td>451</td>\n",
       "      <td>6393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X3</th>\n",
       "      <td>11520</td>\n",
       "      <td>17178</td>\n",
       "      <td>4069</td>\n",
       "      <td>3532</td>\n",
       "      <td>4445</td>\n",
       "      <td>48687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X4</th>\n",
       "      <td>19975</td>\n",
       "      <td>29554</td>\n",
       "      <td>8865</td>\n",
       "      <td>7551</td>\n",
       "      <td>4870</td>\n",
       "      <td>54855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1         3786              7127   1501                  628          1   \n",
       "Zone K2        11285             16440   5103                 4056       2897   \n",
       "Zone K3        12959             16218   3926                 3871       3660   \n",
       "Zone X1         1725              3711    752                  192        451   \n",
       "Zone X3        11520             17178   4069                 3532       4445   \n",
       "Zone X4        19975             29554   8865                 7551       4870   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1       33067  \n",
       "Zone K2       57500  \n",
       "Zone K3       68234  \n",
       "Zone X1        6393  \n",
       "Zone X3       48687  \n",
       "Zone X4       54855  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_zones = pd.crosstab(df.district, df.violation)\n",
    "all_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zone K1</th>\n",
       "      <td>3786</td>\n",
       "      <td>7127</td>\n",
       "      <td>1501</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>33067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>11285</td>\n",
       "      <td>16440</td>\n",
       "      <td>5103</td>\n",
       "      <td>4056</td>\n",
       "      <td>2897</td>\n",
       "      <td>57500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>12959</td>\n",
       "      <td>16218</td>\n",
       "      <td>3926</td>\n",
       "      <td>3871</td>\n",
       "      <td>3660</td>\n",
       "      <td>68234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1         3786              7127   1501                  628          1   \n",
       "Zone K2        11285             16440   5103                 4056       2897   \n",
       "Zone K3        12959             16218   3926                 3871       3660   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1       33067  \n",
       "Zone K2       57500  \n",
       "Zone K3       68234  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_zones=all_zones.loc[\"Zone K1\":\"Zone K3\"]\n",
    "k_zones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting violations by district"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've created a frequency table focused on the ``\"K\"`` zones, you'll visualize the data to help you compare what violations are being caught in each zone.\n",
    "\n",
    "First you'll create a **bar plot**, which is an appropriate plot type since you're comparing categorical data. Then you'll create a **stacked bar plot** in order to get a slightly different look at the data. Which plot do you find to be more insightful?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS 1/2**\n",
    "\n",
    "*   Create a bar plot of ``k_zones``.\n",
    "*   Display the plot and examine it. What do you notice about each of the zones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=k_zones.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS 2/2**\n",
    "\n",
    "*   Create a stacked bar plot of ``k_zones``.\n",
    "*   Display the plot and examine it. Do you notice anything different about the data than you did previously?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rsOzsbDIzM4mOjmbbtm307t2bzZs3c9ZZZ4U7tLDRNuMVS0Xuv/K0zbhGFBXYkSNH6NWrF5mZmTjneP755yt1khCRklGiqMBq1ap10ucmRESKS4vZIiLiS4lCRER8KVGIiIgvJQoREfGlRFGGdu3axYABA2jRogUXXXQRo0aN4vjx46Snp+fZqTX3hn8iIuFWKa96avdiu1OfVAxrhxX8iefcnHMMGjSIe++9l/nz55Odnc3w4cN59NFHadu2LWlpaSft/1RS2dnZREVFnZa2REQ0oigj77//PtHR0dxxxx1A8NPQEydO5G9/+xsPPvggKSkpxMXFhTYA3LBhA0lJSTRv3pzJkyeH2vm///s/EhMTiYuL42c/+xnZ2dlA8AZCjz/+OJ07d2bp0qVl/wZFpMJSoigj69evp2PHjnnKateuTUxMDI899hjJycmkp6eH9lratGkT77zzDsuXL+e3v/0tmZmZbNy4kZSUFD766CPS09OJiooK7ZV0+PBhYmNjWbZsGT169Cjz9yciFVelnHoKB+ccZlbk8muuuYbq1atTvXp1GjRowFdffcV7773HypUrSUhIAODo0aM0aNAACI5QbrjhhjP7JkSkUlKiKCNt27bltddey1P23XffsXPnzgLXEwraZts5x7Bhw/jd73530vnR0dFalxCRM0JTT2Wkd+/eHDlyhJdeegkILjg/8MAD3H777TRs2LDQrbjztzFnzhy+/vprIHj7VN3QR0TOtFIlCjP7pZmtN7N1ZjbLzKLN7HwzW2RmW7zv5+U6/2Ez22pmn5lZ31zlHc1srXdssnlzMWZW3cxSvPJlZhZTmnjDycyYO3cur776Ki1atKBly5ZER0fz5JNP0qtXLzZs2JBnMbsgbdq04X//93/p06cPgUCAK6+8kj179pThuxCRyqjEU09m1gQYCbRxzh01s1eAIUAb4D3n3AQzGwOMAR4yszbe8bZAY+BfZtbSOZcNPA8MBz4B3gT6AW8BdwH/dc5dbGZDgN8DBd8LtBiKcjnrmXDBBRfwz3/+86Ty6tWrs2LFikLrrVu3LvQ4OTm5wJsLHTp06PQEKSKST2mnnqoCNcysKnA2sBsYAOTc/eZFYKD3eAAw2zn3vXNuO7AVSDSzRkBt59xSF7w5xkv56uS0NQfonTPaEBGRslHiROGc+w/wNLAD2AMccM69CzR0zu3xztkDNPCqNAF25mpil1fWxHucvzxPHedcFnAAKPq9QEVEpNRKnCi8tYcBQDOCU0nnmNlP/KoUUOZ8yv3q5I9luJmlmVna3r17/QMXEZFiKc3U0xXAdufcXudcJvA60A34yptOwvv+tXf+LuCCXPWbEpyq2uU9zl+ep443vVUH+DZ/IM65qc65Ts65TvXr1y/FWxIRkfxKkyh2AF3M7Gxv3aA3sBFYAAzzzhkGzPceLwCGeFcyNQNaAMu96amDZtbFa+e2fHVy2hoMvO8q2k2+RUTKuRJf9eScW2Zmc4BVQBawGpgK1AReMbO7CCaTG73z13tXRm3wzr/Pu+IJ4F5gBlCD4NVOb3nl04GXzWwrwZHEkJLGKyIiJVOqq56cc79xzrV2zsU65271rmja55zr7Zxr4X3/Ntf5451zFznnWjnn3spVnua1cZFz7v6cUYNz7phz7kbn3MXOuUTn3OeliTecoqKiiIuLIzY2luuuu479+/eXqJ3du3czePDgQo/v37+f5557rkRtP/nkk3med+vWrUTt5Jg1axbjx48v9HhGRgaxsbG+bWRkZPCPf/yjVHGISOlUyi08Nra+5LS2d8mmjac8p0aNGqSnpwMwbNgwnn32WR599NFiv1bjxo2ZM2dOocdzEsWIESNOOnaq7ceffPJJHnnkkdDzjz/+uNjx5fb2228zcuTIUrWRkyhuvvnmUrUjIiWnLTzCoGvXrvznP/8BYNu2bfTr14+OHTty6aWXsmnTplB5ly5dSEhI4PHHH6dmzZpA3r/C169fH9pyPBAIsGXLFsaMGcO2bduIi4tj9OjRpKam0qtXL26++WbatQveh2PgwIF07NiRtm3bMnXqVADGjBnD0aNHiYuL45ZbbgEIvaZzjtGjRxMbG0u7du1Cnx5PTU0lKSmJwYMH07p1a2655RZylpCcc6Snp9OhQwfGjRvHrbfeyuWXX06LFi2YNm3aST+TjIwMLr30Ujp06ECHDh1CSWrMmDF8+OGHxMXFMXHiRLKzsxk9ejQJCQkEAgH++te/ArBnzx569uwZGrV9+OGHp7nXRCqvSjmiCKfs7Gzee+897rrrLgCGDx/OlClTaNGiBcuWLWPEiBG8//77jBo1ilGjRjF06FCmTJlSYFtTpkxh1KhR3HLLLRw/fpzs7GwmTJjAunXrQqOX1NRUli9fzrp162jWrBkAL7zwAueffz5Hjx4lISGBG264gQkTJvCXv/wlVC+3119/nfT0dD799FO++eYbEhIS6NmzJwCrV69m/fr1NG7cmO7du/PRRx/Ro0cPVq9eTfv27UM7465Zs4ZPPvmEw4cPEx8fzzXXXJPnNRo0aMCiRYuIjo5my5YtDB06lLS0NCZMmMDTTz/NwoULAZg6dSp16tRhxYoVfP/993Tv3p0+ffrw+uuv07dvXx599FGys7M5cuRIqftKRIKUKMpIzl/rGRkZdOzYkSuvvJJDhw7x8ccfc+ONN4bO+/777wFYunQp8+bNA+Dmm2/m17/+9Ultdu3alfHjx7Nr1y4GDRpEixYtCnztxMTEUJIAmDx5MnPnzgVg586dbNmyhbp1C/8c45IlSxg6dChRUVE0bNiQyy67jBUrVlC7dm0SExNp2jR4dXPO++vRowdvv/02V111VaiNAQMGUKNGDWrUqEGvXr1Yvnw5cXFxoeOZmZncf//9oftsbN68ucBY3n33XdasWROafjtw4ABbtmwhISGBO++8k8zMTAYOHJinbREpHU09lZGcNYovvviC48eP8+yzz3LixAnOPfdc0tPTQ18bN556vSPHzTffzIIFC6hRowZ9+/bl/fffL/C8c845J/Q4NTWVf/3rXyxdupRPP/2U+Ph4jh075vs6flckF7QdOgR/offp0yd0LP/OK/mfT5w4kYYNG/Lpp5+SlpbG8ePHC43lz3/+c+jntX37dvr06UPPnj354IMPaNKkCbfeemtol14RKT0lijJWp04dJk+ezNNPP02NGjVo1qwZr776KhD8Jfjpp58C0KVLl9D9K2bPnl1gW59//jnNmzdn5MiR9O/fnzVr1lCrVi3fLcsPHDjAeeedx9lnn82mTZv45JNPQseqVatGZmbmSXV69uxJSkoK2dnZ7N27lw8++IDExETf18jKysozSpk/fz7Hjh1j3759pKamhm6+lLtOo0aNqFKlCi+//HLoFq/530/fvn15/vnnQ3Fu3ryZw4cP88UXX9CgQQN++tOfctddd7Fq1apC4xOR4lGiCIP4+Hjat2/P7NmzmTlzJtOnT6d9+/a0bduW+fODnzWcNGkSf/rTn0hMTGTPnj3UqVPnpHZSUlKIjY0lLi6OTZs2cdttt1G3bl26d+9ObGwso0ePPqlOv379yMrKIhAIMHbsWLp06RI6Nnz4cAKBQGgxO8f1119PIBCgffv2XH755fzhD3/gRz/6UaHvb9GiRVxxxRV5yhITE7nmmmvo0qULY8eOpXHjxnmOjxgxghdffJEuXbqwefPm0CgoEAhQtWpV2rdvz8SJE7n77rtp06YNHTp0IDY2lp/97GdkZWWRmppKXFwc8fHxvPbaa4waNeoUvSAiRWUV7YPOnTp1cmlpaXnKNm7cyCWXnN5LYs+0I0eOUKNGDcyM2bNnM2vWrFASKe/uvvtu7r777lASGjduHDVr1ixwnaWs5f+38Mfka8MYzZn3QMrCcIdwRlXk/ivrvjOzlc65TgUd02J2ObVy5Uruv/9+nHOce+65vPDCC+EOqcj+9re/hTsEETmNlCjKqUsvvTS0XhHpxo0bF+4QRKQUtEYhIiK+lChERMSXEoWIiPhSohAREV9KFGVk/PjxtG3blkAgQFxcHMuWLStRO/PmzWPDhg0FHrv99tt9d5bNL/cGg+np6bz55psliklEKrZKedXTs/cUvNVFSd035XLf40uXLmXhwoWsWrWK6tWr88033xS6RcWpzJs3j2uvvZY2bdqUqH5h0tPTSUtL4+qrrz6t7YpI5NOIogzs2bOHevXqhfZFqlevXuiTyStXruSyyy6jY8eO9O3blz179gAwbdo0EhISaN++PTfccANHjhzh448/ZsGCBYwePZq4uDi2bdt20mv961//4tJLL6Vly5ahHVcL25o7x/Hjx3n88cdJSUkhLi4utI24iAgoUZSJPn36sHPnTlq2bMmIESP497//DQR3TP35z3/OnDlzWLlyJXfeeWfoZkaDBg1ixYoVfPrpp1xyySVMnz6dbt260b9/f5566inS09O56KKLTnqtjIwM/v3vf/PGG29wzz33cOzYMaZPnx7amnvFihVMmzaN7du3h+qcddZZPPHEEyQnJ5Oenk5ycnLZ/GBEJCJUyqmnslazZk1WrlzJhx9+yOLFi0lOTmbChAl06tSJdevWceWVVwLBv/wbNWoEwLp163jsscfYv38/hw4dom/fvkV6rZtuuokqVarQokULmjdvzqZNmwrdmrtly5Zn5g2LSIWiRFFGoqKiSEpKIikpiXbt2vHiiy+G7jK3dOnSk86//fbbmTdvHu3bt2fGjBmkpqYW6XUK2s47Z2vu/MkmIyOjpG9HRCoRTT2Vgc8++4wtW7aEnqenp3PhhRfSqlUr9u7dG0oUmZmZrF+/HoCDBw/SqFEjMjMzmTlzZqjuqbYRf/XVVzlx4gTbtm3j888/p1WrVoVuzZ3bqdoVkcpLiaIMHDp0iGHDhtGmTRsCgQAbNmxg3LhxnHXWWcyZM4eHHnqI9u3bExcXF7pX9P/8z//QuXNnrrzySlq3bh1qa8iQITz11FPEx8cXuJjdqlUrLrvsMq666iqmTJlCdHR0oVtz59arVy82bNigxWwROYm2GZdKRduMVywVuf/K0zbjGlGIiIgvJQoREfGlRCEiIr6UKERExJcShYiI+FKiEBERX0oUZeh0bTVeFElJSeRcJnz11Vezf//+M/ZaIlKxlWoLDzM7F/gbEAs44E7gMyAFiAEygJucc//1zn8YuAvIBkY6597xyjsCM4AawJvAKOecM7PqwEtAR2AfkOycyyhNzHD6r70uyvXOp3Or8eLSfSZEpDRKO6J4BnjbOdcaaA9sBMYA7znnWgDvec8xszbAEKAt0A94zsyivHaeB4YDLbyvfl75XcB/nXMXAxOB35cy3rApbKvxmJgYHnroIRITE0lMTGTr1q0A7N27lxtuuIGEhAQSEhL46KOPADh8+DB33nknCQkJxMfHM3/+fACOHj3KkCFDCAQCJCcnc/To0dBrx8TE8M0335CRkcEll1zCT3/6U9q2bUufPn1C561YsYJAIEDXrl0ZPXp06IZGIiIlThRmVhvoCUwHcM4dd87tBwYAL3qnvQgM9B4PAGY75753zm0HtgKJZtYIqO2cW+qCHxN/KV+dnLbmAL0t/653EaKwrcYBateuzfLly7n//vv5xS9+AcCoUaP45S9/yYoVK3jttde4++67geD01eWXX86KFStYvHgxo0eP5vDhwzz//POcffbZrFmzhkcffZSVK1cWGMeWLVu47777WL9+Peeeey6vvfYaAHfccQdTpkxh6dKlREVFFVhXRCqn0kw9NQf2An83s/bASmAU0NA5twfAObfHzBp45zcBPslVf5dXluk9zl+eU2en11aWmR0A6gLf5A7EzIYTHJHw4x//uBRv6cwpbKtxgKFDh4a+//KXvwSCNyDKfcvT7777joMHD/Luu++yYMECnn76aQCOHTvGjh07+OCDDxg5ciQAgUCAQCBQYBzNmjUjLi4OgI4dO5KRkcH+/fs5ePAg3bp1A+Dmm28O3fRIRKQ0iaIq0AH4uXNumZk9gzfNVIiCRgLOp9yvTt4C56YCUyG415Nf0OFU0FbjkHdr8JzHJ06cYOnSpdSoUSNPG845XnvtNVq1anVS+0UZbOVMfeXEc/ToUSrafl8icnqVZo1iF7DLOZdz6c4cgonjK286Ce/717nOvyBX/abAbq+8aQHleeqYWVWgDvBtKWIOm8K2GgdCu7WmpKTQtWtXIDhV9Ze//CXP+QB9+/blz3/+c+iX++rVqwHo2bNnaDvydevWsWbNmiLHdt5551GrVi0++SQ44Js9e3ZJ3qKIVFAlThTOuS+BnWaW86dtb2ADsAAY5pUNA+Z7jxcAQ8ysupk1I7hovdybpjpoZl289Yfb8tXJaWsw8L6L0D9/C9tqHOD777+nc+fOPPPMM0ycOBGAyZMnk5aWRiAQoE2bNkyZMgWAsWPHkpmZSSAQIDY2lrFjxwJw7733cujQIQKBAH/4wx9ITEwsVnzTp09n+PDhdO3aFeccderUOX1vXkQiWqm2GTezOIKXx54FfA7cQTD5vAL8GNgB3Oic+9Y7/1GCl9BmAb9wzr3llXfih8tj3yI4neXMLBp4GYgnOJIY4pz73C+mSNtmPCYmhrS0NOrVqxfWOA4dOkTNmjUBmDBhAnv27OGZZ54Ja0xngrYZr1gqcv+Vp23GS/U5CudcOlBQw70LOX88ML6A8jSCn8XIX34MuLE0MUrRvPHGG/zud78jKyuLCy+8kBkzZoQ7JBEpJ3TP7DArL/etTk5OJjk5OdxhiEg5pC08RETEV6VJFBG6Bi6nkf4NiJRMpUgU0dHR7Nu3T78oKjHnHPv27SM6OjrcoYhEnEqxRtG0aVN27drF3r17wx2KhFF0dDRNmzY99YkikkelSBTVqlWjWbNm4Q5DRCQiVYqpJxERKTklChER8aVEISIivpQoRETElxKFiIj4UqIQERFfShQiIuJLiUJERHwpUYiIiC8lChER8aVEISIivpQoRETElxKFiIj4UqIQERFfShQiIuKrUtyP4kz6Y/K14Q7hjHogZWG4QxCRMNOIQkREfClRiIiILyUKERHxpUQhIiK+lChERMSXEoWIiPhSohAREV+lThRmFmVmq81soff8fDNbZGZbvO/n5Tr3YTPbamafmVnfXOUdzWytd2yymZlXXt3MUrzyZWYWU9p4RUSkeE7HiGIUsDHX8zHAe865FsB73nPMrA0wBGgL9AOeM7Mor87zwHCghffVzyu/C/ivc+5iYCLw+9MQr4iIFEOpPpltZk2Ba4DxwK+84gFAkvf4RSAVeMgrn+2c+x7YbmZbgUQzywBqO+eWem2+BAwE3vLqjPPamgP8xczMOedKE7eIVAzR5/3q1CdJqZV2RDEJeBA4kausoXNuD4D3vYFX3gTYmeu8XV5ZE+9x/vI8dZxzWcABoG7+IMxsuJmlmVna3r17S/mWREQktxKPKMzsWuBr59xKM0sqSpUCypxPuV+dvAXOTQWmAnTq1EmjDSky/UUqcmqlmXrqDvQ3s6uBaKC2mf0f8JWZNXLO7TGzRsDX3vm7gAty1W8K7PbKmxZQnrvOLjOrCtQBvi1FzCIiUkwlnnpyzj3snGvqnIshuEj9vnPuJ8ACYJh32jBgvvd4ATDEu5KpGcFF6+Xe9NRBM+viXe10W746OW0N9l5DIwYRkTJ0JrYZnwC8YmZ3ATuAGwGcc+vN7BVgA5AF3Oecy/bq3AvMAGoQXMR+yyufDrzsLXx/SzAhiYhIGToticI5l0rw6iacc/uA3oWcN57gFVL5y9OA2ALKj+ElGhERCQ/duEgqtctT7wt3CGfYxlOfEsEqdv+Vn77TFh4iIuJLiUJERHwpUYiIiC8lChER8aVEISIivpQoRETEly6PFZGIddPDFfdX2NpwB5BLxf0pixRBRf5FA+Xrl41ELk09iYiILyUKERHxpUQhIiK+lChERMSXEoWIiPhSohAREV8V+9pAEanQ1m7fEe4QKgWNKERExJcShYiI+NLUk1RqmroQOTWNKERExJcShYiI+FKiEBERX0oUIiLiS4lCRER8KVGIiIgvJQoREfGlRCEiIr6UKERExJcShYiI+CpxojCzC8xssZltNLP1ZjbKKz/fzBaZ2Rbv+3m56jxsZlvN7DMz65urvKOZrfWOTTYz88qrm1mKV77MzGJK8V5FRKQESjOiyAIecM5dAnQB7jOzNsAY4D3nXAvgPe853rEhQFugH/CcmUV5bT0PDAdaeF/9vPK7gP865y4GJgK/L0W8IiJSAiVOFM65Pc65Vd7jg8BGoAkwAHjRO+1FYKD3eAAw2zn3vXNuO7AVSDSzRkBt59xS55wDXspXJ6etOUDvnNGGiIiUjdOyRuFNCcUDy4CGzrk9EEwmQAPvtCbAzlzVdnllTbzH+cvz1HHOZQEHgLoFvP5wM0szs7S9e/eejrckIiKeUicKM6sJvAb8wjn3nd+pBZQ5n3K/OnkLnJvqnOvknOtUv379U4UsIiLFUKpEYWbVCCaJmc65173ir7zpJLzvX3vlu4ALclVvCuz2ypsWUJ6njplVBeoA35YmZhERKZ7SXPVkwHRgo3PuT7kOLQCGeY+HAfNzlQ/xrmRqRnDRerk3PXXQzLp4bd6Wr05OW4OB9711DBERKSOlucNdd+BWYK2ZpXtljwATgFfM7C5gB3AjgHNuvZm9AmwgeMXUfc65bK/evcAMoAbwlvcFwUT0spltJTiSGFKKeEVEpARKnCicc0soeA0BoHchdcYD4wsoTwNiCyg/hpdoREQkPPTJbBER8aVEISIivpQoRETElxKFiIj4UqIQERFfShQiIuJLiUJERHwpUYiIiK/SfDJbgKs/3RbuEEQqrZhj/wh3CGdMRrgDyEWJopRuerhi/wjXhjsAEQk7TT2JiIiviv3nsMgpVOSpCyhf0xcSuZQoSmnt9h3hDkFE5IzS1JOIiPhSohAREV9KFCIi4kuJQkREfClRiIiILyUKERHxpUQhIiK+lChERMSXEoWIiPhSohAREV9KFCIi4kuJQkREfGlTwFLS7qMiUtFpRCEiIr6UKERExJcShYiI+IqIRGFm/czsMzPbamZjwh2PiEhlUu4ThZlFAc8CVwFtgKFm1ia8UYmIVB7lPlEAicBW59znzrnjwGxgQJhjEhGpNCLh8tgmwM5cz3cBnXOfYGbDgeHe00Nm9lkZxRYO9YBvyurF7Pdl9UqVhvovclX0vruwsAORkCisgDKX54lzU4GpZRNOeJlZmnOuU7jjkJJR/0Wuytx3kTD1tAu4INfzpsDuMMUiIlLpREKiWAG0MLNmZnYWMARYEOaYREQqjXI/9eScyzKz+4F3gCjgBefc+jCHFU6VYoqtAlP/Ra5K23fmnDv1WSIiUmlFwtSTiIiEkRKFiIj4UqIQERFfShQRyMzuCHcMcmpm1trMeptZzXzl/cIVkxSNmSWaWYL3uI2Z/crMrg53XOGixewIZGY7nHM/DnccUjgzGwncB2wE4oBRzrn53rFVzrkOYQxPfJjZbwjuLVcVWERwJ4hU4ArgHefc+PBFFx5KFOWUma0p7BDQ0jlXvSzjkeIxs7VAV+fcITOLAeYALzvnnjGz1c65+PBGKIXx+i4OqA58CTR1zn1nZjWAZc65QDjjC4dy/zmKSqwh0Bf4b75yAz4u+3CkmKKcc4cAnHMZZpYEzDGzCyl4WxopP7Kcc9nAETPb5pz7DsA5d9TMToQ5trDQGkX5tRCo6Zz7It9XBsFhsJRvX5pZXM4TL2lcS3BjuXbhCkqK5LiZne097phTaGZ1gEqZKDT1FIHM7Cxvy3Upp8ysKcG/TL8s4Fh359xHYQhLisDMqjvnvi+gvB7wI+fcujCEFVYaUZRTZja2kPI6wLtlHI4U3x2FJInaQKVbDI0wDxZSngn8pSwDKS+UKMqvS80szy8UM/sR8AGwODwhSTEU1n8fAu+HJyQpIr//e5Wy75Qoyq/+QHsz+xOAmbUAlgDPOed+G9bIpCj8+u+JsEYmp6K+y0drFOWYmVUjeOvXTKAr8Avn3NzwRiVFpf6LXOq7vJQoyikz+5X3sBrBOdMPCQ59AXDO/SkccUnRqP8il/ruZPocRflVK9fjyQWUSfmm/otc6rt8NKIQERFfWswWERFfShQiIuJLiUJERHwpUZRzZtbQzKab2Vve8zZmdle445KiUf9FLvXdD5Qoyr8ZwDtAY+/5ZuAX4QpGim0G6r9INQP1HaBEEQnqOedewdu10jmXBWSHNyQpBvVf5FLfeZQoyr/DZlYXcABm1gU4EN6QpBjUf5FLfefRB+7Kv18BC4CLzOwjoD4wOLwhSTGo/yKX+s6jD9xFADOrCrQieGe0z5xzmWEOSYpB/Re51HdBShQRwMy6ATHkGgE6514KW0BSLOq/yKW+C9LUUzlnZi8DFwHp/LCQ5oBK9481Eqn/Ipf67gcaUZRzZrYRaOPUURFJ/Re51Hc/0FVP5d864EfhDkJKTP0XudR3Hk09lX/1gA1mthwI3fDdOdc/fCFJMaj/Ipf6zqNEUf6NC3cAUirjwh2AlNi4cAdQXmiNIgKYWUMgwXu63Dn3dTjjkeJR/0Uu9V2Q1ijKOTO7CVgO3AjcBCwzs0r5oZ9IpP6LXOq7H2hEUc6Z2afAlTl/yZhZfeBfzrn24Y1MikL9F7nUdz/QiKL8q5JvuLsP9VskUf9FLvWdR4vZ5d/bZvYOMMt7ngy8FcZ4pHjUf5FLfefR1FMEMLNBQA+C+8184JybG+aQpBjUf5FLfRekRFFOmVlf59w7hRy70Tn3alnHJEWn/otc6ruTVcr5tgjxppktNrMmBRx7uMyjkeJS/0Uu9V0+ShTl1xrgH8AnZnZjvmMWhnikeNR/kUt9l48SRfnlnHPTgN7Ag2b2dzM7O+dYGOOSolH/RS71XT5KFOWcc24z0BX4ClhtZp3DHJIUg/ovcqnvfqDF7HLKzFY75+LzlSUBLwD1nXO1whGXFI36L3Kp706mz1GUX7/NX+CcSzWzjsDPwhCPFI/6L3Kp7/LRiEJERHxpjUJERHwpUYiIiC+tUYiUkJmNAw4BtQlu7/CvQs4bCGx2zm0o5Pg9wBHn3EuFHI8Bujnn/nEawhYpNiUKkVJyzj1+ilMGAguBkxKFmVV1zk05Rf0Y4GaCHwITKXNazBYpBjN7FLgN2AnsBVYCscBC59wcM5sA9AeygHeB1wkmiQPe1w3AdOBjoDuwAKgFHHLOPW1mFwNTgPpANsGb5swELgG2Ay865yaWzbsVCdKIQqSIvMsjhwDxBP/vrCKYKHKOnw9cD7R2zjkzO9c5t9/MFuAlEu88gHOdc5d5z8flepmZwATn3Fwziya4jjgG+LVz7toz/R5FCqLFbJGiuxSY65w74pz7juBoILfvgGPA37ztqY/4tJWSv8DMagFNcrayds4dc875tSFSJpQoRIqn0Lla51wWkAi8RnBd4m2fdg4XUFYpN5yT8k+JQqToPgCuN7Ma3l//1+U+aGY1gTrOuTeBXwBx3qGDBNchfHmjlF3eVVKYWXVvM7oi1Rc5U5QoRIrIObeK4JRROsFRw4f5TqkFLDSzNcC/gV965bOB0Wa22swuOsXL3AqM9Nr4GPgRwW2vs8zsUzP7pW9tkTNAVz2JiIgvjShERMSXEoWIiPhSohAREV9KFCIi4kuJQkREfClRiIiILyUKERHxpUQhIiK+/h8XyJnRQyW7gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks=k_zones.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting stop durations to numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the traffic stops dataset, the ``stop_duration`` column tells you approximately how long the driver was detained by the officer. Unfortunately, the durations are stored as ``strings``, such as ``'0-15 Min'``. How can you make this data easier to analyze?\n",
    "\n",
    "In this exercise, you'll convert the **stop durations** to ``integers``. Because the precise durations are not available, you'll have to estimate the numbers using reasonable values:\n",
    "\n",
    "*   Convert ``'0-15 Min'`` to ``8``\n",
    "*   Convert ``'16-30 Min'`` to ``23``\n",
    "*   Convert ``'30+ Min'`` to ``45``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Print the **unique values** in the ``stop_duration`` column. (This has been done for you.)\n",
    "*   Create a ``dictionary`` called ``mapping`` that maps the ``stop_duration`` strings to the integers specified above.\n",
    "*   Convert the ``stop_duration`` strings to integers using the ``mapping``, and store the results in a new column called ``stop_minutes``.\n",
    "*   Print the unique values in the ``stop_minutes`` column, to verify that the durations were properly converted to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 23, 45], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping={'0-15 Min':8,'16-30 Min':23,'30+ Min':45,\"1\":45,\"2\":45}\n",
    "df[\"stop_minutes\"]=df.stop_duration.map(mapping)\n",
    "df.stop_minutes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15 Min     386646\n",
       "16-30 Min     76320\n",
       "30+ Min       17612\n",
       "1                 5\n",
       "2                 1\n",
       "Name: stop_duration, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_duration.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     386646\n",
       "23     76320\n",
       "45     17618\n",
       "Name: stop_minutes, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_minutes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     386646\n",
       "23     76320\n",
       "45     17618\n",
       "Name: stop_minutes, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stop_minutes.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting stop length"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "If you were stopped for a particular violation, how long might you expect to be detained?\n",
    "\n",
    "In this exercise, you'll visualize the **average length** of time drivers are stopped for each **type** of **violation**. Rather than using the ``violation`` column in this exercise, you'll use ``violation_raw`` since it contains more detailed descriptions of the violations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   For each value in the ``violation_raw`` column, calculate the **mean number** of ``stop_minutes`` that a driver is detained.\n",
    "*   Save the resulting ``Series`` as a new object, ``stop_length``.\n",
    "*   Sort ``stop_length`` by its values, and then visualize it using a **horizontal bar plot**.\n",
    "*   Display the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "violation_raw\n",
       "APB                                 19.0\n",
       "Call for Service                    22.0\n",
       "Equipment/Inspection Violation      11.0\n",
       "Motorist Assist/Courtesy            18.0\n",
       "Other Traffic Violation             14.0\n",
       "Registration Violation              14.0\n",
       "Seatbelt Violation                  10.0\n",
       "Special Detail/Directed Patrol      15.0\n",
       "Speeding                            11.0\n",
       "Suspicious Person                   18.0\n",
       "Violation of City/Town Ordinance    13.0\n",
       "Warrant                             20.0\n",
       "Name: stop_minutes, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_length=round(df.groupby(\"violation_raw\").stop_minutes.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "violation_raw\n",
       "Seatbelt Violation                  10.0\n",
       "Speeding                            11.0\n",
       "Equipment/Inspection Violation      11.0\n",
       "Violation of City/Town Ordinance    13.0\n",
       "Registration Violation              14.0\n",
       "Other Traffic Violation             14.0\n",
       "Special Detail/Directed Patrol      15.0\n",
       "Motorist Assist/Courtesy            18.0\n",
       "Suspicious Person                   18.0\n",
       "APB                                 19.0\n",
       "Warrant                             20.0\n",
       "Call for Service                    22.0\n",
       "Name: stop_minutes, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_length.sort_values(inplace=True)\n",
    "round(stop_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s=stop_length.plot.barh()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"police_3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}