1.Proje _1234.ipynb

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   "cell_type": "markdown",
   "source": [
    "<h1><p style=\"text-align: center;\">Python ile Veri Analizi <br>Proje No:  1</p><h1> - Trafik Polisi Kontrol Noktaları <img src=\"https://docs.google.com/uc?id=17CPCwi3_VvzcS87TOsh4_U8eExOhL6Ki\" class=\"img-fluid\" alt=\"CLRSWY\" width=\"200\" height=\"100\"> "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Analizinize başlamadan önce, onunla çalışmayı daha verimli bir süreç haline getirmek için önce veri setini incelemeniz ve temizlemeniz çok önemlidir. Stanford Open Policing Project veri kümesini öğrenirken veri türlerini düzeltme, eksik değerleri ele alma, sütunları ve satırları çıkartma alıştırması yapacaksınız."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Veri setinin incelenmesi"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Veri setinin incelenmesi\r\n",
    "Stanford Açık Polis Projesi tarafından toplanan Rhode Island'daki trafik duraklarına ilişkin bir veri kümesini analiz edeceksiniz."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Analizinize başlamadan önce, veri kümesine aşina olmanız önemlidir. Veri kümesini panda ile okuyacak, ilk birkaç satırı inceleyecek ve ardından eksik değerlerini sayacaksınız."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**TALİMATLAR**\n",
    "* ``pd``. takma adını kullanarak pandaları içe aktarın.\n",
    "* Police.csv dosyasını ``ri`` adlı bir ``DataFrame``'e okuyun\n",
    "* DataFrame'in ilk 5 satırını inceleyin (``\"head\"`` olarak bilinir).\n",
    "* Her sütundaki eksik değerlerin sayısını sayın: Hangi DataFrame öğelerinin eksik olduğunu kontrol etmek için ``.isnull()'``u kullanın ve ardından her sütundaki ``True`` değerlerinin sayısını saymak için .sum()'u alın.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "source": [
    "import pandas as pd"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "source": [
    "ri=pd.read_csv(\"police.csv\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\C\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (8,16) 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"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "source": [
    "ri.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>state</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_fips</th>\n",
       "      <th>fine_grained_location</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>...</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type_raw</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RI-2005-00003</td>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>11:30</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id state   stop_date stop_time location_raw  county_name  \\\n",
       "0  RI-2005-00001    RI  2005-01-02     01:55      Zone K1          NaN   \n",
       "1  RI-2005-00002    RI  2005-01-02     20:30      Zone X4          NaN   \n",
       "2  RI-2005-00003    RI  2005-01-04     11:30      Zone X1          NaN   \n",
       "3  RI-2005-00004    RI  2005-01-04     12:55      Zone X4          NaN   \n",
       "4  RI-2005-00005    RI  2005-01-06     01:30      Zone X4          NaN   \n",
       "\n",
       "   county_fips  fine_grained_location police_department driver_gender  ...  \\\n",
       "0          NaN                    NaN               600             M  ...   \n",
       "1          NaN                    NaN               500             M  ...   \n",
       "2          NaN                    NaN                 0           NaN  ...   \n",
       "3          NaN                    NaN               500             M  ...   \n",
       "4          NaN                    NaN               500             M  ...   \n",
       "\n",
       "   search_conducted  search_type_raw search_type contraband_found  \\\n",
       "0             False              NaN         NaN            False   \n",
       "1             False              NaN         NaN            False   \n",
       "2             False              NaN         NaN            False   \n",
       "3             False              NaN         NaN            False   \n",
       "4             False              NaN         NaN            False   \n",
       "\n",
       "  stop_outcome is_arrested stop_duration out_of_state drugs_related_stop  \\\n",
       "0     Citation       False      0-15 Min        False              False   \n",
       "1     Citation       False     16-30 Min        False              False   \n",
       "2          NaN         NaN           NaN          NaN              False   \n",
       "3     Citation       False      0-15 Min        False              False   \n",
       "4     Citation       False      0-15 Min        False              False   \n",
       "\n",
       "   district  \n",
       "0   Zone K1  \n",
       "1   Zone X4  \n",
       "2   Zone X1  \n",
       "3   Zone X4  \n",
       "4   Zone X4  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 147
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "source": [
    "ri.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                            0\n",
       "state                         0\n",
       "stop_date                    10\n",
       "stop_time                    10\n",
       "location_raw                  0\n",
       "county_name              509681\n",
       "county_fips              509681\n",
       "fine_grained_location    509681\n",
       "police_department            10\n",
       "driver_gender             29097\n",
       "driver_age_raw            29049\n",
       "driver_age                30695\n",
       "driver_race_raw           29073\n",
       "driver_race               29073\n",
       "violation_raw             29073\n",
       "violation                 29073\n",
       "search_conducted             10\n",
       "search_type_raw          491919\n",
       "search_type              491919\n",
       "contraband_found              0\n",
       "stop_outcome              29073\n",
       "is_arrested               29073\n",
       "stop_duration             29073\n",
       "out_of_state              29881\n",
       "drugs_related_stop            0\n",
       "district                      0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 148
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Sütunları çıkartma işlemi"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Often, a DataFrame will contain columns that are not useful to your analysis. Such columns should be dropped from the ``DataFrame``, to make it easier for you to focus on the remaining columns.\n",
    "\n",
    "You'll drop the ``county_name`` column because it only contains missing values, and you'll drop the ``state`` column because all of the traffic stops took place in one state (Rhode Island). Thus, these columns can be dropped because they contain no useful information."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Sütunları çıkartmak\n",
    "Çoğu zaman, bir DataFrame, analiziniz için yararlı olmayan sütunlar içerir. Kalan sütunlara odaklanmanızı kolaylaştırmak için bu tür sütunlar DataFrame'den çıkarılmalıdır.\n",
    "Yalnızca eksik değerler içerdiğinden ilçe_adı (county_name) sütununu çıkaracaksınız ve tüm trafik durakları tek bir eyalette (Rhode Island) gerçekleştiği için eyalet sütununu çıkaracaksınız. Bu nedenle, bu sütunlar hiçbir yararlı bilgi içermediklerinden atlanabilir.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Examine the ``DataFrame``'s shape to find out the number of rows and columns.\n",
    "\n",
    "*   Drop the columns that almost consist of missing values. \n",
    "\n",
    "*   Examine the ``.shape`` again to verify that there are now two fewer columns."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "TALİMATLAR\n",
    "• Satır ve sütun sayısını bulmak için DataFrame'in şeklini inceleyin.\n",
    "• Hemen hemen tüm değerleri eksik olan sütunları çıkarın\n",
    "• Çıkarma işleminin yapıldığını denetlemek ve sütün sayısının 2 azaldığnı doğrulamak için .shape'i  komutuyla kontrol edin\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(509681, 26)"
      ]
     },
     "metadata": {},
     "execution_count": 149
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "source": [
    "ri.drop([\"county_name\",\"state\" ], axis=1, inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(509681, 24)"
      ]
     },
     "metadata": {},
     "execution_count": 151
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "source": [
    "ri.dropna(how='all', axis=1, inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(509681, 22)"
      ]
     },
     "metadata": {},
     "execution_count": 153
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Satırları çıkartma"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "When you know that a specific column will be critical to your analysis, and only a small fraction of rows are missing a value in that column, it often makes sense to remove those rows from the dataset.\n",
    "\n",
    "During this course, the ``driver_gender`` column will be critical to many of your analyses. Because only a small fraction of rows are missing ``driver_gender``, we'll drop those rows from the dataset."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Belirli bir sütunun analiziniz için kritik olacağını bildiğinizde ve satırların yalnızca küçük bir bölümünde bu sütunda bir değer eksik olduğunda, bu satırları veri kümesinden çıkarmak genellikle mantıklıdır.\n",
    "Bu kurs sırasında, driver_gender sütunu, analizlerinizin çoğu için kritik olacaktır. Satırların yalnızca küçük bir bölümünde driver_gender eksik olduğundan, bu satırları veri kümesinden çıkaracağız.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Count the number of missing values in each column.\n",
    "\n",
    "*   Drop all rows that are missing ``driver_gender`` by passing the column name to the subset parameter of ``.dropna()``.\n",
    "*   Count the number of missing values in each column again, to verify that none of the remaining rows are missing ``driver_gender``.\n",
    "*   Examine the ``DataFrame``'s ``.shape`` to see how many rows and columns remain."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**TALİMATLAR**\n",
    "* Her sütundaki eksik değerlerin sayısını sayın.\n",
    "* Sütun adını `.dropna()`'nın alt küme parametresine ileterek, `driver_gender` eksik olan tüm satırları çıkarın.\n",
    "* Kalan satırlardan hiçbirinin eksik ``driver_gender`` olmadığını doğrulamak için her sütundaki eksik değerlerin sayısını tekrar sayın.\n",
    "* Kaç satır ve sütun kaldığını görmek için ``DataFrame`` 'in ``.shape``'ini inceleyin.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "source": [
    "ri.isna().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                         0\n",
       "stop_date                 10\n",
       "stop_time                 10\n",
       "location_raw               0\n",
       "police_department         10\n",
       "driver_gender          29097\n",
       "driver_age_raw         29049\n",
       "driver_age             30695\n",
       "driver_race_raw        29073\n",
       "driver_race            29073\n",
       "violation_raw          29073\n",
       "violation              29073\n",
       "search_conducted          10\n",
       "search_type_raw       491919\n",
       "search_type           491919\n",
       "contraband_found           0\n",
       "stop_outcome           29073\n",
       "is_arrested            29073\n",
       "stop_duration          29073\n",
       "out_of_state           29881\n",
       "drugs_related_stop         0\n",
       "district                   0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 154
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "source": [
    "ri.dropna(subset=['driver_gender'],inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "source": [
    "ri.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                         0\n",
       "stop_date                  0\n",
       "stop_time                  0\n",
       "location_raw               0\n",
       "police_department          0\n",
       "driver_gender              0\n",
       "driver_age_raw             1\n",
       "driver_age              1638\n",
       "driver_race_raw            0\n",
       "driver_race                0\n",
       "violation_raw              0\n",
       "violation                  0\n",
       "search_conducted           0\n",
       "search_type_raw       462822\n",
       "search_type           462822\n",
       "contraband_found           0\n",
       "stop_outcome               0\n",
       "is_arrested                0\n",
       "stop_duration              0\n",
       "out_of_state             808\n",
       "drugs_related_stop         0\n",
       "district                   0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 156
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 22)"
      ]
     },
     "metadata": {},
     "execution_count": 157
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Bir data tipini düzeltme"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We know that the ``is_arrested`` column currently has the ``object`` data type. In this exercise, we'll change the data type to ``bool``, which is the most suitable type for a column containing ``True`` and ``False`` values.\n",
    "\n",
    "Fixing the data type will enable us to use mathematical operations on the ``is_arrested`` column that would not be possible otherwise."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Bir veri türünü düzeltme\n",
    "is_arrested sütununun şu anda nesne veri türüne sahip olduğunu biliyoruz. Bu alıştırmada, True ve False değerlerini içeren bir sütun için en uygun tür olan veri türünü bool olarak değiştireceğiz.\n",
    "\n",
    "Veri türünü düzeltmek, is_arrested sütununda başka türlü mümkün olmayan matematiksel işlemleri kullanmamızı sağlayacaktır.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Examine the head of the ``is_arrested`` column to verify that it contains ``True`` and ``False`` values.\n",
    "*   Check the current data type of ``is_arrested``.\n",
    "*   Use the ``.astype()`` method to convert ``is_arrested`` to a ``bool`` column.\n",
    "*   Check the new data type of ``is_arrested``, to confirm that it is now a ``bool`` column."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "TALİMATLAR\n",
    "• Doğru ve Yanlış değerleri içerdiğini doğrulamak için is_arrested sütununun başlığını inceleyin.\n",
    "• is_arrested'ın mevcut veri tipini kontrol edin.\n",
    "• is_arrested'ı bir bool sütununa dönüştürmek için .astype() yöntemini kullanın.\n",
    "• Artık bir bool sütunu olduğunu doğrulamak için is_arrested yeni veri türünü kontrol edin.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "source": [
    "ri.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>...</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type_raw</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   stop_date stop_time location_raw police_department  \\\n",
       "0  RI-2005-00001  2005-01-02     01:55      Zone K1               600   \n",
       "1  RI-2005-00002  2005-01-02     20:30      Zone X4               500   \n",
       "3  RI-2005-00004  2005-01-04     12:55      Zone X4               500   \n",
       "4  RI-2005-00005  2005-01-06     01:30      Zone X4               500   \n",
       "5  RI-2005-00006  2005-01-12     08:05      Zone X1                 0   \n",
       "\n",
       "  driver_gender  driver_age_raw  driver_age driver_race_raw driver_race  ...  \\\n",
       "0             M          1985.0        20.0               W       White  ...   \n",
       "1             M          1987.0        18.0               W       White  ...   \n",
       "3             M          1986.0        19.0               W       White  ...   \n",
       "4             M          1978.0        27.0               B       Black  ...   \n",
       "5             M          1973.0        32.0               B       Black  ...   \n",
       "\n",
       "  search_conducted search_type_raw search_type contraband_found stop_outcome  \\\n",
       "0            False             NaN         NaN            False     Citation   \n",
       "1            False             NaN         NaN            False     Citation   \n",
       "3            False             NaN         NaN            False     Citation   \n",
       "4            False             NaN         NaN            False     Citation   \n",
       "5            False             NaN         NaN            False     Citation   \n",
       "\n",
       "   is_arrested stop_duration out_of_state drugs_related_stop district  \n",
       "0        False      0-15 Min        False              False  Zone K1  \n",
       "1        False     16-30 Min        False              False  Zone X4  \n",
       "3        False      0-15 Min        False              False  Zone X4  \n",
       "4        False      0-15 Min        False              False  Zone X4  \n",
       "5        False       30+ Min         True              False  Zone X1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 158
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "source": [
    "ri.dtypes "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                     object\n",
       "stop_date              object\n",
       "stop_time              object\n",
       "location_raw           object\n",
       "police_department      object\n",
       "driver_gender          object\n",
       "driver_age_raw        float64\n",
       "driver_age            float64\n",
       "driver_race_raw        object\n",
       "driver_race            object\n",
       "violation_raw          object\n",
       "violation              object\n",
       "search_conducted       object\n",
       "search_type_raw        object\n",
       "search_type            object\n",
       "contraband_found         bool\n",
       "stop_outcome           object\n",
       "is_arrested            object\n",
       "stop_duration          object\n",
       "out_of_state           object\n",
       "drugs_related_stop       bool\n",
       "district               object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 159
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#verileri True,False şeklinde kayıtlı  5 sütunun yalnız 2 sinin veri tipleri  bool"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "source": [
    "ri.search_conducted=ri.search_conducted.astype(\"bool\")\n",
    "ri.is_arrested=ri.is_arrested.astype(\"bool\")\n",
    "ri.out_of_state=ri.out_of_state.astype(\"bool\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "source": [
    "ri.dtypes"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                     object\n",
       "stop_date              object\n",
       "stop_time              object\n",
       "location_raw           object\n",
       "police_department      object\n",
       "driver_gender          object\n",
       "driver_age_raw        float64\n",
       "driver_age            float64\n",
       "driver_race_raw        object\n",
       "driver_race            object\n",
       "violation_raw          object\n",
       "violation              object\n",
       "search_conducted         bool\n",
       "search_type_raw        object\n",
       "search_type            object\n",
       "contraband_found         bool\n",
       "stop_outcome           object\n",
       "is_arrested              bool\n",
       "stop_duration          object\n",
       "out_of_state             bool\n",
       "drugs_related_stop       bool\n",
       "district               object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 161
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## nesne  ( object) olan  sütunları bir araya getirme "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Currently, the date and time of each traffic stop are stored in separate object columns: ``stop_date`` and ``stop_time``.\n",
    "\n",
    "You'll combine these two columns into a single column, and then convert it to ``datetime`` format. This will enable convenient date-based attributes that we'll use later in the course."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Nesne sütunlarını birleştirme\n",
    "Şu anda, her trafik durağının tarihi ve saati ayrı nesne sütunlarında depolanır: stop_date ve stop_time.\n",
    "Bu iki sütunu tek bir sütunda birleştirecek ve ardından onu tarih saat biçimine dönüştüreceksiniz. Bu, kursun ilerleyen bölümlerinde kullanacağımız uygun tarih tabanlı özellikleri etkinleştirecektir.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*    Use a string method to concatenate ``stop_date`` and ``stop_time`` (separated by a space), and store the result in ``combined``.\n",
    "*    Convert ``combined`` to ``datetime`` format, and store the result in a new column named ``stop_datetime``.\n",
    "*    Examine the ``DataFrame`` ``.dtypes`` to confirm that ``stop_datetime`` is a datetime column."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "TALİMATLAR\n",
    "• stop_date ve stop_time'ı (bir boşlukla ayrılmış) birleştirmek için bir dize yöntemi kullanın ve sonucu birleşik olarak saklayın.\n",
    "• Birleşik tarih saat biçimine dönüştürün ve sonucu stop_datetime adlı yeni bir sütunda saklayın.\n",
    "• stop_datetime öğesinin bir tarih saat sütunu olduğunu doğrulamak için DataFrame .dtypes'i inceleyin."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "source": [
    "combined=ri.stop_date+\" \"+ri.stop_time"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "source": [
    "ri[\"stop_datetime\"]=combined.astype(\"datetime64\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "source": [
    "ri.dtypes"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                            object\n",
       "stop_date                     object\n",
       "stop_time                     object\n",
       "location_raw                  object\n",
       "police_department             object\n",
       "driver_gender                 object\n",
       "driver_age_raw               float64\n",
       "driver_age                   float64\n",
       "driver_race_raw               object\n",
       "driver_race                   object\n",
       "violation_raw                 object\n",
       "violation                     object\n",
       "search_conducted                bool\n",
       "search_type_raw               object\n",
       "search_type                   object\n",
       "contraband_found                bool\n",
       "stop_outcome                  object\n",
       "is_arrested                     bool\n",
       "stop_duration                 object\n",
       "out_of_state                    bool\n",
       "drugs_related_stop              bool\n",
       "district                      object\n",
       "stop_datetime         datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 164
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "source": [
    "ri.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <td>2005-01-02</td>\n",
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       "      <td>1987.0</td>\n",
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       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>2005-01-04 12:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2005-01-06</td>\n",
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       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>2005-01-06 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>2005-01-12 08:05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id   stop_date stop_time location_raw police_department  \\\n",
       "0  RI-2005-00001  2005-01-02     01:55      Zone K1               600   \n",
       "1  RI-2005-00002  2005-01-02     20:30      Zone X4               500   \n",
       "3  RI-2005-00004  2005-01-04     12:55      Zone X4               500   \n",
       "4  RI-2005-00005  2005-01-06     01:30      Zone X4               500   \n",
       "5  RI-2005-00006  2005-01-12     08:05      Zone X1                 0   \n",
       "\n",
       "  driver_gender  driver_age_raw  driver_age driver_race_raw driver_race  ...  \\\n",
       "0             M          1985.0        20.0               W       White  ...   \n",
       "1             M          1987.0        18.0               W       White  ...   \n",
       "3             M          1986.0        19.0               W       White  ...   \n",
       "4             M          1978.0        27.0               B       Black  ...   \n",
       "5             M          1973.0        32.0               B       Black  ...   \n",
       "\n",
       "  search_type_raw search_type  contraband_found stop_outcome is_arrested  \\\n",
       "0             NaN         NaN             False     Citation       False   \n",
       "1             NaN         NaN             False     Citation       False   \n",
       "3             NaN         NaN             False     Citation       False   \n",
       "4             NaN         NaN             False     Citation       False   \n",
       "5             NaN         NaN             False     Citation       False   \n",
       "\n",
       "   stop_duration out_of_state  drugs_related_stop district       stop_datetime  \n",
       "0       0-15 Min        False               False  Zone K1 2005-01-02 01:55:00  \n",
       "1      16-30 Min        False               False  Zone X4 2005-01-02 20:30:00  \n",
       "3       0-15 Min        False               False  Zone X4 2005-01-04 12:55:00  \n",
       "4       0-15 Min        False               False  Zone X4 2005-01-06 01:30:00  \n",
       "5        30+ Min         True               False  Zone X1 2005-01-12 08:05:00  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 165
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The last step that you'll take in this chapter is to set the ``stop_datetime`` column as the ``DataFrame``'s index. By replacing the default index with a ``DatetimeIndex``, you'll make it easier to analyze the dataset by date and time, which will come in handy later in the course."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Bu bölümde atacağınız son adım, stop_datetime sütununu DataFrame'in dizini olarak ayarlamaktır. Varsayılan dizini bir DatetimeIndex ile değiştirerek, veri kümesini tarih ve saate göre analiz etmeyi kolaylaştıracaksınız, bu da kursun ilerleyen bölümlerinde kullanışlı olacaktır."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Set ``stop_datetime`` as the ``DataFrame`` index.\n",
    "*   Examine the index to verify that it is a ``DatetimeIndex``.\n",
    "*   Examine the ``DataFrame`` columns to confirm that ``stop_datetime`` is no longer one of the columns."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "TALİMATLAR\n",
    "• stop_datetime'ı DataFrame in index i olarak ayarlayın.\n",
    "• Dizini bir DatetimeIndex olduğunu doğrulamak için inceleyin.\n",
    "• stop_datetime öğesinin artık sütunlardan biri olmadığını doğrulamak için DataFrame sütunlarını inceleyin.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "source": [
    "ri=ri.set_index(\"stop_datetime\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "source": [
    "ri.head()"
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   "outputs": [
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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_conducted search_type_raw  search_type  \\\n",
       "stop_datetime                                                       \n",
       "2005-01-02 01:55:00            False             NaN          NaN   \n",
       "2005-01-02 20:30:00            False             NaN          NaN   \n",
       "2005-01-04 12:55:00            False             NaN          NaN   \n",
       "2005-01-06 01:30:00            False             NaN          NaN   \n",
       "2005-01-12 08:05:00            False             NaN          NaN   \n",
       "\n",
       "                    contraband_found stop_outcome  is_arrested stop_duration  \\\n",
       "stop_datetime                                                                  \n",
       "2005-01-02 01:55:00            False     Citation        False      0-15 Min   \n",
       "2005-01-02 20:30:00            False     Citation        False     16-30 Min   \n",
       "2005-01-04 12:55:00            False     Citation        False      0-15 Min   \n",
       "2005-01-06 01:30:00            False     Citation        False      0-15 Min   \n",
       "2005-01-12 08:05:00            False     Citation        False       30+ Min   \n",
       "\n",
       "                     out_of_state drugs_related_stop  district  \n",
       "stop_datetime                                                   \n",
       "2005-01-02 01:55:00         False              False   Zone K1  \n",
       "2005-01-02 20:30:00         False              False   Zone X4  \n",
       "2005-01-04 12:55:00         False              False   Zone X4  \n",
       "2005-01-06 01:30:00         False              False   Zone X4  \n",
       "2005-01-12 08:05:00          True              False   Zone X1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 167
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "source": [
    "ri.to_csv(\"police_1.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 22)"
      ]
     },
     "metadata": {},
     "execution_count": 169
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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\"> "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Does the ``gender`` of a driver have an impact on police behavior during a traffic stop? **In this chapter**, you will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Examining traffic violations"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Before comparing the violations being committed by each gender, you should examine the ``violations`` committed by all drivers to get a baseline understanding of the data.\n",
    "\n",
    "In this exercise, you'll count the unique values in the ``violation`` column, and then separately express those counts as proportions."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "> Before starting your work in this section **repeat the steps which you did in the previos chapter for preparing the data.** Continue to this chapter based on where you were in the end of the previous chapter."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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_1.csv\", index_col=[\"stop_datetime\"])\n",
    "\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "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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       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 20:30:00</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04 12:55:00</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06 01:30:00</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 08:05:00</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                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_conducted search_type_raw  search_type  \\\n",
       "stop_datetime                                                       \n",
       "2005-01-02 01:55:00            False             NaN          NaN   \n",
       "2005-01-02 20:30:00            False             NaN          NaN   \n",
       "2005-01-04 12:55:00            False             NaN          NaN   \n",
       "2005-01-06 01:30:00            False             NaN          NaN   \n",
       "2005-01-12 08:05:00            False             NaN          NaN   \n",
       "\n",
       "                    contraband_found stop_outcome  is_arrested stop_duration  \\\n",
       "stop_datetime                                                                  \n",
       "2005-01-02 01:55:00            False     Citation        False      0-15 Min   \n",
       "2005-01-02 20:30:00            False     Citation        False     16-30 Min   \n",
       "2005-01-04 12:55:00            False     Citation        False      0-15 Min   \n",
       "2005-01-06 01:30:00            False     Citation        False      0-15 Min   \n",
       "2005-01-12 08:05:00            False     Citation        False       30+ Min   \n",
       "\n",
       "                     out_of_state drugs_related_stop  district  \n",
       "stop_datetime                                                   \n",
       "2005-01-02 01:55:00         False              False   Zone K1  \n",
       "2005-01-02 20:30:00         False              False   Zone X4  \n",
       "2005-01-04 12:55:00         False              False   Zone X4  \n",
       "2005-01-06 01:30:00         False              False   Zone X4  \n",
       "2005-01-12 08:05:00          True              False   Zone X1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 170
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "source": [
    "df.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 22)"
      ]
     },
     "metadata": {},
     "execution_count": 171
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Count the unique values in the ``violation`` column, to see what violations are being committed by all drivers.\n",
    "*   Express the violation counts as proportions of the total."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "source": [
    "pd.DataFrame(df.violation.value_counts()/1000)\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>violation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Speeding</th>\n",
       "      <td>268.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moving violation</th>\n",
       "      <td>90.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equipment</th>\n",
       "      <td>61.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>24.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registration/plates</th>\n",
       "      <td>19.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat belt</th>\n",
       "      <td>16.324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     violation\n",
       "Speeding               268.736\n",
       "Moving violation        90.228\n",
       "Equipment               61.250\n",
       "Other                   24.216\n",
       "Registration/plates     19.830\n",
       "Seat belt               16.324"
      ]
     },
     "metadata": {},
     "execution_count": 172
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "source": [
    "pd.DataFrame(prc(df.violation.value_counts(normalize=True)))\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>violation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Speeding</th>\n",
       "      <td>55.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moving violation</th>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equipment</th>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registration/plates</th>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat belt</th>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     violation\n",
       "Speeding                  55.9\n",
       "Moving violation          18.8\n",
       "Equipment                 12.7\n",
       "Other                      5.0\n",
       "Registration/plates        4.1\n",
       "Seat belt                  3.4"
      ]
     },
     "metadata": {},
     "execution_count": 173
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing violations by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The question we're trying to answer is whether male and female drivers tend to commit different types of traffic violations.\n",
    "\n",
    "You'll first create a ``DataFrame`` for each gender, and then analyze the ``violations`` in each ``DataFrame`` separately."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a ``DataFrame``, female, that only contains rows in which ``driver_gender`` is ``'F'``.\n",
    "*   Create a ``DataFrame``, male, that only contains rows in which ``driver_gender`` is ``'M'``.\n",
    "*   Count the ``violations`` committed by female drivers and express them as proportions.\n",
    "*   Count the violations committed by male drivers and express them as proportions."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "source": [
    "dF=df[df.driver_gender==\"F\"]\n",
    "dM=df[df.driver_gender==\"M\"]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "source": [
    "dF.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>...</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type_raw</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2005-02-24 01:20:00</th>\n",
       "      <td>RI-2005-00016</td>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>Zone X3</td>\n",
       "      <td>200</td>\n",
       "      <td>F</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-14 10:00:00</th>\n",
       "      <td>RI-2005-00019</td>\n",
       "      <td>2005-03-14</td>\n",
       "      <td>10:00</td>\n",
       "      <td>Zone K3</td>\n",
       "      <td>300</td>\n",
       "      <td>F</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-29 23:20:00</th>\n",
       "      <td>RI-2005-00026</td>\n",
       "      <td>2005-03-29</td>\n",
       "      <td>23:20</td>\n",
       "      <td>Zone K3</td>\n",
       "      <td>300</td>\n",
       "      <td>F</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06-06 13:20:00</th>\n",
       "      <td>RI-2005-00035</td>\n",
       "      <td>2005-06-06</td>\n",
       "      <td>13:20</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>F</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06-18 16:30:00</th>\n",
       "      <td>RI-2005-00037</td>\n",
       "      <td>2005-06-18</td>\n",
       "      <td>16:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>F</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                id   stop_date stop_time location_raw  \\\n",
       "stop_datetime                                                           \n",
       "2005-02-24 01:20:00  RI-2005-00016  2005-02-24     01:20      Zone X3   \n",
       "2005-03-14 10:00:00  RI-2005-00019  2005-03-14     10:00      Zone K3   \n",
       "2005-03-29 23:20:00  RI-2005-00026  2005-03-29     23:20      Zone K3   \n",
       "2005-06-06 13:20:00  RI-2005-00035  2005-06-06     13:20      Zone X4   \n",
       "2005-06-18 16:30:00  RI-2005-00037  2005-06-18     16:30      Zone X4   \n",
       "\n",
       "                    police_department driver_gender  driver_age_raw  \\\n",
       "stop_datetime                                                         \n",
       "2005-02-24 01:20:00               200             F          1983.0   \n",
       "2005-03-14 10:00:00               300             F          1984.0   \n",
       "2005-03-29 23:20:00               300             F          1971.0   \n",
       "2005-06-06 13:20:00               500             F          1986.0   \n",
       "2005-06-18 16:30:00               500             F          1964.0   \n",
       "\n",
       "                     driver_age driver_race_raw driver_race  ...  \\\n",
       "stop_datetime                                                ...   \n",
       "2005-02-24 01:20:00        22.0               W       White  ...   \n",
       "2005-03-14 10:00:00        21.0               W       White  ...   \n",
       "2005-03-29 23:20:00        34.0               W       White  ...   \n",
       "2005-06-06 13:20:00        19.0               W       White  ...   \n",
       "2005-06-18 16:30:00        41.0               W       White  ...   \n",
       "\n",
       "                    search_conducted search_type_raw  search_type  \\\n",
       "stop_datetime                                                       \n",
       "2005-02-24 01:20:00            False             NaN          NaN   \n",
       "2005-03-14 10:00:00            False             NaN          NaN   \n",
       "2005-03-29 23:20:00            False             NaN          NaN   \n",
       "2005-06-06 13:20:00            False             NaN          NaN   \n",
       "2005-06-18 16:30:00            False             NaN          NaN   \n",
       "\n",
       "                    contraband_found   stop_outcome  is_arrested  \\\n",
       "stop_datetime                                                      \n",
       "2005-02-24 01:20:00            False       Citation        False   \n",
       "2005-03-14 10:00:00            False       Citation        False   \n",
       "2005-03-29 23:20:00            False       Citation        False   \n",
       "2005-06-06 13:20:00            False       Citation        False   \n",
       "2005-06-18 16:30:00            False  Arrest Driver         True   \n",
       "\n",
       "                    stop_duration  out_of_state drugs_related_stop  district  \n",
       "stop_datetime                                                                 \n",
       "2005-02-24 01:20:00      0-15 Min          True              False   Zone X3  \n",
       "2005-03-14 10:00:00      0-15 Min         False              False   Zone K3  \n",
       "2005-03-29 23:20:00      0-15 Min          True              False   Zone K3  \n",
       "2005-06-06 13:20:00      0-15 Min         False              False   Zone X4  \n",
       "2005-06-18 16:30:00       30+ Min         False              False   Zone X4  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 175
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "source": [
    "prc(dF.violation.value_counts(normalize=True))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Speeding               65.7\n",
       "Moving violation       13.7\n",
       "Equipment              10.7\n",
       "Registration/plates     4.3\n",
       "Other                   2.9\n",
       "Seat belt               2.7\n",
       "Name: violation, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 176
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "source": [
    "prc(dM.violation.value_counts(normalize=True))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Speeding               52.2\n",
       "Moving violation       20.7\n",
       "Equipment              13.5\n",
       "Other                   5.8\n",
       "Registration/plates     4.1\n",
       "Seat belt               3.7\n",
       "Name: violation, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 177
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing speeding outcomes by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "When a driver is pulled over for speeding, many people believe that gender has an impact on whether the driver will receive a ticket or a warning. Can you find evidence of this in the dataset?\n",
    "\n",
    "First, you'll create two ``DataFrames`` of drivers who were stopped for ``speeding``: one containing ***females*** and the other containing ***males***.\n",
    "\n",
    "Then, for each **gender**, you'll use the ``stop_outcome`` column to calculate what percentage of stops resulted in a ``\"Citation\"`` (meaning a ticket) versus a ``\"Warning\"``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a ``DataFrame``, ``female_and_speeding``, that only includes female drivers who were stopped for speeding.\n",
    "*   Create a ``DataFrame``, ``male_and_speeding``, that only includes male drivers who were stopped for speeding.\n",
    "*   Count the **stop outcomes** for the female drivers and express them as proportions.\n",
    "*   Count the **stop outcomes** for the male drivers and express them as proportions."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "source": [
    "#female_and_speeding\n",
    "dFspeed=dF[dF.violation==\"Speeding\"]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "source": [
    "#male_and_speeding\n",
    "dMspeed=dM[dM.violation==\"Speeding\"]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "source": [
    "prc(dFspeed.stop_outcome.value_counts(normalize=True))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Citation            95.3\n",
       "Warning              3.9\n",
       "Arrest Driver        0.5\n",
       "Arrest Passenger     0.1\n",
       "N/D                  0.1\n",
       "No Action            0.1\n",
       "Name: stop_outcome, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 180
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "source": [
    "prc(dMspeed.stop_outcome.value_counts(normalize=True))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Citation            94.5\n",
       "Warning              3.6\n",
       "Arrest Driver        1.6\n",
       "Arrest Passenger     0.1\n",
       "N/D                  0.1\n",
       "No Action            0.1\n",
       "Name: stop_outcome, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 181
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "source": [
    "# Erkekler ve bayanların trafik ihlali yapmaları durumunda aldıkları ceza oranları yaklaşık aynı "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "raw",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "source": [
    "dMspeed.violation.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Speeding    182538\n",
       "Name: violation, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 183
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "source": [
    "dFspeed.violation.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Speeding    86198\n",
       "Name: violation, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 184
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "source": [
    "# Erkekler bayanlardan daha çok daha  fazla sayıda  hız ihlali yapmış"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculating the search rate"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "During a traffic stop, the police officer sometimes conducts a search of the vehicle. In this exercise, you'll calculate the percentage of all stops that result in a vehicle search, also known as the **search rate**."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Check the data type of ``search_conducted`` to confirm that it's a ``Boolean Series``.\n",
    "*   Calculate the search rate by counting the ``Series`` values and expressing them as proportions.\n",
    "*   Calculate the search rate by taking the mean of the ``Series``. (It should match the proportion of ``True`` values calculated above.)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "source": [
    "df.search_conducted.dtype"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dtype('bool')"
      ]
     },
     "metadata": {},
     "execution_count": 186
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "source": [
    "prc(df.search_conducted.value_counts(normalize=True))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "False    96.3\n",
       "True      3.7\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 187
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "source": [
    "prc(df.search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.7"
      ]
     },
     "metadata": {},
     "execution_count": 188
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing search rates by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "You'll compare the rates at which **female** and **male** drivers are searched during a traffic stop. Remember that the vehicle search rate across all stops is about **3.8%**.\n",
    "\n",
    "First, you'll filter the ``DataFrame`` by gender and calculate the search rate for each group separately. Then, you'll perform the same calculation for both genders at once using a ``.groupby()``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS 1/3**\n",
    "\n",
    "*   Filter the ``DataFrame`` to only include **female** drivers, and then calculate the search rate by taking the mean of ``search_conducted``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "source": [
    "prc(df[df.driver_gender==\"F\"].search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1.9"
      ]
     },
     "metadata": {},
     "execution_count": 189
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS 2/3**\n",
    "\n",
    "*   Filter the ``DataFrame`` to only include **male** drivers, and then repeat the search rate calculation."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "source": [
    "prc(df[df.driver_gender==\"M\"].search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4.4"
      ]
     },
     "metadata": {},
     "execution_count": 190
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS 3/3**\n",
    "\n",
    "*   Group by driver gender to calculate the search rate for both groups simultaneously. (It should match the previous results.)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "source": [
    "prc(df.groupby(\"driver_gender\").search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "driver_gender\n",
       "F    1.9\n",
       "M    4.4\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 191
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Adding a second factor to the analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Even though the search rate for males is much higher than for females, it's possible that the difference is mostly due to a second factor.\n",
    "\n",
    "For example, you might hypothesize that the search rate varies by violation type, and the difference in search rate between males and females is because they tend to commit different violations.\n",
    "\n",
    "You can test this hypothesis by examining the search rate for each combination of gender and violation. If the hypothesis was true, you would find that males and females are searched at about the same rate for each violation. Find out below if that's the case!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS 1/2**\n",
    "\n",
    "*   Use a ``.groupby()`` to calculate the search rate for each combination of gender and violation. Are males and females searched at about the same rate for each violation?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "source": [
    "prc(df.groupby([\"driver_gender\",\"violation\"]).search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "driver_gender  violation          \n",
       "F              Equipment               4.0\n",
       "               Moving violation        3.8\n",
       "               Other                   4.6\n",
       "               Registration/plates     5.5\n",
       "               Seat belt               1.8\n",
       "               Speeding                0.8\n",
       "M              Equipment               7.1\n",
       "               Moving violation        5.9\n",
       "               Other                   4.6\n",
       "               Registration/plates    10.4\n",
       "               Seat belt               3.2\n",
       "               Speeding                2.7\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 192
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS 2/2**\n",
    "\n",
    "*   Reverse the ordering to group by violation before gender. The results may be easier to compare when presented this way."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "source": [
    "prc(df.groupby([\"violation\",\"driver_gender\"]).search_conducted.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "violation            driver_gender\n",
       "Equipment            F                 4.0\n",
       "                     M                 7.1\n",
       "Moving violation     F                 3.8\n",
       "                     M                 5.9\n",
       "Other                F                 4.6\n",
       "                     M                 4.6\n",
       "Registration/plates  F                 5.5\n",
       "                     M                10.4\n",
       "Seat belt            F                 1.8\n",
       "                     M                 3.2\n",
       "Speeding             F                 0.8\n",
       "                     M                 2.7\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 193
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Counting protective frisks"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "During a vehicle search, the police officer may pat down the driver to check if they have a weapon. This is known as a ``\"protective frisk.\"``\n",
    "\n",
    "You'll first check to see how many times \"Protective Frisk\" was the only search type. Then, you'll use a string method to locate all instances in which the driver was frisked."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Count the ``search_type`` values to see how many times ``\"Protective Frisk\"`` was the only search type.\n",
    "*   Create a new column, frisk, that is ``True`` if ``search_type`` contains the string ``\"Protective Frisk\"`` and ``False`` otherwise.\n",
    "*   Check the data type of frisk to confirm that it's a ``Boolean Series``.\n",
    "*   Take the sum of frisk to count the total number of frisks."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "source": [
    "df.search_type.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Incident to Arrest                                          6998\n",
       "Probable Cause                                              4989\n",
       "Reasonable Suspicion                                        1141\n",
       "Inventory                                                   1101\n",
       "Protective Frisk                                             879\n",
       "Incident to Arrest,Inventory                                 649\n",
       "Incident to Arrest,Probable Cause                            552\n",
       "Probable Cause,Reasonable Suspicion                          334\n",
       "Probable Cause,Protective Frisk                              221\n",
       "Incident to Arrest,Protective Frisk                          158\n",
       "Incident to Arrest,Inventory,Probable Cause                  151\n",
       "Inventory,Probable Cause                                     132\n",
       "Protective Frisk,Reasonable Suspicion                         83\n",
       "Incident to Arrest,Inventory,Protective Frisk                 77\n",
       "Incident to Arrest,Probable Cause,Protective Frisk            74\n",
       "Inventory,Protective Frisk                                    52\n",
       "Incident to Arrest,Reasonable Suspicion                       49\n",
       "Probable Cause,Protective Frisk,Reasonable Suspicion          31\n",
       "Incident to Arrest,Probable Cause,Reasonable Suspicion        31\n",
       "Inventory,Reasonable Suspicion                                17\n",
       "Inventory,Probable Cause,Protective Frisk                     14\n",
       "Incident to Arrest,Inventory,Reasonable Suspicion             12\n",
       "Incident to Arrest,Protective Frisk,Reasonable Suspicion       8\n",
       "Inventory,Probable Cause,Reasonable Suspicion                  8\n",
       "Inventory,Protective Frisk,Reasonable Suspicion                1\n",
       "Name: search_type, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 194
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "source": [
    "df[\"frisk\"]=df.search_type==\"Protective Frisk\" \n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>...</th>\n",
       "      <th>search_type_raw</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>frisk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2005-01-02 01:55:00</th>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 20:30:00</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04 12:55:00</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06 01:30:00</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 08:05:00</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                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]"
      ]
     },
     "metadata": {},
     "execution_count": 195
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "source": [
    "(df.frisk==True).sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "879"
      ]
     },
     "metadata": {},
     "execution_count": 196
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing frisk rates by gender"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "You'll compare the rates at which female and male drivers are frisked during a search. Are males frisked more often than females, perhaps because police officers consider them to be higher risk?\n",
    "\n",
    "Before doing any calculations, it's important to filter the ``DataFrame`` to only include the relevant subset of data, namely stops in which a search was conducted."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a ``DataFrame``, searched, that only contains rows in which ``search_conducted`` is ``True``.\n",
    "*   Take the mean of the frisk column to find out what percentage of searches included a frisk.\n",
    "*   Calculate the frisk rate for each gender using a ``.groupby()``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "source": [
    "dfsc=df[df.search_conducted==True]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "source": [
    "prc(dfsc.frisk.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4.9"
      ]
     },
     "metadata": {},
     "execution_count": 198
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "source": [
    "prc(dfsc.groupby(\"driver_gender\").frisk.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "driver_gender\n",
       "F    4.2\n",
       "M    5.1\n",
       "Name: frisk, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 199
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "source": [
    "# Araba kontrolü esnasındaki Bayan ve erkek üst arama oranları yaklaşık aynı  %4,2 F -%5 M"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "source": [
    "df.to_csv(\"police_2.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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\"> "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Calculating the hourly arrest rate"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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``"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "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"
     ]
    },
    {
     "output_type": "execute_result",
     "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>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>...</th>\n",
       "      <th>search_type_raw</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>frisk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2005-01-02 01:55:00</th>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 20:30:00</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04 12:55:00</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06 01:30:00</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 08:05:00</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                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]"
      ]
     },
     "metadata": {},
     "execution_count": 202
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "source": [
    "df.is_arrested.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "False    463981\n",
       "True      16603\n",
       "Name: is_arrested, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 203
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "source": [
    "prc(df.is_arrested.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "metadata": {},
     "execution_count": 204
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "source": [
    "prc(df.groupby(df.index.hour).is_arrested.mean())\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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"
      ]
     },
     "metadata": {},
     "execution_count": 205
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "source": [
    "hourly_arrest_rate=df.groupby(df.index.hour).is_arrested.mean()\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting the hourly arrest rate"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "source": [
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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    "***"
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   "cell_type": "markdown",
   "source": [
    "## Plotting drug-related stops"
   ],
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   "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."
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   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "source": [
    "prc(df.drugs_related_stop.resample(\"A\").mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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"
      ]
     },
     "metadata": {},
     "execution_count": 209
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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."
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "source": [
    "annual_drug_rate = prc( df.drugs_related_stop.resample(\"A\").mean())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "needs_background": "light"
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    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing drug and search rates (to be deleted)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "source": [
    "annual_searh_rate=prc(df.search_conducted.resample(\"y\").mean())\n",
    "annual_searh_rate"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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"
      ]
     },
     "metadata": {},
     "execution_count": 213
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "source": [
    "annual=pd.concat([annual_drug_rate,annual_searh_rate],axis=1)\n",
    "annual"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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"
      ]
     },
     "metadata": {},
     "execution_count": 214
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "source": [
    "ap=annual.plot(subplots=True)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
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   "cell_type": "markdown",
   "source": [
    "***"
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   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tallying violations by district"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "source": [
    "all_zones = pd.crosstab(df.district, df.violation)\n",
    "all_zones"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "  <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",
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       "      <th>Zone K1</th>\n",
       "      <td>3786</td>\n",
       "      <td>7127</td>\n",
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       "      <th>Zone K2</th>\n",
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       "      <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  "
      ]
     },
     "metadata": {},
     "execution_count": 216
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "source": [
    "k_zones=all_zones.loc[\"Zone K1\":\"Zone K3\"]\n",
    "k_zones"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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  "
      ]
     },
     "metadata": {},
     "execution_count": 217
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting violations by district"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "source": [
    "k=k_zones.plot.bar()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "source": [
    "ks=k_zones.plot.bar(stacked=True)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
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   "cell_type": "markdown",
   "source": [
    "***"
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   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Converting stop durations to numbers"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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``"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 8, 23, 45], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 220
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "source": [
    "df.stop_duration.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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"
      ]
     },
     "metadata": {},
     "execution_count": 221
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "source": [
    "df.stop_minutes.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "8     386646\n",
       "23     76320\n",
       "45     17618\n",
       "Name: stop_minutes, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 222
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "source": [
    "df.stop_minutes.value_counts(dropna=False)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "8     386646\n",
       "23     76320\n",
       "45     17618\n",
       "Name: stop_minutes, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 223
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting stop length"
   ],
   "metadata": {}
  },
  {
   "cell_type": "raw",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "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."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "source": [
    "stop_length=round(df.groupby(\"violation_raw\").stop_minutes.mean())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "source": [
    "stop_length.sort_values(inplace=True)\n",
    "round(stop_length)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "violation_raw\n",
       "Seatbelt Violation                  10.0\n",
       "Equipment/Inspection Violation      11.0\n",
       "Speeding                            11.0\n",
       "Violation of City/Town Ordinance    13.0\n",
       "Other Traffic Violation             14.0\n",
       "Registration 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"
      ]
     },
     "metadata": {},
     "execution_count": 225
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "source": [
    "s=stop_length.plot.barh()\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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  {
   "cell_type": "code",
   "execution_count": 227,
   "source": [
    "df.to_csv(\"police_3.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
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  {
   "cell_type": "markdown",
   "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": [
    "In this chapter, you will use a **second dataset** to explore the impact of **weather conditions** on police behavior during traffic stops. You will practice **merging** and **reshaping** datasets, assessing whether a data source is trustworthy, working with **categorical** data, and other advanced skills."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting the temperature"
   ],
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  },
  {
   "cell_type": "markdown",
   "source": [
    "In this exercise, you'll examine the ``temperature`` columns from the ``weather`` dataset to assess whether the data seems trustworthy. First you'll print the summary statistics, and then you'll visualize the data using a **box plot**.\n",
    "\n",
    "When deciding whether the values seem reasonable, keep in mind that the temperature is measured in degrees **Fahrenheit**, not Celsius!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Read ``weather.csv`` into a ``DataFrame`` named ``weather``.\n",
    "*   Select the temperature columns (``TMIN``, ``TAVG``, ``TMAX``) and print their ``summary statistics`` using the ``.describe()`` method.\n",
    "*   Create a **box plot** to visualize the temperature columns.\n",
    "*   Display the plot."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def prc(x):return round(x*100,1)            # yüzde (%) gösteriminde kolaylık için\n",
    "def celcius(x): return   (x - 32) / 1.8     # fahrenheit i celcius a çevirir\n",
    "\n",
    "we=pd.read_csv(\"weather.csv\")\n",
    "we.head()"
   ],
   "outputs": [
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       "       STATION        DATE  AWND  TAVG  TMAX  TMIN  WT01  WT02  WT03  WT04  \\\n",
       "0  USC00379423  2005-01-01   NaN   NaN  47.0  28.0   NaN   NaN   NaN   NaN   \n",
       "1  USC00379423  2005-01-02   NaN   NaN  52.0  24.0   NaN   NaN   NaN   NaN   \n",
       "2  USC00379423  2005-01-03   NaN   NaN  48.0  27.0   NaN   NaN   NaN   NaN   \n",
       "3  USC00379423  2005-01-04   NaN   NaN  54.0  40.0   NaN   NaN   NaN   NaN   \n",
       "4  USC00379423  2005-01-05   NaN   NaN  44.0  31.0   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...  WT11  WT13  WT14  WT15  WT16  WT17  WT18  WT19  WT21  WT22  \n",
       "0  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
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       "3  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
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     },
     "metadata": {},
     "execution_count": 228
    }
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  {
   "cell_type": "code",
   "execution_count": 229,
   "source": [
    "we.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(8022, 26)"
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     "metadata": {},
     "execution_count": 229
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  {
   "cell_type": "code",
   "execution_count": 230,
   "source": [
    "we.isnull().sum()"
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   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "STATION       0\n",
       "DATE          0\n",
       "AWND       4005\n",
       "TAVG       6805\n",
       "TMAX         17\n",
       "TMIN         26\n",
       "WT01       6245\n",
       "WT02       7801\n",
       "WT03       7795\n",
       "WT04       7903\n",
       "WT05       7662\n",
       "WT06       7997\n",
       "WT07       7943\n",
       "WT08       7618\n",
       "WT09       7953\n",
       "WT10       8020\n",
       "WT11       8019\n",
       "WT13       6847\n",
       "WT14       7447\n",
       "WT15       8016\n",
       "WT16       6696\n",
       "WT17       8010\n",
       "WT18       7677\n",
       "WT19       8018\n",
       "WT21       8004\n",
       "WT22       7990\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 230
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "source": [
    "round(we[[\"TMIN\", \"TAVG\", \"TMAX\"]].describe(),1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>TMIN</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7996.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>8005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.1</td>\n",
       "      <td>52.5</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.4</td>\n",
       "      <td>17.8</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         TMIN    TAVG    TMAX\n",
       "count  7996.0  1217.0  8005.0\n",
       "mean     42.1    52.5    61.2\n",
       "std      17.4    17.8    18.5\n",
       "min     -10.0     6.0    13.0\n",
       "25%      29.0    39.0    46.0\n",
       "50%      42.0    54.0    62.0\n",
       "75%      57.0    68.0    77.0\n",
       "max      77.0    86.0   102.0"
      ]
     },
     "metadata": {},
     "execution_count": 231
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "source": [
    "we[[\"TMIN\", \"TAVG\", \"TMAX\"]].plot.box(grid=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 232
    },
    {
     "output_type": "display_data",
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "source": [
    "# Görselleştirmeyi alışkın olduğumuz Celcius sıcaklık birimine dönüştürelim"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "source": [
    "we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]]=celcius(we[[\"TMIN\", \"TAVG\", \"TMAX\"]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "source": [
    "round(we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]].describe())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>CTMIN</th>\n",
       "      <th>CTAVG</th>\n",
       "      <th>CTMAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7996.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>8005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-23.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CTMIN   CTAVG   CTMAX\n",
       "count  7996.0  1217.0  8005.0\n",
       "mean      6.0    11.0    16.0\n",
       "std      10.0    10.0    10.0\n",
       "min     -23.0   -14.0   -11.0\n",
       "25%      -2.0     4.0     8.0\n",
       "50%       6.0    12.0    17.0\n",
       "75%      14.0    20.0    25.0\n",
       "max      25.0    30.0    39.0"
      ]
     },
     "metadata": {},
     "execution_count": 235
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "source": [
    "we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]].plot.box(grid=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 236
    },
    {
     "output_type": "display_data",
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting the temperature difference"
   ],
   "metadata": {}
  },
  {
   "cell_type": "raw",
   "source": [
    "In this exercise, you'll continue to assess ``whether`` the dataset seems trustworthy by plotting the difference between the maximum and minimum temperatures.\n",
    "\n",
    "What do you notice about the resulting **histogram**? Does it match your expectations, or do you see anything unusual?"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a new column in the ``weather`` ``DataFrame`` named ``TDIFF`` that represents the difference between the maximum and minimum temperatures.\n",
    "*   Print the summary statistics for ``TDIFF`` using the ``.describe()`` method.\n",
    "*   Create a histogram with ``20 bins`` to visualize ``TDIFF``.\n",
    "*   Display the plot."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "source": [
    "we[\"TDIFF\"]=we.TMAX-we.TMIN"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "source": [
    "we.head()"
   ],
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>AWND</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
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       "      <th>WT02</th>\n",
       "      <th>WT03</th>\n",
       "      <th>WT04</th>\n",
       "      <th>...</th>\n",
       "      <th>WT16</th>\n",
       "      <th>WT17</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT19</th>\n",
       "      <th>WT21</th>\n",
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       "      <th>CTAVG</th>\n",
       "      <th>CTMAX</th>\n",
       "      <th>TDIFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-02</td>\n",
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       "      <td>24.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>2005-01-03</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.888889</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>31.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "       STATION        DATE  AWND  TAVG  TMAX  TMIN  WT01  WT02  WT03  WT04  \\\n",
       "0  USC00379423  2005-01-01   NaN   NaN  47.0  28.0   NaN   NaN   NaN   NaN   \n",
       "1  USC00379423  2005-01-02   NaN   NaN  52.0  24.0   NaN   NaN   NaN   NaN   \n",
       "2  USC00379423  2005-01-03   NaN   NaN  48.0  27.0   NaN   NaN   NaN   NaN   \n",
       "3  USC00379423  2005-01-04   NaN   NaN  54.0  40.0   NaN   NaN   NaN   NaN   \n",
       "4  USC00379423  2005-01-05   NaN   NaN  44.0  31.0   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...  WT16  WT17  WT18  WT19  WT21  WT22     CTMIN  CTAVG      CTMAX  TDIFF  \n",
       "0  ...   NaN   NaN   NaN   NaN   NaN   NaN -2.222222    NaN   8.333333   19.0  \n",
       "1  ...   NaN   NaN   NaN   NaN   NaN   NaN -4.444444    NaN  11.111111   28.0  \n",
       "2  ...   NaN   NaN   NaN   NaN   NaN   NaN -2.777778    NaN   8.888889   21.0  \n",
       "3  ...   NaN   NaN   NaN   NaN   NaN   NaN  4.444444    NaN  12.222222   14.0  \n",
       "4  ...   NaN   NaN   NaN   NaN   NaN   NaN -0.555556    NaN   6.666667   13.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 238
    }
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  {
   "cell_type": "code",
   "execution_count": 239,
   "source": [
    "we.TDIFF.dtypes"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "metadata": {},
     "execution_count": 239
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "source": [
    "round(we.TDIFF.describe(),1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "count    7994.0\n",
       "mean       19.1\n",
       "std         7.0\n",
       "min       -25.0\n",
       "25%        14.0\n",
       "50%        19.0\n",
       "75%        24.0\n",
       "max        48.0\n",
       "Name: TDIFF, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 240
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "source": [
    "we.TDIFF.plot.hist(20, grid=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "metadata": {},
     "execution_count": 241
    },
    {
     "output_type": "display_data",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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    "***"
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  {
   "cell_type": "markdown",
   "source": [
    "## Counting bad weather conditions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The ``weather`` ``DataFrame`` contains ``20`` columns that start with ``'WT'``, each of which represents a bad weather condition. For example:\n",
    "\n",
    "*   ``WT05`` indicates ``\"Hail\"``\n",
    "*   ``WT11`` indicates ``\"High or damaging winds\"``\n",
    "*   ``WT17`` indicates ``\"Freezing rain\"``\n",
    "\n",
    "For every row in the dataset, each ``WT`` column contains either a ``1`` (meaning the condition was present that day) or ``NaN`` (meaning the condition was not present).\n",
    "\n",
    "In this exercise, you'll quantify ``\"how bad\"`` the weather was each day by counting the number of ``1`` values in each row."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Copy the columns ``WT01`` through ``WT22`` from ``weather`` to a new ``DataFrame`` named ``WT``.\n",
    "*   Calculate the sum of each row in ``WT``, and store the results in a new weather column named ``bad_conditions``.\n",
    "*   Replace any ``missing values`` in ``bad_conditions`` with a ``0``. (This has been done for you.)\n",
    "*   Create a **histogram** to visualize ``bad_conditions``, and then display the plot."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "source": [
    "wt=we.iloc[:,6:26]\n",
    "wt.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WT01  WT02  WT03  WT04  WT05  WT06  WT07  WT08  WT09  WT10  WT11  WT13  \\\n",
       "0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   WT14  WT15  WT16  WT17  WT18  WT19  WT21  WT22  \n",
       "0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  "
      ]
     },
     "metadata": {},
     "execution_count": 242
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "source": [
    "we[\"bad_conditions\"]=wt.sum(axis=1)\n",
    "we.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>AWND</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>WT01</th>\n",
       "      <th>WT02</th>\n",
       "      <th>WT03</th>\n",
       "      <th>WT04</th>\n",
       "      <th>...</th>\n",
       "      <th>WT17</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT19</th>\n",
       "      <th>WT21</th>\n",
       "      <th>WT22</th>\n",
       "      <th>CTMIN</th>\n",
       "      <th>CTAVG</th>\n",
       "      <th>CTMAX</th>\n",
       "      <th>TDIFF</th>\n",
       "      <th>bad_conditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-2.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-2.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.888889</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-0.555556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION        DATE  AWND  TAVG  TMAX  TMIN  WT01  WT02  WT03  WT04  \\\n",
       "0  USC00379423  2005-01-01   NaN   NaN  47.0  28.0   NaN   NaN   NaN   NaN   \n",
       "1  USC00379423  2005-01-02   NaN   NaN  52.0  24.0   NaN   NaN   NaN   NaN   \n",
       "2  USC00379423  2005-01-03   NaN   NaN  48.0  27.0   NaN   NaN   NaN   NaN   \n",
       "3  USC00379423  2005-01-04   NaN   NaN  54.0  40.0   NaN   NaN   NaN   NaN   \n",
       "4  USC00379423  2005-01-05   NaN   NaN  44.0  31.0   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...  WT17  WT18  WT19  WT21  WT22     CTMIN  CTAVG      CTMAX  TDIFF  \\\n",
       "0  ...   NaN   NaN   NaN   NaN   NaN -2.222222    NaN   8.333333   19.0   \n",
       "1  ...   NaN   NaN   NaN   NaN   NaN -4.444444    NaN  11.111111   28.0   \n",
       "2  ...   NaN   NaN   NaN   NaN   NaN -2.777778    NaN   8.888889   21.0   \n",
       "3  ...   NaN   NaN   NaN   NaN   NaN  4.444444    NaN  12.222222   14.0   \n",
       "4  ...   NaN   NaN   NaN   NaN   NaN -0.555556    NaN   6.666667   13.0   \n",
       "\n",
       "   bad_conditions  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 243
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "source": [
    "we.bad_conditions.plot.hist(20, grid=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "metadata": {},
     "execution_count": 244
    },
    {
     "output_type": "display_data",
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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   "cell_type": "markdown",
   "source": [
    "***"
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   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Rating the weather conditions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "In the previous exercise, you counted the number of bad weather conditions each day. In this exercise, you'll use the counts to create a *rating system** for the weather.\n",
    "\n",
    "The counts range from ``0`` to ``9``, and should be converted to ratings as follows:\n",
    "\n",
    "*   Convert ``0`` to ``'good'``\n",
    "*   Convert ``1`` through ``4`` to ``'bad'``\n",
    "*   Convert ``5`` through ``9`` to ``'worse'``"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Count the **unique** values in the ``bad_conditions`` column and sort the ``index``. \n",
    "*   Create a ``dictionary`` called ``mapping`` that maps the ``bad_conditions`` integers to strings as specified above.\n",
    "*   Convert the ``bad_conditions`` integers to strings using the ``mapping`` and store the results in a new column called ``rating``.\n",
    "*   Count the **unique** values in ``rating`` to verify that the integers were properly converted to strings."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "source": [
    "we.bad_conditions.sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "6779.0"
      ]
     },
     "metadata": {},
     "execution_count": 245
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "source": [
    "we.bad_conditions.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0    5738\n",
       "1.0     628\n",
       "4.0     476\n",
       "3.0     380\n",
       "2.0     368\n",
       "5.0     282\n",
       "6.0     101\n",
       "7.0      41\n",
       "9.0       4\n",
       "8.0       4\n",
       "Name: bad_conditions, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 246
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "source": [
    "we.groupby(\"STATION\").bad_conditions.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "STATION      bad_conditions\n",
       "USC00371874  1.0                  7\n",
       "             2.0                  1\n",
       "USC00371875  1.0                  6\n",
       "USC00379423  0.0               3989\n",
       "             1.0                  2\n",
       "USW00014765  0.0               1749\n",
       "             1.0                613\n",
       "             4.0                476\n",
       "             3.0                380\n",
       "             2.0                367\n",
       "             5.0                282\n",
       "             6.0                101\n",
       "             7.0                 41\n",
       "             8.0                  4\n",
       "             9.0                  4\n",
       "Name: bad_conditions, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 247
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "source": [
    "we.STATION.value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "USW00014765    4017\n",
       "USC00379423    3991\n",
       "USC00371874       8\n",
       "USC00371875       6\n",
       "Name: STATION, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 248
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "source": [
    "we.bad_conditions.value_counts().sort_index()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0    5738\n",
       "1.0     628\n",
       "2.0     368\n",
       "3.0     380\n",
       "4.0     476\n",
       "5.0     282\n",
       "6.0     101\n",
       "7.0      41\n",
       "8.0       4\n",
       "9.0       4\n",
       "Name: bad_conditions, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 249
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "source": [
    "mapping={0:\"good\",1:\"bad\",2:\"bad\",3:\"bad\",4:\"bad\",5:\"worse\",6:\"worse\",7:\"worse\",8:\"worse\",9:\"worse\"}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "source": [
    "we[\"rating\"]=we.bad_conditions.map(mapping)\n",
    "we.rating.unique()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['good', 'bad', 'worse'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 251
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "source": [
    "we.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.888889</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00379423</td>\n",
       "      <td>2005-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.555556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION        DATE  AWND  TAVG  TMAX  TMIN  WT01  WT02  WT03  WT04  \\\n",
       "0  USC00379423  2005-01-01   NaN   NaN  47.0  28.0   NaN   NaN   NaN   NaN   \n",
       "1  USC00379423  2005-01-02   NaN   NaN  52.0  24.0   NaN   NaN   NaN   NaN   \n",
       "2  USC00379423  2005-01-03   NaN   NaN  48.0  27.0   NaN   NaN   NaN   NaN   \n",
       "3  USC00379423  2005-01-04   NaN   NaN  54.0  40.0   NaN   NaN   NaN   NaN   \n",
       "4  USC00379423  2005-01-05   NaN   NaN  44.0  31.0   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...  WT18  WT19  WT21  WT22     CTMIN  CTAVG      CTMAX  TDIFF  \\\n",
       "0  ...   NaN   NaN   NaN   NaN -2.222222    NaN   8.333333   19.0   \n",
       "1  ...   NaN   NaN   NaN   NaN -4.444444    NaN  11.111111   28.0   \n",
       "2  ...   NaN   NaN   NaN   NaN -2.777778    NaN   8.888889   21.0   \n",
       "3  ...   NaN   NaN   NaN   NaN  4.444444    NaN  12.222222   14.0   \n",
       "4  ...   NaN   NaN   NaN   NaN -0.555556    NaN   6.666667   13.0   \n",
       "\n",
       "   bad_conditions  rating  \n",
       "0             0.0    good  \n",
       "1             0.0    good  \n",
       "2             0.0    good  \n",
       "3             0.0    good  \n",
       "4             0.0    good  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 252
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Changing the data type to category"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Since the ``rating`` column only has a few possible values, you'll change its data type to ``category`` in order to store the data more efficiently. You'll also specify a logical order for the categories, which will be useful for future exercises."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "*   Create a ``list`` object called ``cats`` that lists the weather ratings in a logical order: ``'good'``, ``'bad'``, ``'worse'``.\n",
    "*   Change the data type of the ``rating`` column from ``object`` to ``category``. Make sure to use the ``cats list`` to define the category ordering.\n",
    "*   Examine the ``head`` of the ``rating`` column to confirm that the categories are logically ordered."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "source": [
    "cats=(\"good\",\"bad\",\"worse\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "source": [
    "cat_dtype = pd.api.types.CategoricalDtype(categories=cats, ordered=True)\n",
    "we.rating=we.rating.astype(cat_dtype)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "source": [
    "we.rating.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0    good\n",
       "1    good\n",
       "2    good\n",
       "3    good\n",
       "4    good\n",
       "Name: rating, dtype: category\n",
       "Categories (3, object): ['good' < 'bad' < 'worse']"
      ]
     },
     "metadata": {},
     "execution_count": 255
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Preparing the DataFrames"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "In this exercise, you'll prepare the **traffic stop** and **weather rating** ``DataFrames`` so that they're ready to be merged:\n",
    "\n",
    "With the ``ri`` ``DataFrame``, you'll move the ``stop_datetime`` index to a ``column`` since the index will be lost during the merge.\n",
    "\n",
    "With the ``weather`` ``DataFrame``, you'll select the ``DATE`` and ``rating`` columns and put them in a new ``DataFrame``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "* Reset the ``index`` of the ``ri`` ``DataFrame``.\n",
    "\n",
    "* Examine the ``head`` of ``ri`` to verify that ``stop_datetime`` is now a ``DataFrame`` column, \n",
    "and the ``index`` is now the default ``integer`` index.\n",
    "\n",
    "* Create a new ``DataFrame`` named ``weather_rating`` that contains only the ``DATE`` and ``rating`` columns from the ``weather`` ``DataFrame``.\n",
    "\n",
    "* Examine the ``head`` of ``weather_rating`` to verify that it contains the proper columns."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "> Before starting your work from this part of this chapter **repeat the steps which you did in the first chapter for preparing the ``ri`` data.** Continue to this chapter based on where you were in the end of the first chapter."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "source": [
    "weather_rating=we[we.STATION==\"USW00014765\"][[\"DATE\",\"rating\"]]\n",
    "weather_rating"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4005</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>bad</td>\n",
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       "    <tr>\n",
       "      <th>4006</th>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4007</th>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>2005-01-05</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8017</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8019</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8020</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8021</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4017 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE rating\n",
       "4005  2005-01-01    bad\n",
       "4006  2005-01-02    bad\n",
       "4007  2005-01-03    bad\n",
       "4008  2005-01-04    bad\n",
       "4009  2005-01-05    bad\n",
       "...          ...    ...\n",
       "8017  2015-12-27    bad\n",
       "8018  2015-12-28   good\n",
       "8019  2015-12-29    bad\n",
       "8020  2015-12-30    bad\n",
       "8021  2015-12-31    bad\n",
       "\n",
       "[4017 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 256
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "source": [
    "ri=pd.read_csv(\"police_3.csv\")\n",
    "ri.head()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "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"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th>stop_datetime</th>\n",
       "      <th>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>...</th>\n",
       "      <th>search_type</th>\n",
       "      <th>contraband_found</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>frisk</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
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       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
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       "      <td>Citation</td>\n",
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       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-04 12:55:00</td>\n",
       "      <td>RI-2005-00004</td>\n",
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       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-01-06 01:30:00</td>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-01-12 08:05:00</td>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stop_datetime             id   stop_date stop_time location_raw  \\\n",
       "0  2005-01-02 01:55:00  RI-2005-00001  2005-01-02     01:55      Zone K1   \n",
       "1  2005-01-02 20:30:00  RI-2005-00002  2005-01-02     20:30      Zone X4   \n",
       "2  2005-01-04 12:55:00  RI-2005-00004  2005-01-04     12:55      Zone X4   \n",
       "3  2005-01-06 01:30:00  RI-2005-00005  2005-01-06     01:30      Zone X4   \n",
       "4  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  driver_age driver_race_raw  \\\n",
       "0               600             M          1985.0        20.0               W   \n",
       "1               500             M          1987.0        18.0               W   \n",
       "2               500             M          1986.0        19.0               W   \n",
       "3               500             M          1978.0        27.0               B   \n",
       "4                 0             M          1973.0        32.0               B   \n",
       "\n",
       "   ... search_type contraband_found stop_outcome  is_arrested stop_duration  \\\n",
       "0  ...         NaN            False     Citation        False      0-15 Min   \n",
       "1  ...         NaN            False     Citation        False     16-30 Min   \n",
       "2  ...         NaN            False     Citation        False      0-15 Min   \n",
       "3  ...         NaN            False     Citation        False      0-15 Min   \n",
       "4  ...         NaN            False     Citation        False       30+ Min   \n",
       "\n",
       "  out_of_state  drugs_related_stop district  frisk stop_minutes  \n",
       "0        False               False  Zone K1  False            8  \n",
       "1        False               False  Zone X4  False           23  \n",
       "2        False               False  Zone X4  False            8  \n",
       "3        False               False  Zone X4  False            8  \n",
       "4         True               False  Zone X1  False           45  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 257
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Merging the DataFrames"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Merging the DataFrames\n",
    "In this exercise, you'll ``merge`` the ``ri`` and ``weather_rating`` ``DataFrames`` into a new ``DataFrame``, ``ri_weather``.\n",
    "\n",
    "The ``DataFrames`` will be joined using the ``stop_date`` column from ``ri`` and the ``DATE`` column from ``weather_rating``. Thankfully the date formatting matches exactly, which is not always the case!\n",
    "\n",
    "Once the merge is complete, you'll set ``stop_datetime`` as the index, which is the column you saved in the previous exercise."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "- Examine the shape of the ``ri`` ``DataFrame``.\n",
    "``Merge`` the ``ri`` and ``weather_rating`` ``DataFrames`` using a ``left join``.\n",
    "- Examine the ``shape`` of ``ri_weather`` to confirm that it has two more columns but the same number of rows as ``ri``.\n",
    "- Replace the ``index`` of ``ri_weather`` with the ``stop_datetime`` column."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "source": [
    "ri.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 25)"
      ]
     },
     "metadata": {},
     "execution_count": 258
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "source": [
    "weather_rating.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(4017, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 259
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "source": [
    "ri_weather = pd.merge(left=ri, right=weather_rating, left_on='stop_date', right_on='DATE', how='left').drop_duplicates()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "source": [
    "ri_weather.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 27)"
      ]
     },
     "metadata": {},
     "execution_count": 261
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "source": [
    "ri_weather=ri_weather.drop_duplicates()\n",
    "ri_weather.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(480584, 27)"
      ]
     },
     "metadata": {},
     "execution_count": 262
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "source": [
    "ri_weather.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>stop_datetime</th>\n",
       "      <th>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>...</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>frisk</th>\n",
       "      <th>stop_minutes</th>\n",
       "      <th>DATE</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-02 01:55:00</td>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-02 20:30:00</td>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-01-04 12:55:00</td>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-01-06 01:30:00</td>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>worse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-01-12 08:05:00</td>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>worse</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stop_datetime             id   stop_date stop_time location_raw  \\\n",
       "0  2005-01-02 01:55:00  RI-2005-00001  2005-01-02     01:55      Zone K1   \n",
       "1  2005-01-02 20:30:00  RI-2005-00002  2005-01-02     20:30      Zone X4   \n",
       "2  2005-01-04 12:55:00  RI-2005-00004  2005-01-04     12:55      Zone X4   \n",
       "3  2005-01-06 01:30:00  RI-2005-00005  2005-01-06     01:30      Zone X4   \n",
       "4  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  driver_age driver_race_raw  \\\n",
       "0               600             M          1985.0        20.0               W   \n",
       "1               500             M          1987.0        18.0               W   \n",
       "2               500             M          1986.0        19.0               W   \n",
       "3               500             M          1978.0        27.0               B   \n",
       "4                 0             M          1973.0        32.0               B   \n",
       "\n",
       "   ... stop_outcome is_arrested stop_duration  out_of_state  \\\n",
       "0  ...     Citation       False      0-15 Min         False   \n",
       "1  ...     Citation       False     16-30 Min         False   \n",
       "2  ...     Citation       False      0-15 Min         False   \n",
       "3  ...     Citation       False      0-15 Min         False   \n",
       "4  ...     Citation       False       30+ Min          True   \n",
       "\n",
       "  drugs_related_stop district  frisk stop_minutes        DATE rating  \n",
       "0              False  Zone K1  False            8  2005-01-02    bad  \n",
       "1              False  Zone X4  False           23  2005-01-02    bad  \n",
       "2              False  Zone X4  False            8  2005-01-04    bad  \n",
       "3              False  Zone X4  False            8  2005-01-06  worse  \n",
       "4              False  Zone X1  False           45  2005-01-12  worse  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 263
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "source": [
    "ri_weather.set_index(\"stop_datetime\",inplace=True)\n",
    "ri_weather.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>location_raw</th>\n",
       "      <th>police_department</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_age_raw</th>\n",
       "      <th>driver_age</th>\n",
       "      <th>driver_race_raw</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>...</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>out_of_state</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>frisk</th>\n",
       "      <th>stop_minutes</th>\n",
       "      <th>DATE</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2005-01-02 01:55:00</th>\n",
       "      <td>RI-2005-00001</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>01:55</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>600</td>\n",
       "      <td>M</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K1</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 20:30:00</th>\n",
       "      <td>RI-2005-00002</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>20:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-04 12:55:00</th>\n",
       "      <td>RI-2005-00004</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>W</td>\n",
       "      <td>White</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06 01:30:00</th>\n",
       "      <td>RI-2005-00005</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>01:30</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>500</td>\n",
       "      <td>M</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>worse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 08:05:00</th>\n",
       "      <td>RI-2005-00006</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>08:05</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>B</td>\n",
       "      <td>Black</td>\n",
       "      <td>...</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>30+ Min</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>2005-01-12</td>\n",
       "      <td>worse</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                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  ... stop_outcome  \\\n",
       "stop_datetime                                                ...                \n",
       "2005-01-02 01:55:00        20.0               W       White  ...     Citation   \n",
       "2005-01-02 20:30:00        18.0               W       White  ...     Citation   \n",
       "2005-01-04 12:55:00        19.0               W       White  ...     Citation   \n",
       "2005-01-06 01:30:00        27.0               B       Black  ...     Citation   \n",
       "2005-01-12 08:05:00        32.0               B       Black  ...     Citation   \n",
       "\n",
       "                    is_arrested  stop_duration out_of_state  \\\n",
       "stop_datetime                                                 \n",
       "2005-01-02 01:55:00       False       0-15 Min        False   \n",
       "2005-01-02 20:30:00       False      16-30 Min        False   \n",
       "2005-01-04 12:55:00       False       0-15 Min        False   \n",
       "2005-01-06 01:30:00       False       0-15 Min        False   \n",
       "2005-01-12 08:05:00       False        30+ Min         True   \n",
       "\n",
       "                    drugs_related_stop  district  frisk  stop_minutes  \\\n",
       "stop_datetime                                                           \n",
       "2005-01-02 01:55:00              False   Zone K1  False             8   \n",
       "2005-01-02 20:30:00              False   Zone X4  False            23   \n",
       "2005-01-04 12:55:00              False   Zone X4  False             8   \n",
       "2005-01-06 01:30:00              False   Zone X4  False             8   \n",
       "2005-01-12 08:05:00              False   Zone X1  False            45   \n",
       "\n",
       "                           DATE  rating  \n",
       "stop_datetime                            \n",
       "2005-01-02 01:55:00  2005-01-02     bad  \n",
       "2005-01-02 20:30:00  2005-01-02     bad  \n",
       "2005-01-04 12:55:00  2005-01-04     bad  \n",
       "2005-01-06 01:30:00  2005-01-06   worse  \n",
       "2005-01-12 08:05:00  2005-01-12   worse  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 264
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing arrest rates by weather rating"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Do police officers arrest drivers more often when the weather is bad? Find out below!\n",
    "\n",
    "**INSTRUCTIONS**\n",
    "- **First**, you'll calculate the **overall arrest rate**.\n",
    "\n",
    "- **Then**, you'll calculate the **arrest rate** for each of the **weather ratings** you previously assigned.\n",
    "\n",
    "- **Finally**, you'll add **violation type** as a second factor in the analysis, to see if that accounts for any differences in the arrest rate.\n",
    "\n",
    "Since you previously defined a logical order for the weather categories, ``good < bad < worse``, they will be sorted that way in the results."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "source": [
    "prc(ri_weather.is_arrested.value_counts(normalize=True))\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "False    96.5\n",
       "True      3.5\n",
       "Name: is_arrested, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 265
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "source": [
    "prc(ri_weather.is_arrested.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "metadata": {},
     "execution_count": 292
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "source": [
    "# (toplam tutuklama yüzdesi)\",)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "source": [
    "wri=pd.crosstab(ri_weather.rating, ri_weather.is_arrested)\n",
    "wri"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>is_arrested</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>214855</td>\n",
       "      <td>7333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad</th>\n",
       "      <td>208806</td>\n",
       "      <td>7618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worse</th>\n",
       "      <td>40320</td>\n",
       "      <td>1652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_arrested   False  True \n",
       "rating                    \n",
       "good         214855   7333\n",
       "bad          208806   7618\n",
       "worse         40320   1652"
      ]
     },
     "metadata": {},
     "execution_count": 268
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "source": [
    "wri[\"Arrested_Percent\"]=prc(ri_weather.groupby(\"rating\").is_arrested.mean())\n",
    "wri"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>is_arrested</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>Arrested_Percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>214855</td>\n",
       "      <td>7333</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad</th>\n",
       "      <td>208806</td>\n",
       "      <td>7618</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worse</th>\n",
       "      <td>40320</td>\n",
       "      <td>1652</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_arrested   False  True  Arrested_Percent\n",
       "rating                                     \n",
       "good         214855  7333               3.3\n",
       "bad          208806  7618               3.5\n",
       "worse         40320  1652               3.9"
      ]
     },
     "metadata": {},
     "execution_count": 269
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "source": [
    "prc(ri_weather.groupby(\"rating\").is_arrested.mean().mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3.6"
      ]
     },
     "metadata": {},
     "execution_count": 270
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "source": [
    "#  Hava durumu  kötüleştikçe tutuklama oranları artıyor"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "source": [
    "# **Hava durum kaydı tüm günler için mevcut ** \n",
    "# ri_weather.rating.value_counts().sum()  = 480584"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "source": [
    "lll=prc(ri_weather.groupby([\"violation\",\"rating\"]).is_arrested.mean())\n",
    "lll"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "violation            rating\n",
       "Equipment            good      5.6\n",
       "                     bad       6.0\n",
       "                     worse     9.4\n",
       "Moving violation     good      5.6\n",
       "                     bad       5.8\n",
       "                     worse     6.2\n",
       "Other                good      8.2\n",
       "                     bad       8.5\n",
       "                     worse     5.9\n",
       "Registration/plates  good      7.7\n",
       "                     bad       9.3\n",
       "                     worse     9.9\n",
       "Seat belt            good      2.6\n",
       "                     bad       2.9\n",
       "                     worse     0.0\n",
       "Speeding             good      1.3\n",
       "                     bad       1.3\n",
       "                     worse     1.7\n",
       "Name: is_arrested, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 273
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "source": [
    "round(lll.mean(),1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "5.4"
      ]
     },
     "metadata": {},
     "execution_count": 274
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "source": [
    "#  ihlal yapıldığı zamanlarda tutuklama yüzdesi, yüzde artıyor"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Selecting From a mult-indexed Series"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The output of a single ``.groupby()`` operation on multiple columns is a ``Series`` with a ``MultiIndex``. Working with this type of object is similar to working with a ``DataFrame``:\n",
    "\n",
    "The ``outer`` index level is like the ``DataFrame`` rows.\n",
    "The ``inner`` index level is like the ``DataFrame`` columns.\n",
    "In this exercise, you'll practice accessing data from a multi-indexed ``Series`` using the ``.loc[]`` accessor."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "- Save the output of the ``.groupby()`` operation from the last exercise as a new object, ``arrest_rate``.\n",
    "- Print the ``arrest_rate`` ``Series`` and examine it.\n",
    "- Print the arrest rate for ``moving violations`` in bad weather.\n",
    "- Print the arrest rates for ``speeding violations`` in all three weather conditions."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "source": [
    "arrest_rate = prc(ri_weather.groupby(['rating', 'violation']).is_arrested.mean())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "source": [
    "arrest_rate"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "rating  violation          \n",
       "good    Equipment              5.6\n",
       "        Moving violation       5.6\n",
       "        Other                  8.2\n",
       "        Registration/plates    7.7\n",
       "        Seat belt              2.6\n",
       "        Speeding               1.3\n",
       "bad     Equipment              6.0\n",
       "        Moving violation       5.8\n",
       "        Other                  8.5\n",
       "        Registration/plates    9.3\n",
       "        Seat belt              2.9\n",
       "        Speeding               1.3\n",
       "worse   Equipment              9.4\n",
       "        Moving violation       6.2\n",
       "        Other                  5.9\n",
       "        Registration/plates    9.9\n",
       "        Seat belt              0.0\n",
       "        Speeding               1.7\n",
       "Name: is_arrested, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 277
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "source": [
    "#  kötü hava da \"Moving violations\" durumundaki yakalama oranı "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "source": [
    "arrest_rate.loc[\"bad\", \"Moving violation\"] "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "metadata": {},
     "execution_count": 279
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "source": [
    "# hız ihlali yapıldığı zamanlarda daki tutuklama oranları "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "source": [
    "prc(ri_weather[ri_weather.violation==\"Speeding\"].is_arrested.mean())"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "metadata": {},
     "execution_count": 281
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Reshaping the arrest rate data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "In this exercise, you'll start by **reshaping** the ``arrest_rate`` ``Series`` into a ``DataFrame``. This is a useful step when working with any multi-indexed ``Series``, since it enables you to access the full range of ``DataFrame`` methods.\n",
    "\n",
    "Then, you'll create the exact same ``DataFrame`` using a ``pivot table``. This is a great example of how pandas often gives you more than one way to reach the same result!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**INSTRUCTIONS**\n",
    "\n",
    "- ``unstack`` the ``arrest_rate`` ``Series`` to ``reshape`` it into a ``DataFrame``.\n",
    "- Create the exact same ``DataFrame`` using a ``pivot table``! Each of the three ``.pivot_table()`` parameters should be specified as one of the ``ri_weather`` columns."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "source": [
    "arrest_rate.unstack()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>rating</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>good</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worse</th>\n",
       "      <td>9.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "rating                                                                          \n",
       "good             5.6               5.6    8.2                  7.7        2.6   \n",
       "bad              6.0               5.8    8.5                  9.3        2.9   \n",
       "worse            9.4               6.2    5.9                  9.9        0.0   \n",
       "\n",
       "violation  Speeding  \n",
       "rating               \n",
       "good            1.3  \n",
       "bad             1.3  \n",
       "worse           1.7  "
      ]
     },
     "metadata": {},
     "execution_count": 282
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "source": [
    "veri=pd.pivot_table(ri_weather,values='is_arrested', index='rating',columns='violation',\n",
    "                    aggfunc=np.mean)\n",
    "prc(veri)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>rating</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>good</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worse</th>\n",
       "      <td>9.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "rating                                                                          \n",
       "good             5.6               5.6    8.2                  7.7        2.6   \n",
       "bad              6.0               5.8    8.5                  9.3        2.9   \n",
       "worse            9.4               6.2    5.9                  9.9        0.0   \n",
       "\n",
       "violation  Speeding  \n",
       "rating               \n",
       "good            1.3  \n",
       "bad             1.3  \n",
       "worse           1.7  "
      ]
     },
     "metadata": {},
     "execution_count": 283
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "source": [
    "fg=prc(veri.transpose())\n",
    "fg"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>rating</th>\n",
       "      <th>good</th>\n",
       "      <th>bad</th>\n",
       "      <th>worse</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>violation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Equipment</th>\n",
       "      <td>5.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moving violation</th>\n",
       "      <td>5.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>8.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Registration/plates</th>\n",
       "      <td>7.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat belt</th>\n",
       "      <td>2.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Speeding</th>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "rating               good  bad  worse\n",
       "violation                            \n",
       "Equipment             5.6  6.0    9.4\n",
       "Moving violation      5.6  5.8    6.2\n",
       "Other                 8.2  8.5    5.9\n",
       "Registration/plates   7.7  9.3    9.9\n",
       "Seat belt             2.6  2.9    0.0\n",
       "Speeding              1.3  1.3    1.7"
      ]
     },
     "metadata": {},
     "execution_count": 284
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "source": [
    "# Grafikle izleyelim"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "source": [
    "ff=fg.plot.bar( subplots=True,rot=45,figsize=(10,5))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "source": [
    "# Hava durumu çok kötü \"worse \" olduğunda herkes kemerlerini takıyormuş :)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "source": [
    "ri_weather.to_csv(\"ri_weather.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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