04.file.ipynb

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   "source": [
    "<h1><p style=\"text-align: center;\">Data Analysis with Python <br>Project - 1</p><h1> - Traffic Police Stops <img src=\"https://docs.google.com/uc?id=17CPCwi3_VvzcS87TOsh4_U8eExOhL6Ki\" class=\"img-fluid\" alt=\"CLRSWY\" width=\"200\" height=\"100\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>WT11</th>\n",
       "      <th>WT13</th>\n",
       "      <th>WT14</th>\n",
       "      <th>WT15</th>\n",
       "      <th>WT16</th>\n",
       "      <th>WT17</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT19</th>\n",
       "      <th>WT21</th>\n",
       "      <th>WT22</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>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",
       "<p>5 rows × 26 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",
       "   ...  WT11  WT13  WT14  WT15  WT16  WT17  WT18  WT19  WT21  WT22  \n",
       "0  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "1  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "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]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8022, 26)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " round(we[[\"TMIN\", \"TAVG\", \"TMAX\"]].describe(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "we[[\"TMIN\", \"TAVG\", \"TMAX\"]].plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Görselleştirmeyi alışkın olduğumuz Celcius sıcaklık birimine dönüştürelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]]=celcius(we[[\"TMIN\", \"TAVG\", \"TMAX\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " round(we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "we[[\"CTMIN\", \"CTAVG\", \"CTMAX\"]].plot.box(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the temperature difference"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "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?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "we[\"TDIFF\"]=we.TMAX-we.TMIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>WT16</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",
       "    </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>NaN</td>\n",
       "      <td>-2.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.0</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>NaN</td>\n",
       "      <td>-4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>28.0</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>NaN</td>\n",
       "      <td>-2.777778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.888889</td>\n",
       "      <td>21.0</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>NaN</td>\n",
       "      <td>4.444444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.222222</td>\n",
       "      <td>14.0</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>NaN</td>\n",
       "      <td>-0.555556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 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",
       "   ...  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]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.TDIFF.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(we.TDIFF.describe(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "we.TDIFF.plot.hist(20, grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting bad weather conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT01</th>\n",
       "      <th>WT02</th>\n",
       "      <th>WT03</th>\n",
       "      <th>WT04</th>\n",
       "      <th>WT05</th>\n",
       "      <th>WT06</th>\n",
       "      <th>WT07</th>\n",
       "      <th>WT08</th>\n",
       "      <th>WT09</th>\n",
       "      <th>WT10</th>\n",
       "      <th>WT11</th>\n",
       "      <th>WT13</th>\n",
       "      <th>WT14</th>\n",
       "      <th>WT15</th>\n",
       "      <th>WT16</th>\n",
       "      <th>WT17</th>\n",
       "      <th>WT18</th>\n",
       "      <th>WT19</th>\n",
       "      <th>WT21</th>\n",
       "      <th>WT22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <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  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wt=we.iloc[:,6:26]\n",
    "wt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we[\"bad_conditions\"]=wt.sum(axis=1)\n",
    "we.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "we.bad_conditions.plot.hist(20, grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rating the weather conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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'``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6779.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.bad_conditions.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.bad_conditions.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.groupby(\"STATION\").bad_conditions.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USW00014765    4017\n",
       "USC00379423    3991\n",
       "USC00371874       8\n",
       "USC00371875       6\n",
       "Name: STATION, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.STATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.bad_conditions.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping={0:\"good\",1:\"bad\",2:\"bad\",3:\"bad\",4:\"bad\",5:\"worse\",6:\"worse\",7:\"worse\",8:\"worse\",9:\"worse\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['good', 'bad', 'worse'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we[\"rating\"]=we.bad_conditions.map(mapping)\n",
    "we.rating.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>rating</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>-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]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the data type to category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats=(\"good\",\"bad\",\"worse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_dtype = pd.api.types.CategoricalDtype(categories=cats, ordered=True)\n",
    "we.rating=we.rating.astype(cat_dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "we.rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>4006</th>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>bad</td>\n",
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       "    <tr>\n",
       "      <th>4007</th>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>bad</td>\n",
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       "    <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]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_rating=we[we.STATION==\"USW00014765\"][[\"DATE\",\"rating\"]]\n",
    "weather_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "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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       "      <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",
       "      <th>stop_minutes</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>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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       "      <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",
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       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>23</td>\n",
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       "      <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",
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       "      <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",
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       "    <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]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri=pd.read_csv(\"police_3.csv\")\n",
    "ri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging the DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480584, 25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4017, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri_weather = pd.merge(left=ri, right=weather_rating, left_on='stop_date', right_on='DATE', how='left').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480584, 27)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri_weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480584, 27)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri_weather=ri_weather.drop_duplicates()\n",
    "ri_weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri_weather.set_index(\"stop_datetime\",inplace=True)\n",
    "ri_weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing arrest rates by weather rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    96.5\n",
       "True      3.5\n",
       "Name: is_arrested, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(ri_weather.is_arrested.value_counts(normalize=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03454755048024903"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ri_weather.is_arrested.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (toplam tutuklama yüzdesi)\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wri=pd.crosstab(ri_weather.rating, ri_weather.is_arrested)\n",
    "wri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wri[\"Arrested_Percent\"]=prc(ri_weather.groupby(\"rating\").is_arrested.mean())\n",
    "wri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(ri_weather.groupby(\"rating\").is_arrested.mean().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Hava durumu  kötüleştikçe tutukla oranları artıyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# **Hava durum kaydı tüm günler için mevcut ** \n",
    "# ri_weather.rating.value_counts().sum()  = 480584"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lll=prc(ri_weather.groupby([\"violation\",\"rating\"]).is_arrested.mean())\n",
    "lll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.4"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(lll.mean(),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  ihlal yapıldığı zamanlarda tutuklama yüzdesi, yüzde artıyor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting From a mult-indexed Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrest_rate = prc(ri_weather.groupby(['rating', 'violation']).is_arrested.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrest_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  kötü hava da \"Moving violations\" durumundaki yakalama oranı "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrest_rate.loc[\"bad\", \"Moving violation\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hız ihlali yapıldığı zamanlarda daki tutuklama oranları "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prc(ri_weather[ri_weather.violation==\"Speeding\"].is_arrested.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping the arrest rate data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrest_rate.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri=pd.pivot_table(ri_weather,values='is_arrested', index='rating',columns='violation',\n",
    "                    aggfunc=np.mean)\n",
    "prc(veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fg=prc(veri.transpose())\n",
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grafikle izleyelim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ff=fg.plot.bar( subplots=True,rot=45,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hava durumu çok kötü \"worse \" olduğunda herkes kemerlerini takıyormuş :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri_weather.to_csv(\"ri_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}