__PRACTICE_Module2_HANDLING_MISSING_VALUE_WEATHER_DATA.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.facebook.com/codemakerz\"><img src=\"https://scontent.ffjr1-4.fna.fbcdn.net/v/t1.0-9/36189148_736466693143793_2172101683281133568_n.png?_nc_cat=107&_nc_eui2=AeHzxv3SUcQBOfijLP-cEnHkX4z9XQXdeau__2MlErWZ1x07aZ1zx1PzJUDDxL6cpr7oPqYiifggXDptgtP8W5iCoDRjcdILDBYZ5Ig40dqi8Q&_nc_oc=AQmMCNXdzelFB2rdtpk8wN8nC410Wm2yKupYfYS1FxHNejTF0Jhr1G3WIZORKRF3TvFpohMB8Puw29Txxan8CW05&_nc_ht=scontent.ffjr1-4.fna&oh=7b13627e991a4d1b508923041bd7bc22&oe=5D8A7B03\" />\n",
    "</a>\n",
    "\n",
    "\n",
    "You can download:\n",
    "> Datasets at: https://github.com/martandsingh/datasets/master/cars/imports-85.data <br/>\n",
    "> Git Repo for dataset: https://github.com/martandsingh/datasets.git <br/>\n",
    "> Jupyter Notebook(Code): https://github.com/martandsingh/dataanalytics <br/>\n",
    "\n",
    "<h2>PRACTICE:  Missing Value Handling In Tempature DB </h2>\n",
    "    A small DB for better understanding of how we can deal with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing value for weather data\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://github.com/martandsingh/datasets/master/weather_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017       -99999          7  Sunny\n",
       "2  1/3/2017           28     -99999   Snow\n",
       "3  1/4/2017       -99999          7      0\n",
       "4  1/5/2017           32     -99999   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day            object\n",
       "temperature     int64\n",
       "windspeed       int64\n",
       "event          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First lets check datasets. We can see temprature and windspeed already in string format so,\n",
    "# We dont have to chage their types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-28548.714285714286\n"
     ]
    }
   ],
   "source": [
    "# HERE WE CAN SEE OUR MISSING VALUES ARE REPRESENTED AS -99999\n",
    "# LETS REPLACE THEM WITH MEAN IN CASE OF temprature and windspeed. Replace with mode in cse of event as\n",
    "# IT is text data\n",
    "temp_mean = df['temperature'].mean(axis=0) # axis = 0 means average of column\n",
    "print(temp_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AS we can see because of -99999 our mean is highly affected, So we need to change them in nan first\n",
    "df.replace(-99999, np.nan, inplace=True) # inplace=True mean it will chnange the main copy of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017          NaN        7.0      0\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets change for event as 0 can also affect our mode (most frequent value)\n",
    "df.replace('0', np.nan, inplace=True) # we write '0' as string as its dtype is string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
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       "      <td>Rain</td>\n",
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       "      <td>1/2/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017          NaN        7.0    NaN\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0    NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # Finally we have rplace all the missing values with out numpy nan. Now lets chnage them with values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.4\n"
     ]
    }
   ],
   "source": [
    "temp_mean = df['temperature'].mean(axis=0)\n",
    "print(temp_mean) # now you can see the mean is highly changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace temperature empty column with mean value\n",
    "df['temperature'].replace(np.nan, temp_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
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       "      <td>Rain</td>\n",
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       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>31.4</td>\n",
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       "      <td>Sunny</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>31.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017         31.4        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017         31.4        7.0    NaN\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0    NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # You can see values has been changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.4\n"
     ]
    }
   ],
   "source": [
    "# Lets replace windspeed in the same way.\n",
    "wind_mean = df['windspeed'].mean(axis=0)\n",
    "print(wind_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['windspeed'].replace(np.nan, wind_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
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       "      <th>event</th>\n",
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       "      <td>Rain</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
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       "      <td>Sunny</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>31.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017         31.4        7.0  Sunny\n",
       "2  1/3/2017         28.0        5.4   Snow\n",
       "3  1/4/2017         31.4        7.0    NaN\n",
       "4  1/5/2017         32.0        5.4   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0    NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rain     2\n",
       "Sunny    2\n",
       "Snow     1\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now in case of event will not take mean as event is string type. HEre we will choose mode(Most frequent Value)\n",
    "# lets check value first\n",
    "df['event'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rain\n"
     ]
    }
   ],
   "source": [
    "# We can see The values and there counts. Luckily numpy has inbuilt function to choose mode.\n",
    "event_mode = df['event'].value_counts().idxmax()\n",
    "print(event_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['event'].replace(np.nan, event_mode, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>31.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>31.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017         31.4        7.0  Sunny\n",
       "2  1/3/2017         28.0        5.4   Snow\n",
       "3  1/4/2017         31.4        7.0   Rain\n",
       "4  1/5/2017         32.0        5.4   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0   Rain"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CONGRATS WE HAVE REPLACED ALL THE MISSING VALUES.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'temp bins')"
      ]
     },
     "execution_count": 32,
     "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": [
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "from matplotlib import pyplot\n",
    "plt.pyplot.hist(df[\"temperature\"])\n",
    "\n",
    "# set x/y labels and plot title\n",
    "plt.pyplot.xlabel(\"temperature\")\n",
    "plt.pyplot.ylabel(\"count\")\n",
    "plt.pyplot.title(\"temp bins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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