suven consultants internship project 1 of data analytics with python

 {

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# ANALYSIS OF METEOROLOGICAL DATA\n",

    "## BY TANISHA RAKSHIT"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [],

   "source": [

    "import numpy as np \n",

    "import pandas as pd \n",

    "import matplotlib.pyplot as plt"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(96453, 12)"

      ]

     },

     "execution_count": 4,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "#loading the dataset\n",

    "df = pd.read_csv(r'C:\\Users\\TANISHA RAKSHIT\\Desktop\\weatherHistory.csv')\n",

    "df.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "Formatted Date               object\n",

       "Summary                      object\n",

       "Precip Type                  object\n",

       "Temperature (C)             float64\n",

       "Apparent Temperature (C)    float64\n",

       "Humidity                    float64\n",

       "Wind Speed (km/h)           float64\n",

       "Wind Bearing (degrees)      float64\n",

       "Visibility (km)             float64\n",

       "Loud Cover                  float64\n",

       "Pressure (millibars)        float64\n",

       "Daily Summary                object\n",

       "dtype: object"

      ]

     },

     "execution_count": 5,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "df.dtypes"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "0       2006-03-31 22:00:00+00:00\n",

       "1       2006-03-31 23:00:00+00:00\n",

       "2       2006-04-01 00:00:00+00:00\n",

       "3       2006-04-01 01:00:00+00:00\n",

       "4       2006-04-01 02:00:00+00:00\n",

       "                   ...           \n",

       "96448   2016-09-09 17:00:00+00:00\n",

       "96449   2016-09-09 18:00:00+00:00\n",

       "96450   2016-09-09 19:00:00+00:00\n",

       "96451   2016-09-09 20:00:00+00:00\n",

       "96452   2016-09-09 21:00:00+00:00\n",

       "Name: Formatted Date, Length: 96453, dtype: datetime64[ns, UTC]"

      ]

     },

     "execution_count": 6,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "df['Formatted Date'] = pd.to_datetime(df['Formatted Date'], utc=True)\n",

    "df['Formatted Date']"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "Formatted Date              datetime64[ns, UTC]\n",

       "Summary                                  object\n",

       "Precip Type                              object\n",

       "Temperature (C)                         float64\n",

       "Apparent Temperature (C)                float64\n",

       "Humidity                                float64\n",

       "Wind Speed (km/h)                       float64\n",

       "Wind Bearing (degrees)                  float64\n",

       "Visibility (km)                         float64\n",

       "Loud Cover                              float64\n",

       "Pressure (millibars)                    float64\n",

       "Daily Summary                            object\n",

       "dtype: object"

      ]

     },

     "execution_count": 7,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "df.dtypes"

   ]

  },

  {

   "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>Summary</th>\n",

       "      <th>Precip Type</th>\n",

       "      <th>Temperature (C)</th>\n",

       "      <th>Apparent Temperature (C)</th>\n",

       "      <th>Humidity</th>\n",

       "      <th>Wind Speed (km/h)</th>\n",

       "      <th>Wind Bearing (degrees)</th>\n",

       "      <th>Visibility (km)</th>\n",

       "      <th>Loud Cover</th>\n",

       "      <th>Pressure (millibars)</th>\n",

       "      <th>Daily Summary</th>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>Formatted Date</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>2006-03-31 22:00:00+00:00</th>\n",

       "      <td>Partly Cloudy</td>\n",

       "      <td>rain</td>\n",

       "      <td>9.472222</td>\n",

       "      <td>7.388889</td>\n",

       "      <td>0.89</td>\n",

       "      <td>14.1197</td>\n",

       "      <td>251.0</td>\n",

       "      <td>15.8263</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1015.13</td>\n",

       "      <td>Partly cloudy throughout the day.</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-03-31 23:00:00+00:00</th>\n",

       "      <td>Partly Cloudy</td>\n",

       "      <td>rain</td>\n",

       "      <td>9.355556</td>\n",

       "      <td>7.227778</td>\n",

       "      <td>0.86</td>\n",

       "      <td>14.2646</td>\n",

       "      <td>259.0</td>\n",

       "      <td>15.8263</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1015.63</td>\n",

       "      <td>Partly cloudy throughout the day.</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-04-01 00:00:00+00:00</th>\n",

       "      <td>Mostly Cloudy</td>\n",

       "      <td>rain</td>\n",

       "      <td>9.377778</td>\n",

       "      <td>9.377778</td>\n",

       "      <td>0.89</td>\n",

       "      <td>3.9284</td>\n",

       "      <td>204.0</td>\n",

       "      <td>14.9569</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1015.94</td>\n",

       "      <td>Partly cloudy throughout the day.</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-04-01 01:00:00+00:00</th>\n",

       "      <td>Partly Cloudy</td>\n",

       "      <td>rain</td>\n",

       "      <td>8.288889</td>\n",

       "      <td>5.944444</td>\n",

       "      <td>0.83</td>\n",

       "      <td>14.1036</td>\n",

       "      <td>269.0</td>\n",

       "      <td>15.8263</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1016.41</td>\n",

       "      <td>Partly cloudy throughout the day.</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-04-01 02:00:00+00:00</th>\n",

       "      <td>Mostly Cloudy</td>\n",

       "      <td>rain</td>\n",

       "      <td>8.755556</td>\n",

       "      <td>6.977778</td>\n",

       "      <td>0.83</td>\n",

       "      <td>11.0446</td>\n",

       "      <td>259.0</td>\n",

       "      <td>15.8263</td>\n",

       "      <td>0.0</td>\n",

       "      <td>1016.51</td>\n",

       "      <td>Partly cloudy throughout the day.</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                                 Summary Precip Type  Temperature (C)  \\\n",

       "Formatted Date                                                          \n",

       "2006-03-31 22:00:00+00:00  Partly Cloudy        rain         9.472222   \n",

       "2006-03-31 23:00:00+00:00  Partly Cloudy        rain         9.355556   \n",

       "2006-04-01 00:00:00+00:00  Mostly Cloudy        rain         9.377778   \n",

       "2006-04-01 01:00:00+00:00  Partly Cloudy        rain         8.288889   \n",

       "2006-04-01 02:00:00+00:00  Mostly Cloudy        rain         8.755556   \n",

       "\n",

       "                           Apparent Temperature (C)  Humidity  \\\n",

       "Formatted Date                                                  \n",

       "2006-03-31 22:00:00+00:00                  7.388889      0.89   \n",

       "2006-03-31 23:00:00+00:00                  7.227778      0.86   \n",

       "2006-04-01 00:00:00+00:00                  9.377778      0.89   \n",

       "2006-04-01 01:00:00+00:00                  5.944444      0.83   \n",

       "2006-04-01 02:00:00+00:00                  6.977778      0.83   \n",

       "\n",

       "                           Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",

       "Formatted Date                                                         \n",

       "2006-03-31 22:00:00+00:00            14.1197                   251.0   \n",

       "2006-03-31 23:00:00+00:00            14.2646                   259.0   \n",

       "2006-04-01 00:00:00+00:00             3.9284                   204.0   \n",

       "2006-04-01 01:00:00+00:00            14.1036                   269.0   \n",

       "2006-04-01 02:00:00+00:00            11.0446                   259.0   \n",

       "\n",

       "                           Visibility (km)  Loud Cover  Pressure (millibars)  \\\n",

       "Formatted Date                                                                 \n",

       "2006-03-31 22:00:00+00:00          15.8263         0.0               1015.13   \n",

       "2006-03-31 23:00:00+00:00          15.8263         0.0               1015.63   \n",

       "2006-04-01 00:00:00+00:00          14.9569         0.0               1015.94   \n",

       "2006-04-01 01:00:00+00:00          15.8263         0.0               1016.41   \n",

       "2006-04-01 02:00:00+00:00          15.8263         0.0               1016.51   \n",

       "\n",

       "                                               Daily Summary  \n",

       "Formatted Date                                                \n",

       "2006-03-31 22:00:00+00:00  Partly cloudy throughout the day.  \n",

       "2006-03-31 23:00:00+00:00  Partly cloudy throughout the day.  \n",

       "2006-04-01 00:00:00+00:00  Partly cloudy throughout the day.  \n",

       "2006-04-01 01:00:00+00:00  Partly cloudy throughout the day.  \n",

       "2006-04-01 02:00:00+00:00  Partly cloudy throughout the day.  "

      ]

     },

     "execution_count": 8,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "df = df.set_index('Formatted Date')\n",

    "df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "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>Apparent Temperature (C)</th>\n",

       "      <th>Humidity</th>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>Formatted Date</th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>2005-12-01 00:00:00+00:00</th>\n",

       "      <td>-4.050000</td>\n",

       "      <td>0.890000</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-01-01 00:00:00+00:00</th>\n",

       "      <td>-4.173708</td>\n",

       "      <td>0.834610</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-02-01 00:00:00+00:00</th>\n",

       "      <td>-2.990716</td>\n",

       "      <td>0.843467</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-03-01 00:00:00+00:00</th>\n",

       "      <td>1.969780</td>\n",

       "      <td>0.778737</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2006-04-01 00:00:00+00:00</th>\n",

       "      <td>12.098827</td>\n",

       "      <td>0.728625</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                           Apparent Temperature (C)  Humidity\n",

       "Formatted Date                                               \n",

       "2005-12-01 00:00:00+00:00                 -4.050000  0.890000\n",

       "2006-01-01 00:00:00+00:00                 -4.173708  0.834610\n",

       "2006-02-01 00:00:00+00:00                 -2.990716  0.843467\n",

       "2006-03-01 00:00:00+00:00                  1.969780  0.778737\n",

       "2006-04-01 00:00:00+00:00                 12.098827  0.728625"

      ]

     },

     "execution_count": 9,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "#after resampling\n",

    "data_columns = ['Apparent Temperature (C)', 'Humidity']\n",

    "df_monthly_mean = df[data_columns].resample('MS').mean()\n",

    "df_monthly_mean.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<AxesSubplot:title={'center':'Variation in Apparent Temperature and Humidity with time'}, xlabel='Formatted Date'>"

      ]

     },

     "execution_count": 10,

     "metadata": {},

     "output_type": "execute_result"

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 1008x432 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "#Plotting the variation in Apparent Temperature and Humidity with time\n",

    "import seaborn as sns\n",

    "import warnings\n",

    "warnings.filterwarnings(\"ignore\")\n",

    "plt.figure(figsize=(14,6))\n",

    "plt.title(\"Variation in Apparent Temperature and Humidity with time\")\n",

    "sns.lineplot(data=df_monthly_mean)\n",

    "#From the plot, we can say that humidity remained almost constant in these 10 years. Even the average apparent temperature is almost same (as peaks lie on the same line)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "                           Apparent Temperature (C)  Humidity\n",

      "Formatted Date                                               \n",

      "2006-04-01 00:00:00+00:00                 12.098827  0.728625\n",

      "2007-04-01 00:00:00+00:00                 11.894421  0.536361\n",

      "2008-04-01 00:00:00+00:00                 11.183688  0.693194\n",

      "2009-04-01 00:00:00+00:00                 14.267076  0.567847\n",

      "2010-04-01 00:00:00+00:00                 11.639406  0.706875\n",

      "2011-04-01 00:00:00+00:00                 12.978997  0.591625\n",

      "2012-04-01 00:00:00+00:00                 11.782770  0.650222\n",

      "2013-04-01 00:00:00+00:00                 12.045563  0.677667\n",

      "2014-04-01 00:00:00+00:00                 12.486181  0.691403\n",

      "2015-04-01 00:00:00+00:00                 10.632801  0.547764\n",

      "2016-04-01 00:00:00+00:00                 12.731427  0.659972\n"

     ]

    },

    {

     "data": {

      "text/plain": [

       "Apparent Temperature (C)    float64\n",

       "Humidity                    float64\n",

       "dtype: object"

      ]

     },

     "execution_count": 11,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "#retrieving the data of a particular month from every year, say April\n",

    "df1 = df_monthly_mean[df_monthly_mean.index.month==4]\n",

    "print(df1)\n",

    "df1.dtypes"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "metadata": {},

   "outputs": [

    {

     "ename": "ConversionError",

     "evalue": "Failed to convert value(s) to axis units: ['04-01-2006', '04-01-2007', '04-01-2008', '04-01-2009', '04-01-2010', '04-01-2011', '04-01-2012', '04-01-2013', '04-01-2014', '04-01-2015', '04-01-2016']",

     "output_type": "error",

     "traceback": [

      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",

      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m   1522\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1523\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1524\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(value, unit, axis)\u001b[0m\n\u001b[0;32m   1895\u001b[0m         \"\"\"\n\u001b[1;32m-> 1896\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mdate2num\u001b[1;34m(d)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'datetime64[us]'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;31mValueError\u001b[0m: Error parsing datetime string \"04-01-2016\" at position 8",

      "\nThe above exception was the direct cause of the following exception:\n",

      "\u001b[1;31mConversionError\u001b[0m                           Traceback (most recent call last)",

      "\u001b[1;32m<ipython-input-12-5f5fff0942bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2006-04-01'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'2016-04-01'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Apparent Temperature (C)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Apparent Temperature (C)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2006-04-01'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'2016-04-01'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Humidity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Humidity'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'04-01-2006'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2007'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2008'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2009'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2010'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2011'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2012'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2013'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2014'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2015'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'04-01-2016'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmdates\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDateFormatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%d %m %Y'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'center right'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    449\u001b[0m                 \u001b[1;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mset_ticks\u001b[1;34m(self, ticks, minor)\u001b[0m\n\u001b[0;32m   1809\u001b[0m         \"\"\"\n\u001b[0;32m   1810\u001b[0m         \u001b[1;31m# XXX if the user changes units, the information will be lost here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1811\u001b[1;33m         \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1812\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1813\u001b[0m             \u001b[0mxleft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxright\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;32mc:\\users\\tanisha rakshit\\desktop\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m   1524\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1525\u001b[0m             raise munits.ConversionError('Failed to convert value(s) to axis '\n\u001b[1;32m-> 1526\u001b[1;33m                                          f'units: {x!r}') from e\n\u001b[0m\u001b[0;32m   1527\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",

      "\u001b[1;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: ['04-01-2006', '04-01-2007', '04-01-2008', '04-01-2009', '04-01-2010', '04-01-2011', '04-01-2012', '04-01-2013', '04-01-2014', '04-01-2015', '04-01-2016']"

     ]

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 1080x360 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "import matplotlib.dates as mdates\n",

    "fig, ax = plt.subplots(figsize=(15,5))\n",

    "ax.plot(df1.loc['2006-04-01':'2016-04-01', 'Apparent Temperature (C)'], marker='o', linestyle='-',label='Apparent Temperature (C)')\n",

    "ax.plot(df1.loc['2006-04-01':'2016-04-01', 'Humidity'], marker='o', linestyle='-',label='Humidity')\n",

    "ax.set_xticks(['04-01-2006','04-01-2007','04-01-2008','04-01-2009','04-01-2010','04-01-2011','04-01-2012','04-01-2013','04-01-2014','04-01-2015','04-01-2016'])\n",

    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %m %Y'))\n",

    "ax.legend(loc = 'center right')\n",

    "ax.set_xlabel('Month of April')"

   ]

  }

 ],

 "metadata": {

  "kernelspec": {

   "display_name": "Python 3",

   "language": "python",

   "name": "python3"

  }

 },

 "nbformat": 4,

 "nbformat_minor": 4

}