PMS2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
    "                               AutoMinorLocator)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jan</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>16.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>March</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>19.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>28.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>21.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sept</td>\n",
       "      <td>18.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>19.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>18.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.7</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>30.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010\n",
       "0     jan  13.0  18.2  21.5  21.7  25.3  23.8  24.4  29.1  30.0  30.0\n",
       "1     Feb  16.4  19.4  21.6  13.7  26.7  23.0  24.8  28.8  29.0  29.0\n",
       "2   March  22.0  19.0  22.3  20.0  26.0  25.6  27.5  29.0  29.0  29.0\n",
       "3   April  19.5  17.2  21.1  21.5  25.0  24.5  20.5  28.7  29.2  29.2\n",
       "4     May  20.0  11.2  21.6  21.4  24.0  24.3  26.2  29.2  29.5  29.5\n",
       "5    June  20.0  18.5  21.1  22.4  22.0  24.0  26.0  30.2  29.0  29.0\n",
       "6    July  20.0  16.0  20.8  23.4  23.0  24.5  26.9  30.8  30.1  30.1\n",
       "7     Aug  18.0  19.1  21.5  21.8  22.2  23.5  27.1  29.0  27.6  27.6\n",
       "8    Sept  18.2  15.5  19.6  21.5  25.0  26.2  27.5  30.0  29.0  29.0\n",
       "9     Oct  18.2  20.5  20.2  22.8  26.0  27.3  27.7  27.7  26.2  26.2\n",
       "10    Nov  19.0  17.1  21.0  22.7  20.7  25.5  27.5  30.0  23.7  23.7\n",
       "11    Dec  19.4  21.5  21.9  24.8  22.0  25.5  28.2  30.6  26.7  26.7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/temperature.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>28.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>21.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>19.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.7</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>30.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2001  2002  2003  2004  2005  2006  2007  2008  2009  2010\n",
       "0   13.0  18.2  21.5  21.7  25.3  23.8  24.4  29.1  30.0  30.0\n",
       "1   16.4  19.4  21.6  13.7  26.7  23.0  24.8  28.8  29.0  29.0\n",
       "2   22.0  19.0  22.3  20.0  26.0  25.6  27.5  29.0  29.0  29.0\n",
       "3   19.5  17.2  21.1  21.5  25.0  24.5  20.5  28.7  29.2  29.2\n",
       "4   20.0  11.2  21.6  21.4  24.0  24.3  26.2  29.2  29.5  29.5\n",
       "5   20.0  18.5  21.1  22.4  22.0  24.0  26.0  30.2  29.0  29.0\n",
       "6   20.0  16.0  20.8  23.4  23.0  24.5  26.9  30.8  30.1  30.1\n",
       "7   18.0  19.1  21.5  21.8  22.2  23.5  27.1  29.0  27.6  27.6\n",
       "8   18.2  15.5  19.6  21.5  25.0  26.2  27.5  30.0  29.0  29.0\n",
       "9   18.2  20.5  20.2  22.8  26.0  27.3  27.7  27.7  26.2  26.2\n",
       "10  19.0  17.1  21.0  22.7  20.7  25.5  27.5  30.0  23.7  23.7\n",
       "11  19.4  21.5  21.9  24.8  22.0  25.5  28.2  30.6  26.7  26.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop([\"month\"], axis=1) # removing month column\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
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       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
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       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>25.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>28.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>21.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>19.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.7</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>30.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2001  2002  2003  2004  2005  2006  2007  2008  2009  2010\n",
       "Month                                                            \n",
       "0      13.0  18.2  21.5  21.7  25.3  23.8  24.4  29.1  30.0  30.0\n",
       "1      16.4  19.4  21.6  13.7  26.7  23.0  24.8  28.8  29.0  29.0\n",
       "2      22.0  19.0  22.3  20.0  26.0  25.6  27.5  29.0  29.0  29.0\n",
       "3      19.5  17.2  21.1  21.5  25.0  24.5  20.5  28.7  29.2  29.2\n",
       "4      20.0  11.2  21.6  21.4  24.0  24.3  26.2  29.2  29.5  29.5\n",
       "5      20.0  18.5  21.1  22.4  22.0  24.0  26.0  30.2  29.0  29.0\n",
       "6      20.0  16.0  20.8  23.4  23.0  24.5  26.9  30.8  30.1  30.1\n",
       "7      18.0  19.1  21.5  21.8  22.2  23.5  27.1  29.0  27.6  27.6\n",
       "8      18.2  15.5  19.6  21.5  25.0  26.2  27.5  30.0  29.0  29.0\n",
       "9      18.2  20.5  20.2  22.8  26.0  27.3  27.7  27.7  26.2  26.2\n",
       "10     19.0  17.1  21.0  22.7  20.7  25.5  27.5  30.0  23.7  23.7\n",
       "11     19.4  21.5  21.9  24.8  22.0  25.5  28.2  30.6  26.7  26.7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.name='Month' #naming index as month\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "fig,ax = plt.subplots(figsize = [20,6])\n",
    "ax.boxplot(data)\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.tick_params(axis='both',which='major', length=9, direction=\"in\")\n",
    "ax.tick_params(axis='y',which='minor', length=4, direction=\"in\")\n",
    "ax.tick_params(axis='x', rotation= 45)\n",
    "ax.set_xlabel('Month',fontsize =18,color='b')\n",
    "ax.set_ylabel('Temperature($^o$C)',fontsize =18,color='b')\n",
    "ax.set_xticks(np.arange(1,13))\n",
    "ax.set_xticklabels(['Jan', 'Feb', 'March', 'April', 'May','Jun', 'July', 'Aug', 'Sept', 'Oct','Nov', 'Dec'])\n",
    "plt.title('Monthly variation of temperature',fontsize =24,color='r')\n",
    "fig.tight_layout() \n",
    "plt.savefig(\"image/box1.pdf\", dpi = 300) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "fig,ax = plt.subplots(figsize = [20,6])\n",
    "ax.boxplot(data.T)\n",
    "ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "ax.tick_params(axis='both',which='major', length=9, direction=\"in\")\n",
    "ax.tick_params(axis='y',which='minor', length=4, direction=\"in\")\n",
    "ax.tick_params(axis='x', rotation= 45)\n",
    "ax.set_xlabel('Year',fontsize =18,color='b')\n",
    "ax.set_ylabel('Temperature($^o$C)',fontsize =18,color='b')\n",
    "ax.set_xticks(np.arange(1,11))\n",
    "ax.set_xticklabels(['2001', '2002', '2003', '2004', '2005','2006', '2007', '2008', '2009', '2010'])\n",
    "plt.title('Annual variation of temperature',fontsize =24,color='r')\n",
    "fig.tight_layout() \n",
    "plt.savefig(\"image/box2.pdf\", dpi = 300) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001    18.641667\n",
      "2002    17.766667\n",
      "2003    21.183333\n",
      "2004    21.475000\n",
      "2005    23.991667\n",
      "2006    24.808333\n",
      "2007    26.191667\n",
      "2008    29.425000\n",
      "2009    28.250000\n",
      "2010    28.250000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "myear= np.mean(data,axis =0)\n",
    "print(myear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month\n",
      "0     23.70\n",
      "1     23.24\n",
      "2     24.94\n",
      "3     23.64\n",
      "4     23.69\n",
      "5     24.22\n",
      "6     24.56\n",
      "7     23.74\n",
      "8     24.15\n",
      "9     24.28\n",
      "10    23.09\n",
      "11    24.73\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mmonth=np.mean(data,axis =1)\n",
    "print(mmonth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001    2.158494\n",
      "2002    2.585966\n",
      "2003    0.705731\n",
      "2004    2.608679\n",
      "2005    1.873258\n",
      "2006    1.184243\n",
      "2007    2.048356\n",
      "2008    0.863255\n",
      "2009    1.801157\n",
      "2010    1.801157\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sdyear=np.std(data,axis =0)\n",
    "print(sdyear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month\n",
      "0     5.149563\n",
      "1     5.166856\n",
      "2     3.645051\n",
      "3     4.117329\n",
      "4     5.327936\n",
      "5     3.913771\n",
      "6     4.666733\n",
      "7     3.666661\n",
      "8     4.851443\n",
      "9     3.360595\n",
      "10    3.695795\n",
      "11    3.303952\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sdmonth=np.std(data,axis = 1)\n",
    "print(sdmonth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [20,6])\n",
    "fig,ax = plt.subplots()\n",
    "plt.errorbar(data.index, mmonth,yerr=sdmonth,fmt='-o')\n",
    "plt.xlabel('Month',fontsize =14,color='b')\n",
    "plt.ylabel('Temperature($^o$C)',fontsize =18,color='b')\n",
    "plt.xticks(data.index, ('Jan', 'Feb', 'March', 'April', 'May','Jun', 'July', 'Aug', 'Sept', 'Oct','Nov', 'Dec'),fontsize =18)\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Monthly variation of temperature ',fontsize =24,color='g')\n",
    "plt.tight_layout() \n",
    "plt.savefig(\"image/errorbar1.pdf\", dpi = 300) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [20,6])\n",
    "fig,ax = plt.subplots()\n",
    "plt.bar(data.columns, myear,yerr=sdyear,align='center', color=\"c\", ecolor='black')\n",
    "plt.xlabel('Year',fontsize =14,color='b')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Temperature($^o$C)',fontsize =14,color='b')\n",
    "plt.title('Annual variation of temperature',fontsize =18,color='g')\n",
    "plt.tight_layout() \n",
    "plt.savefig(\"image/errorbar2.pdf\", dpi = 300) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}