final project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Dennis Yiaile Project, PHYS104**\n",
    "\n",
    "### This file contains the montly rainfall of India as recorded by different stations in India from 1901 to 2015. The goal of this project is to determine the months that India received high rainfall and the months that India received low rainfall. I also aim to identify the year from 1901 to 2015 that in India received high rainfall and low rainfall. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the libraries \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read the css file\n",
    "rainfall = pd.read_csv(\"rainfall_in_india_1901-2015.csv\", delimiter = \",\" )  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After reading the file, I want to return my table to see the data in it. You will need to scroll horizontally to see all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4111</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>85.9</td>\n",
       "      <td>107.2</td>\n",
       "      <td>153.6</td>\n",
       "      <td>350.2</td>\n",
       "      <td>254.0</td>\n",
       "      <td>255.2</td>\n",
       "      <td>117.4</td>\n",
       "      <td>184.3</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1533.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>196.2</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>316.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2012</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>76.8</td>\n",
       "      <td>21.2</td>\n",
       "      <td>327.0</td>\n",
       "      <td>231.5</td>\n",
       "      <td>381.2</td>\n",
       "      <td>179.8</td>\n",
       "      <td>145.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1405.5</td>\n",
       "      <td>19.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>1119.5</td>\n",
       "      <td>167.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4113</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2013</td>\n",
       "      <td>26.2</td>\n",
       "      <td>34.4</td>\n",
       "      <td>37.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>88.3</td>\n",
       "      <td>426.2</td>\n",
       "      <td>296.4</td>\n",
       "      <td>154.4</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>26.7</td>\n",
       "      <td>1426.3</td>\n",
       "      <td>60.6</td>\n",
       "      <td>131.1</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>177.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2014</td>\n",
       "      <td>53.2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14.9</td>\n",
       "      <td>57.4</td>\n",
       "      <td>244.1</td>\n",
       "      <td>116.1</td>\n",
       "      <td>466.1</td>\n",
       "      <td>132.2</td>\n",
       "      <td>169.2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>69.3</td>\n",
       "      <td>76.7</td>\n",
       "      <td>958.5</td>\n",
       "      <td>290.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>87.1</td>\n",
       "      <td>133.1</td>\n",
       "      <td>296.6</td>\n",
       "      <td>257.5</td>\n",
       "      <td>146.4</td>\n",
       "      <td>160.4</td>\n",
       "      <td>165.4</td>\n",
       "      <td>231.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1642.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>223.9</td>\n",
       "      <td>860.9</td>\n",
       "      <td>555.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4116 rows × 19 columns</p>\n",
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      ],
      "text/plain": [
       "                    SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0     ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1     ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2     ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3     ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4     ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "...                         ...   ...   ...    ...   ...    ...    ...    ...   \n",
       "4111                LAKSHADWEEP  2011   5.1    2.8   3.1   85.9  107.2  153.6   \n",
       "4112                LAKSHADWEEP  2012  19.2    0.1   1.6   76.8   21.2  327.0   \n",
       "4113                LAKSHADWEEP  2013  26.2   34.4  37.5    5.3   88.3  426.2   \n",
       "4114                LAKSHADWEEP  2014  53.2   16.1   4.4   14.9   57.4  244.1   \n",
       "4115                LAKSHADWEEP  2015   2.2    0.5   3.7   87.1  133.1  296.6   \n",
       "\n",
       "        JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0     365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1     228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2     728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3     502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4     368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "...     ...    ...    ...    ...    ...    ...     ...      ...      ...   \n",
       "4111  350.2  254.0  255.2  117.4  184.3   14.9  1533.7      7.9    196.2   \n",
       "4112  231.5  381.2  179.8  145.9   12.4    8.8  1405.5     19.3     99.6   \n",
       "4113  296.4  154.4  180.0   72.8   78.1   26.7  1426.3     60.6    131.1   \n",
       "4114  116.1  466.1  132.2  169.2   59.0   62.3  1395.0     69.3     76.7   \n",
       "4115  257.5  146.4  160.4  165.4  231.0  159.0  1642.9      2.7    223.9   \n",
       "\n",
       "      Jun-Sep  Oct-Dec  \n",
       "0      1696.3    980.3  \n",
       "1      2185.9    716.7  \n",
       "2      1874.0    690.6  \n",
       "3      1977.6    571.0  \n",
       "4      1624.9    630.8  \n",
       "...       ...      ...  \n",
       "4111   1013.0    316.6  \n",
       "4112   1119.5    167.1  \n",
       "4113   1057.0    177.6  \n",
       "4114    958.5    290.5  \n",
       "4115    860.9    555.4  \n",
       "\n",
       "[4116 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall    #returning the content of the table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now I'm returning the first eight rows of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
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       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1906</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556.1</td>\n",
       "      <td>733.3</td>\n",
       "      <td>247.7</td>\n",
       "      <td>320.5</td>\n",
       "      <td>164.3</td>\n",
       "      <td>267.8</td>\n",
       "      <td>128.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>2534.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>556.1</td>\n",
       "      <td>1465.8</td>\n",
       "      <td>475.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1907</td>\n",
       "      <td>110.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>21.6</td>\n",
       "      <td>616.3</td>\n",
       "      <td>305.2</td>\n",
       "      <td>443.9</td>\n",
       "      <td>377.6</td>\n",
       "      <td>200.4</td>\n",
       "      <td>264.4</td>\n",
       "      <td>648.9</td>\n",
       "      <td>245.6</td>\n",
       "      <td>3347.9</td>\n",
       "      <td>110.7</td>\n",
       "      <td>751.2</td>\n",
       "      <td>1327.1</td>\n",
       "      <td>1158.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1908</td>\n",
       "      <td>20.9</td>\n",
       "      <td>85.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>693.6</td>\n",
       "      <td>481.4</td>\n",
       "      <td>699.9</td>\n",
       "      <td>428.8</td>\n",
       "      <td>170.7</td>\n",
       "      <td>208.1</td>\n",
       "      <td>196.9</td>\n",
       "      <td>3576.4</td>\n",
       "      <td>106.0</td>\n",
       "      <td>591.0</td>\n",
       "      <td>2303.7</td>\n",
       "      <td>575.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR    JAN    FEB    MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901   49.2   87.1   29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902    0.0  159.8   12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903   12.7  144.0    0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904    9.4   14.7    0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905    1.3    0.0    3.3   26.9  279.5  628.7   \n",
       "5  ANDAMAN & NICOBAR ISLANDS  1906   36.6    0.0    0.0    0.0  556.1  733.3   \n",
       "6  ANDAMAN & NICOBAR ISLANDS  1907  110.7    0.0  113.3   21.6  616.3  305.2   \n",
       "7  ANDAMAN & NICOBAR ISLANDS  1908   20.9   85.1    0.0   29.0  562.0  693.6   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "5  247.7  320.5  164.3  267.8  128.9   79.2  2534.4     36.6    556.1   \n",
       "6  443.9  377.6  200.4  264.4  648.9  245.6  3347.9    110.7    751.2   \n",
       "7  481.4  699.9  428.8  170.7  208.1  196.9  3576.4    106.0    591.0   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  \n",
       "5   1465.8    475.9  \n",
       "6   1327.1   1158.9  \n",
       "7   2303.7    575.7  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.head(8)   #returning the first eight rows in the table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you didn't see all the columns in the above cells, don't worry I have simplified this for you, below are the name of columns in my table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SUBDIVISION', 'YEAR', 'JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',\n",
       "       'AUG', 'SEP', 'OCT', 'NOV', 'DEC', 'ANNUAL', 'Jan-Feb', 'Mar-May',\n",
       "       'Jun-Sep', 'Oct-Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.columns #returning the table columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good that you saw all the columns. I'm not comfortable with columns that have been written in capital laters or abbreviated. I have decided to play with some codes and rename my columns in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Subdivision', 'Year', 'January', 'February', 'March', 'April', 'May',\n",
       "       'June', 'July', 'August', 'September', 'October', 'November',\n",
       "       'December', 'Annual', 'Jan-Feb', 'Mar-May', 'Jun-Sep', 'Oct-Dec'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code renames columns and returns the new columns'''\n",
    "\n",
    "rainfall.columns = [\"Subdivision\", \"Year\", \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\", \"Annual\", \"Jan-Feb\", \"Mar-May\", \"Jun-Sep\", \"Oct-Dec\"] #renaming columns\n",
    "rainfall.columns     #returning the new columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm returning the first 5 raws to see if the columns changed using the attribute head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subdivision</th>\n",
       "      <th>Year</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Subdivision  Year  January  February  March  April    May  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901     49.2      87.1   29.2    2.3  528.8   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902      0.0     159.8   12.2    0.0  446.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903     12.7     144.0    0.0    1.0  235.1   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904      9.4      14.7    0.0  202.4  304.5   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905      1.3       0.0    3.3   26.9  279.5   \n",
       "\n",
       "    June   July  August  September  October  November  December  Annual  \\\n",
       "0  517.5  365.1   481.1      332.6    388.5     558.2      33.6  3373.2   \n",
       "1  537.1  228.9   753.7      666.2    197.2     359.0     160.5  3520.7   \n",
       "2  479.9  728.4   326.7      339.0    181.2     284.4     225.0  2957.4   \n",
       "3  495.1  502.0   160.1      820.4    222.2     308.7      40.1  3079.6   \n",
       "4  628.7  368.7   330.5      297.0    260.7      25.4     344.7  2566.7   \n",
       "\n",
       "   Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  \n",
       "0    136.3    560.3   1696.3    980.3  \n",
       "1    159.8    458.3   2185.9    716.7  \n",
       "2    156.7    236.1   1874.0    690.6  \n",
       "3     24.1    506.9   1977.6    571.0  \n",
       "4      1.3    309.7   1624.9    630.8  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.head() #returning the first five raws containing the new columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 2\n",
    "\n",
    "\n",
    "**Using loc and np.where methods**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to use loc to find a subset of my data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subdivision</th>\n",
       "      <th>Year</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1906</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556.1</td>\n",
       "      <td>733.3</td>\n",
       "      <td>247.7</td>\n",
       "      <td>320.5</td>\n",
       "      <td>164.3</td>\n",
       "      <td>267.8</td>\n",
       "      <td>128.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>2534.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>556.1</td>\n",
       "      <td>1465.8</td>\n",
       "      <td>475.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Subdivision  Year  January  February  March  April    May  \\\n",
       "5  ANDAMAN & NICOBAR ISLANDS  1906     36.6       0.0    0.0    0.0  556.1   \n",
       "\n",
       "    June   July  August  September  October  November  December  Annual  \\\n",
       "5  733.3  247.7   320.5      164.3    267.8     128.9      79.2  2534.4   \n",
       "\n",
       "   Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  \n",
       "5     36.6    556.1   1465.8    475.9  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.loc[[5]] #returning the raw on the 5th position(sixth raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subdivision</th>\n",
       "      <th>Year</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1906</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556.1</td>\n",
       "      <td>733.3</td>\n",
       "      <td>247.7</td>\n",
       "      <td>320.5</td>\n",
       "      <td>164.3</td>\n",
       "      <td>267.8</td>\n",
       "      <td>128.9</td>\n",
       "      <td>79.2</td>\n",
       "      <td>2534.4</td>\n",
       "      <td>36.6</td>\n",
       "      <td>556.1</td>\n",
       "      <td>1465.8</td>\n",
       "      <td>475.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1907</td>\n",
       "      <td>110.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.3</td>\n",
       "      <td>21.6</td>\n",
       "      <td>616.3</td>\n",
       "      <td>305.2</td>\n",
       "      <td>443.9</td>\n",
       "      <td>377.6</td>\n",
       "      <td>200.4</td>\n",
       "      <td>264.4</td>\n",
       "      <td>648.9</td>\n",
       "      <td>245.6</td>\n",
       "      <td>3347.9</td>\n",
       "      <td>110.7</td>\n",
       "      <td>751.2</td>\n",
       "      <td>1327.1</td>\n",
       "      <td>1158.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1908</td>\n",
       "      <td>20.9</td>\n",
       "      <td>85.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>693.6</td>\n",
       "      <td>481.4</td>\n",
       "      <td>699.9</td>\n",
       "      <td>428.8</td>\n",
       "      <td>170.7</td>\n",
       "      <td>208.1</td>\n",
       "      <td>196.9</td>\n",
       "      <td>3576.4</td>\n",
       "      <td>106.0</td>\n",
       "      <td>591.0</td>\n",
       "      <td>2303.7</td>\n",
       "      <td>575.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1910</td>\n",
       "      <td>26.6</td>\n",
       "      <td>22.7</td>\n",
       "      <td>206.3</td>\n",
       "      <td>89.3</td>\n",
       "      <td>224.5</td>\n",
       "      <td>472.7</td>\n",
       "      <td>264.3</td>\n",
       "      <td>337.4</td>\n",
       "      <td>626.6</td>\n",
       "      <td>208.2</td>\n",
       "      <td>267.3</td>\n",
       "      <td>153.5</td>\n",
       "      <td>2899.4</td>\n",
       "      <td>49.3</td>\n",
       "      <td>520.1</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>629.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122.5</td>\n",
       "      <td>327.3</td>\n",
       "      <td>649.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>187.1</td>\n",
       "      <td>464.5</td>\n",
       "      <td>333.8</td>\n",
       "      <td>94.5</td>\n",
       "      <td>247.1</td>\n",
       "      <td>2687.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>449.8</td>\n",
       "      <td>1553.6</td>\n",
       "      <td>675.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Subdivision  Year  January  February  March  April    May  \\\n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905      1.3       0.0    3.3   26.9  279.5   \n",
       "5  ANDAMAN & NICOBAR ISLANDS  1906     36.6       0.0    0.0    0.0  556.1   \n",
       "6  ANDAMAN & NICOBAR ISLANDS  1907    110.7       0.0  113.3   21.6  616.3   \n",
       "7  ANDAMAN & NICOBAR ISLANDS  1908     20.9      85.1    0.0   29.0  562.0   \n",
       "8  ANDAMAN & NICOBAR ISLANDS  1910     26.6      22.7  206.3   89.3  224.5   \n",
       "9  ANDAMAN & NICOBAR ISLANDS  1911      0.0       8.4    0.0  122.5  327.3   \n",
       "\n",
       "    June   July  August  September  October  November  December  Annual  \\\n",
       "4  628.7  368.7   330.5      297.0    260.7      25.4     344.7  2566.7   \n",
       "5  733.3  247.7   320.5      164.3    267.8     128.9      79.2  2534.4   \n",
       "6  305.2  443.9   377.6      200.4    264.4     648.9     245.6  3347.9   \n",
       "7  693.6  481.4   699.9      428.8    170.7     208.1     196.9  3576.4   \n",
       "8  472.7  264.3   337.4      626.6    208.2     267.3     153.5  2899.4   \n",
       "9  649.0  253.0   187.1      464.5    333.8      94.5     247.1  2687.2   \n",
       "\n",
       "   Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  \n",
       "4      1.3    309.7   1624.9    630.8  \n",
       "5     36.6    556.1   1465.8    475.9  \n",
       "6    110.7    751.2   1327.1   1158.9  \n",
       "7    106.0    591.0   2303.7    575.7  \n",
       "8     49.3    520.1   1701.0    629.0  \n",
       "9      8.4    449.8   1553.6    675.4  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.loc[4:9] #returning all the items from the fifth to the tenth raw(position 4 to 9 raw) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now I want to return all the raws and a subset of columns from Year to December using loc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Year</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4111</th>\n",
       "      <td>2011</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>85.9</td>\n",
       "      <td>107.2</td>\n",
       "      <td>153.6</td>\n",
       "      <td>350.2</td>\n",
       "      <td>254.0</td>\n",
       "      <td>255.2</td>\n",
       "      <td>117.4</td>\n",
       "      <td>184.3</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>2012</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>76.8</td>\n",
       "      <td>21.2</td>\n",
       "      <td>327.0</td>\n",
       "      <td>231.5</td>\n",
       "      <td>381.2</td>\n",
       "      <td>179.8</td>\n",
       "      <td>145.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4113</th>\n",
       "      <td>2013</td>\n",
       "      <td>26.2</td>\n",
       "      <td>34.4</td>\n",
       "      <td>37.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>88.3</td>\n",
       "      <td>426.2</td>\n",
       "      <td>296.4</td>\n",
       "      <td>154.4</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>2014</td>\n",
       "      <td>53.2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14.9</td>\n",
       "      <td>57.4</td>\n",
       "      <td>244.1</td>\n",
       "      <td>116.1</td>\n",
       "      <td>466.1</td>\n",
       "      <td>132.2</td>\n",
       "      <td>169.2</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>2015</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>87.1</td>\n",
       "      <td>133.1</td>\n",
       "      <td>296.6</td>\n",
       "      <td>257.5</td>\n",
       "      <td>146.4</td>\n",
       "      <td>160.4</td>\n",
       "      <td>165.4</td>\n",
       "      <td>231.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4116 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  January  February  March  April    May   June   July  August  \\\n",
       "0     1901     49.2      87.1   29.2    2.3  528.8  517.5  365.1   481.1   \n",
       "1     1902      0.0     159.8   12.2    0.0  446.1  537.1  228.9   753.7   \n",
       "2     1903     12.7     144.0    0.0    1.0  235.1  479.9  728.4   326.7   \n",
       "3     1904      9.4      14.7    0.0  202.4  304.5  495.1  502.0   160.1   \n",
       "4     1905      1.3       0.0    3.3   26.9  279.5  628.7  368.7   330.5   \n",
       "...    ...      ...       ...    ...    ...    ...    ...    ...     ...   \n",
       "4111  2011      5.1       2.8    3.1   85.9  107.2  153.6  350.2   254.0   \n",
       "4112  2012     19.2       0.1    1.6   76.8   21.2  327.0  231.5   381.2   \n",
       "4113  2013     26.2      34.4   37.5    5.3   88.3  426.2  296.4   154.4   \n",
       "4114  2014     53.2      16.1    4.4   14.9   57.4  244.1  116.1   466.1   \n",
       "4115  2015      2.2       0.5    3.7   87.1  133.1  296.6  257.5   146.4   \n",
       "\n",
       "      September  October  November  December  \n",
       "0         332.6    388.5     558.2      33.6  \n",
       "1         666.2    197.2     359.0     160.5  \n",
       "2         339.0    181.2     284.4     225.0  \n",
       "3         820.4    222.2     308.7      40.1  \n",
       "4         297.0    260.7      25.4     344.7  \n",
       "...         ...      ...       ...       ...  \n",
       "4111      255.2    117.4     184.3      14.9  \n",
       "4112      179.8    145.9      12.4       8.8  \n",
       "4113      180.0     72.8      78.1      26.7  \n",
       "4114      132.2    169.2      59.0      62.3  \n",
       "4115      160.4    165.4     231.0     159.0  \n",
       "\n",
       "[4116 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.loc[:,\"Year\":\"December\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "Now, I want to return a specific subset of raws and columns using loc. Since I be will be dealing with months, I want to return the columns for the years and the 12 months only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>January</th>\n",
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       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1917</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>112.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>295.9</td>\n",
       "      <td>301.1</td>\n",
       "      <td>394.8</td>\n",
       "      <td>437.4</td>\n",
       "      <td>471.8</td>\n",
       "      <td>238.1</td>\n",
       "      <td>108.3</td>\n",
       "      <td>236.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1918</td>\n",
       "      <td>77.4</td>\n",
       "      <td>6.9</td>\n",
       "      <td>11.4</td>\n",
       "      <td>10.7</td>\n",
       "      <td>729.3</td>\n",
       "      <td>710.8</td>\n",
       "      <td>200.9</td>\n",
       "      <td>455.4</td>\n",
       "      <td>303.3</td>\n",
       "      <td>227.0</td>\n",
       "      <td>366.9</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1919</td>\n",
       "      <td>10.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>283.9</td>\n",
       "      <td>542.5</td>\n",
       "      <td>246.5</td>\n",
       "      <td>259.8</td>\n",
       "      <td>170.7</td>\n",
       "      <td>186.2</td>\n",
       "      <td>340.4</td>\n",
       "      <td>258.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1920</td>\n",
       "      <td>122.3</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>237.4</td>\n",
       "      <td>546.9</td>\n",
       "      <td>294.4</td>\n",
       "      <td>467.4</td>\n",
       "      <td>505.4</td>\n",
       "      <td>397.5</td>\n",
       "      <td>262.9</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1921</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>351.2</td>\n",
       "      <td>282.7</td>\n",
       "      <td>487.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>581.2</td>\n",
       "      <td>360.7</td>\n",
       "      <td>118.2</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1922</td>\n",
       "      <td>245.3</td>\n",
       "      <td>34.3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>323.1</td>\n",
       "      <td>289.7</td>\n",
       "      <td>506.1</td>\n",
       "      <td>425.8</td>\n",
       "      <td>307.4</td>\n",
       "      <td>511.7</td>\n",
       "      <td>162.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>192.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1923</td>\n",
       "      <td>79.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.3</td>\n",
       "      <td>293.5</td>\n",
       "      <td>808.4</td>\n",
       "      <td>636.9</td>\n",
       "      <td>182.2</td>\n",
       "      <td>560.5</td>\n",
       "      <td>131.9</td>\n",
       "      <td>197.4</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1924</td>\n",
       "      <td>28.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>89.7</td>\n",
       "      <td>191.2</td>\n",
       "      <td>261.2</td>\n",
       "      <td>493.3</td>\n",
       "      <td>290.9</td>\n",
       "      <td>251.2</td>\n",
       "      <td>331.1</td>\n",
       "      <td>378.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1925</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>50.4</td>\n",
       "      <td>282.2</td>\n",
       "      <td>663.8</td>\n",
       "      <td>241.8</td>\n",
       "      <td>278.2</td>\n",
       "      <td>201.9</td>\n",
       "      <td>249.5</td>\n",
       "      <td>271.5</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1926</td>\n",
       "      <td>122.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>198.4</td>\n",
       "      <td>370.0</td>\n",
       "      <td>195.3</td>\n",
       "      <td>523.7</td>\n",
       "      <td>719.3</td>\n",
       "      <td>443.8</td>\n",
       "      <td>148.4</td>\n",
       "      <td>560.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1927</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.8</td>\n",
       "      <td>108.6</td>\n",
       "      <td>504.1</td>\n",
       "      <td>433.3</td>\n",
       "      <td>195.2</td>\n",
       "      <td>370.1</td>\n",
       "      <td>126.2</td>\n",
       "      <td>327.5</td>\n",
       "      <td>274.1</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  January  February  March  April    May   June   July  August  \\\n",
       "15  1917      8.0       3.6  112.0    4.5  295.9  301.1  394.8   437.4   \n",
       "16  1918     77.4       6.9   11.4   10.7  729.3  710.8  200.9   455.4   \n",
       "17  1919     10.2      18.0    0.0   35.5  283.9  542.5  246.5   259.8   \n",
       "18  1920    122.3       7.4    3.1   13.0  237.4  546.9  294.4   467.4   \n",
       "19  1921     13.2       3.1    0.0   37.5  351.2  282.7  487.1   330.0   \n",
       "20  1922    245.3      34.3   15.6  323.1  289.7  506.1  425.8   307.4   \n",
       "21  1923     79.5       0.0    NaN   91.3  293.5  808.4  636.9   182.2   \n",
       "22  1924     28.7       0.0   14.8   89.7  191.2  261.2  493.3   290.9   \n",
       "23  1925     36.6       0.0    8.6   50.4  282.2  663.8  241.8   278.2   \n",
       "24  1926    122.1       0.0    0.0    0.5  198.4  370.0  195.3   523.7   \n",
       "25  1927      3.0      17.5   17.8  108.6  504.1  433.3  195.2   370.1   \n",
       "\n",
       "    September  October  November  December  \n",
       "15      471.8    238.1     108.3     236.9  \n",
       "16      303.3    227.0     366.9     175.0  \n",
       "17      170.7    186.2     340.4     258.4  \n",
       "18      505.4    397.5     262.9      85.5  \n",
       "19      581.2    360.7     118.2      41.5  \n",
       "20      511.7    162.0     541.0     192.2  \n",
       "21      560.5    131.9     197.4      70.6  \n",
       "22      251.2    331.1     378.6       NaN  \n",
       "23      201.9    249.5     271.5     196.0  \n",
       "24      719.3    443.8     148.4     560.7  \n",
       "25      126.2    327.5     274.1      65.5  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.loc[15:25,\"Year\":\"December\"]  #returning raws from position 15th to 25th and columns\n",
    "                                       #from Year to December"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I understand that I have not used np.where in element 2. Don't worry about that. I have employed np.where and loc in the next elements specifically element 3. Therefore, note that element 3 contain some contents of element 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 3   and Test Suite\n",
    "\n",
    "**Using classes, methods, for loops, and np.where**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to find the total amount of rainfall received in each month for all the years. I have decided to use a class, methods, and for loops. This code contains a method that returns the length of the array that I will be taking as my input and a method that prints the sum of monthly rainfall for all the years in a statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''This code calculates the total amount of rainfall received in each month for all the years. \n",
    "Also, this code returns the length of the  array that I will be taking as my input and \n",
    "print the total monthly rainfall for all the years in a statement.'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class Monthly():                    #creating a class called monthly\n",
    "    def __init__(self, mymonths):   #\"dunder\" method with one attribute\n",
    "        self.mymonths = mymonths    #instant attribute\n",
    "  \n",
    "    def monthlysum(self):           #this method returns the total monthly rainfall for all the years\n",
    "        for i in self.mymonths:     #creating a for loop\n",
    "            monthlyrainfall = rainfall[i].dropna()   #removing all unrecorded data\n",
    "            sumeachmonth = sum(monthlyrainfall)       #summing monthly rainfall \n",
    "            print (\"The total rainfall received in %s for all the years is %d.\" %(i, sumeachmonth))  #printing the total sum in a statement\n",
    "        \n",
    "    def length(self):              #this method returns the length of my input in a statement \n",
    "        length = len(self.mymonths)                        #calculating the length of my input\n",
    "        return(\"The length of this data is %s.\" %(length)) #returning the length \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to test my class and methods by calling them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['January', 'February', 'March', 'April', 'May', 'June', 'July',\n",
       "       'August', 'September', 'October', 'November', 'December'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = rainfall.columns[2:14]   #creating an array for all the columns from the 2nd to the 14th position\n",
    "                                  #and assigning it to a variable months\n",
    "months                            #returning the columns containing my months names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total rainfall received in January for all the years is 77952.\n",
      "The total rainfall received in February for all the years is 89685.\n",
      "The total rainfall received in March for all the years is 112446.\n",
      "The total rainfall received in April for all the years is 177340.\n",
      "The total rainfall received in May for all the years is 352670.\n",
      "The total rainfall received in June for all the years is 946493.\n",
      "The total rainfall received in July for all the years is 1426703.\n",
      "The total rainfall received in August for all the years is 1193563.\n",
      "The total rainfall received in September for all the years is 811157.\n",
      "The total rainfall received in October for all the years is 392438.\n",
      "The total rainfall received in November for all the years is 163650.\n",
      "The total rainfall received in December for all the years is 77482.\n"
     ]
    }
   ],
   "source": [
    "mydata = Monthly(months) #Instantiate mydata as a object in the class \"Monthly\"\n",
    "mydata.monthlysum()      #calling my \"monthlysum()\" method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The length of this data is 12.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.length()   #calling my \"length()\" method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these values, it seems like India received higher rainfall from May to September and lower rainfall from November to March. Specifically, the highest rainfal is received in July while the lowest is received in December. \n",
    "\n",
    "Now I want to return all the raws for the December and July columns."
   ]
  },
  {
   "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>December</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>344.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4111</th>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4113</th>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4116 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      December\n",
       "0         33.6\n",
       "1        160.5\n",
       "2        225.0\n",
       "3         40.1\n",
       "4        344.7\n",
       "...        ...\n",
       "4111      14.9\n",
       "4112       8.8\n",
       "4113      26.7\n",
       "4114      62.3\n",
       "4115     159.0\n",
       "\n",
       "[4116 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall[[\"December\"]]   #returning all the raws of december column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>July</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>365.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>728.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>368.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4111</th>\n",
       "      <td>350.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>231.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4113</th>\n",
       "      <td>296.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4114</th>\n",
       "      <td>116.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>257.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4116 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       July\n",
       "0     365.1\n",
       "1     228.9\n",
       "2     728.4\n",
       "3     502.0\n",
       "4     368.7\n",
       "...     ...\n",
       "4111  350.2\n",
       "4112  231.5\n",
       "4113  296.4\n",
       "4114  116.1\n",
       "4115  257.5\n",
       "\n",
       "[4116 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall[[\"July\"]]  ##returning all the raws of july column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time round I'm return the maximum and minimum of all the July and December rainfall in a statement. Remember that my goal was to indetify the months and year in which India received high and Low rainfall. I'm using July and December because they have the largest and smallest total amount of rainfall respectively. You will understand in the next cells how the min and max for these months is helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum rainfall in July for all the years is 2362.8 and the minimum is 0.0\n"
     ]
    }
   ],
   "source": [
    "#returning the maximum and minimum rainfall received in july for all the years.\n",
    "print(\"The maximum rainfall in July for all the years is\", max(rainfall.July), \"and the minimum is\", min(rainfall.July))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum rainfall in December for all the years is 617.5 and the minimum is 0.0\n"
     ]
    }
   ],
   "source": [
    "#returning the maximum and minimum rainfall received in December for all the years\n",
    "print(\"The maximum rainfall in December for all the years is\", max(rainfall.December), \"and the minimum is\", min(rainfall.December))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'm going to calculate the maximum of each month in a loop, sum all of them, and calculate the medium using np.percentile. I will use this medium to come up with the name of months during which India experience high and low rainfall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "798.5999999999999\n"
     ]
    }
   ],
   "source": [
    "'''This code returns the medium of all the maximum amount of rainfall received in each month'''\n",
    "\n",
    "months = rainfall.columns[2:14]  #creating an array containing names of all months and naming it 'months'\n",
    "totalmaxima =[]                  #creating an empty array 'totalmaxima'\n",
    "for m in range(len(months)):     #using for loops within the range of 'months' length\n",
    "    allmax = max(rainfall[months[m]])    #calculating maximum for each month\n",
    "    totalmaxima.append(allmax)    #adding maximum values to our array\n",
    "medium_max = np.percentile(totalmaxima, 50)  #calculating the medium of all maximum values\n",
    "print(medium_max)                            #printing the medium value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The months that India receives higher rainfall are....\n",
      "May\n",
      "June\n",
      "July\n",
      "August\n",
      "September\n",
      "October\n",
      "The months that India receives low rainfall are....\n",
      "January\n",
      "February\n",
      "March\n",
      "April\n",
      "November\n",
      "December\n"
     ]
    }
   ],
   "source": [
    "'''This code returns the months that India receives high rainfall and low rainfall'''\n",
    "\n",
    "#These first section code prints high rainfall months\n",
    "\n",
    "print(\"The months that India receives higher rainfall are....\") #printing statement\n",
    "for m in range(len(months)):             #using for loops within the range of 'months' length\n",
    "    allmax = max(rainfall[months[m]])    #calculating maximum for each month\n",
    "    if (allmax > medium_max):            #maximum greater than medium\n",
    "        print(months[m])                 #printing the name of the months\n",
    "\n",
    "\n",
    "#The second section code prints high rainfall months\n",
    "\n",
    "print(\"The months that India receives low rainfall are....\")  #printing a statement\n",
    "for m in range(len(months)):               #using for loops within the range of 'months' length\n",
    "    allmax = max(rainfall[months[m]])      #calculating maximum for each month\n",
    "    if (allmax < medium_max ):             #maximum less than medium\n",
    "        print(months[m])                   #printing the name of the months\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now know that India receive high rainfall From May to October and low rainfall from November to March. Using the maximum and minimum analysis for december and july, I want to identify the years from 1901 to 2015 that India received high rainfall and low rainfall using **np.where and loc.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to use np.where and loc in the next cells to determine the years that India received high rainfall and low rainfall. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10    1912\n",
       " Name: Year, dtype: int64,\n",
       " 1806    2005\n",
       " Name: Year, dtype: int64,\n",
       " 114    1920\n",
       " Name: Year, dtype: int64,\n",
       " 128    1934\n",
       " Name: Year, dtype: int64,\n",
       " 142    1948\n",
       " Name: Year, dtype: int64,\n",
       " 112    1918\n",
       " Name: Year, dtype: int64,\n",
       " 142    1948\n",
       " Name: Year, dtype: int64,\n",
       " 149    1958\n",
       " Name: Year, dtype: int64,\n",
       " 119    1925\n",
       " Name: Year, dtype: int64,\n",
       " 113    1919\n",
       " Name: Year, dtype: int64,\n",
       " 6    1907\n",
       " Name: Year, dtype: int64,\n",
       " 35    1937\n",
       " Name: Year, dtype: int64]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns an array containing all the years that each month receives maximum rainfall'''\n",
    "\n",
    "maxyears=[]                          #creating an empty array called maxyears\n",
    "months = rainfall.columns[2:14]      #months names columns\n",
    "for m in range(len(months)):         #using for loops and connecting it to variable months length\n",
    "    montharray = np.array(rainfall[months[m]])                #converting all the months data to arrays\n",
    "    monthhigher = np.where( montharray == max(montharray) )   #using np where to find the position\n",
    "                                                              #where all months receive maximum rainfall\n",
    "    maxyears.append(rainfall.loc[monthhigher][\"Year\"])        #using loc after np where condition\n",
    "maxyears                                                      #returning years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you looked at this data, you will notice that the number of years between 1920 to 1950 is higher. This means that India probably received the highest rainfall around 1920 to 1950s. \n",
    "\n",
    "To come up with an accurate, or close, estimation of the year that India received the highest rainfall, I decided to use the month in which India received higher rainfall -July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142    1948\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns the year that India receives high rainfall'''\n",
    "\n",
    "julyarray = np.array(rainfall.July)       #converting 'july' data into an array\n",
    "julyhigher = np.where(julyarray == max(julyarray))    #finding the position of the maximum value of jularray\n",
    "rainfall.loc[julyhigher][\"Year\"]          #returning the year that India receives high rainfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fantastic. If you look at my first estimation, 1948 is within 1920 to 1950. Also, if you noted, it is the only year that appeared twice in the list of years that India received the highest rainfall. I returned the list in the cell above. Therefore, it appears that India received higher rainfall in 1948. \n",
    "\n",
    "\n",
    "\n",
    "If you clearly saw my table at the beginning, there was a column that contain the total Annual rainfall(total monthly rainfall received in a given year). It is obvious that when annual rainfall is at its maximum, India received the highest rainfall that year. If my calculations above were correct, I should get the year in which Annual rainfall is maximum to be around 1948."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142    1948\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns the year that India receives high rainfall'''\n",
    "\n",
    "annualarray = np.array(rainfall.Annual)       #converting 'Annual' data into an array\n",
    "annualhigher = np.where(annualarray == max(annualarray) )  #finding the position of the maximum value of annualarray\n",
    "rainfall.loc[annualhigher][\"Year\"]           #returning the year that India receives high rainfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, it is now clear that **1948** is the year that India received the highest rainfall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because using annual rainfall is an accurate method which doesn't require a lot of calculations, I decided to use this methods to find the year when India received the lowest rainfall. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834    1918\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns the year that India receives low rainfall'''\n",
    "\n",
    "annualarray = np.array(rainfall.Annual)              #converting 'Annual' data into an array\n",
    "annuallower = np.where(annualarray == min(annualarray) ) #finding the position of the minimum value of annualarray\n",
    "rainfall.loc[annuallower][\"Year\"]             #returning the year that India receives low rainfall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It now appears that India receives the highest rainfall in **1918.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 4 and Test Suite\n",
    "\n",
    "**Ploting curves**\n",
    "\n",
    "\n",
    "Now I want to represent this information on graphs to see the relationship between these variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are graphs for July, December, and Annual rainfall vs Years from 1901 to 2015. If all my calculations above were corrrect, you will notice on these graphs that the highest rainfall received in India is around 1930s - 1950 and the lowest rainfall received is around 1918. It could be hard to notice this by looking at one curve but you should be able to see some similarity in terms of the highest and lowest rainfall after looking at the three curves. \n",
    "\n",
    "\n",
    "\n",
    "Again, I identified at the beginning that India receive its highest rainfall in June and lowest rainfall in India. For the next two curves, you will notice that the y-axis values which is the amount of rainfall are smaller for December and larger for July.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1901., 1920., 1939., 1958., 1977., 1996., 2015.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a range using np.linspace\n",
    "mydate = np.linspace(1901, 2015, 7) #1901 is the start, 2015 is the end and 7 is the steps\n",
    "mydate       #returning my scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "'''This code plots the amount of rainfall received in July for all the years'''\n",
    "\n",
    "plt.plot(rainfall.Year, rainfall.July)   #ploting year vs july rainfall\n",
    "plt.xlabel(\"Date (Years)\")               #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in July (mm)\")      #labeling y-axis\n",
    "plt.title(\"Indian Rainfall in July from 1901-2015\") #title\n",
    "plt.xticks(mydate, rotation=45);         #adding an appropriate x-axis scale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "'''This code plots the amount of rainfall received in December for all the years'''\n",
    "\n",
    "plt.plot(rainfall.Year, rainfall.December) #ploting year vs December  rainfall\n",
    "plt.xlabel(\"Date (Years)\")                 #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in December (mm)\")    #labeling y-axis\n",
    "plt.title(\"Indian Rainfall in December from 1901-2015\") #Title\n",
    "plt.xticks(mydate, rotation=45);            #adding an appropriate x-axis scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "'''This code plots the amount of rainfall received Annualy for all the years'''\n",
    "\n",
    "plt.plot(rainfall.Year, (rainfall.Annual))     #ploting year vs Annual  rainfall\n",
    "plt.xlabel(\"Date (Years)\")                     #labeling x-axis\n",
    "plt.ylabel(\"Annual rainfall (mm)\")             #labeling y-axis\n",
    "plt.title(\"Indian Annual Rainfall from 1901-2015\") #Title\n",
    "plt.xticks(mydate, rotation=45);                #adding an appropriate x-axis scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the three graphs above, it's almost clear that the highest amount of rainfall was received in the year 1948. Also, the month of July received the highest amount of rainfall compared to December for all the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to plot monthly rainfall vs each other. I will begin by ploting months that receive high rainfall and low rainfall. Later, I will focus on low vs high rainfall months specifically December and July. Notice the shape of high vs high, low vs low, and low vs high plots.\n",
    "\n",
    "\n",
    "**High vs High** and **Low vs Low rainfall months** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rainfall in November vs December')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''This code plots the amount of rainfall received in June and July for all the years'''\n",
    "\n",
    "plt.figure(figsize=(30, 30))       #size of the curve figure\n",
    "plt.subplot(4, 4, 1)               #ploting suplot \n",
    "plt.plot(rainfall.June, (rainfall.July), \".\", c = \"g\")  #ploting June vs July rainfall\n",
    "plt.xlabel(\"Rainfall June (mm)\")      #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in July (mm)\")   #labeling y-axis\n",
    "plt.title(\"Rainfall in June vs July\") #Title \n",
    "\n",
    "plt.figure(figsize=(30, 30))          #size of the curve figure\n",
    "plt.subplot(4, 4, 2)                  #ploting suplot\n",
    "plt.plot(rainfall.November, (rainfall.December), \".\") #ploting November vs December rainfall\n",
    "plt.xlabel(\"Rainfall in November (mm)\")   #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in December (mm)\")    #labeling y-axis\n",
    "plt.title(\"Rainfall in November vs December\")  #Title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph of June vs July shows that the amount of rainfall received in these months is directly proportional in some way. Is it because India receive high rainfall in these months? Also, the graph of December vs November shows that the amount of rainfall received in these months is directly proportional in some way. Is this because India receives low rainfall during this period? To answer this question, I decided to plot a graph of low vs high rainfall months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to compare the amount of rainfall received in December(lowest rainfall month) and July(highest rainfall month) on a graph. Watch closely because I will be using this data for modeling and correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rainfall in December vs July')"
      ]
     },
     "execution_count": 31,
     "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": [
    "'''This code plots the amount of rainfall received in December and July for all the years'''\n",
    "\n",
    "plt.plot(rainfall.December, (rainfall.July), \".\")    #ploting December vs July rainfall\n",
    "plt.xlabel(\"Rainfall in December (mm)\")              #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in July (mm)\")                  #labeling y-axis\n",
    "plt.title(\"Rainfall in December vs July\")            #Title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good. This is what I expected. I expected the graph of december vs July rainfall to be inversely proportional compared to those of June vs July and December vs November that are directly propertional. Therefore, I expect the slope of this curve to be negative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 5\n",
    "\n",
    "\n",
    "#### Fitting a curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using December and July curves, I want to plot a fitting line on top of my December vs July curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, one of the challenges that I encoutered using this data set was unrecorded data. To solve this, I had to replace all unrecorded data subsets for these two months with zero. I could not use this method, dropna(), because the number of unrecorded data for different months was not the same and this could affect the x and y values when fitting a curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4116 4116\n"
     ]
    }
   ],
   "source": [
    "'''This code replaces all NaN data with zero and returns the length of the new data'''\n",
    "\n",
    "december = rainfall.December.replace(np.nan, 0)   #replacing unrecorded data in December with zero \n",
    "july = rainfall.July.replace(np.nan, 0)           #replacing unrecorded data in July with zero\n",
    "print(len(july), len(december) )                  #returning the length of the new data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, I can now go ahead to fit a line on the december vs july graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope of this curve is -0.118534 and the y intercept is 348.855198\n"
     ]
    },
    {
     "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": [
    "'''This code plots a fit line on the December vs July curve \n",
    "   and return the slope and y intercept parameters'''\n",
    "\n",
    "p = np.polyfit(december, july, 1)         #1st polynomial fit line curve\n",
    "bestfit = p[0]*december + p[1]            #calculating the bestline equation\n",
    "plt.plot(december, july, \".\")             #ploting december vs july curve\n",
    "plt.plot(december, bestfit)               #ploting a fit line curve\n",
    "plt.xlabel(\"Rainfall in December (mm)\")   #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in July (mm)\")       #labeling y-axis\n",
    "plt.title(\"Rainfall in December vs July\") #Title\n",
    "print(\"The slope of this curve is %f and the y intercept is %f\" %(p[0], p[1])) #returning the slope and y-intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I mentioned that December and July are Inversely proportional. Inversely proportional variables have negative slopes. After looking at the slope of this curve, -0.118534, I can confirm that December and July are inversely proportional. That is, December is a low rainfall month and July is a high rainfall month. I also Identified this while working on correlation in the cells below -after residuals.\n",
    "\n",
    "\n",
    "### Element 6\n",
    "\n",
    "#### Residuals\n",
    "\n",
    "After ploting a fit line, I want to access the validity of my model using residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope of this curve is -0.118534 and the y intercept is 348.855198\n"
     ]
    },
    {
     "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": [
    "'''This code plots a fit line on the December vs July curve \n",
    "and return the slope and y intercept parameters'''\n",
    "\n",
    "p = np.polyfit(december, july, 1)    #1st polynomial fit line curve\n",
    "bestfit = p[0]*december + p[1]       #calculating the bestline \n",
    "residuals = july - bestfit           #calculating residuals\n",
    "plt.plot(december, residuals, \".\")   #ploting december vs residual curve\n",
    "plt.plot(december, bestfit)          #ploting the best line curve\n",
    "plt.xlabel(\"Rainfall in December (mm)\")           #labeling x-axis\n",
    "plt.ylabel(\"Rainfall in July residual (mm)\")      #labeling y-axis\n",
    "plt.title(\"Rainfall in December vs July residual\") #Title\n",
    "print(\"The slope of this curve is %f and the y intercept is %f\" %(p[0], p[1])) ##returning the slope and y-intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation  and Test Suite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to use the correlation coeffient to identify the correlation between the amount of rainfall in December and July as well as months around December and July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.019427065767893357"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.December.corr(rainfall.July) #correlation between rainfall in dec and july"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7412853933626072"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.June.corr(rainfall.July)  #correlation between rainfall in Jun and july"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45140657807895135"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall.November.corr(rainfall.December) #correlation between rainfall in nove and dec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #3f53c6;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col2 {\n",
       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col3 {\n",
       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col4 {\n",
       "            background-color:  #e5d8d1;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col5 {\n",
       "            background-color:  #dbdcde;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col6 {\n",
       "            background-color:  #9fbfff;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col7 {\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col8 {\n",
       "            background-color:  #bcd2f7;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col9 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #d7dce3;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col11 {\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col0 {\n",
       "            background-color:  #5572df;\n",
       "            color:  #000000;\n",
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       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #445acc;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #7699f6;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b5cdfa;\n",
       "            color:  #000000;\n",
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       "            background-color:  #8caffe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #465ecf;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #dadce0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6b8df0;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col3 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col4 {\n",
       "            background-color:  #92b4fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col5 {\n",
       "            background-color:  #5b7ae5;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col6 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col7 {\n",
       "            background-color:  #4055c8;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col8 {\n",
       "            background-color:  #6282ea;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col9 {\n",
       "            background-color:  #9dbdff;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col10 {\n",
       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
       "        }    #T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col11 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >January</th>        <th class=\"col_heading level0 col1\" >February</th>        <th class=\"col_heading level0 col2\" >March</th>        <th class=\"col_heading level0 col3\" >April</th>        <th class=\"col_heading level0 col4\" >May</th>        <th class=\"col_heading level0 col5\" >June</th>        <th class=\"col_heading level0 col6\" >July</th>        <th class=\"col_heading level0 col7\" >August</th>        <th class=\"col_heading level0 col8\" >September</th>        <th class=\"col_heading level0 col9\" >October</th>        <th class=\"col_heading level0 col10\" >November</th>        <th class=\"col_heading level0 col11\" >December</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row0\" class=\"row_heading level0 row0\" >January</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col1\" class=\"data row0 col1\" >0.46</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col2\" class=\"data row0 col2\" >0.4</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col3\" class=\"data row0 col3\" >0.21</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col4\" class=\"data row0 col4\" >0.13</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col5\" class=\"data row0 col5\" >-0.034</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col6\" class=\"data row0 col6\" >-0.052</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col7\" class=\"data row0 col7\" >0.012</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col8\" class=\"data row0 col8\" >0.024</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col9\" class=\"data row0 col9\" >0.012</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col10\" class=\"data row0 col10\" >0.067</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row1\" class=\"row_heading level0 row1\" >February</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col0\" class=\"data row1 col0\" >0.46</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col1\" class=\"data row1 col1\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col2\" class=\"data row1 col2\" >0.58</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col3\" class=\"data row1 col3\" >0.37</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col4\" class=\"data row1 col4\" >0.2</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col5\" class=\"data row1 col5\" >0.034</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col6\" class=\"data row1 col6\" >0.016</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col7\" class=\"data row1 col7\" >0.072</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col8\" class=\"data row1 col8\" >0.08</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col9\" class=\"data row1 col9\" >-0.0046</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col10\" class=\"data row1 col10\" >-0.023</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row1_col11\" class=\"data row1 col11\" >0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row2\" class=\"row_heading level0 row2\" >March</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col0\" class=\"data row2 col0\" >0.4</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col1\" class=\"data row2 col1\" >0.58</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col3\" class=\"data row2 col3\" >0.56</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col4\" class=\"data row2 col4\" >0.36</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col5\" class=\"data row2 col5\" >0.17</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col6\" class=\"data row2 col6\" >0.097</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col7\" class=\"data row2 col7\" >0.14</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col8\" class=\"data row2 col8\" >0.18</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col9\" class=\"data row2 col9\" >0.086</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col10\" class=\"data row2 col10\" >0.0088</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row2_col11\" class=\"data row2 col11\" >0.14</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row3\" class=\"row_heading level0 row3\" >April</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col0\" class=\"data row3 col0\" >0.21</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col1\" class=\"data row3 col1\" >0.37</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col2\" class=\"data row3 col2\" >0.56</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col3\" class=\"data row3 col3\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col4\" class=\"data row3 col4\" >0.65</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col5\" class=\"data row3 col5\" >0.46</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col6\" class=\"data row3 col6\" >0.27</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col7\" class=\"data row3 col7\" >0.26</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col8\" class=\"data row3 col8\" >0.38</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col9\" class=\"data row3 col9\" >0.37</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col10\" class=\"data row3 col10\" >0.17</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row3_col11\" class=\"data row3 col11\" >0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row4\" class=\"row_heading level0 row4\" >May</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col0\" class=\"data row4 col0\" >0.13</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col1\" class=\"data row4 col1\" >0.2</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col2\" class=\"data row4 col2\" >0.36</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col3\" class=\"data row4 col3\" >0.65</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col4\" class=\"data row4 col4\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col5\" class=\"data row4 col5\" >0.57</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col6\" class=\"data row4 col6\" >0.33</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col7\" class=\"data row4 col7\" >0.33</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col8\" class=\"data row4 col8\" >0.49</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col9\" class=\"data row4 col9\" >0.53</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col10\" class=\"data row4 col10\" >0.35</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row4_col11\" class=\"data row4 col11\" >0.25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row5\" class=\"row_heading level0 row5\" >June</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col0\" class=\"data row5 col0\" >-0.034</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col1\" class=\"data row5 col1\" >0.034</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col2\" class=\"data row5 col2\" >0.17</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col3\" class=\"data row5 col3\" >0.46</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col4\" class=\"data row5 col4\" >0.57</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col5\" class=\"data row5 col5\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col6\" class=\"data row5 col6\" >0.74</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col7\" class=\"data row5 col7\" >0.66</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col8\" class=\"data row5 col8\" >0.55</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col9\" class=\"data row5 col9\" >0.49</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col10\" class=\"data row5 col10\" >0.23</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row5_col11\" class=\"data row5 col11\" >0.089</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row6\" class=\"row_heading level0 row6\" >July</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col0\" class=\"data row6 col0\" >-0.052</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col1\" class=\"data row6 col1\" >0.016</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col2\" class=\"data row6 col2\" >0.097</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col3\" class=\"data row6 col3\" >0.27</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col4\" class=\"data row6 col4\" >0.33</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col5\" class=\"data row6 col5\" >0.74</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col6\" class=\"data row6 col6\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col7\" class=\"data row6 col7\" >0.69</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col8\" class=\"data row6 col8\" >0.51</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col9\" class=\"data row6 col9\" >0.3</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col10\" class=\"data row6 col10\" >0.043</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row6_col11\" class=\"data row6 col11\" >-0.019</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row7\" class=\"row_heading level0 row7\" >August</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col0\" class=\"data row7 col0\" >0.012</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col1\" class=\"data row7 col1\" >0.072</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col2\" class=\"data row7 col2\" >0.14</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col3\" class=\"data row7 col3\" >0.26</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col4\" class=\"data row7 col4\" >0.33</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col5\" class=\"data row7 col5\" >0.66</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col6\" class=\"data row7 col6\" >0.69</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col7\" class=\"data row7 col7\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col8\" class=\"data row7 col8\" >0.5</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col9\" class=\"data row7 col9\" >0.25</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col10\" class=\"data row7 col10\" >0.017</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row7_col11\" class=\"data row7 col11\" >0.0016</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row8\" class=\"row_heading level0 row8\" >September</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col0\" class=\"data row8 col0\" >0.024</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col1\" class=\"data row8 col1\" >0.08</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col2\" class=\"data row8 col2\" >0.18</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col3\" class=\"data row8 col3\" >0.38</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col4\" class=\"data row8 col4\" >0.49</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col5\" class=\"data row8 col5\" >0.55</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col6\" class=\"data row8 col6\" >0.51</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col7\" class=\"data row8 col7\" >0.5</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col8\" class=\"data row8 col8\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col9\" class=\"data row8 col9\" >0.38</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col10\" class=\"data row8 col10\" >0.15</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row8_col11\" class=\"data row8 col11\" >0.11</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row9\" class=\"row_heading level0 row9\" >October</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col0\" class=\"data row9 col0\" >0.012</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col1\" class=\"data row9 col1\" >-0.0046</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col2\" class=\"data row9 col2\" >0.086</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col3\" class=\"data row9 col3\" >0.37</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col4\" class=\"data row9 col4\" >0.53</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col5\" class=\"data row9 col5\" >0.49</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col6\" class=\"data row9 col6\" >0.3</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col7\" class=\"data row9 col7\" >0.25</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col8\" class=\"data row9 col8\" >0.38</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col9\" class=\"data row9 col9\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col10\" class=\"data row9 col10\" >0.48</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row9_col11\" class=\"data row9 col11\" >0.28</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row10\" class=\"row_heading level0 row10\" >November</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col0\" class=\"data row10 col0\" >0.067</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col1\" class=\"data row10 col1\" >-0.023</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col2\" class=\"data row10 col2\" >0.0088</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col3\" class=\"data row10 col3\" >0.17</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col4\" class=\"data row10 col4\" >0.35</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col5\" class=\"data row10 col5\" >0.23</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col6\" class=\"data row10 col6\" >0.043</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col7\" class=\"data row10 col7\" >0.017</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col8\" class=\"data row10 col8\" >0.15</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col9\" class=\"data row10 col9\" >0.48</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col10\" class=\"data row10 col10\" >1.0</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row10_col11\" class=\"data row10 col11\" >0.45</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3level0_row11\" class=\"row_heading level0 row11\" >December</th>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col1\" class=\"data row11 col1\" >0.13</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col2\" class=\"data row11 col2\" >0.14</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col3\" class=\"data row11 col3\" >0.13</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col4\" class=\"data row11 col4\" >0.25</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col5\" class=\"data row11 col5\" >0.089</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col6\" class=\"data row11 col6\" >-0.019</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col7\" class=\"data row11 col7\" >0.0016</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col8\" class=\"data row11 col8\" >0.11</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col9\" class=\"data row11 col9\" >0.28</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col10\" class=\"data row11 col10\" >0.45</td>\n",
       "                        <td id=\"T_e9a6e1a8_b36c_11eb_a6e2_d094661f38e3row11_col11\" class=\"data row11 col11\" >1.0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb773f8f490>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This code plot a colored table of all correlations\n",
    "#between the pairs of months columns my data frame\n",
    "\n",
    "monthly = rainfall.loc[:,\"January\":\"December\"]  #variable containing all the months raws\n",
    "monthly.corr(method = 'pearson').style.format(\"{:.2}\").background_gradient(cmap=plt.get_cmap('coolwarm'), axis=1) #ploting a colored table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, the correlation between months with high rainfall is larger. Also,the correlation between months with lower rainfall is larger. However, the correlation between months with high rainfall vs those with lower rainfall is smaller. This summarises my argument that India receives higher rainfall from May to September and lower rainfall from November to March. Further, India receives the highest rainfall in July and low rainfall in December."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using dictionaries, I want to use some of the months names as keys and their data in the table as values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['July', 'Dec'])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Ths code create two keys and their corresponding value and return the keys and values'''\n",
    "\n",
    "monthly_dict ={\"July\":rainfall.July, \"Dec\":rainfall.December}  #dictionary with keys july and Dec and\n",
    "                                                                #values rainfall.July and rainfall.December\n",
    "monthly_dict.keys()    #returning the keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       365.1\n",
       " 1       228.9\n",
       " 2       728.4\n",
       " 3       502.0\n",
       " 4       368.7\n",
       "         ...  \n",
       " 4111    350.2\n",
       " 4112    231.5\n",
       " 4113    296.4\n",
       " 4114    116.1\n",
       " 4115    257.5\n",
       " Name: July, Length: 4116, dtype: float64,\n",
       " 0        33.6\n",
       " 1       160.5\n",
       " 2       225.0\n",
       " 3        40.1\n",
       " 4       344.7\n",
       "         ...  \n",
       " 4111     14.9\n",
       " 4112      8.8\n",
       " 4113     26.7\n",
       " 4114     62.3\n",
       " 4115    159.0\n",
       " Name: December, Length: 4116, dtype: float64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " monthly_dict[\"July\"], monthly_dict[\"Dec\"]  #returning values inside July and Dec keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 8\n",
    "**Adding new columns and Combining data**\n",
    "\n",
    "Using the \"monthly_dict\" dictionaries above, I want to calculate the average daily rainfall in July for all the years and combined them in an array. I will also do that for the average daily rainfall in December. After that, I will add two new  columns to specific positions of my table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''This code calculate the average daily rainfall in July and December for all the years\n",
    "and add them to an empty array using for loop'''\n",
    "\n",
    "dailyJuly = []                  #creating an empty array 'dailyJuly'\n",
    "for j in monthly_dict[\"July\"]:  #creating a for loop condition for each item in the key 'July'\n",
    "    dailyJuly.append(j/30)      #adding july daily rainfall to my array\n",
    "\n",
    "dailyDec = []                   #creating an empty array 'dailyDec'\n",
    "for f in monthly_dict[\"Dec\"]:   #creating a for loop condition for each item in the key 'Dec'\n",
    "    dailyDec.append(f/31)       #adding dec daily rainfall to my array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''This code add new columns to my original rainfall table'''\n",
    "\n",
    "rainfall.insert(9, \"AverageDailyInJuly\", dailyJuly)   #inserting a new column called 'AverageDailyInJuly' to position 9.\n",
    "rainfall.insert(15, \"AverageDailyInDec\", dailyDec)    #inserting a new column called 'AverageDailyInDec' to position 15."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to return my new table if the column have been added to the right position. You will need to scroll horizontally to see all all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Subdivision</th>\n",
       "      <th>Year</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>AverageDailyInJuly</th>\n",
       "      <th>...</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "      <th>AverageDailyInDec</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>12.170000</td>\n",
       "      <td>...</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>1.083871</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>7.630000</td>\n",
       "      <td>...</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>5.177419</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>24.280000</td>\n",
       "      <td>...</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>7.258065</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>16.733333</td>\n",
       "      <td>...</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.293548</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>12.290000</td>\n",
       "      <td>...</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>11.119355</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Subdivision  Year  January  February  March  April    May  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901     49.2      87.1   29.2    2.3  528.8   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902      0.0     159.8   12.2    0.0  446.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903     12.7     144.0    0.0    1.0  235.1   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904      9.4      14.7    0.0  202.4  304.5   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905      1.3       0.0    3.3   26.9  279.5   \n",
       "\n",
       "    June   July  AverageDailyInJuly  ...  September  October  November  \\\n",
       "0  517.5  365.1           12.170000  ...      332.6    388.5     558.2   \n",
       "1  537.1  228.9            7.630000  ...      666.2    197.2     359.0   \n",
       "2  479.9  728.4           24.280000  ...      339.0    181.2     284.4   \n",
       "3  495.1  502.0           16.733333  ...      820.4    222.2     308.7   \n",
       "4  628.7  368.7           12.290000  ...      297.0    260.7      25.4   \n",
       "\n",
       "   December  AverageDailyInDec  Annual  Jan-Feb  Mar-May  Jun-Sep  Oct-Dec  \n",
       "0      33.6           1.083871  3373.2    136.3    560.3   1696.3    980.3  \n",
       "1     160.5           5.177419  3520.7    159.8    458.3   2185.9    716.7  \n",
       "2     225.0           7.258065  2957.4    156.7    236.1   1874.0    690.6  \n",
       "3      40.1           1.293548  3079.6     24.1    506.9   1977.6    571.0  \n",
       "4     344.7          11.119355  2566.7      1.3    309.7   1624.9    630.8  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returning the first five raws in a table using head()\n",
    "rainfall.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Element 9\n",
    "\n",
    "\n",
    "#### Numerical Integration\n",
    "\n",
    "\n",
    "Now I want to Integrate my data. I will be using the new 'averagedailyJuly' column with its corresponding 'July' column. This will return the total amount of rainfall received in July for all the years. Also, I will test the validity of my integration formular using averagesailyInDec and Dec columns.\n",
    "\n",
    "\n",
    "I will first find the total amount of rainfall received in July for all the years using the 'averagedailyInJuly' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1426703.7000000004"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the total amount of average daily rainfall received in July for all the years\n",
    "#Calculating the total amount of rainfall received in July by multplying total average by 30 days \n",
    "daysInJuly  = 30                              #number of days in july\n",
    "sum(rainfall['AverageDailyInJuly'].dropna()) * daysInJuly #calculating the total rainfall received in July"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to confirm if this is actually the correct sum by finding the total amount of rainfall received in July using the 'July' column directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1426703.6999999983"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(rainfall.July.dropna()) #finding total amount of rainfall received in July"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, I actually got very closer answers. \n",
    "\n",
    "#### Test Suite\n",
    "My exact answer should be between the two values when I use Integration.\n",
    "\n",
    "Now I want to use integration to find the total amount of rainfall received in July for all the years. I will be using the equation **y = mx** where **y** is july monthly rainfall(rainfall.July), **x** is average daily rainfall in july(rainfall[\"averagedailyInJuly]), and m is total the number of days in July(30). I will find the total amount of rainfall received in july, for all the years, by finding the area under the curve *y = mx*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1426703.7"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns the total amount of rainfall received in July for all the years.'''\n",
    "width = 1                           #interval of year for the years 1901 to 2015\n",
    "daysInJuly  = 30                    #number of days in july\n",
    "x = rainfall['AverageDailyInJuly']  #assigning x to 'AveragedailyInuly' column\n",
    "y = daysInJuly * x                  #creating a variable y and assigning it 30 * x \n",
    "np.sum(y)*width                     #total sum of y (rainfall.July)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was actually more accurate. I now want to use a function that returns the area under y=mx curve as the total amount of rainfall received in july."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1426703.7"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code creates a function that returns the total amount of rainfall \n",
    "received in a given month for all the years.'''\n",
    "\n",
    "width = 1                           #interval of year for the years 1901 to 2015\n",
    "daysInJuly  = 30                    #number of days in july\n",
    "x = rainfall['AverageDailyInJuly']  #assigning x to 'AveragedailyInJuly' column\n",
    "def totalsum(total, days):               #defining a function that takes in two variable\n",
    "    return days * total                  #calculating the rainfall.July\n",
    "np.sum(totalsum(x, 30)*width)                #total sum of rainfall in July total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, that is actually the total amount of rainfall received in July for all the years.\n",
    "\n",
    "\n",
    "#### Test Suite\n",
    "\n",
    "Lastly, I want to test the total amounf of rainfall received in december using two different methods to check if my integration formula is correct. The values should be very close after using different methods. Remember that the number of days in december is 31."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77482.5999999998, 77482.6)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''This code returns the total amount of rainfall received in India in December\n",
    "for all the years using two different methods'''\n",
    "\n",
    "d = rainfall['AverageDailyInDec']      #creating a variable 'd' and assigning it to all the data\n",
    "                                       #in 'AverageDailyInDec'column\n",
    "sum(rainfall.December.dropna()), np.sum(totalsum(d, 31)*width)  #returning the total amount of rainfall in Dec\n",
    "                                                                 #for all the years using two methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, this is actually the same answer. This proves that my integration method is working and it is more accurate to use. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "\n",
    "* ```In conclusion, I have noticed that India receives high rainfall from May to September and Low rainfall from November to March. I confirmed this by comparing the sum of the monthly rainfall received for all the years and the maximum amount for each month for all the years. Also, I have applied the maximum values together with annual amount of rainfall to identify 1948 as the year in which India receive low rainfall. Through all this, I have answered my project question and this confirms that India doesn't experience double-maxima rainfall yearly hence it is not located in an Equitorial Climate Zone.```"
   ]
  }
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