{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "India_Temp.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pl_jXQV8hpfD"
      },
      "source": [
        "#importing libraries \n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import plotly as px\n",
        "import plotly.graph_objects as go\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ghMSofHFhpfN",
        "outputId": "ddaf4443-e5a7-4ae6-a7ac-59f509136a37"
      },
      "source": [
        "#Loading the Dataset\n",
        "data = pd.read_csv('India_Temp_2011.csv')\n",
        "data.head() "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Annual</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Oct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1901</td>\n",
              "      <td>28.96</td>\n",
              "      <td>23.27</td>\n",
              "      <td>31.46</td>\n",
              "      <td>31.27</td>\n",
              "      <td>27.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1902</td>\n",
              "      <td>29.22</td>\n",
              "      <td>25.75</td>\n",
              "      <td>31.76</td>\n",
              "      <td>31.09</td>\n",
              "      <td>26.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1903</td>\n",
              "      <td>28.47</td>\n",
              "      <td>24.24</td>\n",
              "      <td>30.71</td>\n",
              "      <td>30.92</td>\n",
              "      <td>26.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1904</td>\n",
              "      <td>28.49</td>\n",
              "      <td>23.62</td>\n",
              "      <td>30.95</td>\n",
              "      <td>30.67</td>\n",
              "      <td>26.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1905</td>\n",
              "      <td>28.30</td>\n",
              "      <td>22.25</td>\n",
              "      <td>30.00</td>\n",
              "      <td>31.33</td>\n",
              "      <td>26.57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Annual    Jan    Mar    Jun    Oct\n",
              "0  1901   28.96  23.27  31.46  31.27  27.25\n",
              "1  1902   29.22  25.75  31.76  31.09  26.49\n",
              "2  1903   28.47  24.24  30.71  30.92  26.26\n",
              "3  1904   28.49  23.62  30.95  30.67  26.40\n",
              "4  1905   28.30  22.25  30.00  31.33  26.57"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCgAVCvUhpfR",
        "outputId": "375c3673-a1f0-45e9-8aad-3470a0e13b28"
      },
      "source": [
        "#Exploratory data analysis on dataset through different functions\n",
        "data.shape "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(111, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqLszVpYhpfT",
        "outputId": "c91355db-3c15-4031-c830-593d81338d54"
      },
      "source": [
        "data.columns.values  # column names"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Year', 'Annual', 'Jan', 'Mar', 'Jun', 'Oct'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkOsP6gAhpfV",
        "outputId": "c9190aee-ec6d-4b2b-9c13-c9e21a7622b4"
      },
      "source": [
        "data.info() # to check data-type and null values in all columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 111 entries, 0 to 110\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Year    111 non-null    int64  \n",
            " 1   Annual  111 non-null    float64\n",
            " 2   Jan     111 non-null    float64\n",
            " 3   Mar     111 non-null    float64\n",
            " 4   Jun     111 non-null    float64\n",
            " 5   Oct     111 non-null    float64\n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 5.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2UtiUdhShpfY",
        "outputId": "dc35034b-4b5a-47d3-909f-7d4616ee2f8e"
      },
      "source": [
        "data.describe() #to find out mean,median , max values of each column"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Annual</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Oct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>111.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>111.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1956.000000</td>\n",
              "      <td>29.116216</td>\n",
              "      <td>24.540901</td>\n",
              "      <td>31.444865</td>\n",
              "      <td>31.157928</td>\n",
              "      <td>27.133514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>32.186954</td>\n",
              "      <td>0.453954</td>\n",
              "      <td>0.788018</td>\n",
              "      <td>0.639510</td>\n",
              "      <td>0.383994</td>\n",
              "      <td>0.566916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1901.000000</td>\n",
              "      <td>28.110000</td>\n",
              "      <td>22.250000</td>\n",
              "      <td>29.920000</td>\n",
              "      <td>30.240000</td>\n",
              "      <td>25.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1928.500000</td>\n",
              "      <td>28.760000</td>\n",
              "      <td>24.030000</td>\n",
              "      <td>31.025000</td>\n",
              "      <td>30.905000</td>\n",
              "      <td>26.670000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1956.000000</td>\n",
              "      <td>29.070000</td>\n",
              "      <td>24.510000</td>\n",
              "      <td>31.460000</td>\n",
              "      <td>31.160000</td>\n",
              "      <td>27.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1983.500000</td>\n",
              "      <td>29.420000</td>\n",
              "      <td>25.030000</td>\n",
              "      <td>31.865000</td>\n",
              "      <td>31.370000</td>\n",
              "      <td>27.515000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>30.290000</td>\n",
              "      <td>27.440000</td>\n",
              "      <td>33.460000</td>\n",
              "      <td>32.240000</td>\n",
              "      <td>28.530000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year      Annual         Jan         Mar         Jun         Oct\n",
              "count   111.000000  111.000000  111.000000  111.000000  111.000000  111.000000\n",
              "mean   1956.000000   29.116216   24.540901   31.444865   31.157928   27.133514\n",
              "std      32.186954    0.453954    0.788018    0.639510    0.383994    0.566916\n",
              "min    1901.000000   28.110000   22.250000   29.920000   30.240000   25.740000\n",
              "25%    1928.500000   28.760000   24.030000   31.025000   30.905000   26.670000\n",
              "50%    1956.000000   29.070000   24.510000   31.460000   31.160000   27.180000\n",
              "75%    1983.500000   29.420000   25.030000   31.865000   31.370000   27.515000\n",
              "max    2011.000000   30.290000   27.440000   33.460000   32.240000   28.530000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEh4KH5Chpfa",
        "outputId": "0dbbb1c3-8b9a-4aeb-ab1e-ccf413e03400"
      },
      "source": [
        "#Understanding Target Variable\n",
        "data.Annual.unique()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([28.96, 29.22, 28.47, 28.49, 28.3 , 28.73, 28.65, 28.83, 28.39,\n",
              "       28.53, 28.62, 28.95, 28.67, 28.66, 28.94, 28.82, 28.11, 28.76,\n",
              "       28.86, 28.8 , 28.74, 28.7 , 28.59, 28.98, 29.15, 29.09, 29.03,\n",
              "       28.71, 28.85, 28.88, 29.46, 28.89, 28.97, 29.37, 28.84, 29.16,\n",
              "       29.43, 28.92, 28.63, 28.64, 29.33, 29.02, 29.31, 28.72, 29.04,\n",
              "       29.41, 29.14, 29.07, 29.61, 29.47, 29.44, 29.26, 29.27, 29.23,\n",
              "       29.63, 29.58, 29.32, 29.11, 29.28, 29.72, 29.55, 29.18, 30.18,\n",
              "       29.05, 29.7 , 29.81, 29.75, 29.99, 30.23, 29.79, 29.6 , 30.06,\n",
              "       29.84, 29.64, 30.29, 30.12, 29.82])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGMiG1rthpfc",
        "outputId": "bf95eb06-edd9-435b-f8e9-dde5313067ec"
      },
      "source": [
        "data.Annual.value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.76    4\n",
              "28.89    4\n",
              "28.66    3\n",
              "28.80    3\n",
              "28.70    3\n",
              "        ..\n",
              "30.12    1\n",
              "28.30    1\n",
              "28.95    1\n",
              "29.82    1\n",
              "28.97    1\n",
              "Name: Annual, Length: 77, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rA7SZ8lThpff",
        "outputId": "9dab7403-d9e8-4628-c306-2c1f3d2cbe72"
      },
      "source": [
        "data.Jan.unique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([23.27, 25.75, 24.24, 23.62, 22.25, 23.03, 24.23, 24.42, 23.52,\n",
              "       24.2 , 23.9 , 24.88, 24.25, 24.59, 23.22, 24.57, 24.52, 23.57,\n",
              "       23.71, 23.64, 23.91, 24.43, 23.73, 23.94, 24.73, 23.76, 24.21,\n",
              "       23.53, 23.2 , 24.55, 24.51, 24.13, 24.53, 23.41, 24.11, 23.31,\n",
              "       24.46, 24.37, 24.03, 24.02, 23.86, 25.49, 23.99, 24.49, 24.16,\n",
              "       25.17, 24.71, 24.9 , 24.4 , 23.87, 25.43, 25.48, 24.17, 24.29,\n",
              "       24.67, 25.54, 25.31, 23.68, 24.99, 25.19, 25.35, 24.34, 24.12,\n",
              "       24.61, 25.15, 24.36, 25.21, 24.62, 25.29, 24.64, 25.07, 25.39,\n",
              "       24.74, 24.6 , 25.09, 25.68, 26.3 , 24.97, 25.11, 24.82, 25.88,\n",
              "       25.37, 25.32, 24.96, 27.44, 25.73, 24.72, 26.5 , 25.95, 25.33])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nm-AyMXhpfj",
        "outputId": "a059e4f9-9ef4-4fe7-cc84-2de528251f64"
      },
      "source": [
        "data.Jan .value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.99    4\n",
              "24.51    3\n",
              "23.62    3\n",
              "25.49    2\n",
              "24.90    2\n",
              "        ..\n",
              "24.59    1\n",
              "23.91    1\n",
              "23.90    1\n",
              "25.35    1\n",
              "25.75    1\n",
              "Name: Jan, Length: 90, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmmX10DEhpfm",
        "outputId": "c9a6f986-d1ea-42b1-946d-f0afc554859d"
      },
      "source": [
        "data.Mar.unique()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([31.46, 31.76, 30.71, 30.95, 30.  , 31.11, 29.92, 31.43, 31.02,\n",
              "       31.14, 30.7 , 31.1 , 30.89, 30.73, 31.06, 31.88, 30.06, 30.68,\n",
              "       31.17, 30.4 , 32.05, 31.21, 31.4 , 31.44, 31.47, 30.21, 30.72,\n",
              "       31.51, 31.72, 30.94, 31.71, 30.42, 31.28, 31.15, 30.84, 31.74,\n",
              "       30.76, 30.66, 32.12, 31.8 , 30.8 , 31.03, 31.19, 31.5 , 31.78,\n",
              "       31.27, 30.67, 31.13, 32.19, 31.89, 30.88, 31.53, 30.41, 31.73,\n",
              "       31.69, 31.16, 31.31, 31.04, 31.24, 32.02, 32.03, 31.58, 31.49,\n",
              "       31.92, 31.62, 31.45, 31.65, 31.57, 31.7 , 32.2 , 31.64, 30.79,\n",
              "       32.51, 31.37, 31.75, 31.35, 31.32, 31.61, 31.85, 32.4 , 32.07,\n",
              "       31.26, 32.45, 32.22, 32.61, 33.06, 32.69, 31.81, 32.08, 32.32,\n",
              "       32.11, 32.57, 33.46])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "enHGGYUYhpfo",
        "outputId": "59e881de-a3c9-4961-f248-4dd8abdcdd95"
      },
      "source": [
        "data.Mar .value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.84    3\n",
              "31.17    3\n",
              "31.89    3\n",
              "31.69    2\n",
              "32.19    2\n",
              "        ..\n",
              "31.14    1\n",
              "30.66    1\n",
              "31.32    1\n",
              "30.40    1\n",
              "30.00    1\n",
              "Name: Mar, Length: 93, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgdEYNrihpfp",
        "outputId": "a4b66c57-0f81-46e0-c985-50601ae8cf28"
      },
      "source": [
        "data.Jun.unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([31.27, 31.09, 30.92, 30.67, 31.33, 30.86, 30.8 , 30.72, 30.33,\n",
              "       30.48, 31.14, 31.15, 30.84, 31.51, 30.52, 30.24, 31.11, 31.08,\n",
              "       30.81, 30.9 , 30.98, 30.96, 31.03, 31.16, 31.25, 30.41, 31.22,\n",
              "       30.85, 30.68, 30.59, 31.06, 30.93, 31.37, 30.99, 30.83, 31.48,\n",
              "       31.23, 31.2 , 30.88, 31.13, 31.12, 30.25, 31.07, 31.29, 30.75,\n",
              "       31.28, 30.82, 31.24, 31.32, 31.54, 31.55, 30.91, 31.66, 31.39,\n",
              "       30.66, 31.87, 31.36, 31.34, 31.44, 32.24, 31.18, 31.45, 31.47,\n",
              "       31.31, 32.01, 31.19, 31.77, 31.75, 31.61, 32.02, 31.72, 31.42,\n",
              "       31.84, 31.4 , 31.43])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUt1RlsIhpfr",
        "outputId": "d24ffc78-a28b-4b0b-bb50-df9802a3ff52"
      },
      "source": [
        "data.Jun .value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31.55    4\n",
              "31.28    4\n",
              "31.11    4\n",
              "31.25    4\n",
              "30.80    3\n",
              "        ..\n",
              "31.87    1\n",
              "30.33    1\n",
              "30.66    1\n",
              "30.91    1\n",
              "32.02    1\n",
              "Name: Jun, Length: 75, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qyFwkLvhpft",
        "outputId": "31438799-d072-4f2b-d8e2-5d0dd262fe97"
      },
      "source": [
        "data.Oct .unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([27.25, 26.49, 26.26, 26.4 , 26.57, 27.29, 27.36, 26.64, 26.88,\n",
              "       26.2 , 26.31, 26.42, 27.18, 26.32, 25.74, 26.77, 26.6 , 27.45,\n",
              "       26.43, 26.38, 26.59, 26.61, 26.72, 26.73, 26.27, 26.9 , 26.97,\n",
              "       26.92, 26.94, 26.69, 26.71, 27.05, 27.24, 27.62, 27.23, 27.33,\n",
              "       27.16, 26.82, 27.3 , 26.7 , 26.79, 26.58, 27.77, 27.26, 27.56,\n",
              "       26.46, 26.37, 27.01, 27.03, 26.3 , 26.65, 27.  , 27.2 , 27.19,\n",
              "       27.71, 27.5 , 27.17, 27.21, 26.99, 27.76, 27.59, 27.64, 27.67,\n",
              "       27.51, 27.46, 27.35, 27.4 , 27.82, 27.57, 27.49, 27.74, 27.52,\n",
              "       28.52, 27.83, 27.95, 28.53, 28.13, 28.36, 27.7 , 27.65, 27.78,\n",
              "       28.03, 28.29, 27.96, 28.23])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rd6LKA6Rhpfv",
        "outputId": "7527a43e-fe9e-41f1-c4bb-23808a9b0d22"
      },
      "source": [
        "data.Oct.value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.24    3\n",
              "27.50    3\n",
              "27.26    3\n",
              "27.62    2\n",
              "26.97    2\n",
              "        ..\n",
              "26.64    1\n",
              "27.16    1\n",
              "26.32    1\n",
              "26.60    1\n",
              "27.25    1\n",
              "Name: Oct, Length: 85, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0LNnSgshpfx",
        "outputId": "83a36d8f-6d05-4cb3-ef9c-f922f63ac24f"
      },
      "source": [
        "#Data Visualization starts here\n",
        "#To check missing values\n",
        "\n",
        "sns.heatmap(data.isnull(),cbar=False,yticklabels=False,cmap = 'viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKI0wfn1hpfy",
        "outputId": "e3803cb9-239b-45cb-a8cb-dc5c52d4625d"
      },
      "source": [
        "#To check correlation\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "sns.heatmap(data.corr(),cmap='Blues',annot=False) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYOyAllLhpf0",
        "outputId": "3c21909b-1c31-41b0-a04d-d0264296406d"
      },
      "source": [
        "#Columns correlation matrix\n",
        "k = 12 #number of variables for heatmap\n",
        "cols = data.corr().nlargest(k, 'Annual')['Annual'].index\n",
        "cm = data[cols].corr()\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(cm, annot=True, cmap = 'viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVYX0z0fhpf4",
        "outputId": "8a16018f-fe2a-45af-963d-e7678a0dfb8a"
      },
      "source": [
        "#To check Outliers\n",
        "\n",
        "l = data.columns.values\n",
        "number_of_columns=6\n",
        "number_of_columns=int(np.ceil(np.sqrt(len(\"Annual,Jan,Mar,Jun,Oct\")))) # convert float value into int \n",
        "\n",
        "number_of_rows = len(l)-1/number_of_columns\n",
        "plt.figure(figsize=(number_of_columns,5*number_of_rows))\n",
        "for i in range(0,len(l)):\n",
        "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
        "    sns.set_style('whitegrid')\n",
        "    sns.boxplot(data[l[i]],color='green',orient='v')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x8MMPx4c+9KGYO3du9O/fv/k+69atiz/90z+NiIgxY8bE008/3eb69rjtttviwgsvbF5rR9bXUfvz2LS1j/2d89prr93vx6InKLbnxsbG+Ju/+ZtYs2ZNScZr7wxb0+7vnePHj4+ysv/f6eHDh8d//dd/RUTEI488Etu2bYuGhobmX08iIioqKqKhoaHF7RUVFfHmm292bIftqKioiP79+0dDQ0PMmDEjZs6cGYVCIXr16tVirq5eV6kU21+xc++osrKymD17dixcuDDGjx9f9KwOpGL72XOxPvnkk7F8+fL4yle+0uJrDjvssJg2bVp873vfi2uvvTauvPLK2LlzZ6vjHX/88XHVVVfFnXfeGcOHD49//Md/bDFeQ0NDiyD36dOnzfEiIjZv3hy/+MUvYsqUKfvsqa31dUZnH5u29rG/c76bx6Kn2HvPw4cPjxNOOKFk47V3hq3p9At/S5Ysidtvvz2++tWvxvvf//543/veF/3794/Gxsbm+zQ2NsYRRxzR4vbGxsYYMGBAZ6dr1WuvvRZf/vKX49xzz42JEye2eJ1sz1wZ6yqVvfdX7Nw744Ybboif/vSnMX/+/Ghqamq+vav2v/d+IiLuv//+uOaaa2LZsmX7vAY/cuTI+PznPx+9evWKkSNHxsCBA2PTpk2tjnfWWWfFxz/+8YiIOOuss+LZZ59tMd7e18KuXbtaPLEotr4HHnggzjnnnBbPiDu6vs7ozGPT3j72Z8633nrrXT0WPcXeey71eG2dYWs6HeDHHnsslixZEsuWLYv6+vo49dRT4/jjj49169ZFU1NTvPnmm/Hiiy/GRz/60Rg7dmw89thjERGxZs2aOOmkkzo7XVF1dXVx8cUXx6xZs+ILX/hCREQcd9xx8fjjjzfPdfLJJ3f5ukql2P6KnXtHrF69Om6//faIiDj88MOjV69e8fGPf3yfszqQiu3nRz/6USxfvjyqq6tj+PDh+3zNypUr4/rrr4+I3S97NTQ0xKBBg1odb9q0afHUU09FRMQvfvGL+NjHPtZivLFjxzb/url+/fr46Ec/2ub69oxz2mmnFd1TW+vrqP15bNrax/7O+eCDD+73Y9ETFNtzsR+q72a89s6wNR16L4jf//73cfnll8c999wT//7v/x633nprHH744fHJT34yvvGNb0RExD333BM//OEPo1AoxNe+9rUYP358bNu2LWbPnh2bNm2Kvn37xi233FKSB27RokXxk5/8JEaNGtV827x582LRokWxY8eOGDVqVCxatCj69OnTpesqlWL7u+iii4qee3veeuut+OY3vxl1dXWxc+fO+Ou//us4+uijY/78+fuc1YGy937efvvteP755+Ooo45qfob3iU98ImbMmBFXXXVVzJw5MyorK+Ob3/xmvPrqq9GrV6+48sorm18DL3Y+M2fOjJtuuin69u0blZWVsXDhwujfv3/zeEOGDIkFCxbEb37zmygUCrFkyZI4+uijWx3vu9/9bpx33nlx1113tXgW2pH1dVRnHpuO7GN/55w7d24MHTp0vx6LnqDYns8888yI2P0af2VlZaf+CqIzZ9geb8YDkMQ/xABIIsAASQQYIIkAAyQRYIAkaQGeMWNGLFu2rPnjxsbGGD9+fNTW1mYtKdWyZcvi05/+dIs/xD8QHn/88Q7/CVtPMHXq1HjxxRezl9FtPP7443HsscfG/fff3+L2iRMnxpw5c5JW1f10l+smLcALFiyIu+66K1544YWI2P2vSi644IIYPXp01pJS/fjHP44JEybEv/3bv2UvhR5u1KhRcd999zV//Nxzz3Xqn67TdTr/bxhL5Mgjj4z58+fH1VdfHZdffnls2LAhLrvssrjkkkuiqakpysvLY+HChTF06NC45ZZb4umnn47GxsY4+uij47rrrovbbrstfvnLX8Zbb70Vixcv7tQfo3c3jz/+eIwYMSL+8i//MmbNmhVTpkyJqVOnxujRo+P555+PhoaGuPXWW6NQKMQVV1wRQ4YMiQ0bNsQf/dEfxbXXXtvij8lffPHFWLBgQVRXV8cDDzwQd955Z/M8t956a+IuD5wtW7bEpZdeGk1NTVFfXx9f//rX48wzz4yJEyfGKaecEs8991z06tUrli5d2uK9QQ5Wo0ePjpdffjm2bt0aAwYMiHvvvTcmTpwYr732Wixfvjx+9rOfxc6dO+OII46I2267Le67775YtWpV7Nq1K2bMmBF//Md/nL2FLvEP//APccopp+zzfdOV103qa8Cnn356jBw5MubMmRPXX3993HDDDTF16tSorq6OadOmxc033xwNDQ0xYMCA+P73vx933313rF+/Pl5//fWI2P2T/u677+7R8Y2IWLFiRZx//vkxatSo6NevX/zqV7+KiIjjjz8+7rjjjjj11FObnxm//PLLsXjx4lixYkWsWbOmzX+T//LLL8eyZcuiuro6Ro4cGT//+c+7ZD9drba2Ni666KL4/ve/H/Pnz2/+odPY2Bif+9znYvny5TF48OD9euernuqss86KBx98MAqFQjz11FNx4oknxq5du6K+vj7uuOOO+MEPfhA7d+6MX//61xERMWDAgLjrrrsOmfi2pSuvm7RnwHtMmjQptm/fHh/4wAfiN7/5Tdx+++3xT//0T1EoFKJv375RXl4eb7zxRlx++eXxnve8J956663YsWNHROx+k5Ce7n//939jzZo18cYbb0R1dXU0NDTE8uXLI2L3+1tERAwZMqT5zcFHjBjR/G5YgwYNavM14/e///0xe/bsqKioiJdeeqlHvYVgWxobG6Nfv37Rt2/fiIg4+eSTY9myZbFy5cro1atXi3fq2nOGQ4cOPeCvr3cnEydOjAULFsTw4cOb30+id+/e0bdv3+bvpf/5n/9pPquD4XupPXtfN3veda6Yrrpu0gP8TqNGjYqLL744xo4dGy+++GI88cQTsWbNmnjttdfi7//+7+ONN95o/qkeEQf0/+LQVe69994477zzYvbs2RERsW3btjjjjDNafbezYhdNeXl58zPhZ555JiIi3nzzzfjOd74Tjz76aETsfi+Jg+Vfnc+ZMye+9KUvxSmnnBKbN2+OJUuWxLRp02LcuHGxatWq+Nd//dfm+7b1TXYwGz58eLz11ltRXV3d/BJfQ0NDPPTQQ7FixYrYtm1bTJky5aD6XmrP3tfNqaeeus/3zR5ddd10qwDPnj07FixYEE1NTbF9+/aYN29eDBs2LJYuXRp/8Rd/Ef369Yvhw4fHxo0bs5daMitWrIgbb7yx+ePDDz88/vzP/zxWrlzZ4TE++9nPxsyZM+OJJ55ofkvG/v37x9ixY2Py5Mnxnve8JwYMGBAbN26MYcOGlXwPXe2iiy6KRYsWRXl5eUyePDmGDh0aixcvjttvvz2GDh0aW7ZsyV5itzBhwoT40Y9+FCNHjowNGzZEnz594vDDD48pU6ZEv379YtCgQQfV91J79r5uzj777H2+b7qaN+MBSHLw/94B0E0JMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJSvJuaOvXr4/y8vJ9bt/zf7boSh2ds6mp6aB5f1ygZypJgMvLy6Oqqmqf22tqaorefiB1dM6ampouWA1A67wEAZBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSFKWOfnixYujtra21c/X1dVFRERlZWWL20ePHh3z5s07oGsDONBSA1xbWxtrn1wbOwfsLPr5sq27l1dbX7vPbQA9XXrNdg7YGfWfqi/6uYFrB0ZEtPj8ntsAejqvAQMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQpGQBXr16daxevbpUwx0QPWGNwKGjrFQDrVq1KiIiJk2aVKohS64nrBE4dHgJAiCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQpKxUA9XV1cWmTZti6tSpzbc1NjZGRUVFq19TU1MTvXd17mdA76beUVNT02Ked2przpqamhg0aFCn5gM4UDwDBkhSsmfAlZWVUVlZGdXV1c231dTURFVVVatfM3Xq1Pj5Cz/v1Dy7yndF1TFVLeZ5p7bmbO1ZM0AGz4ABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEnKSjXQeeedV6qhDpiesEbg0FGyAE+aNKlUQx0wPWGNwKHDSxAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCRl6QvYWhYD1w5s9XMR0eLze24D6OlSazZ69Og2P19XVxcREZWVlZ36OoCeIDXA8+bNy5weIJXXgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCBJr0KhUHi3g6xfvz7Ky8tLsZ4u09TUFGPGjMleBnAIK0mAAeg8L0EAJBFggCQCDJBEgAGSCDBAkrJSDLJjx46YO3duvPLKK/GHP/whLrvsshgzZkxcffXVsXXr1nj77bfjxhtvjBEjRpRiujbnPeqoo+Kaa66JPn36xIc//OFYvHhx9O7t5wzQ/ZQkwPfee28MHDgwbrrpptiyZUtMnjw5PvWpT8XEiRNjwoQJsXbt2njppZdKHuBi837sYx+Lr3/96zFu3Li44oor4tFHH43TTz+9pPMClEJJAnz22WfH+PHjmz/u06dPPPnkk3HsscfGV77ylfjgBz8Y8+bNK8VU7c5bVVUV9fX1USgUorGxMcrKSrJFgJIrye/mFRUV0b9//2hoaIgZM2bEzJkz45VXXokBAwbEHXfcEUOHDo3vfve7pZiq3Xn3vOzw2c9+NjZv3hyf/OQnSz4vQCmU7MXR1157Lb785S/HueeeGxMnToyBAwc2/+p/+umnx9NPP12qqdqcd/HixXHnnXfGAw88EJMmTYrrr7/+gMwL8G6VJMB1dXVx8cUXx6xZs+ILX/hCREScdNJJ8dhjj0VExBNPPBHHHHNMKaZqd973vve90b9//4iIGDx4cGzdurXk8wKUQkneC2LRokXxk5/8JEaNGtV82/XXXx9XX311bNu2Lfr37x+33HJLvPe97323U7U779/+7d/GzTffHGVlZdG3b99YuHBhDBs2rKTzApSCN+MBSOIPZAGSCDBAEgEGSCLAAEkEGCBJt/h3uhs2bIgbb7wx6uvrY8eOHTF69Oi48sorm/+e951effXVqK2t9f4OQI+X/gx4+/btMX369Ljkkkuiuro67r777jjhhBPiiiuuKHr/tWvXxpNPPtnFqwQovfQAP/roo/GJT3wiTjjhhObbJk+eHFu2bImXXnopLrzwwrjgggvir/7qr6Kuri6WLVsW9913Xzz88MOJqwZ499JfgtiwYUPRt6kcNmxYnH/++fHtb387TjvttLj//vujtrY2vvrVr8ZLL70UZ5xxRsJqAUon/RnwBz7wgfj973+/z+0vv/xyNDU1xYknnhgRERMmTIhPf/rTXb08gAMmPcBnnHFG/Od//mc89dRTzbetWLEijjzyyBg3blz8+te/jojdb75eXV0dvXv3jl27dmUtF6BkusV7Qfzud7+LJUuWRH19fbz99ttx7LHHxlVXXRVbtmyJv/u7v4tdu3bFYYcdFjfddFO8+uqr8Y1vfCNmzJgRn/vc57KXDrDfukWAAQ5F6S9BAByqBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSPL/AOUBxIcM4cimAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x2088 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jropVLHshpf6",
        "outputId": "a548b155-c724-4ff1-c530-4cd436af89a9"
      },
      "source": [
        "#To check distribution-Skewness\n",
        "\n",
        "plt.figure(figsize=(2*number_of_columns,5*number_of_rows))\n",
        "for i in range(0,len(l)):\n",
        "    plt.subplot(number_of_rows + 1,number_of_columns,i+1)\n",
        "    sns.distplot(data[l[i]],kde=True) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x2088 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPe0wPUKhpf8"
      },
      "source": [
        "#prepare some data- annual temp\n",
        "annual=np.array ([28.96,29.22,28.47,28.49,28.3,28.73,28.65,28.83,28.39,28.53,28.62,28.95,28.67,28.66,28.94,28.82,28.11,28.66,\n",
        "             28.66,28.76,28.86,28.8,28.74,28.8,28.67,28.7,28.59,28.98,28.76,28.65,29.15,29.09,28.49,29.03,28.76,28.71,\n",
        "             28.7,28.7,28.85,28.88,29.46,28.98,28.8,28.89,28.97,29.37,28.84,28.73,28.89,28.47,29.09,29.16,29.43,28.92,\n",
        "            28.76,28.63,28.64,29.33,29.02,29.31,28.72,28.89,29.04,29.09,29.16,29.41,29.14,29.07,29.61,29.47,29.15,29.31,\n",
        "             29.44,29.26,28.89,29.27,29.41,29.23,29.63,29.58,29.32,29.11,29.11,29.28,29.61,29.33,29.72,29.55,29.18,29.14,\n",
        "             29.32,29.23,29.55,29.46,30.18,29.58,29.05,29.7,29.81,29.75,29.99,30.23,29.75,29.79,29.6,30.06,29.84,29.64,30.29,\n",
        "             30.12,29.82])\n",
        "\n",
        "\n",
        "x= np.array ([1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,\n",
        "               1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,\n",
        "               1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,\n",
        "               1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,\n",
        "               1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,\n",
        "                2011])\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "J6ZC7U0shpf-",
        "outputId": "ba2b2f88-13b5-4cc6-a8da-b6af5626f481"
      },
      "source": [
        "#Plotting annual temp readings- Line plot\n",
        "\n",
        "fig = go.Figure(data=go.Scatter(x=x, y=annual))\n",
        "# Edit the layout\n",
        "fig.update_layout(title='Annual Temperature(1901-2011)',\n",
        "                   xaxis_title='Years',\n",
        "                   yaxis_title='Temperature (degrees C)')\n",
        "fig.show()\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"a8d8c5ac-11c6-4c03-946b-f7ddf799f8be\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
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              "                        [{\"type\": \"scatter\", \"x\": [1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011], \"y\": [28.96, 29.22, 28.47, 28.49, 28.3, 28.73, 28.65, 28.83, 28.39, 28.53, 28.62, 28.95, 28.67, 28.66, 28.94, 28.82, 28.11, 28.66, 28.66, 28.76, 28.86, 28.8, 28.74, 28.8, 28.67, 28.7, 28.59, 28.98, 28.76, 28.65, 29.15, 29.09, 28.49, 29.03, 28.76, 28.71, 28.7, 28.7, 28.85, 28.88, 29.46, 28.98, 28.8, 28.89, 28.97, 29.37, 28.84, 28.73, 28.89, 28.47, 29.09, 29.16, 29.43, 28.92, 28.76, 28.63, 28.64, 29.33, 29.02, 29.31, 28.72, 28.89, 29.04, 29.09, 29.16, 29.41, 29.14, 29.07, 29.61, 29.47, 29.15, 29.31, 29.44, 29.26, 28.89, 29.27, 29.41, 29.23, 29.63, 29.58, 29.32, 29.11, 29.11, 29.28, 29.61, 29.33, 29.72, 29.55, 29.18, 29.14, 29.32, 29.23, 29.55, 29.46, 30.18, 29.58, 29.05, 29.7, 29.81, 29.75, 29.99, 30.23, 29.75, 29.79, 29.6, 30.06, 29.84, 29.64, 30.29, 30.12, 29.82]}],\n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Annual Temperature(1901-2011)\"}, \"xaxis\": {\"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"title\": {\"text\": \"Temperature (degrees C)\"}}},\n",
              "                        {\"responsive\": true}\n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mP1dX5vAjLK7",
        "outputId": "f2a255e1-4b05-4481-d5d0-c36748582489"
      },
      "source": [
        "pip install chart_studio"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting chart_studio\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/ce/330794a6b6ca4b9182c38fc69dd2a9cbff60fd49421cb8648ee5fee352dc/chart_studio-1.1.0-py3-none-any.whl (64kB)\n",
            "\r\u001b[K     |█████                           | 10kB 13.8MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 20kB 12.7MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 30kB 7.1MB/s eta 0:00:01\r\u001b[K     |████████████████████▍           | 40kB 6.8MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 51kB 3.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 61kB 4.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 3.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.3.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from chart_studio) (1.15.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from chart_studio) (4.4.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Installing collected packages: chart-studio\n",
            "Successfully installed chart-studio-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_2yVucVhpgZ"
      },
      "source": [
        "#Hosting visualization using chartstudio\n",
        "import chart_studio\n",
        "import chart_studio.plotly as py"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dxo-tJ3Thpgb"
      },
      "source": [
        "chart_studio.tools.set_credentials_file(username='TROPICSU', api_key='sqFXkXIxv94tXI4BCvUI')\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "n5QWuVFnhpgc",
        "outputId": "a1bf9e24-ee3c-4f3d-cc4a-ce845c8ed38d"
      },
      "source": [
        "#Push your visualization to your account using this code\n",
        "py.plot(fig, filename = 'Annual_temp_plot', auto_open=True) "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://plotly.com/~TROPICSU/2/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKAFISCMhpge"
      },
      "source": [
        "#create HTML for big datasets\n",
        "import plotly.io as pio\n",
        "pio.write_html(fig, file='index.html', auto_open=True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcmGFc6Uhpg2"
      },
      "source": [
        "#prepare some data- JUN TO SEPT\n",
        "j =([31.27,31.09,30.92,30.67,31.33,30.86,30.8,30.72,30.33,30.48,31.14,31.15,30.92,30.84,31.51,30.52,30.24,31.11,30.8,31.08,\n",
        "     30.81,30.9,30.98,30.96,30.67,31.14,30.8,31.14,31.03,30.98,31.16,31.25,30.41,31.22,30.85,30.68,31.22,30.59,31.06,30.93,\n",
        "    31.37,30.99,30.83,31.25,31.48,31.23,31.2,30.84,30.88,30.48,31.13,31.11,31.12,30.9,30.72,30.25,31.07,31.23,30.99,31.29,\n",
        "    30.75,31.08,31.28,30.82,31.37,31.24,31.32,31.54,31.55,31.16,30.91,31.66,31.28,31.39,30.66,31.09,31.22,31.11,31.87,31.36,\n",
        "    31.34,31.55,31.48,31.09,31.28,31.44,32.24,31.34,31.11,31.18,31.45,31.28,31.47,31.31,32.01,31.19,31.77,31.75,31.55,31.25,\n",
        "     31.61,32.02,31.72,31.42,31.84,31.39,31.4,31.25,32.24,31.43,31.55])\n",
        "\n",
        "x= np.array ([1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,\n",
        "               1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,\n",
        "               1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,\n",
        "               1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,\n",
        "               1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,\n",
        "                2011])\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "owk1MHXThpg4",
        "outputId": "6c19b942-d540-47fe-86cc-ca9e95e9698f"
      },
      "source": [
        "#Plotting JUN-SEPT readings\n",
        "\n",
        "fig = go.Figure(data=go.Scatter(x=x, y=j))\n",
        "\n",
        "# Edit the layout\n",
        "fig.update_layout(title='June to September Temperature',\n",
        "                   xaxis_title='Years',\n",
        "                   yaxis_title='Temperature (degrees C)')\n",
        "fig.show()\n",
        "\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<head><meta charset=\"utf-8\" /></head>\n",
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              "                        [{\"type\": \"scatter\", \"x\": [1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011], \"y\": [31.27, 31.09, 30.92, 30.67, 31.33, 30.86, 30.8, 30.72, 30.33, 30.48, 31.14, 31.15, 30.92, 30.84, 31.51, 30.52, 30.24, 31.11, 30.8, 31.08, 30.81, 30.9, 30.98, 30.96, 30.67, 31.14, 30.8, 31.14, 31.03, 30.98, 31.16, 31.25, 30.41, 31.22, 30.85, 30.68, 31.22, 30.59, 31.06, 30.93, 31.37, 30.99, 30.83, 31.25, 31.48, 31.23, 31.2, 30.84, 30.88, 30.48, 31.13, 31.11, 31.12, 30.9, 30.72, 30.25, 31.07, 31.23, 30.99, 31.29, 30.75, 31.08, 31.28, 30.82, 31.37, 31.24, 31.32, 31.54, 31.55, 31.16, 30.91, 31.66, 31.28, 31.39, 30.66, 31.09, 31.22, 31.11, 31.87, 31.36, 31.34, 31.55, 31.48, 31.09, 31.28, 31.44, 32.24, 31.34, 31.11, 31.18, 31.45, 31.28, 31.47, 31.31, 32.01, 31.19, 31.77, 31.75, 31.55, 31.25, 31.61, 32.02, 31.72, 31.42, 31.84, 31.39, 31.4, 31.25, 32.24, 31.43, 31.55]}],\n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"June to September Temperature\"}, \"xaxis\": {\"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"title\": {\"text\": \"Temperature (degrees C)\"}}},\n",
              "                        {\"responsive\": true}\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV_7D-tYhpg5"
      },
      "source": [
        "chart_studio.tools.set_credentials_file(username='TROPICSU', api_key='sqFXkXIxv94tXI4BCvUI')\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "C-1N-iaohpg6",
        "outputId": "f42b2f8d-4cce-4d1f-ad1f-8fd1db20d747"
      },
      "source": [
        "#Push your visualization to your account using this code\n",
        "py.plot(fig, filename = 'June-Sept plot', auto_open=True) "
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://plotly.com/~TROPICSU/6/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COTc3yk-hpg7"
      },
      "source": [
        "#prepare some data -OCT-DEC\n",
        "\n",
        "o=([27.25,26.49,26.26,26.4,26.57,27.29,27.36,26.64,26.88,26.2,26.31,26.57,26.42,26.4,27.18,26.32,25.74,26.77,26.6,27.45,\n",
        "   26.43,26.38,26.43,26.49,26.59,26.61,26.72,26.73,26.27,26.9,26.97,27.18,26.92,26.94,26.88,26.69,26.26,26.71,27.05,27.24,\n",
        "   27.62,27.23,27.33,27.16,26.82,27.3,26.7,26.77,26.79,26.58,27.77,27.26,27.56,26.46,26.59,26.37,26.82,27.01,27.03,27.36,\n",
        "    26.3,26.65,27,27.2,27.62,27.26,26.9,27.19,27.71,27.5,27.17,27.21,26.97,27.03,26.99,27.76,27.59,27.64,27.67,27.51,27.24,\n",
        "   27.26,27.24,27.46,27.35,27.4,27.82,27.76,27.57,27.5,27.4,27.49,27.74,27.52,28.52,27.29,26.65,27.83,27.95,28.53,28.13,28.36,\n",
        "   27.7,27.65,27.5,27.78,28.03,28.29,27.96,27.78,28.23])\n",
        "\n",
        "x= np.array ([1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,\n",
        "               1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,\n",
        "               1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,\n",
        "               1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,\n",
        "               1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,\n",
        "                2011])\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "KpdgMQjnhphI",
        "outputId": "3e64ff65-06f3-4ad5-bd33-adbbcf88db1d"
      },
      "source": [
        "#Plotting OCT-DEC readings\n",
        "\n",
        "fig = go.Figure(data=go.Scatter(x=x, y=o))\n",
        "# Edit the layout\n",
        "fig.update_layout(title='October to December Temperature',\n",
        "                   xaxis_title='Years',\n",
        "                   yaxis_title='Temperature (degrees C)')\n",
        "fig.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"October to December Temperature\"}, \"xaxis\": {\"title\": {\"text\": \"Years\"}}, \"yaxis\": {\"title\": {\"text\": \"Temperature (degrees C)\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('8e289090-ece9-468c-918a-62466967da20');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dE8rDZuPhphK"
      },
      "source": [
        "chart_studio.tools.set_credentials_file(username='TROPICSU', api_key='sqFXkXIxv94tXI4BCvUI')\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MMKM_Ho5hphL",
        "outputId": "fe29a89b-d780-4410-8f7e-22185abf5290"
      },
      "source": [
        "#Push your visualization to your account using this code\n",
        "py.plot(fig, filename = 'Oct-Dec plot', auto_open=True) "
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://plotly.com/~TROPICSU/8/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlCk0ZHAklqA"
      },
      "source": [
        "#End of code"
      ]
    }
  ]
}