Bangladesh_Economic_Indicators_Analysis.ipynb

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      "name": "Bangladesh Economic Indicators Analysis",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/Data-Analysis/blob/master/Bangladesh_Economic_Indicators_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7zx2Gi1hIBG"
      },
      "source": [
        "<h1><u><center><font size=10 color='indigo'>BANGLADESH ECONOMIC INDICATORS ANALYSIS</font></center></u></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVpf1FoHhdB7"
      },
      "source": [
        "# 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FSMbuOKg3fZ",
        "outputId": "bb300f15-15c6-4187-ceda-0eb0985732d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "print(\"All packages have been installed successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All packages have been installed successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TFFKRG7h19g"
      },
      "source": [
        "# 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPRgWXmqhz0z",
        "outputId": "ab606fb2-c00c-4ff4-901b-6127223e166a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df = pd.read_csv('/content/BD_economic_indicators.csv')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>GDP</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>GDP growth</th>\n",
              "      <th>Inflation rate</th>\n",
              "      <th>Unemployed rate</th>\n",
              "      <th>Government debt</th>\n",
              "      <th>Total Investment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>41.2</td>\n",
              "      <td>500</td>\n",
              "      <td>3.10%</td>\n",
              "      <td>15.40%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.44%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>47.4</td>\n",
              "      <td>560</td>\n",
              "      <td>5.60%</td>\n",
              "      <td>14.50%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.16%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>52.0</td>\n",
              "      <td>597</td>\n",
              "      <td>3.20%</td>\n",
              "      <td>12.90%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.36%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>56.5</td>\n",
              "      <td>633</td>\n",
              "      <td>4.60%</td>\n",
              "      <td>9.50%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.56%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>61.0</td>\n",
              "      <td>664</td>\n",
              "      <td>4.20%</td>\n",
              "      <td>10.40%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.48%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  GDP  GDP per capita  ... Unemployed rate Government debt Total Investment\n",
              "0  1980  41.2            500  ...             NaN             NaN           14.44%\n",
              "1  1981  47.4            560  ...             NaN             NaN           17.16%\n",
              "2  1982  52.0            597  ...             NaN             NaN           17.36%\n",
              "3  1983  56.5            633  ...             NaN             NaN           16.56%\n",
              "4  1984  61.0            664  ...             NaN             NaN           16.48%\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "        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>GDP</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>GDP growth</th>\n",
              "      <th>Inflation rate</th>\n",
              "      <th>Unemployed rate</th>\n",
              "      <th>Government debt</th>\n",
              "      <th>Total Investment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2015</td>\n",
              "      <td>581.6</td>\n",
              "      <td>3,638</td>\n",
              "      <td>6.80%</td>\n",
              "      <td>6.20%</td>\n",
              "      <td>4.42%</td>\n",
              "      <td>33.60%</td>\n",
              "      <td>28.89%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2016</td>\n",
              "      <td>629.9</td>\n",
              "      <td>3,900</td>\n",
              "      <td>7.20%</td>\n",
              "      <td>5.70%</td>\n",
              "      <td>4.35%</td>\n",
              "      <td>33.30%</td>\n",
              "      <td>29.65%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2017</td>\n",
              "      <td>690.5</td>\n",
              "      <td>4,231</td>\n",
              "      <td>7.60%</td>\n",
              "      <td>5.60%</td>\n",
              "      <td>4.37%</td>\n",
              "      <td>32.60%</td>\n",
              "      <td>30.51%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2018</td>\n",
              "      <td>753.4</td>\n",
              "      <td>4,630</td>\n",
              "      <td>7.90%</td>\n",
              "      <td>5.60%</td>\n",
              "      <td>4.30%</td>\n",
              "      <td>34.00%</td>\n",
              "      <td>31.23%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2019</td>\n",
              "      <td>817.6</td>\n",
              "      <td>5,028</td>\n",
              "      <td>8.10%</td>\n",
              "      <td>5.50%</td>\n",
              "      <td>4.29%</td>\n",
              "      <td>33.50%</td>\n",
              "      <td>31.60%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year   GDP  GDP per capita  ... Unemployed rate Government debt Total Investment\n",
              "35  2015  581.6          3,638  ...           4.42%          33.60%           28.89%\n",
              "36  2016  629.9          3,900  ...           4.35%          33.30%           29.65%\n",
              "37  2017  690.5          4,231  ...           4.37%          32.60%           30.51%\n",
              "38  2018  753.4          4,630  ...           4.30%          34.00%           31.23%\n",
              "39  2019  817.6          5,028  ...           4.29%          33.50%           31.60%\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6NxsWtaiJIa"
      },
      "source": [
        "# 3. Data exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VV5YqOPiEwN",
        "outputId": "8b8af236-cb4a-4cbc-84fa-f82d0cf41fac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "r,c = df.shape\n",
        "print(\"The number of columns in the dataset = \",c)\n",
        "print(\"The number of rows in the dataset    = \",r)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The number of columns in the dataset =  8\n",
            "The number of rows in the dataset    =  40\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSTV8xXVibJK",
        "outputId": "c8e1187f-70e3-4e89-d4d1-41a453b18a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40 entries, 0 to 39\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Year              40 non-null     int64  \n",
            " 1   GDP               40 non-null     float64\n",
            " 2   GDP per capita    40 non-null     object \n",
            " 3   GDP growth        40 non-null     object \n",
            " 4   Inflation rate    40 non-null     object \n",
            " 5   Unemployed rate   29 non-null     object \n",
            " 6   Government debt   17 non-null     object \n",
            " 7   Total Investment  40 non-null     object \n",
            "dtypes: float64(1), int64(1), object(6)\n",
            "memory usage: 2.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8ttqyAW0Hiz"
      },
      "source": [
        "# 4. Data Cleaning and Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMrZ8_t1lf34"
      },
      "source": [
        "df = df.replace(np.nan,'%')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQwOAplWjTl6",
        "outputId": "3173f387-c15f-425a-9485-4fa59e32bdb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "source": [
        "df.head(15)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>GDP</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>GDP growth</th>\n",
              "      <th>Inflation rate</th>\n",
              "      <th>Unemployed rate</th>\n",
              "      <th>Government debt</th>\n",
              "      <th>Total Investment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>41.2</td>\n",
              "      <td>500</td>\n",
              "      <td>3.10%</td>\n",
              "      <td>15.40%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>14.44%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>47.4</td>\n",
              "      <td>560</td>\n",
              "      <td>5.60%</td>\n",
              "      <td>14.50%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>17.16%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>52.0</td>\n",
              "      <td>597</td>\n",
              "      <td>3.20%</td>\n",
              "      <td>12.90%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>17.36%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>56.5</td>\n",
              "      <td>633</td>\n",
              "      <td>4.60%</td>\n",
              "      <td>9.50%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.56%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>61.0</td>\n",
              "      <td>664</td>\n",
              "      <td>4.20%</td>\n",
              "      <td>10.40%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.48%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1985</td>\n",
              "      <td>65.3</td>\n",
              "      <td>693</td>\n",
              "      <td>3.70%</td>\n",
              "      <td>10.50%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>15.83%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1986</td>\n",
              "      <td>69.3</td>\n",
              "      <td>715</td>\n",
              "      <td>4.00%</td>\n",
              "      <td>10.20%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.18%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1987</td>\n",
              "      <td>73.1</td>\n",
              "      <td>735</td>\n",
              "      <td>2.90%</td>\n",
              "      <td>10.80%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>15.47%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1988</td>\n",
              "      <td>77.5</td>\n",
              "      <td>759</td>\n",
              "      <td>2.40%</td>\n",
              "      <td>9.70%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>15.74%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1989</td>\n",
              "      <td>84.0</td>\n",
              "      <td>801</td>\n",
              "      <td>4.30%</td>\n",
              "      <td>8.70%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.12%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1990</td>\n",
              "      <td>91.1</td>\n",
              "      <td>848</td>\n",
              "      <td>4.60%</td>\n",
              "      <td>10.50%</td>\n",
              "      <td>%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.46%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1991</td>\n",
              "      <td>98.1</td>\n",
              "      <td>892</td>\n",
              "      <td>4.20%</td>\n",
              "      <td>8.30%</td>\n",
              "      <td>2.20%</td>\n",
              "      <td>%</td>\n",
              "      <td>16.90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1992</td>\n",
              "      <td>105.1</td>\n",
              "      <td>935</td>\n",
              "      <td>4.80%</td>\n",
              "      <td>3.60%</td>\n",
              "      <td>2.25%</td>\n",
              "      <td>%</td>\n",
              "      <td>17.31%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1993</td>\n",
              "      <td>112.3</td>\n",
              "      <td>977</td>\n",
              "      <td>4.30%</td>\n",
              "      <td>3.00%</td>\n",
              "      <td>2.37%</td>\n",
              "      <td>%</td>\n",
              "      <td>17.95%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1994</td>\n",
              "      <td>119.9</td>\n",
              "      <td>1,021</td>\n",
              "      <td>4.50%</td>\n",
              "      <td>6.20%</td>\n",
              "      <td>2.44%</td>\n",
              "      <td>%</td>\n",
              "      <td>18.40%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year   GDP  GDP per capita  ... Unemployed rate Government debt Total Investment\n",
              "0   1980   41.2            500  ...               %               %           14.44%\n",
              "1   1981   47.4            560  ...               %               %           17.16%\n",
              "2   1982   52.0            597  ...               %               %           17.36%\n",
              "3   1983   56.5            633  ...               %               %           16.56%\n",
              "4   1984   61.0            664  ...               %               %           16.48%\n",
              "5   1985   65.3            693  ...               %               %           15.83%\n",
              "6   1986   69.3            715  ...               %               %           16.18%\n",
              "7   1987   73.1            735  ...               %               %           15.47%\n",
              "8   1988   77.5            759  ...               %               %           15.74%\n",
              "9   1989   84.0            801  ...               %               %           16.12%\n",
              "10  1990   91.1            848  ...               %               %           16.46%\n",
              "11  1991   98.1            892  ...           2.20%               %           16.90%\n",
              "12  1992  105.1            935  ...           2.25%               %           17.31%\n",
              "13  1993  112.3            977  ...           2.37%               %           17.95%\n",
              "14  1994  119.9          1,021  ...           2.44%               %           18.40%\n",
              "\n",
              "[15 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilDcwSJNifcV"
      },
      "source": [
        "df['GDP growth'] = df['GDP growth'].map(lambda x: x.rstrip('%'))\n",
        "df['Inflation rate'] = df['Inflation rate'].map(lambda x: x.rstrip('%'))\n",
        "df['Total Investment'] = df['Total Investment'].map(lambda x: x.rstrip('%'))\n",
        "df['Unemployed rate'] = df['Unemployed rate'].map(lambda x: x.rstrip('%'))\n",
        "df['Government debt'] = df['Government debt'].map(lambda x: x.rstrip('%'))\n",
        "df['GDP per capita'].replace(',','', regex=True, inplace=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noJkt9eEmxws"
      },
      "source": [
        "df['GDP growth'] = pd.to_numeric(df['GDP growth'],errors='coerce')\n",
        "df['Inflation rate'] = pd.to_numeric(df['Inflation rate'],errors='coerce')\n",
        "df['Total Investment'] = pd.to_numeric(df['Total Investment'],errors='coerce')\n",
        "df['Unemployed rate'] = pd.to_numeric(df['Unemployed rate'],errors='coerce')\n",
        "df['Government debt'] = pd.to_numeric(df['Government debt'],errors='coerce')\n",
        "df['GDP per capita'] = pd.to_numeric(df['GDP per capita'],errors='coerce')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_sVuUS2nuIp"
      },
      "source": [
        "m = df['Unemployed rate'].mean()\n",
        "m1 = df['Government debt'].mean()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3B6grgZoOCH"
      },
      "source": [
        "df['Unemployed rate'] = df['Unemployed rate'].replace(np.nan,m)\n",
        "df['Government debt'] = df['Government debt'].replace(np.nan,m1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmQhVd60qjsU",
        "outputId": "a3dd25ac-81dc-4ae3-f509-3dc8ba1634b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 12,
      "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>GDP</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>GDP growth</th>\n",
              "      <th>Inflation rate</th>\n",
              "      <th>Unemployed rate</th>\n",
              "      <th>Government debt</th>\n",
              "      <th>Total Investment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980</td>\n",
              "      <td>41.2</td>\n",
              "      <td>500</td>\n",
              "      <td>3.1</td>\n",
              "      <td>15.4</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>14.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1981</td>\n",
              "      <td>47.4</td>\n",
              "      <td>560</td>\n",
              "      <td>5.6</td>\n",
              "      <td>14.5</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>17.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1982</td>\n",
              "      <td>52.0</td>\n",
              "      <td>597</td>\n",
              "      <td>3.2</td>\n",
              "      <td>12.9</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>17.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1983</td>\n",
              "      <td>56.5</td>\n",
              "      <td>633</td>\n",
              "      <td>4.6</td>\n",
              "      <td>9.5</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1984</td>\n",
              "      <td>61.0</td>\n",
              "      <td>664</td>\n",
              "      <td>4.2</td>\n",
              "      <td>10.4</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1985</td>\n",
              "      <td>65.3</td>\n",
              "      <td>693</td>\n",
              "      <td>3.7</td>\n",
              "      <td>10.5</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>15.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1986</td>\n",
              "      <td>69.3</td>\n",
              "      <td>715</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1987</td>\n",
              "      <td>73.1</td>\n",
              "      <td>735</td>\n",
              "      <td>2.9</td>\n",
              "      <td>10.8</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>15.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1988</td>\n",
              "      <td>77.5</td>\n",
              "      <td>759</td>\n",
              "      <td>2.4</td>\n",
              "      <td>9.7</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>15.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1989</td>\n",
              "      <td>84.0</td>\n",
              "      <td>801</td>\n",
              "      <td>4.3</td>\n",
              "      <td>8.7</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1990</td>\n",
              "      <td>91.1</td>\n",
              "      <td>848</td>\n",
              "      <td>4.6</td>\n",
              "      <td>10.5</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1991</td>\n",
              "      <td>98.1</td>\n",
              "      <td>892</td>\n",
              "      <td>4.2</td>\n",
              "      <td>8.3</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>16.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1992</td>\n",
              "      <td>105.1</td>\n",
              "      <td>935</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>17.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1993</td>\n",
              "      <td>112.3</td>\n",
              "      <td>977</td>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.370000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>17.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1994</td>\n",
              "      <td>119.9</td>\n",
              "      <td>1021</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.2</td>\n",
              "      <td>2.440000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>18.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1995</td>\n",
              "      <td>128.2</td>\n",
              "      <td>1069</td>\n",
              "      <td>4.8</td>\n",
              "      <td>10.1</td>\n",
              "      <td>2.480000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>19.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1996</td>\n",
              "      <td>137.1</td>\n",
              "      <td>1120</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.510000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>20.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1997</td>\n",
              "      <td>146.8</td>\n",
              "      <td>1175</td>\n",
              "      <td>5.3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.690000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>21.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1998</td>\n",
              "      <td>155.9</td>\n",
              "      <td>1223</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>2.830000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>22.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1999</td>\n",
              "      <td>166.9</td>\n",
              "      <td>1284</td>\n",
              "      <td>5.4</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>22.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2000</td>\n",
              "      <td>180.2</td>\n",
              "      <td>1361</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.270000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>23.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2001</td>\n",
              "      <td>193.2</td>\n",
              "      <td>1434</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>3.550000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>24.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2002</td>\n",
              "      <td>205.7</td>\n",
              "      <td>1501</td>\n",
              "      <td>4.8</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3.960000</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>24.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2003</td>\n",
              "      <td>221.9</td>\n",
              "      <td>1594</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.4</td>\n",
              "      <td>4.320000</td>\n",
              "      <td>44.300000</td>\n",
              "      <td>24.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2004</td>\n",
              "      <td>241.9</td>\n",
              "      <td>1713</td>\n",
              "      <td>6.1</td>\n",
              "      <td>6.1</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>43.500000</td>\n",
              "      <td>24.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2005</td>\n",
              "      <td>265.5</td>\n",
              "      <td>1855</td>\n",
              "      <td>6.3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>42.300000</td>\n",
              "      <td>25.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2006</td>\n",
              "      <td>291.4</td>\n",
              "      <td>2018</td>\n",
              "      <td>6.9</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.590000</td>\n",
              "      <td>42.300000</td>\n",
              "      <td>26.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2007</td>\n",
              "      <td>319.7</td>\n",
              "      <td>2183</td>\n",
              "      <td>6.5</td>\n",
              "      <td>9.1</td>\n",
              "      <td>3.770000</td>\n",
              "      <td>41.900000</td>\n",
              "      <td>26.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2008</td>\n",
              "      <td>344.0</td>\n",
              "      <td>2325</td>\n",
              "      <td>5.5</td>\n",
              "      <td>8.9</td>\n",
              "      <td>4.070000</td>\n",
              "      <td>40.600000</td>\n",
              "      <td>26.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2009</td>\n",
              "      <td>365.0</td>\n",
              "      <td>2441</td>\n",
              "      <td>5.3</td>\n",
              "      <td>4.9</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>39.500000</td>\n",
              "      <td>26.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2010</td>\n",
              "      <td>391.7</td>\n",
              "      <td>2592</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>3.370000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>26.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2011</td>\n",
              "      <td>425.8</td>\n",
              "      <td>2785</td>\n",
              "      <td>6.5</td>\n",
              "      <td>11.5</td>\n",
              "      <td>3.710000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>27.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2012</td>\n",
              "      <td>460.8</td>\n",
              "      <td>2979</td>\n",
              "      <td>6.3</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.040000</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>28.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2013</td>\n",
              "      <td>496.5</td>\n",
              "      <td>3171</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.5</td>\n",
              "      <td>4.430000</td>\n",
              "      <td>35.800000</td>\n",
              "      <td>28.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2014</td>\n",
              "      <td>537.3</td>\n",
              "      <td>3396</td>\n",
              "      <td>6.3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.410000</td>\n",
              "      <td>35.300000</td>\n",
              "      <td>28.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2015</td>\n",
              "      <td>581.6</td>\n",
              "      <td>3638</td>\n",
              "      <td>6.8</td>\n",
              "      <td>6.2</td>\n",
              "      <td>4.420000</td>\n",
              "      <td>33.600000</td>\n",
              "      <td>28.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2016</td>\n",
              "      <td>629.9</td>\n",
              "      <td>3900</td>\n",
              "      <td>7.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>33.300000</td>\n",
              "      <td>29.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2017</td>\n",
              "      <td>690.5</td>\n",
              "      <td>4231</td>\n",
              "      <td>7.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.370000</td>\n",
              "      <td>32.600000</td>\n",
              "      <td>30.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2018</td>\n",
              "      <td>753.4</td>\n",
              "      <td>4630</td>\n",
              "      <td>7.9</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>31.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2019</td>\n",
              "      <td>817.6</td>\n",
              "      <td>5028</td>\n",
              "      <td>8.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.290000</td>\n",
              "      <td>33.500000</td>\n",
              "      <td>31.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year   GDP   ...  Government debt  Total Investment\n",
              "0   1980   41.2  ...        37.694118             14.44\n",
              "1   1981   47.4  ...        37.694118             17.16\n",
              "2   1982   52.0  ...        37.694118             17.36\n",
              "3   1983   56.5  ...        37.694118             16.56\n",
              "4   1984   61.0  ...        37.694118             16.48\n",
              "5   1985   65.3  ...        37.694118             15.83\n",
              "6   1986   69.3  ...        37.694118             16.18\n",
              "7   1987   73.1  ...        37.694118             15.47\n",
              "8   1988   77.5  ...        37.694118             15.74\n",
              "9   1989   84.0  ...        37.694118             16.12\n",
              "10  1990   91.1  ...        37.694118             16.46\n",
              "11  1991   98.1  ...        37.694118             16.90\n",
              "12  1992  105.1  ...        37.694118             17.31\n",
              "13  1993  112.3  ...        37.694118             17.95\n",
              "14  1994  119.9  ...        37.694118             18.40\n",
              "15  1995  128.2  ...        37.694118             19.12\n",
              "16  1996  137.1  ...        37.694118             20.73\n",
              "17  1997  146.8  ...        37.694118             21.82\n",
              "18  1998  155.9  ...        37.694118             22.12\n",
              "19  1999  166.9  ...        37.694118             22.72\n",
              "20  2000  180.2  ...        37.694118             23.81\n",
              "21  2001  193.2  ...        37.694118             24.17\n",
              "22  2002  205.7  ...        37.694118             24.34\n",
              "23  2003  221.9  ...        44.300000             24.68\n",
              "24  2004  241.9  ...        43.500000             24.99\n",
              "25  2005  265.5  ...        42.300000             25.83\n",
              "26  2006  291.4  ...        42.300000             26.14\n",
              "27  2007  319.7  ...        41.900000             26.18\n",
              "28  2008  344.0  ...        40.600000             26.20\n",
              "29  2009  365.0  ...        39.500000             26.21\n",
              "30  2010  391.7  ...        35.500000             26.25\n",
              "31  2011  425.8  ...        36.600000             27.42\n",
              "32  2012  460.8  ...        36.200000             28.26\n",
              "33  2013  496.5  ...        35.800000             28.39\n",
              "34  2014  537.3  ...        35.300000             28.58\n",
              "35  2015  581.6  ...        33.600000             28.89\n",
              "36  2016  629.9  ...        33.300000             29.65\n",
              "37  2017  690.5  ...        32.600000             30.51\n",
              "38  2018  753.4  ...        34.000000             31.23\n",
              "39  2019  817.6  ...        33.500000             31.60\n",
              "\n",
              "[40 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhclzR_SqzAP",
        "outputId": "c7a55e2e-fb10-4dd3-d146-6c741a971f24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40 entries, 0 to 39\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Year              40 non-null     int64  \n",
            " 1   GDP               40 non-null     float64\n",
            " 2   GDP per capita    40 non-null     int64  \n",
            " 3   GDP growth        40 non-null     float64\n",
            " 4   Inflation rate    40 non-null     float64\n",
            " 5   Unemployed rate   40 non-null     float64\n",
            " 6   Government debt   40 non-null     float64\n",
            " 7   Total Investment  40 non-null     float64\n",
            "dtypes: float64(6), int64(2)\n",
            "memory usage: 2.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vByxeW5Sru4K",
        "outputId": "4c056ceb-bd5e-4ef4-8efe-edfdb44302a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "df.describe(include=\"all\").T"
      ],
      "execution_count": 14,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>40.0</td>\n",
              "      <td>1999.500000</td>\n",
              "      <td>11.690452</td>\n",
              "      <td>1980.00</td>\n",
              "      <td>1989.750000</td>\n",
              "      <td>1999.500000</td>\n",
              "      <td>2009.250000</td>\n",
              "      <td>2019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP</th>\n",
              "      <td>40.0</td>\n",
              "      <td>257.557500</td>\n",
              "      <td>215.532485</td>\n",
              "      <td>41.20</td>\n",
              "      <td>89.325000</td>\n",
              "      <td>173.550000</td>\n",
              "      <td>371.675000</td>\n",
              "      <td>817.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP per capita</th>\n",
              "      <td>40.0</td>\n",
              "      <td>1799.400000</td>\n",
              "      <td>1236.066939</td>\n",
              "      <td>500.00</td>\n",
              "      <td>836.250000</td>\n",
              "      <td>1322.500000</td>\n",
              "      <td>2478.750000</td>\n",
              "      <td>5028.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP growth</th>\n",
              "      <td>40.0</td>\n",
              "      <td>5.305000</td>\n",
              "      <td>1.350964</td>\n",
              "      <td>2.40</td>\n",
              "      <td>4.450000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>6.300000</td>\n",
              "      <td>8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inflation rate</th>\n",
              "      <td>40.0</td>\n",
              "      <td>7.577500</td>\n",
              "      <td>3.205723</td>\n",
              "      <td>1.90</td>\n",
              "      <td>5.575000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>15.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unemployed rate</th>\n",
              "      <td>40.0</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>0.698567</td>\n",
              "      <td>2.20</td>\n",
              "      <td>3.345000</td>\n",
              "      <td>3.608276</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Government debt</th>\n",
              "      <td>40.0</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>2.581936</td>\n",
              "      <td>32.60</td>\n",
              "      <td>37.420588</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>37.694118</td>\n",
              "      <td>44.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Investment</th>\n",
              "      <td>40.0</td>\n",
              "      <td>22.455000</td>\n",
              "      <td>5.314517</td>\n",
              "      <td>14.44</td>\n",
              "      <td>17.095000</td>\n",
              "      <td>23.265000</td>\n",
              "      <td>26.220000</td>\n",
              "      <td>31.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count         mean  ...          75%     max\n",
              "Year               40.0  1999.500000  ...  2009.250000  2019.0\n",
              "GDP                40.0   257.557500  ...   371.675000   817.6\n",
              "GDP per capita     40.0  1799.400000  ...  2478.750000  5028.0\n",
              "GDP growth         40.0     5.305000  ...     6.300000     8.1\n",
              "Inflation rate     40.0     7.577500  ...     9.800000    15.4\n",
              "Unemployed rate    40.0     3.608276  ...     4.260000     5.0\n",
              "Government debt    40.0    37.694118  ...    37.694118    44.3\n",
              "Total Investment   40.0    22.455000  ...    26.220000    31.6\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIza7tN9xD3r",
        "outputId": "f566e72c-9a40-4c03-cd98-a710b1887713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        }
      },
      "source": [
        "plt.figure(figsize=(14,7))\n",
        "sns.heatmap(df.corr(),annot=True,cmap='BuPu')\n",
        "plt.xticks(fontsize=16,color='green')\n",
        "plt.yticks(fontsize=16,color='green')\n",
        "plt.title(\"Correlation of Parameters\\n\",fontsize=24,fontweight='bold',color='red')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation of Parameters\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KYwXSEG2kJ1"
      },
      "source": [
        "<h2><u><center><font color=\"blue\" size=10>Time Series Analysis of Data</center></u></h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpNDF2HS-ir5"
      },
      "source": [
        "df1 = df.set_index('Year')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExIw9Bkb3vib",
        "outputId": "fdef2b78-4608-4294-93cb-8ff64aee97f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "df1['GDP '].plot()\n",
        "plt.xticks(fontsize=16,color='blue')\n",
        "plt.xlabel('Years',fontsize=16,color='blue')\n",
        "plt.ylabel('Amount of rise',fontsize=16,color='green')\n",
        "plt.yticks(fontsize=16,color='green')\n",
        "plt.title('GDP rise with years\\n',fontsize=20,fontweight='bold',color='orange')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GDP rise with years\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPsFPf1S-QjY",
        "outputId": "397e2357-3a42-460b-dc78-6297e82fcc77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "df1['GDP per capita'].plot(color='red')\n",
        "plt.xticks(fontsize=16,color='green')\n",
        "plt.xlabel('Years',fontsize=16,color='green')\n",
        "plt.ylabel('Amount of rise',fontsize=16,color='red')\n",
        "plt.yticks(fontsize=16,color='red')\n",
        "plt.title('GDP per capita rise with years\\n',fontsize=20,fontweight='bold',color='orange')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GDP per capita rise with years\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNrEETrV4L6G",
        "outputId": "02ab338f-dcab-4f47-e3ea-7b9534dbc383",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        }
      },
      "source": [
        "# importing pmdarima package\n",
        "\n",
        "!pip install pmdarima"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pmdarima\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/be/62/725b3b6ae0e56c77534de5a8139322e7b863ca53fd5bd6bd3b7de87d0c20/pmdarima-1.7.1-cp36-cp36m-manylinux1_x86_64.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Collecting statsmodels<0.12,>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cb/83/540fd83238a18abe6c2d280fa8e489ac5fcefa1f370f0ca1acd16ae1b860/statsmodels-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (8.7MB)\n",
            "\u001b[K     |████████████████████████████████| 8.7MB 17.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.1.2)\n",
            "Collecting setuptools<50.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/a9/5dc32465951cf4812e9e93b4ad2d314893c2fa6d5f66ce5c057af6e76d85/setuptools-49.6.0-py3-none-any.whl (803kB)\n",
            "\u001b[K     |████████████████████████████████| 808kB 47.7MB/s \n",
            "\u001b[?25hCollecting Cython<0.29.18,>=0.29\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/d7/510ddef0248f3e1e91f9cc7e31c0f35f8954d0af92c5c3fd4c853e859ebe/Cython-0.29.17-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 45.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.18.5)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.16.0)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.6/dist-packages (from statsmodels<0.12,>=0.11->pmdarima) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.5->statsmodels<0.12,>=0.11->pmdarima) (1.15.0)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: statsmodels, setuptools, Cython, pmdarima\n",
            "  Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "  Found existing installation: setuptools 50.3.0\n",
            "    Uninstalling setuptools-50.3.0:\n",
            "      Successfully uninstalled setuptools-50.3.0\n",
            "  Found existing installation: Cython 0.29.21\n",
            "    Uninstalling Cython-0.29.21:\n",
            "      Successfully uninstalled Cython-0.29.21\n",
            "Successfully installed Cython-0.29.17 pmdarima-1.7.1 setuptools-49.6.0 statsmodels-0.11.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pkg_resources",
                  "statsmodels"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAp8CSCP5dWx",
        "outputId": "9bc4c419-c802-4e90-960d-393d9521aaba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "!pip install folium==0.2.1"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting folium==0.2.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/72/dd/75ced7437bfa7cb9a88b96ee0177953062803c3b4cde411a97d98c35adaf/folium-0.2.1.tar.gz (69kB)\n",
            "\r\u001b[K     |████▊                           | 10kB 16.3MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 30kB 2.1MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 40kB 2.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 61kB 2.2MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 2.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: Jinja2 in /usr/local/lib/python3.6/dist-packages (from folium==0.2.1) (2.11.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2->folium==0.2.1) (1.1.1)\n",
            "Building wheels for collected packages: folium\n",
            "  Building wheel for folium (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for folium: filename=folium-0.2.1-cp36-none-any.whl size=79980 sha256=97110f97aed51d27dfd5365a487484e93a3ee20020afc246ea65f3ab3bdda806\n",
            "  Stored in directory: /root/.cache/pip/wheels/b8/09/f0/52d2ef419c2aaf4fb149f92a33e0008bdce7ae816f0dd8f0c5\n",
            "Successfully built folium\n",
            "Installing collected packages: folium\n",
            "  Found existing installation: folium 0.8.3\n",
            "    Uninstalling folium-0.8.3:\n",
            "      Successfully uninstalled folium-0.8.3\n",
            "Successfully installed folium-0.2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7XwCpjA5xsf"
      },
      "source": [
        "import pmdarima as pm\n",
        "from pmdarima.model_selection import train_test_split"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RccQmEz46AEa"
      },
      "source": [
        "train = df[:30].values.reshape(-1,1)\n",
        "valid = df[31:].values.reshape(-1,1)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8I0JcGp6OTC",
        "outputId": "f45cc990-0c35-4e8e-c443-8147d2ee37c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fit your model\n",
        "model = pm.auto_arima(train, seasonal=True, m=6)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/sarimax.py:975: UserWarning:\n",
            "\n",
            "Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/pmdarima/arima/_auto_solvers.py:437: ModelFitWarning:\n",
            "\n",
            "Error fitting  ARIMA(1,0,1)(2,0,1)[6] intercept (if you do not want to see these warnings, run with error_action=\"ignore\").\n",
            "Traceback:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pmdarima/arima/_auto_solvers.py\", line 421, in _fit_candidate_model\n",
            "    fit.fit(x, exogenous=xreg, **fit_params)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pmdarima/arima/arima.py\", line 472, in fit\n",
            "    self._fit(y, exogenous, **fit_args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pmdarima/arima/arima.py\", line 397, in _fit\n",
            "    fit, self.arima_res_ = _fit_wrapper()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/pmdarima/arima/arima.py\", line 389, in _fit_wrapper\n",
            "    **fit_args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/mlemodel.py\", line 659, in fit\n",
            "    skip_hessian=True, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py\", line 526, in fit\n",
            "    full_output=full_output)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/base/optimizer.py\", line 218, in _fit\n",
            "    hess=hessian)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/base/optimizer.py\", line 440, in _fit_lbfgs\n",
            "    **extra_kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py\", line 199, in fmin_l_bfgs_b\n",
            "    **opts)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py\", line 345, in _minimize_lbfgsb\n",
            "    f, g = func_and_grad(x)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/scipy/optimize/lbfgsb.py\", line 290, in func_and_grad\n",
            "    f = fun(x, *args)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/scipy/optimize/optimize.py\", line 327, in function_wrapper\n",
            "    return function(*(wrapper_args + args))\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py\", line 500, in f\n",
            "    return -self.loglike(params, *args) / nobs\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/mlemodel.py\", line 889, in loglike\n",
            "    loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/kalman_filter.py\", line 977, in loglike\n",
            "    kfilter = self._filter(**kwargs)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/kalman_filter.py\", line 897, in _filter\n",
            "    self._initialize_state(prefix=prefix, complex_step=complex_step)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/statespace/representation.py\", line 950, in _initialize_state\n",
            "    complex_step=complex_step)\n",
            "  File \"statsmodels/tsa/statespace/_representation.pyx\", line 1334, in statsmodels.tsa.statespace._representation.dStatespace.initialize\n",
            "  File \"statsmodels/tsa/statespace/_initialization.pyx\", line 288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize\n",
            "  File \"statsmodels/tsa/statespace/_initialization.pyx\", line 406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov\n",
            "  File \"statsmodels/tsa/statespace/_tools.pyx\", line 1206, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov\n",
            "numpy.linalg.LinAlgError: LU decomposition error.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:568: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMD4A36N7gqb",
        "outputId": "99f4cb5e-2786-4b65-bb73-acdcc119545a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "sns.lmplot(x='GDP growth',y='Government debt',data=df)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f45ee0d1e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNehvq_X8LrA",
        "outputId": "608040db-e92b-4712-d97e-4ab745f3ff42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "sns.barplot(x='GDP growth',y='Total Investment',data=df)\n",
        "plt.xticks(fontsize=16,color='green')\n",
        "plt.xlabel('GDP growth',fontsize=16,color='red')\n",
        "plt.ylabel('Total investment',fontsize=16,color='green')\n",
        "plt.yticks(fontsize=16,color='red')\n",
        "plt.title('GDP growth vs Total Investment\\n',fontsize=20,fontweight='bold',color='orange')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GDP growth vs Total Investment\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6ovwpsh9A2e",
        "outputId": "46013c18-8eef-445a-d158-18b285689346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "df['Inflation rate'].plot()\n",
        "df['Government debt'].plot()\n",
        "plt.xticks(fontsize=16,color='blue')\n",
        "plt.xlabel('Years',fontsize=16,color='red')\n",
        "plt.ylabel('Amount of rise',fontsize=16,color='red')\n",
        "plt.yticks(fontsize=16,color='blue')\n",
        "plt.title('GDP effect on Inflation and Government debts\\n',fontsize=20,fontweight='bold',color='orange')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GDP effect on Inflation and Government debts\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9RJSgBH9RPo",
        "outputId": "1b327891-73c5-4ee7-e108-578651ba47c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "sns.set_style('darkgrid')\n",
        "sns.scatterplot(x='GDP growth',y='Unemployed rate',data=df)\n",
        "plt.xticks(fontsize=16,color='green')\n",
        "plt.xlabel('Years',fontsize=16,color='blue')\n",
        "plt.ylabel('Amount of rise',fontsize=16,color='green')\n",
        "plt.yticks(fontsize=16,color='red')\n",
        "plt.title('GDP growth vs Umemployed rate\\n',fontsize=20,fontweight='bold',color='orange')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'GDP growth vs Umemployed rate\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tphE6ZoS_Dmm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}