Manejo de datos.ipynb

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  "metadata": {
    "colab": {
      "name": "Rutina típica de manejo de datos.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Manejo de datos usando Pandas"
      ],
      "metadata": {
        "id": "NpXB3hUOpZwf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1. Prepación de entorno de trabajo"
      ],
      "metadata": {
        "id": "Fb6NSG-SpiAj"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKo2zPLt69v0",
        "outputId": "7d3e50fa-5b53-4a55-eaf4-abdf59ec4fcd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-a1swWuy7PYQ",
        "outputId": "23d3a11c-fa11-4766-89a5-69e81a2955df"
      },
      "source": [
        "%cd'/content/drive/MyDrive/pandas/db1'\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/pandas/db1\n",
            "covid_19_data.csv\t\t     poblacion.csv  test.parquet\n",
            "GlobalLandTemperaturesByCountry.csv  test.csv\t    test.pkl\n",
            "london_merged.csv\t\t     test.hdf\t    test.xlsx\n",
            "Meteorite_Landings.csv\t\t     test.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2. Dependencias"
      ],
      "metadata": {
        "id": "vHJzvVampoei"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQieaPob7oYb"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.3. Analisis exporatorio de los datos"
      ],
      "metadata": {
        "id": "S9W7M82DpsJX"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "BelDsEVl7fll",
        "outputId": "b1f82105-1867-458d-8c80-e5a1e1de868a"
      },
      "source": [
        "df_temp = pd.read_csv('GlobalLandTemperaturesByCountry.csv')\n",
        "df_temp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>dt</th>\n",
              "      <th>AverageTemperature</th>\n",
              "      <th>AverageTemperatureUncertainty</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1743-11-01</td>\n",
              "      <td>4.384</td>\n",
              "      <td>2.294</td>\n",
              "      <td>Åland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1743-12-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Åland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1744-01-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Åland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1744-02-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Åland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1744-03-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Åland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577457</th>\n",
              "      <td>2013-05-01</td>\n",
              "      <td>19.059</td>\n",
              "      <td>1.022</td>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577458</th>\n",
              "      <td>2013-06-01</td>\n",
              "      <td>17.613</td>\n",
              "      <td>0.473</td>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577459</th>\n",
              "      <td>2013-07-01</td>\n",
              "      <td>17.000</td>\n",
              "      <td>0.453</td>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577460</th>\n",
              "      <td>2013-08-01</td>\n",
              "      <td>19.759</td>\n",
              "      <td>0.717</td>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577461</th>\n",
              "      <td>2013-09-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>577462 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                dt  AverageTemperature  AverageTemperatureUncertainty   Country\n",
              "0       1743-11-01               4.384                          2.294     Åland\n",
              "1       1743-12-01                 NaN                            NaN     Åland\n",
              "2       1744-01-01                 NaN                            NaN     Åland\n",
              "3       1744-02-01                 NaN                            NaN     Åland\n",
              "4       1744-03-01                 NaN                            NaN     Åland\n",
              "...            ...                 ...                            ...       ...\n",
              "577457  2013-05-01              19.059                          1.022  Zimbabwe\n",
              "577458  2013-06-01              17.613                          0.473  Zimbabwe\n",
              "577459  2013-07-01              17.000                          0.453  Zimbabwe\n",
              "577460  2013-08-01              19.759                          0.717  Zimbabwe\n",
              "577461  2013-09-01                 NaN                            NaN  Zimbabwe\n",
              "\n",
              "[577462 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RMgdzBp7s1D",
        "outputId": "c75dc285-c2e4-48c7-ac12-f2e00738015c"
      },
      "source": [
        "df_temp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(577462, 4)"
            ]
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          "metadata": {},
          "execution_count": 7
        }
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wWjwavk7yKO",
        "outputId": "95499b06-7edc-463c-ca67-3e6c955d5025"
      },
      "source": [
        "df_temp.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 577462 entries, 0 to 577461\n",
            "Data columns (total 4 columns):\n",
            " #   Column                         Non-Null Count   Dtype  \n",
            "---  ------                         --------------   -----  \n",
            " 0   dt                             577462 non-null  object \n",
            " 1   AverageTemperature             544811 non-null  float64\n",
            " 2   AverageTemperatureUncertainty  545550 non-null  float64\n",
            " 3   Country                        577462 non-null  object \n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 17.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "_CG8Me5R716e",
        "outputId": "a6b93fdf-f5d2-401b-f7f1-db802420d1b8"
      },
      "source": [
        "df_temp['Country'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Åland', 'Afghanistan', 'Africa', 'Albania', 'Algeria',\n",
              "       'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica',\n",
              "       'Antigua And Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Asia',\n",
              "       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',\n",
              "       'Baker Island', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium',\n",
              "       'Belize', 'Benin', 'Bhutan', 'Bolivia',\n",
              "       'Bonaire, Saint Eustatius And Saba', 'Bosnia And Herzegovina',\n",
              "       'Botswana', 'Brazil', 'British Virgin Islands', 'Bulgaria',\n",
              "       'Burkina Faso', 'Burma', 'Burundi', \"Côte D'Ivoire\", 'Cambodia',\n",
              "       'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands',\n",
              "       'Central African Republic', 'Chad', 'Chile', 'China',\n",
              "       'Christmas Island', 'Colombia', 'Comoros',\n",
              "       'Congo (Democratic Republic Of The)', 'Congo', 'Costa Rica',\n",
              "       'Croatia', 'Cuba', 'Curaçao', 'Cyprus', 'Czech Republic',\n",
              "       'Denmark (Europe)', 'Denmark', 'Djibouti', 'Dominica',\n",
              "       'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n",
              "       'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Europe',\n",
              "       'Falkland Islands (Islas Malvinas)', 'Faroe Islands',\n",
              "       'Federated States Of Micronesia', 'Fiji', 'Finland',\n",
              "       'France (Europe)', 'France', 'French Guiana', 'French Polynesia',\n",
              "       'French Southern And Antarctic Lands', 'Gabon', 'Gambia',\n",
              "       'Gaza Strip', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland',\n",
              "       'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey',\n",
              "       'Guinea Bissau', 'Guinea', 'Guyana', 'Haiti',\n",
              "       'Heard Island And Mcdonald Islands', 'Honduras', 'Hong Kong',\n",
              "       'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\n",
              "       'Ireland', 'Isle Of Man', 'Israel', 'Italy', 'Jamaica', 'Japan',\n",
              "       'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kingman Reef',\n",
              "       'Kiribati', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n",
              "       'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
              "       'Luxembourg', 'Macau', 'Macedonia', 'Madagascar', 'Malawi',\n",
              "       'Malaysia', 'Mali', 'Malta', 'Martinique', 'Mauritania',\n",
              "       'Mauritius', 'Mayotte', 'Mexico', 'Moldova', 'Monaco', 'Mongolia',\n",
              "       'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',\n",
              "       'Nepal', 'Netherlands (Europe)', 'Netherlands', 'New Caledonia',\n",
              "       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Niue',\n",
              "       'North America', 'North Korea', 'Northern Mariana Islands',\n",
              "       'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestina',\n",
              "       'Palmyra Atoll', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',\n",
              "       'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n",
              "       'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthélemy',\n",
              "       'Saint Kitts And Nevis', 'Saint Lucia', 'Saint Martin',\n",
              "       'Saint Pierre And Miquelon', 'Saint Vincent And The Grenadines',\n",
              "       'Samoa', 'San Marino', 'Sao Tome And Principe', 'Saudi Arabia',\n",
              "       'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n",
              "       'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',\n",
              "       'Somalia', 'South Africa', 'South America',\n",
              "       'South Georgia And The South Sandwich Isla', 'South Korea',\n",
              "       'Spain', 'Sri Lanka', 'Sudan', 'Suriname',\n",
              "       'Svalbard And Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland',\n",
              "       'Syria', 'Taiwan', 'Tajikistan', 'Tanzania', 'Thailand',\n",
              "       'Timor Leste', 'Togo', 'Tonga', 'Trinidad And Tobago', 'Tunisia',\n",
              "       'Turkey', 'Turkmenistan', 'Turks And Caicas Islands', 'Uganda',\n",
              "       'Ukraine', 'United Arab Emirates', 'United Kingdom (Europe)',\n",
              "       'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',\n",
              "       'Venezuela', 'Vietnam', 'Virgin Islands', 'Western Sahara',\n",
              "       'Yemen', 'Zambia', 'Zimbabwe'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oBgRj6b7_Ht",
        "outputId": "c23c9dc0-58ae-4a9b-8a92-2b2babc9ac76"
      },
      "source": [
        "df_temp['year'] = pd.to_datetime(df_temp['dt'])\n",
        "df_temp['year']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1743-11-01\n",
              "1        1743-12-01\n",
              "2        1744-01-01\n",
              "3        1744-02-01\n",
              "4        1744-03-01\n",
              "            ...    \n",
              "577457   2013-05-01\n",
              "577458   2013-06-01\n",
              "577459   2013-07-01\n",
              "577460   2013-08-01\n",
              "577461   2013-09-01\n",
              "Name: year, Length: 577462, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJcl8RC48KTf"
      },
      "source": [
        "idx = df_temp['year'] > pd.to_datetime('1970-01-01')\n",
        "df_temp = df_temp[idx]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "5cTx9gUu8aiW",
        "outputId": "2a3dfe4c-ae33-4b18-e045-483203d47045"
      },
      "source": [
        "df_temp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dt</th>\n",
              "      <th>AverageTemperature</th>\n",
              "      <th>AverageTemperatureUncertainty</th>\n",
              "      <th>Country</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2715</th>\n",
              "      <td>1970-02-01</td>\n",
              "      <td>-10.835</td>\n",
              "      <td>0.478</td>\n",
              "      <td>Åland</td>\n",
              "      <td>1970-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2716</th>\n",
              "      <td>1970-03-01</td>\n",
              "      <td>-1.583</td>\n",
              "      <td>0.608</td>\n",
              "      <td>Åland</td>\n",
              "      <td>1970-03-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2717</th>\n",
              "      <td>1970-04-01</td>\n",
              "      <td>0.493</td>\n",
              "      <td>0.463</td>\n",
              "      <td>Åland</td>\n",
              "      <td>1970-04-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2718</th>\n",
              "      <td>1970-05-01</td>\n",
              "      <td>6.939</td>\n",
              "      <td>0.565</td>\n",
              "      <td>Åland</td>\n",
              "      <td>1970-05-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2719</th>\n",
              "      <td>1970-06-01</td>\n",
              "      <td>14.252</td>\n",
              "      <td>0.676</td>\n",
              "      <td>Åland</td>\n",
              "      <td>1970-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577457</th>\n",
              "      <td>2013-05-01</td>\n",
              "      <td>19.059</td>\n",
              "      <td>1.022</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2013-05-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577458</th>\n",
              "      <td>2013-06-01</td>\n",
              "      <td>17.613</td>\n",
              "      <td>0.473</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2013-06-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577459</th>\n",
              "      <td>2013-07-01</td>\n",
              "      <td>17.000</td>\n",
              "      <td>0.453</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2013-07-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577460</th>\n",
              "      <td>2013-08-01</td>\n",
              "      <td>19.759</td>\n",
              "      <td>0.717</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2013-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577461</th>\n",
              "      <td>2013-09-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2013-09-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127332 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                dt  AverageTemperature  ...   Country       year\n",
              "2715    1970-02-01             -10.835  ...     Åland 1970-02-01\n",
              "2716    1970-03-01              -1.583  ...     Åland 1970-03-01\n",
              "2717    1970-04-01               0.493  ...     Åland 1970-04-01\n",
              "2718    1970-05-01               6.939  ...     Åland 1970-05-01\n",
              "2719    1970-06-01              14.252  ...     Åland 1970-06-01\n",
              "...            ...                 ...  ...       ...        ...\n",
              "577457  2013-05-01              19.059  ...  Zimbabwe 2013-05-01\n",
              "577458  2013-06-01              17.613  ...  Zimbabwe 2013-06-01\n",
              "577459  2013-07-01              17.000  ...  Zimbabwe 2013-07-01\n",
              "577460  2013-08-01              19.759  ...  Zimbabwe 2013-08-01\n",
              "577461  2013-09-01                 NaN  ...  Zimbabwe 2013-09-01\n",
              "\n",
              "[127332 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMJdH6_o8cvh"
      },
      "source": [
        "df_t_avg = df_temp.groupby(['Country',\n",
        "                 pd.Grouper(key='year', freq='1Y')               \n",
        "]).agg({'AverageTemperature': [np.mean, np.median]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "gvilOFaZ8zcX",
        "outputId": "81b0d78d-e3aa-4736-8901-e6b9d946d728"
      },
      "source": [
        "df_t_avg"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">AverageTemperature</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Afghanistan</th>\n",
              "      <th>1970-12-31</th>\n",
              "      <td>16.005455</td>\n",
              "      <td>16.1990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971-12-31</th>\n",
              "      <td>14.823500</td>\n",
              "      <td>14.9340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972-12-31</th>\n",
              "      <td>12.547167</td>\n",
              "      <td>13.8725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-12-31</th>\n",
              "      <td>14.235500</td>\n",
              "      <td>14.9130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-12-31</th>\n",
              "      <td>13.316750</td>\n",
              "      <td>13.6160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Åland</th>\n",
              "      <th>2009-12-31</th>\n",
              "      <td>6.489083</td>\n",
              "      <td>5.5765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-12-31</th>\n",
              "      <td>4.861917</td>\n",
              "      <td>4.7550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-12-31</th>\n",
              "      <td>7.170750</td>\n",
              "      <td>7.4990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-31</th>\n",
              "      <td>6.063917</td>\n",
              "      <td>6.2540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-12-31</th>\n",
              "      <td>6.229750</td>\n",
              "      <td>6.0140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10692 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       AverageTemperature         \n",
              "                                     mean   median\n",
              "Country     year                                  \n",
              "Afghanistan 1970-12-31          16.005455  16.1990\n",
              "            1971-12-31          14.823500  14.9340\n",
              "            1972-12-31          12.547167  13.8725\n",
              "            1973-12-31          14.235500  14.9130\n",
              "            1974-12-31          13.316750  13.6160\n",
              "...                                   ...      ...\n",
              "Åland       2009-12-31           6.489083   5.5765\n",
              "            2010-12-31           4.861917   4.7550\n",
              "            2011-12-31           7.170750   7.4990\n",
              "            2012-12-31           6.063917   6.2540\n",
              "            2013-12-31           6.229750   6.0140\n",
              "\n",
              "[10692 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "SUN3mQUf85U2",
        "outputId": "3a78d67e-5f55-485f-983c-78a1fc6862b1"
      },
      "source": [
        "df_t_avg.xs('Mexico')['AverageTemperature'].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feff3efa650>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "XpC_q1gT9FYP",
        "outputId": "1a560fe0-f950-4d3f-e24d-d0f67e1a49ae"
      },
      "source": [
        "df_t_avg.xs('Russia')['AverageTemperature'].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feff3db1a90>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "erEg0HHO9JFu",
        "outputId": "6c16fcb6-2bb9-43c5-f21a-3bb941dad572"
      },
      "source": [
        "df_t_med = df_t_avg['AverageTemperature']['median'].reset_index()\n",
        "df_t_med"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>year</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1970-12-31</td>\n",
              "      <td>16.1990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1971-12-31</td>\n",
              "      <td>14.9340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1972-12-31</td>\n",
              "      <td>13.8725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1973-12-31</td>\n",
              "      <td>14.9130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1974-12-31</td>\n",
              "      <td>13.6160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10687</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2009-12-31</td>\n",
              "      <td>5.5765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10688</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>4.7550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10689</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2011-12-31</td>\n",
              "      <td>7.4990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10690</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>6.2540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10691</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2013-12-31</td>\n",
              "      <td>6.0140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10692 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Country       year   median\n",
              "0      Afghanistan 1970-12-31  16.1990\n",
              "1      Afghanistan 1971-12-31  14.9340\n",
              "2      Afghanistan 1972-12-31  13.8725\n",
              "3      Afghanistan 1973-12-31  14.9130\n",
              "4      Afghanistan 1974-12-31  13.6160\n",
              "...            ...        ...      ...\n",
              "10687        Åland 2009-12-31   5.5765\n",
              "10688        Åland 2010-12-31   4.7550\n",
              "10689        Åland 2011-12-31   7.4990\n",
              "10690        Åland 2012-12-31   6.2540\n",
              "10691        Åland 2013-12-31   6.0140\n",
              "\n",
              "[10692 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1W2DVTNh9YLW",
        "outputId": "20460311-e5f7-42aa-86c4-1ff6f539b92b"
      },
      "source": [
        "df_t_med['date'] = df_t_med['year'].dt.year\n",
        "df_t_med['date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1970\n",
              "1        1971\n",
              "2        1972\n",
              "3        1973\n",
              "4        1974\n",
              "         ... \n",
              "10687    2009\n",
              "10688    2010\n",
              "10689    2011\n",
              "10690    2012\n",
              "10691    2013\n",
              "Name: date, Length: 10692, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXjQu7dd9ej2"
      },
      "source": [
        "df_t_med.rename(columns={'median': 'temperature'},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "2DFtEGJS9tON",
        "outputId": "d9baad7a-7a34-46fe-9e46-d55516b4b781"
      },
      "source": [
        "df_t_med"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>year</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1970-12-31</td>\n",
              "      <td>16.1990</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1971-12-31</td>\n",
              "      <td>14.9340</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1972-12-31</td>\n",
              "      <td>13.8725</td>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1973-12-31</td>\n",
              "      <td>14.9130</td>\n",
              "      <td>1973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1974-12-31</td>\n",
              "      <td>13.6160</td>\n",
              "      <td>1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10687</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2009-12-31</td>\n",
              "      <td>5.5765</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10688</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>4.7550</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10689</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2011-12-31</td>\n",
              "      <td>7.4990</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10690</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>6.2540</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10691</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2013-12-31</td>\n",
              "      <td>6.0140</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10692 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Country       year  temperature  date\n",
              "0      Afghanistan 1970-12-31      16.1990  1970\n",
              "1      Afghanistan 1971-12-31      14.9340  1971\n",
              "2      Afghanistan 1972-12-31      13.8725  1972\n",
              "3      Afghanistan 1973-12-31      14.9130  1973\n",
              "4      Afghanistan 1974-12-31      13.6160  1974\n",
              "...            ...        ...          ...   ...\n",
              "10687        Åland 2009-12-31       5.5765  2009\n",
              "10688        Åland 2010-12-31       4.7550  2010\n",
              "10689        Åland 2011-12-31       7.4990  2011\n",
              "10690        Åland 2012-12-31       6.2540  2012\n",
              "10691        Åland 2013-12-31       6.0140  2013\n",
              "\n",
              "[10692 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uVkQZTF19vnB",
        "outputId": "32bf7cc7-e3c4-4a33-ad15-376ece4a8a50"
      },
      "source": [
        "df_t_pivot = df_t_med.pivot_table(values='temperature', index='date', columns='Country')\n",
        "df_t_pivot"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>Afghanistan</th>\n",
              "      <th>Africa</th>\n",
              "      <th>Albania</th>\n",
              "      <th>Algeria</th>\n",
              "      <th>American Samoa</th>\n",
              "      <th>Andorra</th>\n",
              "      <th>Angola</th>\n",
              "      <th>Anguilla</th>\n",
              "      <th>Antigua And Barbuda</th>\n",
              "      <th>Argentina</th>\n",
              "      <th>Armenia</th>\n",
              "      <th>Aruba</th>\n",
              "      <th>Asia</th>\n",
              "      <th>Australia</th>\n",
              "      <th>Austria</th>\n",
              "      <th>Azerbaijan</th>\n",
              "      <th>Bahamas</th>\n",
              "      <th>Bahrain</th>\n",
              "      <th>Baker Island</th>\n",
              "      <th>Bangladesh</th>\n",
              "      <th>Barbados</th>\n",
              "      <th>Belarus</th>\n",
              "      <th>Belgium</th>\n",
              "      <th>Belize</th>\n",
              "      <th>Benin</th>\n",
              "      <th>Bhutan</th>\n",
              "      <th>Bolivia</th>\n",
              "      <th>Bonaire, Saint Eustatius And Saba</th>\n",
              "      <th>Bosnia And Herzegovina</th>\n",
              "      <th>Botswana</th>\n",
              "      <th>Brazil</th>\n",
              "      <th>British Virgin Islands</th>\n",
              "      <th>Bulgaria</th>\n",
              "      <th>Burkina Faso</th>\n",
              "      <th>Burma</th>\n",
              "      <th>Burundi</th>\n",
              "      <th>Cambodia</th>\n",
              "      <th>Cameroon</th>\n",
              "      <th>Canada</th>\n",
              "      <th>Cape Verde</th>\n",
              "      <th>...</th>\n",
              "      <th>South America</th>\n",
              "      <th>South Georgia And The South Sandwich Isla</th>\n",
              "      <th>South Korea</th>\n",
              "      <th>Spain</th>\n",
              "      <th>Sri Lanka</th>\n",
              "      <th>Sudan</th>\n",
              "      <th>Suriname</th>\n",
              "      <th>Svalbard And Jan Mayen</th>\n",
              "      <th>Swaziland</th>\n",
              "      <th>Sweden</th>\n",
              "      <th>Switzerland</th>\n",
              "      <th>Syria</th>\n",
              "      <th>Taiwan</th>\n",
              "      <th>Tajikistan</th>\n",
              "      <th>Tanzania</th>\n",
              "      <th>Thailand</th>\n",
              "      <th>Timor Leste</th>\n",
              "      <th>Togo</th>\n",
              "      <th>Tonga</th>\n",
              "      <th>Trinidad And Tobago</th>\n",
              "      <th>Tunisia</th>\n",
              "      <th>Turkey</th>\n",
              "      <th>Turkmenistan</th>\n",
              "      <th>Turks And Caicas Islands</th>\n",
              "      <th>Uganda</th>\n",
              "      <th>Ukraine</th>\n",
              "      <th>United Arab Emirates</th>\n",
              "      <th>United Kingdom</th>\n",
              "      <th>United Kingdom (Europe)</th>\n",
              "      <th>United States</th>\n",
              "      <th>Uruguay</th>\n",
              "      <th>Uzbekistan</th>\n",
              "      <th>Venezuela</th>\n",
              "      <th>Vietnam</th>\n",
              "      <th>Virgin Islands</th>\n",
              "      <th>Western Sahara</th>\n",
              "      <th>Yemen</th>\n",
              "      <th>Zambia</th>\n",
              "      <th>Zimbabwe</th>\n",
              "      <th>Åland</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>16.1990</td>\n",
              "      <td>25.2360</td>\n",
              "      <td>12.4230</td>\n",
              "      <td>23.2840</td>\n",
              "      <td>26.7180</td>\n",
              "      <td>11.6390</td>\n",
              "      <td>22.9640</td>\n",
              "      <td>27.1510</td>\n",
              "      <td>27.2240</td>\n",
              "      <td>15.1150</td>\n",
              "      <td>11.1180</td>\n",
              "      <td>28.6180</td>\n",
              "      <td>8.6480</td>\n",
              "      <td>21.9360</td>\n",
              "      <td>6.3550</td>\n",
              "      <td>12.4300</td>\n",
              "      <td>25.6190</td>\n",
              "      <td>27.4460</td>\n",
              "      <td>25.5650</td>\n",
              "      <td>27.9370</td>\n",
              "      <td>27.0110</td>\n",
              "      <td>6.5360</td>\n",
              "      <td>10.6590</td>\n",
              "      <td>26.4620</td>\n",
              "      <td>27.0670</td>\n",
              "      <td>13.1690</td>\n",
              "      <td>21.9350</td>\n",
              "      <td>28.3010</td>\n",
              "      <td>10.0870</td>\n",
              "      <td>23.4400</td>\n",
              "      <td>25.4440</td>\n",
              "      <td>26.7220</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>28.1070</td>\n",
              "      <td>25.7410</td>\n",
              "      <td>19.9750</td>\n",
              "      <td>27.1040</td>\n",
              "      <td>24.4220</td>\n",
              "      <td>-1.6320</td>\n",
              "      <td>24.7480</td>\n",
              "      <td>...</td>\n",
              "      <td>22.6690</td>\n",
              "      <td>1.2720</td>\n",
              "      <td>14.4880</td>\n",
              "      <td>13.7550</td>\n",
              "      <td>27.6430</td>\n",
              "      <td>28.3390</td>\n",
              "      <td>26.4830</td>\n",
              "      <td>-5.8620</td>\n",
              "      <td>19.2560</td>\n",
              "      <td>3.2700</td>\n",
              "      <td>7.4020</td>\n",
              "      <td>19.4770</td>\n",
              "      <td>24.2230</td>\n",
              "      <td>6.4740</td>\n",
              "      <td>22.3660</td>\n",
              "      <td>26.8520</td>\n",
              "      <td>26.8290</td>\n",
              "      <td>27.0600</td>\n",
              "      <td>23.6180</td>\n",
              "      <td>26.4450</td>\n",
              "      <td>20.1740</td>\n",
              "      <td>12.9030</td>\n",
              "      <td>17.4190</td>\n",
              "      <td>26.8910</td>\n",
              "      <td>22.8360</td>\n",
              "      <td>10.0670</td>\n",
              "      <td>28.9860</td>\n",
              "      <td>9.7340</td>\n",
              "      <td>9.9730</td>\n",
              "      <td>8.2860</td>\n",
              "      <td>15.7640</td>\n",
              "      <td>15.5410</td>\n",
              "      <td>25.2780</td>\n",
              "      <td>24.6330</td>\n",
              "      <td>26.7220</td>\n",
              "      <td>23.8210</td>\n",
              "      <td>27.4390</td>\n",
              "      <td>22.1330</td>\n",
              "      <td>23.2560</td>\n",
              "      <td>6.9340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>14.9340</td>\n",
              "      <td>24.7755</td>\n",
              "      <td>11.6735</td>\n",
              "      <td>22.7810</td>\n",
              "      <td>26.4845</td>\n",
              "      <td>11.9275</td>\n",
              "      <td>22.4615</td>\n",
              "      <td>26.5295</td>\n",
              "      <td>26.3810</td>\n",
              "      <td>14.7665</td>\n",
              "      <td>8.6390</td>\n",
              "      <td>28.0850</td>\n",
              "      <td>8.1845</td>\n",
              "      <td>22.2985</td>\n",
              "      <td>7.1385</td>\n",
              "      <td>10.7060</td>\n",
              "      <td>25.4960</td>\n",
              "      <td>25.5135</td>\n",
              "      <td>25.3280</td>\n",
              "      <td>26.3180</td>\n",
              "      <td>26.4790</td>\n",
              "      <td>5.7755</td>\n",
              "      <td>10.0065</td>\n",
              "      <td>25.2630</td>\n",
              "      <td>26.9295</td>\n",
              "      <td>12.6415</td>\n",
              "      <td>21.2455</td>\n",
              "      <td>27.2900</td>\n",
              "      <td>10.1885</td>\n",
              "      <td>23.9550</td>\n",
              "      <td>24.9120</td>\n",
              "      <td>26.2470</td>\n",
              "      <td>9.5820</td>\n",
              "      <td>27.9310</td>\n",
              "      <td>24.7345</td>\n",
              "      <td>19.6260</td>\n",
              "      <td>27.0150</td>\n",
              "      <td>24.0725</td>\n",
              "      <td>-3.4755</td>\n",
              "      <td>23.6530</td>\n",
              "      <td>...</td>\n",
              "      <td>21.9110</td>\n",
              "      <td>1.5420</td>\n",
              "      <td>11.9565</td>\n",
              "      <td>12.6455</td>\n",
              "      <td>27.1535</td>\n",
              "      <td>27.9145</td>\n",
              "      <td>25.8920</td>\n",
              "      <td>-10.5280</td>\n",
              "      <td>19.5295</td>\n",
              "      <td>1.6720</td>\n",
              "      <td>8.5215</td>\n",
              "      <td>17.1220</td>\n",
              "      <td>22.1795</td>\n",
              "      <td>5.8390</td>\n",
              "      <td>22.3920</td>\n",
              "      <td>26.6300</td>\n",
              "      <td>26.4135</td>\n",
              "      <td>26.3805</td>\n",
              "      <td>23.8420</td>\n",
              "      <td>26.0095</td>\n",
              "      <td>19.6470</td>\n",
              "      <td>10.6395</td>\n",
              "      <td>14.9685</td>\n",
              "      <td>26.7755</td>\n",
              "      <td>22.7355</td>\n",
              "      <td>7.4325</td>\n",
              "      <td>27.8175</td>\n",
              "      <td>8.7785</td>\n",
              "      <td>8.9780</td>\n",
              "      <td>8.7545</td>\n",
              "      <td>16.5470</td>\n",
              "      <td>12.9860</td>\n",
              "      <td>24.6480</td>\n",
              "      <td>24.1105</td>\n",
              "      <td>26.2470</td>\n",
              "      <td>20.2715</td>\n",
              "      <td>26.5130</td>\n",
              "      <td>21.8265</td>\n",
              "      <td>22.7055</td>\n",
              "      <td>4.4870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>13.8725</td>\n",
              "      <td>25.1230</td>\n",
              "      <td>11.2175</td>\n",
              "      <td>22.4985</td>\n",
              "      <td>26.8205</td>\n",
              "      <td>10.0250</td>\n",
              "      <td>22.6070</td>\n",
              "      <td>26.9150</td>\n",
              "      <td>26.7700</td>\n",
              "      <td>14.3445</td>\n",
              "      <td>11.4950</td>\n",
              "      <td>28.6780</td>\n",
              "      <td>8.6445</td>\n",
              "      <td>22.3470</td>\n",
              "      <td>5.3755</td>\n",
              "      <td>13.6880</td>\n",
              "      <td>25.8145</td>\n",
              "      <td>26.3535</td>\n",
              "      <td>26.0285</td>\n",
              "      <td>26.9035</td>\n",
              "      <td>27.0225</td>\n",
              "      <td>6.0925</td>\n",
              "      <td>8.7840</td>\n",
              "      <td>25.8145</td>\n",
              "      <td>26.7025</td>\n",
              "      <td>12.5800</td>\n",
              "      <td>21.9525</td>\n",
              "      <td>27.8050</td>\n",
              "      <td>9.7260</td>\n",
              "      <td>23.0225</td>\n",
              "      <td>25.1430</td>\n",
              "      <td>26.6150</td>\n",
              "      <td>10.6495</td>\n",
              "      <td>28.2305</td>\n",
              "      <td>25.4830</td>\n",
              "      <td>20.0425</td>\n",
              "      <td>27.2160</td>\n",
              "      <td>24.1850</td>\n",
              "      <td>-6.6775</td>\n",
              "      <td>23.6670</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3110</td>\n",
              "      <td>0.4455</td>\n",
              "      <td>12.5585</td>\n",
              "      <td>12.2105</td>\n",
              "      <td>27.4820</td>\n",
              "      <td>28.9775</td>\n",
              "      <td>26.1590</td>\n",
              "      <td>-5.9665</td>\n",
              "      <td>19.9670</td>\n",
              "      <td>2.2865</td>\n",
              "      <td>6.1615</td>\n",
              "      <td>19.4300</td>\n",
              "      <td>23.0640</td>\n",
              "      <td>4.9110</td>\n",
              "      <td>22.6910</td>\n",
              "      <td>26.9205</td>\n",
              "      <td>26.2535</td>\n",
              "      <td>27.0445</td>\n",
              "      <td>23.2235</td>\n",
              "      <td>26.4690</td>\n",
              "      <td>18.6385</td>\n",
              "      <td>13.2440</td>\n",
              "      <td>16.3415</td>\n",
              "      <td>27.1115</td>\n",
              "      <td>23.2850</td>\n",
              "      <td>9.0295</td>\n",
              "      <td>28.0485</td>\n",
              "      <td>8.6235</td>\n",
              "      <td>8.7990</td>\n",
              "      <td>8.2290</td>\n",
              "      <td>16.9190</td>\n",
              "      <td>13.5640</td>\n",
              "      <td>25.2615</td>\n",
              "      <td>24.5165</td>\n",
              "      <td>26.6150</td>\n",
              "      <td>21.3905</td>\n",
              "      <td>26.8175</td>\n",
              "      <td>21.9800</td>\n",
              "      <td>22.3980</td>\n",
              "      <td>5.6200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>14.9130</td>\n",
              "      <td>25.3445</td>\n",
              "      <td>11.8370</td>\n",
              "      <td>22.9975</td>\n",
              "      <td>27.0120</td>\n",
              "      <td>9.5525</td>\n",
              "      <td>23.0730</td>\n",
              "      <td>27.3360</td>\n",
              "      <td>27.1725</td>\n",
              "      <td>14.8675</td>\n",
              "      <td>9.7335</td>\n",
              "      <td>28.1630</td>\n",
              "      <td>8.5830</td>\n",
              "      <td>22.9775</td>\n",
              "      <td>4.5905</td>\n",
              "      <td>11.8825</td>\n",
              "      <td>25.5835</td>\n",
              "      <td>27.0690</td>\n",
              "      <td>25.9215</td>\n",
              "      <td>27.1555</td>\n",
              "      <td>26.8725</td>\n",
              "      <td>5.7910</td>\n",
              "      <td>8.0960</td>\n",
              "      <td>26.4840</td>\n",
              "      <td>27.3385</td>\n",
              "      <td>13.5435</td>\n",
              "      <td>22.4190</td>\n",
              "      <td>27.7220</td>\n",
              "      <td>9.3100</td>\n",
              "      <td>23.6135</td>\n",
              "      <td>25.2795</td>\n",
              "      <td>27.0685</td>\n",
              "      <td>10.5565</td>\n",
              "      <td>28.4860</td>\n",
              "      <td>25.4180</td>\n",
              "      <td>20.2410</td>\n",
              "      <td>27.4025</td>\n",
              "      <td>24.6665</td>\n",
              "      <td>-3.6645</td>\n",
              "      <td>24.5360</td>\n",
              "      <td>...</td>\n",
              "      <td>22.4800</td>\n",
              "      <td>1.6770</td>\n",
              "      <td>12.8265</td>\n",
              "      <td>12.3910</td>\n",
              "      <td>27.5945</td>\n",
              "      <td>28.6225</td>\n",
              "      <td>26.4350</td>\n",
              "      <td>-9.6135</td>\n",
              "      <td>18.8930</td>\n",
              "      <td>0.4795</td>\n",
              "      <td>5.1230</td>\n",
              "      <td>18.9260</td>\n",
              "      <td>23.2600</td>\n",
              "      <td>5.5885</td>\n",
              "      <td>22.7820</td>\n",
              "      <td>26.9290</td>\n",
              "      <td>27.0445</td>\n",
              "      <td>26.9310</td>\n",
              "      <td>23.9020</td>\n",
              "      <td>26.4575</td>\n",
              "      <td>19.6385</td>\n",
              "      <td>12.1315</td>\n",
              "      <td>15.8780</td>\n",
              "      <td>26.8120</td>\n",
              "      <td>23.0450</td>\n",
              "      <td>8.5070</td>\n",
              "      <td>28.7285</td>\n",
              "      <td>7.4480</td>\n",
              "      <td>7.5175</td>\n",
              "      <td>8.8785</td>\n",
              "      <td>17.5420</td>\n",
              "      <td>13.4735</td>\n",
              "      <td>25.2020</td>\n",
              "      <td>25.0080</td>\n",
              "      <td>27.0685</td>\n",
              "      <td>22.9215</td>\n",
              "      <td>27.2265</td>\n",
              "      <td>22.1090</td>\n",
              "      <td>22.5815</td>\n",
              "      <td>3.5795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974</th>\n",
              "      <td>13.6160</td>\n",
              "      <td>24.8260</td>\n",
              "      <td>11.3210</td>\n",
              "      <td>21.5795</td>\n",
              "      <td>26.6350</td>\n",
              "      <td>8.2045</td>\n",
              "      <td>22.3275</td>\n",
              "      <td>26.6015</td>\n",
              "      <td>26.5190</td>\n",
              "      <td>15.2160</td>\n",
              "      <td>9.3265</td>\n",
              "      <td>27.7935</td>\n",
              "      <td>8.3875</td>\n",
              "      <td>21.6105</td>\n",
              "      <td>5.1555</td>\n",
              "      <td>11.6075</td>\n",
              "      <td>25.2450</td>\n",
              "      <td>26.3585</td>\n",
              "      <td>25.4955</td>\n",
              "      <td>27.2765</td>\n",
              "      <td>26.7885</td>\n",
              "      <td>6.1085</td>\n",
              "      <td>8.3310</td>\n",
              "      <td>25.6020</td>\n",
              "      <td>26.7790</td>\n",
              "      <td>14.0195</td>\n",
              "      <td>21.2395</td>\n",
              "      <td>27.3970</td>\n",
              "      <td>8.3580</td>\n",
              "      <td>22.0050</td>\n",
              "      <td>24.7665</td>\n",
              "      <td>26.1720</td>\n",
              "      <td>10.4035</td>\n",
              "      <td>27.0055</td>\n",
              "      <td>25.5345</td>\n",
              "      <td>19.8995</td>\n",
              "      <td>27.2265</td>\n",
              "      <td>24.0195</td>\n",
              "      <td>-6.0400</td>\n",
              "      <td>23.3540</td>\n",
              "      <td>...</td>\n",
              "      <td>21.8890</td>\n",
              "      <td>1.4885</td>\n",
              "      <td>12.2490</td>\n",
              "      <td>10.8630</td>\n",
              "      <td>27.2295</td>\n",
              "      <td>28.1990</td>\n",
              "      <td>25.8925</td>\n",
              "      <td>-7.0340</td>\n",
              "      <td>19.3190</td>\n",
              "      <td>2.4690</td>\n",
              "      <td>5.4260</td>\n",
              "      <td>19.1350</td>\n",
              "      <td>22.9360</td>\n",
              "      <td>5.6675</td>\n",
              "      <td>22.3410</td>\n",
              "      <td>26.7550</td>\n",
              "      <td>26.4965</td>\n",
              "      <td>26.4465</td>\n",
              "      <td>23.8720</td>\n",
              "      <td>26.2555</td>\n",
              "      <td>18.5315</td>\n",
              "      <td>12.0760</td>\n",
              "      <td>14.3705</td>\n",
              "      <td>26.3520</td>\n",
              "      <td>22.9670</td>\n",
              "      <td>8.4815</td>\n",
              "      <td>27.8570</td>\n",
              "      <td>7.3445</td>\n",
              "      <td>7.3910</td>\n",
              "      <td>8.8610</td>\n",
              "      <td>17.4025</td>\n",
              "      <td>13.2105</td>\n",
              "      <td>25.0760</td>\n",
              "      <td>24.3700</td>\n",
              "      <td>26.1720</td>\n",
              "      <td>21.5755</td>\n",
              "      <td>26.6320</td>\n",
              "      <td>21.6005</td>\n",
              "      <td>21.3255</td>\n",
              "      <td>5.4550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975</th>\n",
              "      <td>13.5130</td>\n",
              "      <td>24.8045</td>\n",
              "      <td>12.3595</td>\n",
              "      <td>23.0740</td>\n",
              "      <td>26.6510</td>\n",
              "      <td>10.4105</td>\n",
              "      <td>22.4210</td>\n",
              "      <td>26.4780</td>\n",
              "      <td>26.3340</td>\n",
              "      <td>15.1515</td>\n",
              "      <td>10.2160</td>\n",
              "      <td>28.1140</td>\n",
              "      <td>8.8600</td>\n",
              "      <td>21.4200</td>\n",
              "      <td>5.8655</td>\n",
              "      <td>12.2135</td>\n",
              "      <td>25.9255</td>\n",
              "      <td>25.5630</td>\n",
              "      <td>25.6860</td>\n",
              "      <td>27.3305</td>\n",
              "      <td>26.4560</td>\n",
              "      <td>6.9855</td>\n",
              "      <td>8.4725</td>\n",
              "      <td>25.8630</td>\n",
              "      <td>26.7795</td>\n",
              "      <td>14.1455</td>\n",
              "      <td>21.8520</td>\n",
              "      <td>27.4675</td>\n",
              "      <td>10.3755</td>\n",
              "      <td>23.1615</td>\n",
              "      <td>25.1770</td>\n",
              "      <td>26.2985</td>\n",
              "      <td>11.1605</td>\n",
              "      <td>26.9675</td>\n",
              "      <td>25.4380</td>\n",
              "      <td>19.8435</td>\n",
              "      <td>27.0700</td>\n",
              "      <td>24.0085</td>\n",
              "      <td>-4.3170</td>\n",
              "      <td>23.7810</td>\n",
              "      <td>...</td>\n",
              "      <td>22.1260</td>\n",
              "      <td>1.8425</td>\n",
              "      <td>13.6235</td>\n",
              "      <td>12.3570</td>\n",
              "      <td>27.2450</td>\n",
              "      <td>27.7055</td>\n",
              "      <td>25.9040</td>\n",
              "      <td>-10.1780</td>\n",
              "      <td>19.0535</td>\n",
              "      <td>2.3690</td>\n",
              "      <td>6.4030</td>\n",
              "      <td>19.0685</td>\n",
              "      <td>23.4175</td>\n",
              "      <td>5.1000</td>\n",
              "      <td>22.5975</td>\n",
              "      <td>26.8635</td>\n",
              "      <td>26.4400</td>\n",
              "      <td>26.3395</td>\n",
              "      <td>23.9175</td>\n",
              "      <td>26.1995</td>\n",
              "      <td>19.1290</td>\n",
              "      <td>12.6860</td>\n",
              "      <td>15.2960</td>\n",
              "      <td>26.8230</td>\n",
              "      <td>22.9845</td>\n",
              "      <td>9.4300</td>\n",
              "      <td>27.6990</td>\n",
              "      <td>8.1145</td>\n",
              "      <td>8.2045</td>\n",
              "      <td>7.9060</td>\n",
              "      <td>17.1810</td>\n",
              "      <td>13.3395</td>\n",
              "      <td>24.9790</td>\n",
              "      <td>24.9900</td>\n",
              "      <td>26.2985</td>\n",
              "      <td>20.5175</td>\n",
              "      <td>26.1155</td>\n",
              "      <td>21.8635</td>\n",
              "      <td>21.7910</td>\n",
              "      <td>6.0170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1976</th>\n",
              "      <td>14.1615</td>\n",
              "      <td>24.8225</td>\n",
              "      <td>12.6080</td>\n",
              "      <td>22.2980</td>\n",
              "      <td>26.6155</td>\n",
              "      <td>9.6625</td>\n",
              "      <td>22.1455</td>\n",
              "      <td>26.9825</td>\n",
              "      <td>26.8425</td>\n",
              "      <td>14.3965</td>\n",
              "      <td>9.0935</td>\n",
              "      <td>28.3905</td>\n",
              "      <td>7.5785</td>\n",
              "      <td>21.4185</td>\n",
              "      <td>7.1560</td>\n",
              "      <td>10.9315</td>\n",
              "      <td>25.4225</td>\n",
              "      <td>26.7010</td>\n",
              "      <td>25.4530</td>\n",
              "      <td>27.0235</td>\n",
              "      <td>26.7760</td>\n",
              "      <td>4.4420</td>\n",
              "      <td>9.8210</td>\n",
              "      <td>25.4535</td>\n",
              "      <td>26.2805</td>\n",
              "      <td>13.0715</td>\n",
              "      <td>21.1130</td>\n",
              "      <td>27.6690</td>\n",
              "      <td>10.9355</td>\n",
              "      <td>22.6565</td>\n",
              "      <td>24.8620</td>\n",
              "      <td>26.6620</td>\n",
              "      <td>10.7210</td>\n",
              "      <td>27.4535</td>\n",
              "      <td>25.1635</td>\n",
              "      <td>19.9425</td>\n",
              "      <td>26.8995</td>\n",
              "      <td>23.8925</td>\n",
              "      <td>-3.5780</td>\n",
              "      <td>23.4605</td>\n",
              "      <td>...</td>\n",
              "      <td>21.9095</td>\n",
              "      <td>2.1665</td>\n",
              "      <td>12.3400</td>\n",
              "      <td>11.4830</td>\n",
              "      <td>27.4010</td>\n",
              "      <td>28.6220</td>\n",
              "      <td>25.8310</td>\n",
              "      <td>-7.1570</td>\n",
              "      <td>19.0210</td>\n",
              "      <td>1.3020</td>\n",
              "      <td>7.4415</td>\n",
              "      <td>18.2675</td>\n",
              "      <td>22.3975</td>\n",
              "      <td>4.5605</td>\n",
              "      <td>22.7670</td>\n",
              "      <td>26.7265</td>\n",
              "      <td>26.1580</td>\n",
              "      <td>26.4210</td>\n",
              "      <td>23.2860</td>\n",
              "      <td>26.1790</td>\n",
              "      <td>19.2235</td>\n",
              "      <td>11.9430</td>\n",
              "      <td>13.9315</td>\n",
              "      <td>27.1975</td>\n",
              "      <td>23.1775</td>\n",
              "      <td>7.3250</td>\n",
              "      <td>28.3100</td>\n",
              "      <td>8.4885</td>\n",
              "      <td>8.6360</td>\n",
              "      <td>8.1965</td>\n",
              "      <td>16.5660</td>\n",
              "      <td>11.3820</td>\n",
              "      <td>25.0590</td>\n",
              "      <td>24.4665</td>\n",
              "      <td>26.6620</td>\n",
              "      <td>20.4625</td>\n",
              "      <td>26.3220</td>\n",
              "      <td>21.6980</td>\n",
              "      <td>21.9880</td>\n",
              "      <td>4.3855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>15.6575</td>\n",
              "      <td>25.0345</td>\n",
              "      <td>11.9870</td>\n",
              "      <td>23.5375</td>\n",
              "      <td>26.7085</td>\n",
              "      <td>10.4055</td>\n",
              "      <td>22.6000</td>\n",
              "      <td>27.2010</td>\n",
              "      <td>27.0085</td>\n",
              "      <td>15.9725</td>\n",
              "      <td>8.2360</td>\n",
              "      <td>28.5885</td>\n",
              "      <td>8.3580</td>\n",
              "      <td>22.6405</td>\n",
              "      <td>6.8190</td>\n",
              "      <td>10.3025</td>\n",
              "      <td>25.2510</td>\n",
              "      <td>26.9490</td>\n",
              "      <td>25.4965</td>\n",
              "      <td>26.4955</td>\n",
              "      <td>27.0555</td>\n",
              "      <td>6.3195</td>\n",
              "      <td>9.7070</td>\n",
              "      <td>25.5090</td>\n",
              "      <td>26.9880</td>\n",
              "      <td>11.9855</td>\n",
              "      <td>21.9350</td>\n",
              "      <td>28.0855</td>\n",
              "      <td>10.4705</td>\n",
              "      <td>22.7440</td>\n",
              "      <td>25.4210</td>\n",
              "      <td>26.8935</td>\n",
              "      <td>10.0760</td>\n",
              "      <td>27.7075</td>\n",
              "      <td>25.2595</td>\n",
              "      <td>20.2540</td>\n",
              "      <td>27.0945</td>\n",
              "      <td>24.3450</td>\n",
              "      <td>-2.9450</td>\n",
              "      <td>24.4010</td>\n",
              "      <td>...</td>\n",
              "      <td>22.5340</td>\n",
              "      <td>1.4905</td>\n",
              "      <td>14.1470</td>\n",
              "      <td>13.0735</td>\n",
              "      <td>27.6640</td>\n",
              "      <td>27.7425</td>\n",
              "      <td>26.2530</td>\n",
              "      <td>-7.3890</td>\n",
              "      <td>20.4265</td>\n",
              "      <td>1.4770</td>\n",
              "      <td>7.3385</td>\n",
              "      <td>17.4700</td>\n",
              "      <td>23.6320</td>\n",
              "      <td>6.2235</td>\n",
              "      <td>23.0310</td>\n",
              "      <td>26.8120</td>\n",
              "      <td>26.1630</td>\n",
              "      <td>26.8305</td>\n",
              "      <td>22.6550</td>\n",
              "      <td>26.6390</td>\n",
              "      <td>19.9580</td>\n",
              "      <td>10.7595</td>\n",
              "      <td>15.6115</td>\n",
              "      <td>26.9005</td>\n",
              "      <td>23.2760</td>\n",
              "      <td>7.9585</td>\n",
              "      <td>29.1745</td>\n",
              "      <td>8.0655</td>\n",
              "      <td>8.1515</td>\n",
              "      <td>9.4840</td>\n",
              "      <td>18.3455</td>\n",
              "      <td>13.8000</td>\n",
              "      <td>25.6165</td>\n",
              "      <td>24.6295</td>\n",
              "      <td>26.8935</td>\n",
              "      <td>22.6755</td>\n",
              "      <td>26.8845</td>\n",
              "      <td>22.1670</td>\n",
              "      <td>22.2910</td>\n",
              "      <td>5.4745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1978</th>\n",
              "      <td>15.2580</td>\n",
              "      <td>24.9135</td>\n",
              "      <td>11.5490</td>\n",
              "      <td>22.5620</td>\n",
              "      <td>26.7250</td>\n",
              "      <td>9.7565</td>\n",
              "      <td>22.7055</td>\n",
              "      <td>27.2320</td>\n",
              "      <td>27.0370</td>\n",
              "      <td>15.3350</td>\n",
              "      <td>9.3725</td>\n",
              "      <td>28.8605</td>\n",
              "      <td>8.6740</td>\n",
              "      <td>22.0440</td>\n",
              "      <td>5.9975</td>\n",
              "      <td>11.4820</td>\n",
              "      <td>26.1575</td>\n",
              "      <td>27.0880</td>\n",
              "      <td>25.6125</td>\n",
              "      <td>27.2520</td>\n",
              "      <td>27.0830</td>\n",
              "      <td>6.0435</td>\n",
              "      <td>9.2650</td>\n",
              "      <td>25.7075</td>\n",
              "      <td>26.8340</td>\n",
              "      <td>13.1970</td>\n",
              "      <td>21.9330</td>\n",
              "      <td>28.0820</td>\n",
              "      <td>9.9120</td>\n",
              "      <td>23.1410</td>\n",
              "      <td>25.2130</td>\n",
              "      <td>26.9675</td>\n",
              "      <td>10.3185</td>\n",
              "      <td>28.2235</td>\n",
              "      <td>25.2435</td>\n",
              "      <td>20.1490</td>\n",
              "      <td>26.8140</td>\n",
              "      <td>24.0850</td>\n",
              "      <td>-5.4740</td>\n",
              "      <td>24.1940</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3675</td>\n",
              "      <td>1.7630</td>\n",
              "      <td>13.0155</td>\n",
              "      <td>12.1645</td>\n",
              "      <td>27.5055</td>\n",
              "      <td>28.2530</td>\n",
              "      <td>26.3990</td>\n",
              "      <td>-9.4320</td>\n",
              "      <td>18.9115</td>\n",
              "      <td>1.4850</td>\n",
              "      <td>6.3045</td>\n",
              "      <td>19.3485</td>\n",
              "      <td>22.4655</td>\n",
              "      <td>6.2655</td>\n",
              "      <td>22.7920</td>\n",
              "      <td>26.8325</td>\n",
              "      <td>26.7905</td>\n",
              "      <td>26.9310</td>\n",
              "      <td>22.8915</td>\n",
              "      <td>26.6885</td>\n",
              "      <td>18.6720</td>\n",
              "      <td>12.1405</td>\n",
              "      <td>15.6020</td>\n",
              "      <td>27.3660</td>\n",
              "      <td>23.0180</td>\n",
              "      <td>8.1770</td>\n",
              "      <td>28.5220</td>\n",
              "      <td>9.1220</td>\n",
              "      <td>9.3405</td>\n",
              "      <td>9.2330</td>\n",
              "      <td>17.1655</td>\n",
              "      <td>13.2965</td>\n",
              "      <td>25.5600</td>\n",
              "      <td>24.4290</td>\n",
              "      <td>26.9675</td>\n",
              "      <td>21.7730</td>\n",
              "      <td>27.0135</td>\n",
              "      <td>22.1010</td>\n",
              "      <td>22.6210</td>\n",
              "      <td>5.7475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979</th>\n",
              "      <td>16.2990</td>\n",
              "      <td>25.3705</td>\n",
              "      <td>11.7765</td>\n",
              "      <td>23.1455</td>\n",
              "      <td>26.8385</td>\n",
              "      <td>10.4145</td>\n",
              "      <td>22.9120</td>\n",
              "      <td>27.0870</td>\n",
              "      <td>26.9765</td>\n",
              "      <td>14.8075</td>\n",
              "      <td>9.8630</td>\n",
              "      <td>28.6780</td>\n",
              "      <td>7.9615</td>\n",
              "      <td>22.5365</td>\n",
              "      <td>5.7620</td>\n",
              "      <td>11.8350</td>\n",
              "      <td>25.8230</td>\n",
              "      <td>27.8140</td>\n",
              "      <td>25.6405</td>\n",
              "      <td>27.4985</td>\n",
              "      <td>27.3110</td>\n",
              "      <td>4.8085</td>\n",
              "      <td>9.7100</td>\n",
              "      <td>26.0405</td>\n",
              "      <td>27.3720</td>\n",
              "      <td>13.3900</td>\n",
              "      <td>21.5180</td>\n",
              "      <td>28.2010</td>\n",
              "      <td>9.6800</td>\n",
              "      <td>24.1515</td>\n",
              "      <td>25.1270</td>\n",
              "      <td>26.7280</td>\n",
              "      <td>9.5395</td>\n",
              "      <td>27.9565</td>\n",
              "      <td>25.3420</td>\n",
              "      <td>20.2900</td>\n",
              "      <td>27.3580</td>\n",
              "      <td>24.5120</td>\n",
              "      <td>-4.4885</td>\n",
              "      <td>24.7885</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3415</td>\n",
              "      <td>0.9005</td>\n",
              "      <td>12.9815</td>\n",
              "      <td>12.3045</td>\n",
              "      <td>27.5455</td>\n",
              "      <td>28.9130</td>\n",
              "      <td>26.4915</td>\n",
              "      <td>-8.0900</td>\n",
              "      <td>20.5830</td>\n",
              "      <td>1.0080</td>\n",
              "      <td>6.6445</td>\n",
              "      <td>19.3485</td>\n",
              "      <td>22.2440</td>\n",
              "      <td>6.6125</td>\n",
              "      <td>22.6980</td>\n",
              "      <td>27.2405</td>\n",
              "      <td>26.5965</td>\n",
              "      <td>27.1650</td>\n",
              "      <td>23.3115</td>\n",
              "      <td>26.8605</td>\n",
              "      <td>19.3595</td>\n",
              "      <td>12.2510</td>\n",
              "      <td>16.9030</td>\n",
              "      <td>26.9845</td>\n",
              "      <td>23.3070</td>\n",
              "      <td>6.5595</td>\n",
              "      <td>28.7470</td>\n",
              "      <td>7.6845</td>\n",
              "      <td>7.7605</td>\n",
              "      <td>9.3975</td>\n",
              "      <td>17.2155</td>\n",
              "      <td>14.2690</td>\n",
              "      <td>25.6650</td>\n",
              "      <td>24.4445</td>\n",
              "      <td>26.7280</td>\n",
              "      <td>22.7670</td>\n",
              "      <td>27.0245</td>\n",
              "      <td>22.2165</td>\n",
              "      <td>22.7160</td>\n",
              "      <td>4.6730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980</th>\n",
              "      <td>16.3110</td>\n",
              "      <td>25.2955</td>\n",
              "      <td>11.9060</td>\n",
              "      <td>23.1765</td>\n",
              "      <td>26.9700</td>\n",
              "      <td>9.8455</td>\n",
              "      <td>23.0965</td>\n",
              "      <td>27.7015</td>\n",
              "      <td>27.5980</td>\n",
              "      <td>15.0240</td>\n",
              "      <td>8.9100</td>\n",
              "      <td>28.7590</td>\n",
              "      <td>8.3455</td>\n",
              "      <td>22.9370</td>\n",
              "      <td>4.9100</td>\n",
              "      <td>11.0045</td>\n",
              "      <td>26.0035</td>\n",
              "      <td>27.5430</td>\n",
              "      <td>25.6820</td>\n",
              "      <td>26.9115</td>\n",
              "      <td>27.4320</td>\n",
              "      <td>6.5505</td>\n",
              "      <td>8.6210</td>\n",
              "      <td>26.0040</td>\n",
              "      <td>27.1530</td>\n",
              "      <td>13.2670</td>\n",
              "      <td>21.8220</td>\n",
              "      <td>28.1240</td>\n",
              "      <td>9.3955</td>\n",
              "      <td>23.0695</td>\n",
              "      <td>25.3230</td>\n",
              "      <td>27.4175</td>\n",
              "      <td>10.7480</td>\n",
              "      <td>28.3695</td>\n",
              "      <td>25.4160</td>\n",
              "      <td>20.5170</td>\n",
              "      <td>26.9990</td>\n",
              "      <td>24.4480</td>\n",
              "      <td>-3.0985</td>\n",
              "      <td>24.6580</td>\n",
              "      <td>...</td>\n",
              "      <td>22.5275</td>\n",
              "      <td>1.8295</td>\n",
              "      <td>11.6460</td>\n",
              "      <td>12.5635</td>\n",
              "      <td>27.9430</td>\n",
              "      <td>28.9115</td>\n",
              "      <td>26.6025</td>\n",
              "      <td>-9.4415</td>\n",
              "      <td>19.6075</td>\n",
              "      <td>1.9840</td>\n",
              "      <td>5.6505</td>\n",
              "      <td>18.3405</td>\n",
              "      <td>22.9385</td>\n",
              "      <td>6.6220</td>\n",
              "      <td>23.1505</td>\n",
              "      <td>27.0640</td>\n",
              "      <td>26.6145</td>\n",
              "      <td>26.7465</td>\n",
              "      <td>23.2560</td>\n",
              "      <td>26.7225</td>\n",
              "      <td>18.5395</td>\n",
              "      <td>11.8370</td>\n",
              "      <td>16.0270</td>\n",
              "      <td>27.7425</td>\n",
              "      <td>23.3255</td>\n",
              "      <td>7.8195</td>\n",
              "      <td>29.5300</td>\n",
              "      <td>8.0395</td>\n",
              "      <td>8.1750</td>\n",
              "      <td>8.9745</td>\n",
              "      <td>18.1700</td>\n",
              "      <td>13.7570</td>\n",
              "      <td>25.4025</td>\n",
              "      <td>24.9190</td>\n",
              "      <td>27.4175</td>\n",
              "      <td>22.7095</td>\n",
              "      <td>27.1850</td>\n",
              "      <td>22.4310</td>\n",
              "      <td>22.2040</td>\n",
              "      <td>4.5410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1981</th>\n",
              "      <td>14.4390</td>\n",
              "      <td>25.2910</td>\n",
              "      <td>13.3060</td>\n",
              "      <td>24.6310</td>\n",
              "      <td>26.8290</td>\n",
              "      <td>11.2490</td>\n",
              "      <td>22.7700</td>\n",
              "      <td>27.4895</td>\n",
              "      <td>27.3860</td>\n",
              "      <td>15.3170</td>\n",
              "      <td>9.3615</td>\n",
              "      <td>28.6780</td>\n",
              "      <td>8.7120</td>\n",
              "      <td>23.2340</td>\n",
              "      <td>7.1215</td>\n",
              "      <td>11.5385</td>\n",
              "      <td>25.4515</td>\n",
              "      <td>26.8710</td>\n",
              "      <td>25.6625</td>\n",
              "      <td>26.4805</td>\n",
              "      <td>27.4550</td>\n",
              "      <td>5.9660</td>\n",
              "      <td>8.9845</td>\n",
              "      <td>25.8845</td>\n",
              "      <td>27.1095</td>\n",
              "      <td>13.0965</td>\n",
              "      <td>21.7255</td>\n",
              "      <td>28.1885</td>\n",
              "      <td>11.4500</td>\n",
              "      <td>22.3525</td>\n",
              "      <td>25.2685</td>\n",
              "      <td>27.1800</td>\n",
              "      <td>11.4455</td>\n",
              "      <td>28.0100</td>\n",
              "      <td>25.5910</td>\n",
              "      <td>20.5595</td>\n",
              "      <td>27.4505</td>\n",
              "      <td>23.8580</td>\n",
              "      <td>-4.5375</td>\n",
              "      <td>24.4005</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3750</td>\n",
              "      <td>1.8185</td>\n",
              "      <td>12.4210</td>\n",
              "      <td>13.1770</td>\n",
              "      <td>27.4510</td>\n",
              "      <td>28.7735</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>-7.2270</td>\n",
              "      <td>18.3175</td>\n",
              "      <td>2.0555</td>\n",
              "      <td>7.5865</td>\n",
              "      <td>18.3090</td>\n",
              "      <td>22.9930</td>\n",
              "      <td>4.7470</td>\n",
              "      <td>22.7970</td>\n",
              "      <td>27.0770</td>\n",
              "      <td>26.4810</td>\n",
              "      <td>27.0045</td>\n",
              "      <td>23.5860</td>\n",
              "      <td>26.8230</td>\n",
              "      <td>21.2485</td>\n",
              "      <td>11.5180</td>\n",
              "      <td>14.4145</td>\n",
              "      <td>27.1660</td>\n",
              "      <td>23.2490</td>\n",
              "      <td>7.9545</td>\n",
              "      <td>28.6970</td>\n",
              "      <td>7.1895</td>\n",
              "      <td>7.2330</td>\n",
              "      <td>9.6250</td>\n",
              "      <td>18.0710</td>\n",
              "      <td>12.1485</td>\n",
              "      <td>25.4545</td>\n",
              "      <td>25.1685</td>\n",
              "      <td>27.1800</td>\n",
              "      <td>22.9300</td>\n",
              "      <td>26.8900</td>\n",
              "      <td>22.3740</td>\n",
              "      <td>21.6195</td>\n",
              "      <td>4.8730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1982</th>\n",
              "      <td>15.0705</td>\n",
              "      <td>25.1225</td>\n",
              "      <td>12.4050</td>\n",
              "      <td>23.0310</td>\n",
              "      <td>26.8895</td>\n",
              "      <td>10.8620</td>\n",
              "      <td>22.8485</td>\n",
              "      <td>26.9350</td>\n",
              "      <td>26.8135</td>\n",
              "      <td>15.5410</td>\n",
              "      <td>9.9095</td>\n",
              "      <td>28.4090</td>\n",
              "      <td>8.7315</td>\n",
              "      <td>22.1050</td>\n",
              "      <td>6.0195</td>\n",
              "      <td>11.9370</td>\n",
              "      <td>25.3505</td>\n",
              "      <td>27.0510</td>\n",
              "      <td>25.8175</td>\n",
              "      <td>26.8630</td>\n",
              "      <td>27.0375</td>\n",
              "      <td>5.9815</td>\n",
              "      <td>9.4850</td>\n",
              "      <td>25.8900</td>\n",
              "      <td>26.6765</td>\n",
              "      <td>12.4010</td>\n",
              "      <td>21.6555</td>\n",
              "      <td>27.7460</td>\n",
              "      <td>10.2365</td>\n",
              "      <td>23.3875</td>\n",
              "      <td>25.4350</td>\n",
              "      <td>26.5140</td>\n",
              "      <td>10.4100</td>\n",
              "      <td>28.1800</td>\n",
              "      <td>25.3380</td>\n",
              "      <td>20.3195</td>\n",
              "      <td>26.9950</td>\n",
              "      <td>23.9245</td>\n",
              "      <td>-5.2400</td>\n",
              "      <td>24.0320</td>\n",
              "      <td>...</td>\n",
              "      <td>22.4205</td>\n",
              "      <td>2.0085</td>\n",
              "      <td>13.2350</td>\n",
              "      <td>12.9710</td>\n",
              "      <td>27.8540</td>\n",
              "      <td>28.5595</td>\n",
              "      <td>26.3435</td>\n",
              "      <td>-8.6855</td>\n",
              "      <td>19.1065</td>\n",
              "      <td>2.6855</td>\n",
              "      <td>6.6365</td>\n",
              "      <td>18.8845</td>\n",
              "      <td>23.1930</td>\n",
              "      <td>5.9250</td>\n",
              "      <td>22.8930</td>\n",
              "      <td>26.5910</td>\n",
              "      <td>25.9385</td>\n",
              "      <td>26.7845</td>\n",
              "      <td>23.4195</td>\n",
              "      <td>26.4970</td>\n",
              "      <td>19.9365</td>\n",
              "      <td>12.1510</td>\n",
              "      <td>16.0500</td>\n",
              "      <td>26.8730</td>\n",
              "      <td>22.9630</td>\n",
              "      <td>7.6625</td>\n",
              "      <td>28.2430</td>\n",
              "      <td>8.6750</td>\n",
              "      <td>8.7895</td>\n",
              "      <td>7.7690</td>\n",
              "      <td>17.7920</td>\n",
              "      <td>14.0850</td>\n",
              "      <td>25.3885</td>\n",
              "      <td>24.4430</td>\n",
              "      <td>26.5140</td>\n",
              "      <td>21.9365</td>\n",
              "      <td>26.1680</td>\n",
              "      <td>22.7425</td>\n",
              "      <td>22.3540</td>\n",
              "      <td>6.4350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983</th>\n",
              "      <td>13.6025</td>\n",
              "      <td>25.4250</td>\n",
              "      <td>12.6770</td>\n",
              "      <td>23.9725</td>\n",
              "      <td>26.9605</td>\n",
              "      <td>11.1960</td>\n",
              "      <td>23.3130</td>\n",
              "      <td>27.3235</td>\n",
              "      <td>27.1775</td>\n",
              "      <td>15.6850</td>\n",
              "      <td>9.7915</td>\n",
              "      <td>28.8825</td>\n",
              "      <td>8.4310</td>\n",
              "      <td>22.2530</td>\n",
              "      <td>7.6185</td>\n",
              "      <td>12.1520</td>\n",
              "      <td>25.4500</td>\n",
              "      <td>25.1865</td>\n",
              "      <td>25.8870</td>\n",
              "      <td>26.9090</td>\n",
              "      <td>27.1310</td>\n",
              "      <td>8.0855</td>\n",
              "      <td>9.7560</td>\n",
              "      <td>25.9880</td>\n",
              "      <td>27.2475</td>\n",
              "      <td>12.5755</td>\n",
              "      <td>22.6180</td>\n",
              "      <td>28.0590</td>\n",
              "      <td>11.3500</td>\n",
              "      <td>24.3635</td>\n",
              "      <td>25.5180</td>\n",
              "      <td>26.9610</td>\n",
              "      <td>11.3670</td>\n",
              "      <td>28.5430</td>\n",
              "      <td>25.5710</td>\n",
              "      <td>20.9425</td>\n",
              "      <td>27.2510</td>\n",
              "      <td>23.9470</td>\n",
              "      <td>-4.0675</td>\n",
              "      <td>24.3705</td>\n",
              "      <td>...</td>\n",
              "      <td>22.7370</td>\n",
              "      <td>1.1670</td>\n",
              "      <td>13.5940</td>\n",
              "      <td>13.2070</td>\n",
              "      <td>27.9245</td>\n",
              "      <td>28.2585</td>\n",
              "      <td>26.8525</td>\n",
              "      <td>-9.3495</td>\n",
              "      <td>20.5310</td>\n",
              "      <td>2.8695</td>\n",
              "      <td>7.5765</td>\n",
              "      <td>17.5990</td>\n",
              "      <td>23.5075</td>\n",
              "      <td>5.1340</td>\n",
              "      <td>23.4135</td>\n",
              "      <td>27.0955</td>\n",
              "      <td>26.8185</td>\n",
              "      <td>27.2290</td>\n",
              "      <td>23.5235</td>\n",
              "      <td>26.8745</td>\n",
              "      <td>20.2350</td>\n",
              "      <td>11.8100</td>\n",
              "      <td>15.6115</td>\n",
              "      <td>27.2430</td>\n",
              "      <td>23.2430</td>\n",
              "      <td>9.7780</td>\n",
              "      <td>27.1970</td>\n",
              "      <td>8.1720</td>\n",
              "      <td>8.2525</td>\n",
              "      <td>8.2395</td>\n",
              "      <td>17.7920</td>\n",
              "      <td>13.9635</td>\n",
              "      <td>25.6870</td>\n",
              "      <td>24.9960</td>\n",
              "      <td>26.9610</td>\n",
              "      <td>23.0355</td>\n",
              "      <td>26.4130</td>\n",
              "      <td>23.0990</td>\n",
              "      <td>23.3410</td>\n",
              "      <td>5.1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>14.4185</td>\n",
              "      <td>25.2940</td>\n",
              "      <td>12.7785</td>\n",
              "      <td>23.3215</td>\n",
              "      <td>26.8765</td>\n",
              "      <td>10.2310</td>\n",
              "      <td>23.0485</td>\n",
              "      <td>26.8730</td>\n",
              "      <td>26.6555</td>\n",
              "      <td>15.0610</td>\n",
              "      <td>9.4810</td>\n",
              "      <td>28.2350</td>\n",
              "      <td>7.8630</td>\n",
              "      <td>22.3420</td>\n",
              "      <td>6.5885</td>\n",
              "      <td>11.6855</td>\n",
              "      <td>25.2615</td>\n",
              "      <td>26.6575</td>\n",
              "      <td>25.4225</td>\n",
              "      <td>27.3540</td>\n",
              "      <td>26.8000</td>\n",
              "      <td>8.3380</td>\n",
              "      <td>9.5375</td>\n",
              "      <td>25.9700</td>\n",
              "      <td>27.1945</td>\n",
              "      <td>13.9775</td>\n",
              "      <td>22.0595</td>\n",
              "      <td>27.4670</td>\n",
              "      <td>10.8400</td>\n",
              "      <td>24.3660</td>\n",
              "      <td>25.3015</td>\n",
              "      <td>26.7050</td>\n",
              "      <td>11.2590</td>\n",
              "      <td>28.5710</td>\n",
              "      <td>25.0275</td>\n",
              "      <td>20.3640</td>\n",
              "      <td>26.8380</td>\n",
              "      <td>24.2640</td>\n",
              "      <td>-3.2890</td>\n",
              "      <td>24.6420</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3715</td>\n",
              "      <td>1.2355</td>\n",
              "      <td>12.3465</td>\n",
              "      <td>12.6025</td>\n",
              "      <td>27.1130</td>\n",
              "      <td>29.2300</td>\n",
              "      <td>26.1190</td>\n",
              "      <td>-4.1095</td>\n",
              "      <td>19.5585</td>\n",
              "      <td>3.5675</td>\n",
              "      <td>6.7965</td>\n",
              "      <td>18.2125</td>\n",
              "      <td>22.6005</td>\n",
              "      <td>5.0195</td>\n",
              "      <td>22.7115</td>\n",
              "      <td>26.7395</td>\n",
              "      <td>26.3165</td>\n",
              "      <td>26.9930</td>\n",
              "      <td>23.6155</td>\n",
              "      <td>26.2045</td>\n",
              "      <td>19.2540</td>\n",
              "      <td>11.6960</td>\n",
              "      <td>15.6185</td>\n",
              "      <td>26.8415</td>\n",
              "      <td>23.1750</td>\n",
              "      <td>9.3965</td>\n",
              "      <td>28.0750</td>\n",
              "      <td>8.3550</td>\n",
              "      <td>8.5465</td>\n",
              "      <td>8.3445</td>\n",
              "      <td>17.7430</td>\n",
              "      <td>13.1145</td>\n",
              "      <td>25.0260</td>\n",
              "      <td>24.6625</td>\n",
              "      <td>26.7050</td>\n",
              "      <td>22.3915</td>\n",
              "      <td>26.5425</td>\n",
              "      <td>22.5665</td>\n",
              "      <td>23.3255</td>\n",
              "      <td>6.7930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985</th>\n",
              "      <td>14.8955</td>\n",
              "      <td>24.9255</td>\n",
              "      <td>12.6220</td>\n",
              "      <td>23.3455</td>\n",
              "      <td>26.8040</td>\n",
              "      <td>11.2395</td>\n",
              "      <td>22.9350</td>\n",
              "      <td>26.9935</td>\n",
              "      <td>26.7665</td>\n",
              "      <td>14.8510</td>\n",
              "      <td>9.5890</td>\n",
              "      <td>28.0270</td>\n",
              "      <td>8.6040</td>\n",
              "      <td>22.1965</td>\n",
              "      <td>6.5250</td>\n",
              "      <td>11.4595</td>\n",
              "      <td>26.0330</td>\n",
              "      <td>26.5245</td>\n",
              "      <td>25.7155</td>\n",
              "      <td>27.2310</td>\n",
              "      <td>26.7695</td>\n",
              "      <td>6.8900</td>\n",
              "      <td>9.7155</td>\n",
              "      <td>25.6225</td>\n",
              "      <td>27.4820</td>\n",
              "      <td>13.5070</td>\n",
              "      <td>21.8845</td>\n",
              "      <td>27.4385</td>\n",
              "      <td>10.5455</td>\n",
              "      <td>23.6740</td>\n",
              "      <td>25.3570</td>\n",
              "      <td>26.5675</td>\n",
              "      <td>10.7890</td>\n",
              "      <td>27.7515</td>\n",
              "      <td>25.2980</td>\n",
              "      <td>20.2170</td>\n",
              "      <td>27.1505</td>\n",
              "      <td>24.2720</td>\n",
              "      <td>-5.1105</td>\n",
              "      <td>24.3275</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3895</td>\n",
              "      <td>1.7830</td>\n",
              "      <td>13.2320</td>\n",
              "      <td>13.2890</td>\n",
              "      <td>27.4085</td>\n",
              "      <td>28.4740</td>\n",
              "      <td>26.1560</td>\n",
              "      <td>-9.4110</td>\n",
              "      <td>20.1825</td>\n",
              "      <td>1.9995</td>\n",
              "      <td>7.6130</td>\n",
              "      <td>18.2035</td>\n",
              "      <td>22.8485</td>\n",
              "      <td>6.1065</td>\n",
              "      <td>22.5400</td>\n",
              "      <td>26.6820</td>\n",
              "      <td>26.7680</td>\n",
              "      <td>27.2585</td>\n",
              "      <td>23.2565</td>\n",
              "      <td>26.3285</td>\n",
              "      <td>20.0520</td>\n",
              "      <td>11.5795</td>\n",
              "      <td>15.4840</td>\n",
              "      <td>27.1880</td>\n",
              "      <td>23.1115</td>\n",
              "      <td>8.2485</td>\n",
              "      <td>27.9090</td>\n",
              "      <td>8.5780</td>\n",
              "      <td>8.7565</td>\n",
              "      <td>9.0720</td>\n",
              "      <td>17.4000</td>\n",
              "      <td>13.2645</td>\n",
              "      <td>24.9395</td>\n",
              "      <td>24.2640</td>\n",
              "      <td>26.5675</td>\n",
              "      <td>21.7620</td>\n",
              "      <td>26.1620</td>\n",
              "      <td>22.4485</td>\n",
              "      <td>22.4450</td>\n",
              "      <td>3.8720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1986</th>\n",
              "      <td>15.1815</td>\n",
              "      <td>25.1730</td>\n",
              "      <td>13.3720</td>\n",
              "      <td>23.5470</td>\n",
              "      <td>27.1245</td>\n",
              "      <td>10.9900</td>\n",
              "      <td>22.8715</td>\n",
              "      <td>26.8440</td>\n",
              "      <td>26.6995</td>\n",
              "      <td>15.5155</td>\n",
              "      <td>10.5700</td>\n",
              "      <td>28.0475</td>\n",
              "      <td>8.6130</td>\n",
              "      <td>22.2580</td>\n",
              "      <td>7.1915</td>\n",
              "      <td>12.4225</td>\n",
              "      <td>26.0135</td>\n",
              "      <td>27.4740</td>\n",
              "      <td>25.6205</td>\n",
              "      <td>26.5340</td>\n",
              "      <td>26.9460</td>\n",
              "      <td>6.8400</td>\n",
              "      <td>9.7240</td>\n",
              "      <td>25.5525</td>\n",
              "      <td>26.7085</td>\n",
              "      <td>11.8370</td>\n",
              "      <td>22.0620</td>\n",
              "      <td>27.6100</td>\n",
              "      <td>11.3860</td>\n",
              "      <td>23.3250</td>\n",
              "      <td>25.3650</td>\n",
              "      <td>26.4680</td>\n",
              "      <td>11.4375</td>\n",
              "      <td>28.0790</td>\n",
              "      <td>24.7745</td>\n",
              "      <td>20.1440</td>\n",
              "      <td>27.0420</td>\n",
              "      <td>24.1695</td>\n",
              "      <td>-4.8195</td>\n",
              "      <td>23.9395</td>\n",
              "      <td>...</td>\n",
              "      <td>22.5275</td>\n",
              "      <td>1.5435</td>\n",
              "      <td>12.2235</td>\n",
              "      <td>12.7690</td>\n",
              "      <td>27.5600</td>\n",
              "      <td>28.4560</td>\n",
              "      <td>26.3520</td>\n",
              "      <td>-7.9930</td>\n",
              "      <td>19.8690</td>\n",
              "      <td>2.2540</td>\n",
              "      <td>7.0035</td>\n",
              "      <td>19.7205</td>\n",
              "      <td>22.6770</td>\n",
              "      <td>5.5895</td>\n",
              "      <td>22.9225</td>\n",
              "      <td>26.8110</td>\n",
              "      <td>26.4585</td>\n",
              "      <td>26.5650</td>\n",
              "      <td>22.8320</td>\n",
              "      <td>26.3620</td>\n",
              "      <td>20.4205</td>\n",
              "      <td>12.8230</td>\n",
              "      <td>15.7825</td>\n",
              "      <td>26.9835</td>\n",
              "      <td>23.4960</td>\n",
              "      <td>9.0475</td>\n",
              "      <td>28.7965</td>\n",
              "      <td>8.5325</td>\n",
              "      <td>8.6855</td>\n",
              "      <td>9.0425</td>\n",
              "      <td>17.9370</td>\n",
              "      <td>13.3115</td>\n",
              "      <td>25.1655</td>\n",
              "      <td>24.6800</td>\n",
              "      <td>26.4680</td>\n",
              "      <td>22.1385</td>\n",
              "      <td>26.2835</td>\n",
              "      <td>22.2760</td>\n",
              "      <td>22.4890</td>\n",
              "      <td>6.4775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>13.6260</td>\n",
              "      <td>25.5695</td>\n",
              "      <td>12.5730</td>\n",
              "      <td>24.7010</td>\n",
              "      <td>27.1080</td>\n",
              "      <td>11.4880</td>\n",
              "      <td>23.5495</td>\n",
              "      <td>27.3765</td>\n",
              "      <td>27.3890</td>\n",
              "      <td>15.6910</td>\n",
              "      <td>7.0025</td>\n",
              "      <td>29.0670</td>\n",
              "      <td>7.8260</td>\n",
              "      <td>23.3055</td>\n",
              "      <td>7.6480</td>\n",
              "      <td>8.8330</td>\n",
              "      <td>25.8125</td>\n",
              "      <td>26.9020</td>\n",
              "      <td>25.9975</td>\n",
              "      <td>27.3550</td>\n",
              "      <td>27.6400</td>\n",
              "      <td>4.8660</td>\n",
              "      <td>10.8445</td>\n",
              "      <td>25.3045</td>\n",
              "      <td>27.5715</td>\n",
              "      <td>12.8815</td>\n",
              "      <td>22.6500</td>\n",
              "      <td>28.5615</td>\n",
              "      <td>11.1400</td>\n",
              "      <td>24.7975</td>\n",
              "      <td>25.7660</td>\n",
              "      <td>27.1615</td>\n",
              "      <td>9.4115</td>\n",
              "      <td>28.9900</td>\n",
              "      <td>25.5110</td>\n",
              "      <td>20.7535</td>\n",
              "      <td>27.3425</td>\n",
              "      <td>24.7320</td>\n",
              "      <td>-3.2360</td>\n",
              "      <td>25.0650</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0975</td>\n",
              "      <td>0.9260</td>\n",
              "      <td>13.2315</td>\n",
              "      <td>13.7255</td>\n",
              "      <td>28.1585</td>\n",
              "      <td>28.4215</td>\n",
              "      <td>27.0110</td>\n",
              "      <td>-9.4650</td>\n",
              "      <td>19.8040</td>\n",
              "      <td>3.5965</td>\n",
              "      <td>8.1755</td>\n",
              "      <td>17.3110</td>\n",
              "      <td>23.2195</td>\n",
              "      <td>3.0620</td>\n",
              "      <td>23.4690</td>\n",
              "      <td>27.3125</td>\n",
              "      <td>26.8115</td>\n",
              "      <td>27.5480</td>\n",
              "      <td>23.1790</td>\n",
              "      <td>27.1705</td>\n",
              "      <td>20.2295</td>\n",
              "      <td>10.5270</td>\n",
              "      <td>12.0260</td>\n",
              "      <td>27.4970</td>\n",
              "      <td>24.0255</td>\n",
              "      <td>6.0225</td>\n",
              "      <td>28.2075</td>\n",
              "      <td>8.6800</td>\n",
              "      <td>8.8405</td>\n",
              "      <td>9.4065</td>\n",
              "      <td>17.8325</td>\n",
              "      <td>9.6325</td>\n",
              "      <td>26.0220</td>\n",
              "      <td>25.2870</td>\n",
              "      <td>27.1615</td>\n",
              "      <td>24.5010</td>\n",
              "      <td>26.7970</td>\n",
              "      <td>23.1640</td>\n",
              "      <td>23.3280</td>\n",
              "      <td>4.4655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1988</th>\n",
              "      <td>15.2810</td>\n",
              "      <td>25.4000</td>\n",
              "      <td>12.3825</td>\n",
              "      <td>24.6335</td>\n",
              "      <td>26.9025</td>\n",
              "      <td>11.9130</td>\n",
              "      <td>23.2305</td>\n",
              "      <td>27.4250</td>\n",
              "      <td>27.2265</td>\n",
              "      <td>14.4780</td>\n",
              "      <td>9.7000</td>\n",
              "      <td>28.2675</td>\n",
              "      <td>8.8560</td>\n",
              "      <td>23.8240</td>\n",
              "      <td>7.5550</td>\n",
              "      <td>12.0510</td>\n",
              "      <td>25.5225</td>\n",
              "      <td>27.5240</td>\n",
              "      <td>25.8190</td>\n",
              "      <td>27.6420</td>\n",
              "      <td>27.1380</td>\n",
              "      <td>5.4890</td>\n",
              "      <td>10.2680</td>\n",
              "      <td>25.7080</td>\n",
              "      <td>27.1760</td>\n",
              "      <td>13.4655</td>\n",
              "      <td>22.4425</td>\n",
              "      <td>27.6645</td>\n",
              "      <td>10.6920</td>\n",
              "      <td>23.5455</td>\n",
              "      <td>25.6060</td>\n",
              "      <td>27.1650</td>\n",
              "      <td>9.4995</td>\n",
              "      <td>27.8840</td>\n",
              "      <td>25.5755</td>\n",
              "      <td>20.6425</td>\n",
              "      <td>27.4905</td>\n",
              "      <td>24.3870</td>\n",
              "      <td>-3.1090</td>\n",
              "      <td>24.6430</td>\n",
              "      <td>...</td>\n",
              "      <td>22.4555</td>\n",
              "      <td>0.6045</td>\n",
              "      <td>13.0165</td>\n",
              "      <td>13.6760</td>\n",
              "      <td>27.6270</td>\n",
              "      <td>28.0385</td>\n",
              "      <td>26.6280</td>\n",
              "      <td>-12.9070</td>\n",
              "      <td>19.6100</td>\n",
              "      <td>1.1910</td>\n",
              "      <td>8.4545</td>\n",
              "      <td>17.9125</td>\n",
              "      <td>22.1410</td>\n",
              "      <td>5.6615</td>\n",
              "      <td>23.1850</td>\n",
              "      <td>27.1565</td>\n",
              "      <td>27.0350</td>\n",
              "      <td>27.1495</td>\n",
              "      <td>24.3065</td>\n",
              "      <td>26.8810</td>\n",
              "      <td>21.4540</td>\n",
              "      <td>11.5515</td>\n",
              "      <td>16.0485</td>\n",
              "      <td>27.0260</td>\n",
              "      <td>23.1775</td>\n",
              "      <td>7.4885</td>\n",
              "      <td>28.8125</td>\n",
              "      <td>8.3375</td>\n",
              "      <td>8.5425</td>\n",
              "      <td>8.9530</td>\n",
              "      <td>16.2830</td>\n",
              "      <td>13.8960</td>\n",
              "      <td>25.2890</td>\n",
              "      <td>24.2045</td>\n",
              "      <td>27.1650</td>\n",
              "      <td>22.5255</td>\n",
              "      <td>27.0845</td>\n",
              "      <td>22.6545</td>\n",
              "      <td>22.8105</td>\n",
              "      <td>3.9515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>14.6015</td>\n",
              "      <td>25.1320</td>\n",
              "      <td>12.9095</td>\n",
              "      <td>23.7400</td>\n",
              "      <td>26.7715</td>\n",
              "      <td>11.1915</td>\n",
              "      <td>22.4795</td>\n",
              "      <td>27.1480</td>\n",
              "      <td>26.9740</td>\n",
              "      <td>15.4785</td>\n",
              "      <td>11.2820</td>\n",
              "      <td>28.4010</td>\n",
              "      <td>8.6025</td>\n",
              "      <td>22.8395</td>\n",
              "      <td>7.4385</td>\n",
              "      <td>13.0840</td>\n",
              "      <td>26.0115</td>\n",
              "      <td>26.7130</td>\n",
              "      <td>25.6710</td>\n",
              "      <td>27.5585</td>\n",
              "      <td>27.0440</td>\n",
              "      <td>7.8820</td>\n",
              "      <td>10.6080</td>\n",
              "      <td>25.6040</td>\n",
              "      <td>26.8640</td>\n",
              "      <td>13.4490</td>\n",
              "      <td>21.6025</td>\n",
              "      <td>27.6745</td>\n",
              "      <td>11.3560</td>\n",
              "      <td>23.1610</td>\n",
              "      <td>25.2590</td>\n",
              "      <td>26.8440</td>\n",
              "      <td>11.9800</td>\n",
              "      <td>27.9425</td>\n",
              "      <td>25.4660</td>\n",
              "      <td>19.8570</td>\n",
              "      <td>27.1370</td>\n",
              "      <td>24.2240</td>\n",
              "      <td>-3.9400</td>\n",
              "      <td>24.6915</td>\n",
              "      <td>...</td>\n",
              "      <td>22.3640</td>\n",
              "      <td>2.2945</td>\n",
              "      <td>13.4820</td>\n",
              "      <td>14.0085</td>\n",
              "      <td>27.3575</td>\n",
              "      <td>28.6510</td>\n",
              "      <td>26.2005</td>\n",
              "      <td>-8.3755</td>\n",
              "      <td>19.2040</td>\n",
              "      <td>2.9025</td>\n",
              "      <td>7.5375</td>\n",
              "      <td>20.3050</td>\n",
              "      <td>22.5190</td>\n",
              "      <td>4.8890</td>\n",
              "      <td>22.4225</td>\n",
              "      <td>26.9190</td>\n",
              "      <td>26.6215</td>\n",
              "      <td>26.7040</td>\n",
              "      <td>23.8370</td>\n",
              "      <td>26.4810</td>\n",
              "      <td>20.1755</td>\n",
              "      <td>13.6730</td>\n",
              "      <td>15.8665</td>\n",
              "      <td>27.2360</td>\n",
              "      <td>22.9200</td>\n",
              "      <td>9.8140</td>\n",
              "      <td>27.7450</td>\n",
              "      <td>8.7065</td>\n",
              "      <td>8.7470</td>\n",
              "      <td>9.6700</td>\n",
              "      <td>17.6955</td>\n",
              "      <td>12.6615</td>\n",
              "      <td>25.1060</td>\n",
              "      <td>24.6570</td>\n",
              "      <td>26.8440</td>\n",
              "      <td>22.6755</td>\n",
              "      <td>25.8545</td>\n",
              "      <td>22.4170</td>\n",
              "      <td>22.5005</td>\n",
              "      <td>5.6370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>14.4935</td>\n",
              "      <td>25.6785</td>\n",
              "      <td>13.4490</td>\n",
              "      <td>24.5480</td>\n",
              "      <td>27.2050</td>\n",
              "      <td>11.3630</td>\n",
              "      <td>22.8845</td>\n",
              "      <td>27.4750</td>\n",
              "      <td>27.3155</td>\n",
              "      <td>15.4390</td>\n",
              "      <td>9.2625</td>\n",
              "      <td>28.4645</td>\n",
              "      <td>9.1645</td>\n",
              "      <td>22.7685</td>\n",
              "      <td>6.8430</td>\n",
              "      <td>11.3530</td>\n",
              "      <td>25.8385</td>\n",
              "      <td>26.8575</td>\n",
              "      <td>25.8980</td>\n",
              "      <td>26.3050</td>\n",
              "      <td>27.4750</td>\n",
              "      <td>7.6130</td>\n",
              "      <td>10.3725</td>\n",
              "      <td>26.0340</td>\n",
              "      <td>27.6235</td>\n",
              "      <td>12.1430</td>\n",
              "      <td>22.1365</td>\n",
              "      <td>28.0420</td>\n",
              "      <td>11.3720</td>\n",
              "      <td>23.7665</td>\n",
              "      <td>25.6170</td>\n",
              "      <td>27.2110</td>\n",
              "      <td>11.3675</td>\n",
              "      <td>28.3065</td>\n",
              "      <td>25.0780</td>\n",
              "      <td>20.4255</td>\n",
              "      <td>27.2930</td>\n",
              "      <td>24.9810</td>\n",
              "      <td>-4.6380</td>\n",
              "      <td>24.3260</td>\n",
              "      <td>...</td>\n",
              "      <td>22.9105</td>\n",
              "      <td>1.6280</td>\n",
              "      <td>12.9370</td>\n",
              "      <td>13.1150</td>\n",
              "      <td>27.6465</td>\n",
              "      <td>29.1060</td>\n",
              "      <td>26.5510</td>\n",
              "      <td>-6.6820</td>\n",
              "      <td>20.0810</td>\n",
              "      <td>3.6325</td>\n",
              "      <td>7.7080</td>\n",
              "      <td>18.9630</td>\n",
              "      <td>22.8585</td>\n",
              "      <td>4.8225</td>\n",
              "      <td>22.9795</td>\n",
              "      <td>27.1195</td>\n",
              "      <td>26.6685</td>\n",
              "      <td>27.5955</td>\n",
              "      <td>23.9020</td>\n",
              "      <td>26.8710</td>\n",
              "      <td>20.7655</td>\n",
              "      <td>12.1875</td>\n",
              "      <td>15.0015</td>\n",
              "      <td>27.4025</td>\n",
              "      <td>23.3435</td>\n",
              "      <td>9.0585</td>\n",
              "      <td>28.8700</td>\n",
              "      <td>9.2085</td>\n",
              "      <td>9.2875</td>\n",
              "      <td>9.4860</td>\n",
              "      <td>18.5705</td>\n",
              "      <td>12.7960</td>\n",
              "      <td>25.5775</td>\n",
              "      <td>24.9875</td>\n",
              "      <td>27.2110</td>\n",
              "      <td>23.2320</td>\n",
              "      <td>26.5170</td>\n",
              "      <td>22.7115</td>\n",
              "      <td>22.8805</td>\n",
              "      <td>6.1945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>14.3920</td>\n",
              "      <td>25.2885</td>\n",
              "      <td>11.5885</td>\n",
              "      <td>23.0320</td>\n",
              "      <td>27.0785</td>\n",
              "      <td>9.9675</td>\n",
              "      <td>22.4500</td>\n",
              "      <td>27.1310</td>\n",
              "      <td>26.9055</td>\n",
              "      <td>14.9220</td>\n",
              "      <td>11.2955</td>\n",
              "      <td>28.4485</td>\n",
              "      <td>9.2555</td>\n",
              "      <td>23.4120</td>\n",
              "      <td>5.6870</td>\n",
              "      <td>13.2390</td>\n",
              "      <td>26.0375</td>\n",
              "      <td>27.0465</td>\n",
              "      <td>25.8225</td>\n",
              "      <td>27.1025</td>\n",
              "      <td>26.7765</td>\n",
              "      <td>6.8790</td>\n",
              "      <td>9.5385</td>\n",
              "      <td>26.2130</td>\n",
              "      <td>27.1145</td>\n",
              "      <td>13.2025</td>\n",
              "      <td>22.4135</td>\n",
              "      <td>27.7510</td>\n",
              "      <td>9.5535</td>\n",
              "      <td>23.6465</td>\n",
              "      <td>25.6580</td>\n",
              "      <td>26.7895</td>\n",
              "      <td>10.4165</td>\n",
              "      <td>28.1310</td>\n",
              "      <td>25.7055</td>\n",
              "      <td>20.4680</td>\n",
              "      <td>27.0050</td>\n",
              "      <td>24.3415</td>\n",
              "      <td>-4.2405</td>\n",
              "      <td>24.3025</td>\n",
              "      <td>...</td>\n",
              "      <td>22.5105</td>\n",
              "      <td>1.5385</td>\n",
              "      <td>12.8350</td>\n",
              "      <td>11.8620</td>\n",
              "      <td>28.1155</td>\n",
              "      <td>28.5530</td>\n",
              "      <td>26.5575</td>\n",
              "      <td>-9.2070</td>\n",
              "      <td>20.0135</td>\n",
              "      <td>2.9050</td>\n",
              "      <td>6.2930</td>\n",
              "      <td>19.6760</td>\n",
              "      <td>22.2825</td>\n",
              "      <td>5.4510</td>\n",
              "      <td>23.0170</td>\n",
              "      <td>26.9550</td>\n",
              "      <td>26.3005</td>\n",
              "      <td>26.8985</td>\n",
              "      <td>23.3495</td>\n",
              "      <td>26.5235</td>\n",
              "      <td>18.6340</td>\n",
              "      <td>12.4785</td>\n",
              "      <td>16.7895</td>\n",
              "      <td>27.1980</td>\n",
              "      <td>23.0655</td>\n",
              "      <td>9.1445</td>\n",
              "      <td>28.5425</td>\n",
              "      <td>8.2215</td>\n",
              "      <td>8.3675</td>\n",
              "      <td>9.6490</td>\n",
              "      <td>17.5385</td>\n",
              "      <td>14.6800</td>\n",
              "      <td>25.4995</td>\n",
              "      <td>24.6115</td>\n",
              "      <td>26.7895</td>\n",
              "      <td>22.2000</td>\n",
              "      <td>27.0470</td>\n",
              "      <td>22.3795</td>\n",
              "      <td>23.3265</td>\n",
              "      <td>5.4660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>13.7520</td>\n",
              "      <td>25.4245</td>\n",
              "      <td>13.5850</td>\n",
              "      <td>23.0325</td>\n",
              "      <td>27.0925</td>\n",
              "      <td>10.1285</td>\n",
              "      <td>23.3055</td>\n",
              "      <td>27.0675</td>\n",
              "      <td>26.9020</td>\n",
              "      <td>14.9460</td>\n",
              "      <td>9.0375</td>\n",
              "      <td>28.7775</td>\n",
              "      <td>8.0045</td>\n",
              "      <td>22.4940</td>\n",
              "      <td>5.9320</td>\n",
              "      <td>11.0120</td>\n",
              "      <td>25.3285</td>\n",
              "      <td>25.3180</td>\n",
              "      <td>25.6765</td>\n",
              "      <td>27.1445</td>\n",
              "      <td>27.1770</td>\n",
              "      <td>4.5765</td>\n",
              "      <td>8.3875</td>\n",
              "      <td>26.1505</td>\n",
              "      <td>26.6930</td>\n",
              "      <td>13.3190</td>\n",
              "      <td>22.0565</td>\n",
              "      <td>27.9635</td>\n",
              "      <td>11.3460</td>\n",
              "      <td>25.1265</td>\n",
              "      <td>25.5640</td>\n",
              "      <td>26.6865</td>\n",
              "      <td>11.9140</td>\n",
              "      <td>27.8100</td>\n",
              "      <td>25.0580</td>\n",
              "      <td>20.5505</td>\n",
              "      <td>27.0905</td>\n",
              "      <td>23.8705</td>\n",
              "      <td>-5.0440</td>\n",
              "      <td>24.4820</td>\n",
              "      <td>...</td>\n",
              "      <td>22.6660</td>\n",
              "      <td>1.2625</td>\n",
              "      <td>12.7945</td>\n",
              "      <td>12.7425</td>\n",
              "      <td>27.6085</td>\n",
              "      <td>28.7310</td>\n",
              "      <td>26.7185</td>\n",
              "      <td>-8.8055</td>\n",
              "      <td>21.4250</td>\n",
              "      <td>-0.1735</td>\n",
              "      <td>6.3310</td>\n",
              "      <td>19.0425</td>\n",
              "      <td>21.9455</td>\n",
              "      <td>5.0470</td>\n",
              "      <td>23.2815</td>\n",
              "      <td>26.9725</td>\n",
              "      <td>26.9170</td>\n",
              "      <td>26.4550</td>\n",
              "      <td>23.0870</td>\n",
              "      <td>26.5445</td>\n",
              "      <td>19.9065</td>\n",
              "      <td>12.0420</td>\n",
              "      <td>14.6450</td>\n",
              "      <td>26.7410</td>\n",
              "      <td>23.0780</td>\n",
              "      <td>7.4290</td>\n",
              "      <td>27.1795</td>\n",
              "      <td>7.3335</td>\n",
              "      <td>7.4085</td>\n",
              "      <td>9.1280</td>\n",
              "      <td>17.5475</td>\n",
              "      <td>12.6130</td>\n",
              "      <td>25.4020</td>\n",
              "      <td>24.3885</td>\n",
              "      <td>26.6865</td>\n",
              "      <td>22.6770</td>\n",
              "      <td>26.2140</td>\n",
              "      <td>23.2185</td>\n",
              "      <td>24.1030</td>\n",
              "      <td>3.2440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>14.0685</td>\n",
              "      <td>25.6620</td>\n",
              "      <td>13.6500</td>\n",
              "      <td>23.7730</td>\n",
              "      <td>27.0890</td>\n",
              "      <td>10.0595</td>\n",
              "      <td>22.8445</td>\n",
              "      <td>27.4010</td>\n",
              "      <td>27.1205</td>\n",
              "      <td>15.4800</td>\n",
              "      <td>8.8195</td>\n",
              "      <td>28.7625</td>\n",
              "      <td>8.2140</td>\n",
              "      <td>22.7905</td>\n",
              "      <td>7.4580</td>\n",
              "      <td>10.8395</td>\n",
              "      <td>25.5950</td>\n",
              "      <td>26.8945</td>\n",
              "      <td>25.7725</td>\n",
              "      <td>26.9070</td>\n",
              "      <td>27.3185</td>\n",
              "      <td>6.6030</td>\n",
              "      <td>10.2160</td>\n",
              "      <td>26.2285</td>\n",
              "      <td>27.3680</td>\n",
              "      <td>12.9475</td>\n",
              "      <td>22.0820</td>\n",
              "      <td>28.1325</td>\n",
              "      <td>11.7905</td>\n",
              "      <td>23.7530</td>\n",
              "      <td>25.6260</td>\n",
              "      <td>27.1385</td>\n",
              "      <td>11.9455</td>\n",
              "      <td>28.5130</td>\n",
              "      <td>25.0725</td>\n",
              "      <td>20.4250</td>\n",
              "      <td>26.9250</td>\n",
              "      <td>24.4260</td>\n",
              "      <td>-4.0330</td>\n",
              "      <td>24.2755</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8530</td>\n",
              "      <td>2.6340</td>\n",
              "      <td>12.0325</td>\n",
              "      <td>11.9005</td>\n",
              "      <td>27.7550</td>\n",
              "      <td>29.2895</td>\n",
              "      <td>26.6325</td>\n",
              "      <td>-7.5120</td>\n",
              "      <td>20.3795</td>\n",
              "      <td>1.8305</td>\n",
              "      <td>7.4305</td>\n",
              "      <td>18.8350</td>\n",
              "      <td>22.7870</td>\n",
              "      <td>4.7045</td>\n",
              "      <td>22.8055</td>\n",
              "      <td>26.8475</td>\n",
              "      <td>26.3570</td>\n",
              "      <td>26.8390</td>\n",
              "      <td>23.5625</td>\n",
              "      <td>26.8855</td>\n",
              "      <td>21.1075</td>\n",
              "      <td>12.0265</td>\n",
              "      <td>14.5565</td>\n",
              "      <td>27.1580</td>\n",
              "      <td>23.2030</td>\n",
              "      <td>8.1405</td>\n",
              "      <td>28.2355</td>\n",
              "      <td>7.9425</td>\n",
              "      <td>7.9970</td>\n",
              "      <td>8.9185</td>\n",
              "      <td>18.5965</td>\n",
              "      <td>12.1805</td>\n",
              "      <td>25.4390</td>\n",
              "      <td>24.2855</td>\n",
              "      <td>27.1385</td>\n",
              "      <td>22.2210</td>\n",
              "      <td>26.5510</td>\n",
              "      <td>22.0050</td>\n",
              "      <td>22.6745</td>\n",
              "      <td>4.7270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>13.6150</td>\n",
              "      <td>25.2650</td>\n",
              "      <td>13.2390</td>\n",
              "      <td>22.9560</td>\n",
              "      <td>27.0020</td>\n",
              "      <td>11.2280</td>\n",
              "      <td>22.7125</td>\n",
              "      <td>27.4880</td>\n",
              "      <td>27.2635</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>11.1105</td>\n",
              "      <td>28.8200</td>\n",
              "      <td>9.1150</td>\n",
              "      <td>22.6400</td>\n",
              "      <td>5.9520</td>\n",
              "      <td>12.6165</td>\n",
              "      <td>26.2240</td>\n",
              "      <td>27.7170</td>\n",
              "      <td>25.9170</td>\n",
              "      <td>26.9760</td>\n",
              "      <td>27.3210</td>\n",
              "      <td>7.1190</td>\n",
              "      <td>9.8565</td>\n",
              "      <td>26.4680</td>\n",
              "      <td>26.6490</td>\n",
              "      <td>12.9100</td>\n",
              "      <td>22.5455</td>\n",
              "      <td>28.1955</td>\n",
              "      <td>10.6615</td>\n",
              "      <td>24.1390</td>\n",
              "      <td>25.5675</td>\n",
              "      <td>27.3550</td>\n",
              "      <td>12.0345</td>\n",
              "      <td>27.5320</td>\n",
              "      <td>24.9230</td>\n",
              "      <td>20.5175</td>\n",
              "      <td>27.0855</td>\n",
              "      <td>24.2560</td>\n",
              "      <td>-3.4375</td>\n",
              "      <td>24.4865</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8910</td>\n",
              "      <td>1.0325</td>\n",
              "      <td>14.2030</td>\n",
              "      <td>13.6050</td>\n",
              "      <td>27.7385</td>\n",
              "      <td>28.4730</td>\n",
              "      <td>26.3430</td>\n",
              "      <td>-9.0315</td>\n",
              "      <td>19.2520</td>\n",
              "      <td>2.8095</td>\n",
              "      <td>7.3960</td>\n",
              "      <td>20.9865</td>\n",
              "      <td>23.3770</td>\n",
              "      <td>4.0770</td>\n",
              "      <td>23.3220</td>\n",
              "      <td>26.7980</td>\n",
              "      <td>26.1450</td>\n",
              "      <td>26.6675</td>\n",
              "      <td>23.0940</td>\n",
              "      <td>26.7110</td>\n",
              "      <td>19.8540</td>\n",
              "      <td>14.4065</td>\n",
              "      <td>14.7525</td>\n",
              "      <td>27.6650</td>\n",
              "      <td>23.2305</td>\n",
              "      <td>9.7615</td>\n",
              "      <td>28.7915</td>\n",
              "      <td>9.1600</td>\n",
              "      <td>9.3670</td>\n",
              "      <td>9.3035</td>\n",
              "      <td>17.4490</td>\n",
              "      <td>12.4145</td>\n",
              "      <td>25.4460</td>\n",
              "      <td>24.6055</td>\n",
              "      <td>27.3550</td>\n",
              "      <td>22.7920</td>\n",
              "      <td>27.1460</td>\n",
              "      <td>22.1780</td>\n",
              "      <td>22.5795</td>\n",
              "      <td>5.2915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>14.4835</td>\n",
              "      <td>25.5005</td>\n",
              "      <td>11.8750</td>\n",
              "      <td>23.0965</td>\n",
              "      <td>27.3490</td>\n",
              "      <td>12.2240</td>\n",
              "      <td>23.2875</td>\n",
              "      <td>27.8540</td>\n",
              "      <td>27.6130</td>\n",
              "      <td>15.2710</td>\n",
              "      <td>9.4595</td>\n",
              "      <td>28.7210</td>\n",
              "      <td>9.4275</td>\n",
              "      <td>22.0365</td>\n",
              "      <td>8.1380</td>\n",
              "      <td>12.0235</td>\n",
              "      <td>26.2775</td>\n",
              "      <td>26.6020</td>\n",
              "      <td>25.8255</td>\n",
              "      <td>27.5590</td>\n",
              "      <td>27.5220</td>\n",
              "      <td>7.4550</td>\n",
              "      <td>11.2485</td>\n",
              "      <td>26.1580</td>\n",
              "      <td>27.1090</td>\n",
              "      <td>13.5695</td>\n",
              "      <td>22.5115</td>\n",
              "      <td>28.1350</td>\n",
              "      <td>10.9185</td>\n",
              "      <td>24.6210</td>\n",
              "      <td>25.8645</td>\n",
              "      <td>27.5390</td>\n",
              "      <td>10.3955</td>\n",
              "      <td>27.9625</td>\n",
              "      <td>25.2825</td>\n",
              "      <td>20.7005</td>\n",
              "      <td>26.9105</td>\n",
              "      <td>24.2250</td>\n",
              "      <td>-3.4570</td>\n",
              "      <td>25.1050</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8240</td>\n",
              "      <td>1.6955</td>\n",
              "      <td>13.0695</td>\n",
              "      <td>15.0110</td>\n",
              "      <td>28.0075</td>\n",
              "      <td>29.0320</td>\n",
              "      <td>26.8320</td>\n",
              "      <td>-7.7170</td>\n",
              "      <td>20.4860</td>\n",
              "      <td>2.9290</td>\n",
              "      <td>8.9155</td>\n",
              "      <td>18.1455</td>\n",
              "      <td>23.1050</td>\n",
              "      <td>4.9775</td>\n",
              "      <td>23.3290</td>\n",
              "      <td>26.9595</td>\n",
              "      <td>26.4590</td>\n",
              "      <td>26.9805</td>\n",
              "      <td>23.7415</td>\n",
              "      <td>27.1145</td>\n",
              "      <td>19.1870</td>\n",
              "      <td>11.1055</td>\n",
              "      <td>16.1335</td>\n",
              "      <td>27.7660</td>\n",
              "      <td>23.5375</td>\n",
              "      <td>8.7330</td>\n",
              "      <td>28.3840</td>\n",
              "      <td>9.1255</td>\n",
              "      <td>9.2630</td>\n",
              "      <td>9.0025</td>\n",
              "      <td>17.1485</td>\n",
              "      <td>14.0485</td>\n",
              "      <td>25.6130</td>\n",
              "      <td>25.2270</td>\n",
              "      <td>27.5390</td>\n",
              "      <td>23.6885</td>\n",
              "      <td>26.8915</td>\n",
              "      <td>22.7600</td>\n",
              "      <td>23.4275</td>\n",
              "      <td>4.4450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>14.5665</td>\n",
              "      <td>25.3650</td>\n",
              "      <td>11.7315</td>\n",
              "      <td>23.2925</td>\n",
              "      <td>27.2245</td>\n",
              "      <td>11.1270</td>\n",
              "      <td>23.1680</td>\n",
              "      <td>27.1605</td>\n",
              "      <td>27.0600</td>\n",
              "      <td>15.4410</td>\n",
              "      <td>9.0820</td>\n",
              "      <td>28.4245</td>\n",
              "      <td>8.1595</td>\n",
              "      <td>22.6105</td>\n",
              "      <td>7.0745</td>\n",
              "      <td>10.9115</td>\n",
              "      <td>25.6205</td>\n",
              "      <td>26.7445</td>\n",
              "      <td>25.6890</td>\n",
              "      <td>27.1980</td>\n",
              "      <td>27.1580</td>\n",
              "      <td>7.5460</td>\n",
              "      <td>10.1800</td>\n",
              "      <td>26.2065</td>\n",
              "      <td>27.0955</td>\n",
              "      <td>13.5350</td>\n",
              "      <td>22.0035</td>\n",
              "      <td>27.7935</td>\n",
              "      <td>10.5910</td>\n",
              "      <td>23.5410</td>\n",
              "      <td>25.7440</td>\n",
              "      <td>26.7825</td>\n",
              "      <td>10.0915</td>\n",
              "      <td>28.5015</td>\n",
              "      <td>25.3070</td>\n",
              "      <td>20.3560</td>\n",
              "      <td>26.9180</td>\n",
              "      <td>24.1910</td>\n",
              "      <td>-4.8425</td>\n",
              "      <td>25.1940</td>\n",
              "      <td>...</td>\n",
              "      <td>22.6940</td>\n",
              "      <td>1.8695</td>\n",
              "      <td>12.1945</td>\n",
              "      <td>13.6520</td>\n",
              "      <td>27.6770</td>\n",
              "      <td>29.0155</td>\n",
              "      <td>26.7665</td>\n",
              "      <td>-6.1565</td>\n",
              "      <td>20.0140</td>\n",
              "      <td>3.2245</td>\n",
              "      <td>7.7115</td>\n",
              "      <td>17.5795</td>\n",
              "      <td>22.6695</td>\n",
              "      <td>4.5355</td>\n",
              "      <td>23.0625</td>\n",
              "      <td>26.8365</td>\n",
              "      <td>26.4625</td>\n",
              "      <td>27.0865</td>\n",
              "      <td>23.4125</td>\n",
              "      <td>26.6900</td>\n",
              "      <td>19.3080</td>\n",
              "      <td>10.7045</td>\n",
              "      <td>14.6145</td>\n",
              "      <td>26.8605</td>\n",
              "      <td>23.4285</td>\n",
              "      <td>8.4445</td>\n",
              "      <td>28.3260</td>\n",
              "      <td>8.0920</td>\n",
              "      <td>8.1855</td>\n",
              "      <td>8.3985</td>\n",
              "      <td>18.4705</td>\n",
              "      <td>11.9735</td>\n",
              "      <td>25.5700</td>\n",
              "      <td>24.0215</td>\n",
              "      <td>26.7825</td>\n",
              "      <td>23.7365</td>\n",
              "      <td>27.0405</td>\n",
              "      <td>22.3725</td>\n",
              "      <td>22.6680</td>\n",
              "      <td>5.0130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>15.2480</td>\n",
              "      <td>25.4135</td>\n",
              "      <td>10.7790</td>\n",
              "      <td>23.5340</td>\n",
              "      <td>26.9840</td>\n",
              "      <td>12.7180</td>\n",
              "      <td>22.6280</td>\n",
              "      <td>27.7875</td>\n",
              "      <td>27.6900</td>\n",
              "      <td>15.7925</td>\n",
              "      <td>10.3735</td>\n",
              "      <td>29.2125</td>\n",
              "      <td>9.9275</td>\n",
              "      <td>22.7840</td>\n",
              "      <td>4.8900</td>\n",
              "      <td>12.9725</td>\n",
              "      <td>26.1995</td>\n",
              "      <td>26.5975</td>\n",
              "      <td>26.1890</td>\n",
              "      <td>25.7965</td>\n",
              "      <td>27.7140</td>\n",
              "      <td>4.6425</td>\n",
              "      <td>9.1105</td>\n",
              "      <td>26.5710</td>\n",
              "      <td>27.4535</td>\n",
              "      <td>11.3955</td>\n",
              "      <td>22.0755</td>\n",
              "      <td>28.3780</td>\n",
              "      <td>8.0780</td>\n",
              "      <td>23.4435</td>\n",
              "      <td>25.4460</td>\n",
              "      <td>27.5165</td>\n",
              "      <td>7.9720</td>\n",
              "      <td>28.5350</td>\n",
              "      <td>24.8085</td>\n",
              "      <td>20.6095</td>\n",
              "      <td>27.1010</td>\n",
              "      <td>24.5470</td>\n",
              "      <td>-4.9055</td>\n",
              "      <td>25.3245</td>\n",
              "      <td>...</td>\n",
              "      <td>23.1505</td>\n",
              "      <td>1.0540</td>\n",
              "      <td>12.9425</td>\n",
              "      <td>15.3555</td>\n",
              "      <td>27.8820</td>\n",
              "      <td>29.3080</td>\n",
              "      <td>26.7535</td>\n",
              "      <td>-8.6125</td>\n",
              "      <td>19.1575</td>\n",
              "      <td>0.8660</td>\n",
              "      <td>6.9265</td>\n",
              "      <td>17.9020</td>\n",
              "      <td>23.1940</td>\n",
              "      <td>7.1075</td>\n",
              "      <td>23.2245</td>\n",
              "      <td>27.0520</td>\n",
              "      <td>26.2475</td>\n",
              "      <td>27.2545</td>\n",
              "      <td>23.2390</td>\n",
              "      <td>26.8085</td>\n",
              "      <td>19.9910</td>\n",
              "      <td>10.8540</td>\n",
              "      <td>18.1180</td>\n",
              "      <td>28.2100</td>\n",
              "      <td>23.2975</td>\n",
              "      <td>6.3000</td>\n",
              "      <td>28.2740</td>\n",
              "      <td>8.8115</td>\n",
              "      <td>8.9650</td>\n",
              "      <td>8.1565</td>\n",
              "      <td>18.1465</td>\n",
              "      <td>16.4050</td>\n",
              "      <td>25.8085</td>\n",
              "      <td>24.8690</td>\n",
              "      <td>27.5165</td>\n",
              "      <td>23.3680</td>\n",
              "      <td>26.9840</td>\n",
              "      <td>22.3355</td>\n",
              "      <td>22.6220</td>\n",
              "      <td>4.1280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>15.7670</td>\n",
              "      <td>25.9285</td>\n",
              "      <td>13.4790</td>\n",
              "      <td>24.4580</td>\n",
              "      <td>27.3975</td>\n",
              "      <td>10.5570</td>\n",
              "      <td>23.5685</td>\n",
              "      <td>27.7630</td>\n",
              "      <td>27.5965</td>\n",
              "      <td>15.7865</td>\n",
              "      <td>11.5605</td>\n",
              "      <td>28.9800</td>\n",
              "      <td>8.9135</td>\n",
              "      <td>23.0175</td>\n",
              "      <td>7.4605</td>\n",
              "      <td>13.4515</td>\n",
              "      <td>26.3355</td>\n",
              "      <td>27.9395</td>\n",
              "      <td>26.3370</td>\n",
              "      <td>27.5600</td>\n",
              "      <td>27.5910</td>\n",
              "      <td>7.0380</td>\n",
              "      <td>9.4480</td>\n",
              "      <td>26.5020</td>\n",
              "      <td>27.3240</td>\n",
              "      <td>13.8255</td>\n",
              "      <td>22.1595</td>\n",
              "      <td>28.3610</td>\n",
              "      <td>11.6925</td>\n",
              "      <td>24.8650</td>\n",
              "      <td>26.1615</td>\n",
              "      <td>27.5600</td>\n",
              "      <td>12.5005</td>\n",
              "      <td>28.7205</td>\n",
              "      <td>26.0295</td>\n",
              "      <td>20.7470</td>\n",
              "      <td>27.9760</td>\n",
              "      <td>24.8150</td>\n",
              "      <td>-1.6480</td>\n",
              "      <td>25.0250</td>\n",
              "      <td>...</td>\n",
              "      <td>23.2985</td>\n",
              "      <td>2.5655</td>\n",
              "      <td>15.4965</td>\n",
              "      <td>13.1095</td>\n",
              "      <td>28.0575</td>\n",
              "      <td>28.5420</td>\n",
              "      <td>27.4190</td>\n",
              "      <td>-6.6925</td>\n",
              "      <td>20.1470</td>\n",
              "      <td>1.7700</td>\n",
              "      <td>7.1895</td>\n",
              "      <td>19.7280</td>\n",
              "      <td>24.1730</td>\n",
              "      <td>6.2000</td>\n",
              "      <td>23.1975</td>\n",
              "      <td>27.4415</td>\n",
              "      <td>27.0960</td>\n",
              "      <td>27.4740</td>\n",
              "      <td>24.4100</td>\n",
              "      <td>27.4210</td>\n",
              "      <td>20.7345</td>\n",
              "      <td>13.6120</td>\n",
              "      <td>16.3295</td>\n",
              "      <td>27.6590</td>\n",
              "      <td>23.4070</td>\n",
              "      <td>9.5630</td>\n",
              "      <td>29.6655</td>\n",
              "      <td>8.3585</td>\n",
              "      <td>8.3550</td>\n",
              "      <td>9.6500</td>\n",
              "      <td>18.0080</td>\n",
              "      <td>13.6335</td>\n",
              "      <td>26.0800</td>\n",
              "      <td>25.3970</td>\n",
              "      <td>27.5600</td>\n",
              "      <td>23.1230</td>\n",
              "      <td>28.1300</td>\n",
              "      <td>22.7895</td>\n",
              "      <td>22.9400</td>\n",
              "      <td>4.5975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>15.5875</td>\n",
              "      <td>25.5770</td>\n",
              "      <td>13.5485</td>\n",
              "      <td>25.5620</td>\n",
              "      <td>26.9660</td>\n",
              "      <td>11.7000</td>\n",
              "      <td>22.6180</td>\n",
              "      <td>27.7335</td>\n",
              "      <td>27.4540</td>\n",
              "      <td>14.9360</td>\n",
              "      <td>10.1780</td>\n",
              "      <td>28.2480</td>\n",
              "      <td>9.2505</td>\n",
              "      <td>22.2180</td>\n",
              "      <td>7.6375</td>\n",
              "      <td>12.4295</td>\n",
              "      <td>25.6370</td>\n",
              "      <td>28.5815</td>\n",
              "      <td>25.8735</td>\n",
              "      <td>27.3800</td>\n",
              "      <td>27.3235</td>\n",
              "      <td>8.4725</td>\n",
              "      <td>10.1935</td>\n",
              "      <td>25.9125</td>\n",
              "      <td>27.5225</td>\n",
              "      <td>14.3675</td>\n",
              "      <td>22.0435</td>\n",
              "      <td>27.8285</td>\n",
              "      <td>11.5735</td>\n",
              "      <td>24.2475</td>\n",
              "      <td>25.7590</td>\n",
              "      <td>27.4675</td>\n",
              "      <td>11.8425</td>\n",
              "      <td>27.8755</td>\n",
              "      <td>25.4870</td>\n",
              "      <td>20.1410</td>\n",
              "      <td>27.1705</td>\n",
              "      <td>24.4165</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>24.8980</td>\n",
              "      <td>...</td>\n",
              "      <td>22.6645</td>\n",
              "      <td>2.7365</td>\n",
              "      <td>13.3850</td>\n",
              "      <td>13.9690</td>\n",
              "      <td>27.9070</td>\n",
              "      <td>27.9940</td>\n",
              "      <td>26.4805</td>\n",
              "      <td>-6.5995</td>\n",
              "      <td>19.6775</td>\n",
              "      <td>3.6510</td>\n",
              "      <td>7.6080</td>\n",
              "      <td>19.3760</td>\n",
              "      <td>22.7155</td>\n",
              "      <td>5.6170</td>\n",
              "      <td>22.9415</td>\n",
              "      <td>26.9260</td>\n",
              "      <td>26.4015</td>\n",
              "      <td>27.3405</td>\n",
              "      <td>23.7255</td>\n",
              "      <td>27.0535</td>\n",
              "      <td>22.0900</td>\n",
              "      <td>12.8880</td>\n",
              "      <td>15.9685</td>\n",
              "      <td>27.1365</td>\n",
              "      <td>23.1095</td>\n",
              "      <td>9.8120</td>\n",
              "      <td>29.9960</td>\n",
              "      <td>9.2195</td>\n",
              "      <td>9.3435</td>\n",
              "      <td>9.0985</td>\n",
              "      <td>16.7775</td>\n",
              "      <td>13.9925</td>\n",
              "      <td>25.2785</td>\n",
              "      <td>24.9145</td>\n",
              "      <td>27.4675</td>\n",
              "      <td>23.6165</td>\n",
              "      <td>28.0860</td>\n",
              "      <td>22.0465</td>\n",
              "      <td>22.3720</td>\n",
              "      <td>6.0360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>16.8560</td>\n",
              "      <td>25.4540</td>\n",
              "      <td>13.9625</td>\n",
              "      <td>24.2205</td>\n",
              "      <td>27.3200</td>\n",
              "      <td>11.0310</td>\n",
              "      <td>22.5755</td>\n",
              "      <td>27.3015</td>\n",
              "      <td>27.1470</td>\n",
              "      <td>15.6365</td>\n",
              "      <td>10.6650</td>\n",
              "      <td>28.3175</td>\n",
              "      <td>9.2910</td>\n",
              "      <td>22.1100</td>\n",
              "      <td>9.2115</td>\n",
              "      <td>12.8780</td>\n",
              "      <td>25.3180</td>\n",
              "      <td>28.6595</td>\n",
              "      <td>25.9740</td>\n",
              "      <td>27.3285</td>\n",
              "      <td>27.1740</td>\n",
              "      <td>9.6060</td>\n",
              "      <td>10.7110</td>\n",
              "      <td>25.8755</td>\n",
              "      <td>27.3295</td>\n",
              "      <td>13.2700</td>\n",
              "      <td>21.9825</td>\n",
              "      <td>27.6650</td>\n",
              "      <td>12.9870</td>\n",
              "      <td>23.4310</td>\n",
              "      <td>25.4135</td>\n",
              "      <td>27.0020</td>\n",
              "      <td>12.2525</td>\n",
              "      <td>28.1720</td>\n",
              "      <td>25.1840</td>\n",
              "      <td>20.4920</td>\n",
              "      <td>27.0025</td>\n",
              "      <td>24.3775</td>\n",
              "      <td>-4.0555</td>\n",
              "      <td>24.3620</td>\n",
              "      <td>...</td>\n",
              "      <td>22.7225</td>\n",
              "      <td>1.5740</td>\n",
              "      <td>13.0410</td>\n",
              "      <td>12.7670</td>\n",
              "      <td>27.5225</td>\n",
              "      <td>28.3095</td>\n",
              "      <td>26.4265</td>\n",
              "      <td>-5.4815</td>\n",
              "      <td>19.2120</td>\n",
              "      <td>4.6550</td>\n",
              "      <td>8.2860</td>\n",
              "      <td>19.0170</td>\n",
              "      <td>23.2220</td>\n",
              "      <td>6.3045</td>\n",
              "      <td>23.0845</td>\n",
              "      <td>26.9260</td>\n",
              "      <td>26.1750</td>\n",
              "      <td>27.3375</td>\n",
              "      <td>24.1965</td>\n",
              "      <td>26.7800</td>\n",
              "      <td>20.9930</td>\n",
              "      <td>12.7110</td>\n",
              "      <td>17.1125</td>\n",
              "      <td>26.8495</td>\n",
              "      <td>23.5235</td>\n",
              "      <td>10.8900</td>\n",
              "      <td>30.2475</td>\n",
              "      <td>8.2685</td>\n",
              "      <td>8.3670</td>\n",
              "      <td>9.5315</td>\n",
              "      <td>18.2065</td>\n",
              "      <td>14.2690</td>\n",
              "      <td>25.4205</td>\n",
              "      <td>24.7545</td>\n",
              "      <td>27.0020</td>\n",
              "      <td>23.0295</td>\n",
              "      <td>28.2775</td>\n",
              "      <td>22.2105</td>\n",
              "      <td>22.7690</td>\n",
              "      <td>7.8450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>16.3620</td>\n",
              "      <td>25.8030</td>\n",
              "      <td>13.7490</td>\n",
              "      <td>25.3240</td>\n",
              "      <td>27.2835</td>\n",
              "      <td>12.6740</td>\n",
              "      <td>22.8960</td>\n",
              "      <td>27.5740</td>\n",
              "      <td>27.5020</td>\n",
              "      <td>15.4380</td>\n",
              "      <td>10.2705</td>\n",
              "      <td>29.0125</td>\n",
              "      <td>9.1210</td>\n",
              "      <td>21.8180</td>\n",
              "      <td>7.7940</td>\n",
              "      <td>12.3055</td>\n",
              "      <td>24.9745</td>\n",
              "      <td>28.2420</td>\n",
              "      <td>26.0725</td>\n",
              "      <td>27.2915</td>\n",
              "      <td>27.4825</td>\n",
              "      <td>8.7850</td>\n",
              "      <td>10.8030</td>\n",
              "      <td>26.1480</td>\n",
              "      <td>27.4790</td>\n",
              "      <td>13.1535</td>\n",
              "      <td>22.0350</td>\n",
              "      <td>28.3935</td>\n",
              "      <td>11.8670</td>\n",
              "      <td>23.3015</td>\n",
              "      <td>25.8505</td>\n",
              "      <td>27.1430</td>\n",
              "      <td>11.8270</td>\n",
              "      <td>28.0125</td>\n",
              "      <td>25.3875</td>\n",
              "      <td>20.6210</td>\n",
              "      <td>27.3510</td>\n",
              "      <td>24.5315</td>\n",
              "      <td>-3.6760</td>\n",
              "      <td>25.2865</td>\n",
              "      <td>...</td>\n",
              "      <td>22.9255</td>\n",
              "      <td>2.6030</td>\n",
              "      <td>14.1725</td>\n",
              "      <td>14.4395</td>\n",
              "      <td>28.0995</td>\n",
              "      <td>28.8300</td>\n",
              "      <td>26.8305</td>\n",
              "      <td>-8.5470</td>\n",
              "      <td>20.3675</td>\n",
              "      <td>3.3020</td>\n",
              "      <td>7.8850</td>\n",
              "      <td>19.6980</td>\n",
              "      <td>22.9645</td>\n",
              "      <td>6.5665</td>\n",
              "      <td>22.9440</td>\n",
              "      <td>27.1680</td>\n",
              "      <td>26.4715</td>\n",
              "      <td>27.3055</td>\n",
              "      <td>24.1455</td>\n",
              "      <td>27.1865</td>\n",
              "      <td>21.9790</td>\n",
              "      <td>12.8920</td>\n",
              "      <td>16.4005</td>\n",
              "      <td>26.6725</td>\n",
              "      <td>23.3780</td>\n",
              "      <td>9.8855</td>\n",
              "      <td>29.4600</td>\n",
              "      <td>9.4945</td>\n",
              "      <td>9.6605</td>\n",
              "      <td>9.4300</td>\n",
              "      <td>18.2560</td>\n",
              "      <td>13.8655</td>\n",
              "      <td>25.7300</td>\n",
              "      <td>25.3055</td>\n",
              "      <td>27.1430</td>\n",
              "      <td>23.2355</td>\n",
              "      <td>28.0270</td>\n",
              "      <td>22.1860</td>\n",
              "      <td>22.7205</td>\n",
              "      <td>5.6560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>16.4970</td>\n",
              "      <td>25.7410</td>\n",
              "      <td>12.5335</td>\n",
              "      <td>23.8485</td>\n",
              "      <td>27.7370</td>\n",
              "      <td>11.8895</td>\n",
              "      <td>23.4880</td>\n",
              "      <td>27.7745</td>\n",
              "      <td>27.5745</td>\n",
              "      <td>15.5300</td>\n",
              "      <td>9.9525</td>\n",
              "      <td>29.1200</td>\n",
              "      <td>8.9365</td>\n",
              "      <td>23.7285</td>\n",
              "      <td>6.6950</td>\n",
              "      <td>12.1050</td>\n",
              "      <td>26.1215</td>\n",
              "      <td>27.8765</td>\n",
              "      <td>26.0920</td>\n",
              "      <td>27.0230</td>\n",
              "      <td>27.4260</td>\n",
              "      <td>6.3980</td>\n",
              "      <td>10.0230</td>\n",
              "      <td>26.5420</td>\n",
              "      <td>27.3980</td>\n",
              "      <td>12.7165</td>\n",
              "      <td>21.9955</td>\n",
              "      <td>28.3530</td>\n",
              "      <td>10.9175</td>\n",
              "      <td>24.4625</td>\n",
              "      <td>25.8745</td>\n",
              "      <td>27.5530</td>\n",
              "      <td>10.5540</td>\n",
              "      <td>28.3580</td>\n",
              "      <td>25.1625</td>\n",
              "      <td>20.6915</td>\n",
              "      <td>27.0640</td>\n",
              "      <td>24.3795</td>\n",
              "      <td>-5.4660</td>\n",
              "      <td>24.4780</td>\n",
              "      <td>...</td>\n",
              "      <td>23.1415</td>\n",
              "      <td>1.5065</td>\n",
              "      <td>13.2740</td>\n",
              "      <td>13.8100</td>\n",
              "      <td>28.3315</td>\n",
              "      <td>29.1090</td>\n",
              "      <td>26.7150</td>\n",
              "      <td>-7.0145</td>\n",
              "      <td>20.4860</td>\n",
              "      <td>1.9555</td>\n",
              "      <td>7.6810</td>\n",
              "      <td>19.3685</td>\n",
              "      <td>23.9110</td>\n",
              "      <td>6.7005</td>\n",
              "      <td>23.2625</td>\n",
              "      <td>26.8485</td>\n",
              "      <td>26.9900</td>\n",
              "      <td>27.2240</td>\n",
              "      <td>23.5895</td>\n",
              "      <td>27.0090</td>\n",
              "      <td>21.0260</td>\n",
              "      <td>12.2045</td>\n",
              "      <td>16.6770</td>\n",
              "      <td>27.9400</td>\n",
              "      <td>23.6165</td>\n",
              "      <td>8.4025</td>\n",
              "      <td>29.5625</td>\n",
              "      <td>8.6430</td>\n",
              "      <td>8.8155</td>\n",
              "      <td>9.2495</td>\n",
              "      <td>18.2875</td>\n",
              "      <td>13.8335</td>\n",
              "      <td>25.7840</td>\n",
              "      <td>25.0420</td>\n",
              "      <td>27.5530</td>\n",
              "      <td>23.0610</td>\n",
              "      <td>27.3385</td>\n",
              "      <td>23.1025</td>\n",
              "      <td>23.1010</td>\n",
              "      <td>3.9725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>15.4800</td>\n",
              "      <td>26.0325</td>\n",
              "      <td>12.1450</td>\n",
              "      <td>25.3615</td>\n",
              "      <td>27.0420</td>\n",
              "      <td>11.1290</td>\n",
              "      <td>23.4450</td>\n",
              "      <td>27.6205</td>\n",
              "      <td>27.5225</td>\n",
              "      <td>15.6955</td>\n",
              "      <td>10.1890</td>\n",
              "      <td>29.1185</td>\n",
              "      <td>9.3175</td>\n",
              "      <td>22.5615</td>\n",
              "      <td>5.2795</td>\n",
              "      <td>12.1880</td>\n",
              "      <td>26.7730</td>\n",
              "      <td>28.3160</td>\n",
              "      <td>26.1660</td>\n",
              "      <td>27.4160</td>\n",
              "      <td>27.6710</td>\n",
              "      <td>5.1940</td>\n",
              "      <td>9.2080</td>\n",
              "      <td>26.7105</td>\n",
              "      <td>27.5975</td>\n",
              "      <td>13.5660</td>\n",
              "      <td>21.7860</td>\n",
              "      <td>28.3645</td>\n",
              "      <td>9.8000</td>\n",
              "      <td>25.2260</td>\n",
              "      <td>25.8715</td>\n",
              "      <td>27.3325</td>\n",
              "      <td>9.7005</td>\n",
              "      <td>28.8950</td>\n",
              "      <td>25.5785</td>\n",
              "      <td>20.7980</td>\n",
              "      <td>27.4115</td>\n",
              "      <td>24.6510</td>\n",
              "      <td>-3.2570</td>\n",
              "      <td>24.6645</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8815</td>\n",
              "      <td>2.0660</td>\n",
              "      <td>13.0905</td>\n",
              "      <td>13.4340</td>\n",
              "      <td>28.1770</td>\n",
              "      <td>29.1350</td>\n",
              "      <td>27.0270</td>\n",
              "      <td>-7.5470</td>\n",
              "      <td>21.0920</td>\n",
              "      <td>1.6430</td>\n",
              "      <td>6.4125</td>\n",
              "      <td>19.4410</td>\n",
              "      <td>23.3250</td>\n",
              "      <td>5.3560</td>\n",
              "      <td>23.6360</td>\n",
              "      <td>27.0040</td>\n",
              "      <td>26.7910</td>\n",
              "      <td>27.5405</td>\n",
              "      <td>24.2060</td>\n",
              "      <td>27.1605</td>\n",
              "      <td>21.8705</td>\n",
              "      <td>12.2845</td>\n",
              "      <td>15.9085</td>\n",
              "      <td>27.8910</td>\n",
              "      <td>23.9225</td>\n",
              "      <td>7.3590</td>\n",
              "      <td>29.7585</td>\n",
              "      <td>8.7030</td>\n",
              "      <td>8.8580</td>\n",
              "      <td>10.2400</td>\n",
              "      <td>17.6225</td>\n",
              "      <td>13.4725</td>\n",
              "      <td>26.0055</td>\n",
              "      <td>25.2690</td>\n",
              "      <td>27.3325</td>\n",
              "      <td>23.8755</td>\n",
              "      <td>27.6280</td>\n",
              "      <td>22.9690</td>\n",
              "      <td>22.8095</td>\n",
              "      <td>4.9515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>14.6700</td>\n",
              "      <td>25.7840</td>\n",
              "      <td>13.6610</td>\n",
              "      <td>24.8325</td>\n",
              "      <td>27.3035</td>\n",
              "      <td>11.3525</td>\n",
              "      <td>23.2425</td>\n",
              "      <td>27.1515</td>\n",
              "      <td>27.0755</td>\n",
              "      <td>15.7490</td>\n",
              "      <td>9.6885</td>\n",
              "      <td>28.3805</td>\n",
              "      <td>8.8170</td>\n",
              "      <td>23.7110</td>\n",
              "      <td>8.1275</td>\n",
              "      <td>11.7590</td>\n",
              "      <td>25.7260</td>\n",
              "      <td>27.4820</td>\n",
              "      <td>26.0565</td>\n",
              "      <td>26.8225</td>\n",
              "      <td>27.2820</td>\n",
              "      <td>7.4700</td>\n",
              "      <td>10.9955</td>\n",
              "      <td>26.2045</td>\n",
              "      <td>27.6745</td>\n",
              "      <td>12.3455</td>\n",
              "      <td>22.4460</td>\n",
              "      <td>27.6760</td>\n",
              "      <td>12.1625</td>\n",
              "      <td>23.1190</td>\n",
              "      <td>25.8560</td>\n",
              "      <td>26.8635</td>\n",
              "      <td>12.1270</td>\n",
              "      <td>28.6385</td>\n",
              "      <td>25.3425</td>\n",
              "      <td>21.0065</td>\n",
              "      <td>27.1500</td>\n",
              "      <td>24.9685</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>24.3560</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0725</td>\n",
              "      <td>1.9755</td>\n",
              "      <td>13.7215</td>\n",
              "      <td>13.1715</td>\n",
              "      <td>27.7455</td>\n",
              "      <td>29.1625</td>\n",
              "      <td>26.8355</td>\n",
              "      <td>-5.9185</td>\n",
              "      <td>20.1125</td>\n",
              "      <td>3.5645</td>\n",
              "      <td>8.5825</td>\n",
              "      <td>19.6740</td>\n",
              "      <td>22.5075</td>\n",
              "      <td>5.2360</td>\n",
              "      <td>23.3925</td>\n",
              "      <td>26.9895</td>\n",
              "      <td>26.8280</td>\n",
              "      <td>27.8150</td>\n",
              "      <td>23.6725</td>\n",
              "      <td>26.9530</td>\n",
              "      <td>20.5785</td>\n",
              "      <td>12.7550</td>\n",
              "      <td>14.4445</td>\n",
              "      <td>27.1325</td>\n",
              "      <td>23.5720</td>\n",
              "      <td>9.1280</td>\n",
              "      <td>29.6765</td>\n",
              "      <td>9.0875</td>\n",
              "      <td>9.2390</td>\n",
              "      <td>10.0290</td>\n",
              "      <td>18.1710</td>\n",
              "      <td>12.4375</td>\n",
              "      <td>25.7210</td>\n",
              "      <td>24.6615</td>\n",
              "      <td>26.8635</td>\n",
              "      <td>23.1790</td>\n",
              "      <td>27.6495</td>\n",
              "      <td>22.4920</td>\n",
              "      <td>22.1475</td>\n",
              "      <td>5.8705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>15.5190</td>\n",
              "      <td>26.0680</td>\n",
              "      <td>12.4485</td>\n",
              "      <td>25.3350</td>\n",
              "      <td>27.4560</td>\n",
              "      <td>12.0625</td>\n",
              "      <td>23.1205</td>\n",
              "      <td>28.0185</td>\n",
              "      <td>27.8795</td>\n",
              "      <td>14.4385</td>\n",
              "      <td>10.2280</td>\n",
              "      <td>28.7300</td>\n",
              "      <td>9.6800</td>\n",
              "      <td>24.2970</td>\n",
              "      <td>7.8025</td>\n",
              "      <td>12.5120</td>\n",
              "      <td>25.8360</td>\n",
              "      <td>27.4580</td>\n",
              "      <td>26.1600</td>\n",
              "      <td>27.1155</td>\n",
              "      <td>28.0955</td>\n",
              "      <td>7.7845</td>\n",
              "      <td>11.9320</td>\n",
              "      <td>26.8300</td>\n",
              "      <td>27.3920</td>\n",
              "      <td>12.9415</td>\n",
              "      <td>21.3100</td>\n",
              "      <td>28.3980</td>\n",
              "      <td>10.8475</td>\n",
              "      <td>24.4590</td>\n",
              "      <td>25.7890</td>\n",
              "      <td>27.7440</td>\n",
              "      <td>10.7665</td>\n",
              "      <td>28.9275</td>\n",
              "      <td>25.5450</td>\n",
              "      <td>21.3250</td>\n",
              "      <td>27.4435</td>\n",
              "      <td>24.7935</td>\n",
              "      <td>-1.9980</td>\n",
              "      <td>25.3895</td>\n",
              "      <td>...</td>\n",
              "      <td>22.7925</td>\n",
              "      <td>2.1145</td>\n",
              "      <td>13.7255</td>\n",
              "      <td>14.3425</td>\n",
              "      <td>28.0790</td>\n",
              "      <td>28.8765</td>\n",
              "      <td>27.2850</td>\n",
              "      <td>-6.8960</td>\n",
              "      <td>20.6430</td>\n",
              "      <td>4.0160</td>\n",
              "      <td>8.4190</td>\n",
              "      <td>18.7795</td>\n",
              "      <td>23.6280</td>\n",
              "      <td>5.9770</td>\n",
              "      <td>24.0775</td>\n",
              "      <td>27.1210</td>\n",
              "      <td>27.0485</td>\n",
              "      <td>27.4340</td>\n",
              "      <td>23.9600</td>\n",
              "      <td>27.3045</td>\n",
              "      <td>21.1685</td>\n",
              "      <td>11.8890</td>\n",
              "      <td>16.6915</td>\n",
              "      <td>27.5155</td>\n",
              "      <td>23.7230</td>\n",
              "      <td>9.4655</td>\n",
              "      <td>29.5985</td>\n",
              "      <td>9.1570</td>\n",
              "      <td>9.3150</td>\n",
              "      <td>9.6430</td>\n",
              "      <td>16.6670</td>\n",
              "      <td>14.4225</td>\n",
              "      <td>25.8770</td>\n",
              "      <td>24.9820</td>\n",
              "      <td>27.7440</td>\n",
              "      <td>24.0055</td>\n",
              "      <td>27.4135</td>\n",
              "      <td>22.9420</td>\n",
              "      <td>22.9180</td>\n",
              "      <td>6.4785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>16.6300</td>\n",
              "      <td>25.9285</td>\n",
              "      <td>13.6685</td>\n",
              "      <td>25.7525</td>\n",
              "      <td>26.9235</td>\n",
              "      <td>13.2795</td>\n",
              "      <td>22.4780</td>\n",
              "      <td>27.9425</td>\n",
              "      <td>27.7930</td>\n",
              "      <td>16.0645</td>\n",
              "      <td>10.9375</td>\n",
              "      <td>29.2380</td>\n",
              "      <td>8.7730</td>\n",
              "      <td>22.8305</td>\n",
              "      <td>8.4635</td>\n",
              "      <td>13.2330</td>\n",
              "      <td>25.6240</td>\n",
              "      <td>28.2520</td>\n",
              "      <td>26.2235</td>\n",
              "      <td>27.4755</td>\n",
              "      <td>27.7460</td>\n",
              "      <td>7.9205</td>\n",
              "      <td>11.4235</td>\n",
              "      <td>26.7270</td>\n",
              "      <td>27.7780</td>\n",
              "      <td>12.8415</td>\n",
              "      <td>21.6060</td>\n",
              "      <td>28.7105</td>\n",
              "      <td>12.2830</td>\n",
              "      <td>22.4600</td>\n",
              "      <td>26.0145</td>\n",
              "      <td>27.7405</td>\n",
              "      <td>11.9710</td>\n",
              "      <td>28.5430</td>\n",
              "      <td>25.6935</td>\n",
              "      <td>20.9875</td>\n",
              "      <td>27.3375</td>\n",
              "      <td>25.0245</td>\n",
              "      <td>-2.1305</td>\n",
              "      <td>24.7355</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8745</td>\n",
              "      <td>2.8795</td>\n",
              "      <td>13.9840</td>\n",
              "      <td>15.3635</td>\n",
              "      <td>27.8950</td>\n",
              "      <td>28.8425</td>\n",
              "      <td>26.9070</td>\n",
              "      <td>-4.3495</td>\n",
              "      <td>19.9945</td>\n",
              "      <td>3.0745</td>\n",
              "      <td>9.1115</td>\n",
              "      <td>19.1800</td>\n",
              "      <td>23.8505</td>\n",
              "      <td>7.3095</td>\n",
              "      <td>23.0075</td>\n",
              "      <td>27.0970</td>\n",
              "      <td>26.3740</td>\n",
              "      <td>27.4910</td>\n",
              "      <td>23.8535</td>\n",
              "      <td>27.3860</td>\n",
              "      <td>22.3865</td>\n",
              "      <td>13.0720</td>\n",
              "      <td>18.0130</td>\n",
              "      <td>27.6900</td>\n",
              "      <td>23.2340</td>\n",
              "      <td>9.5500</td>\n",
              "      <td>29.7750</td>\n",
              "      <td>9.3660</td>\n",
              "      <td>9.4815</td>\n",
              "      <td>9.7290</td>\n",
              "      <td>18.8200</td>\n",
              "      <td>15.3440</td>\n",
              "      <td>25.8240</td>\n",
              "      <td>25.4795</td>\n",
              "      <td>27.7405</td>\n",
              "      <td>23.7470</td>\n",
              "      <td>27.4390</td>\n",
              "      <td>22.2425</td>\n",
              "      <td>22.0520</td>\n",
              "      <td>6.5160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>15.4785</td>\n",
              "      <td>25.6990</td>\n",
              "      <td>13.1770</td>\n",
              "      <td>24.3050</td>\n",
              "      <td>27.4810</td>\n",
              "      <td>12.3455</td>\n",
              "      <td>22.9140</td>\n",
              "      <td>27.8245</td>\n",
              "      <td>27.4715</td>\n",
              "      <td>15.9405</td>\n",
              "      <td>9.4105</td>\n",
              "      <td>28.8000</td>\n",
              "      <td>10.0050</td>\n",
              "      <td>23.4850</td>\n",
              "      <td>8.2980</td>\n",
              "      <td>11.8035</td>\n",
              "      <td>25.4330</td>\n",
              "      <td>28.2310</td>\n",
              "      <td>25.9255</td>\n",
              "      <td>27.3500</td>\n",
              "      <td>27.3810</td>\n",
              "      <td>6.9625</td>\n",
              "      <td>11.6705</td>\n",
              "      <td>26.1065</td>\n",
              "      <td>27.4985</td>\n",
              "      <td>13.8700</td>\n",
              "      <td>22.3115</td>\n",
              "      <td>28.3555</td>\n",
              "      <td>11.4785</td>\n",
              "      <td>24.3655</td>\n",
              "      <td>26.0195</td>\n",
              "      <td>27.4890</td>\n",
              "      <td>11.3930</td>\n",
              "      <td>28.9505</td>\n",
              "      <td>25.2520</td>\n",
              "      <td>20.5590</td>\n",
              "      <td>27.2970</td>\n",
              "      <td>24.4755</td>\n",
              "      <td>-2.9235</td>\n",
              "      <td>24.5510</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0490</td>\n",
              "      <td>1.0420</td>\n",
              "      <td>13.3090</td>\n",
              "      <td>13.8195</td>\n",
              "      <td>27.8995</td>\n",
              "      <td>28.5540</td>\n",
              "      <td>26.8225</td>\n",
              "      <td>-5.9010</td>\n",
              "      <td>19.9700</td>\n",
              "      <td>4.0825</td>\n",
              "      <td>9.7510</td>\n",
              "      <td>18.7815</td>\n",
              "      <td>22.7480</td>\n",
              "      <td>6.2240</td>\n",
              "      <td>23.2940</td>\n",
              "      <td>27.3535</td>\n",
              "      <td>26.8195</td>\n",
              "      <td>27.5410</td>\n",
              "      <td>24.0550</td>\n",
              "      <td>26.9065</td>\n",
              "      <td>20.6920</td>\n",
              "      <td>11.9520</td>\n",
              "      <td>15.7160</td>\n",
              "      <td>27.2175</td>\n",
              "      <td>23.6905</td>\n",
              "      <td>9.0240</td>\n",
              "      <td>29.8550</td>\n",
              "      <td>10.3215</td>\n",
              "      <td>10.5525</td>\n",
              "      <td>9.7040</td>\n",
              "      <td>18.9395</td>\n",
              "      <td>13.8395</td>\n",
              "      <td>25.6715</td>\n",
              "      <td>24.5150</td>\n",
              "      <td>27.4890</td>\n",
              "      <td>23.0445</td>\n",
              "      <td>27.6570</td>\n",
              "      <td>22.2380</td>\n",
              "      <td>22.9095</td>\n",
              "      <td>6.2955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>16.3810</td>\n",
              "      <td>25.6660</td>\n",
              "      <td>13.2985</td>\n",
              "      <td>24.4435</td>\n",
              "      <td>27.0560</td>\n",
              "      <td>11.1975</td>\n",
              "      <td>22.2545</td>\n",
              "      <td>27.3630</td>\n",
              "      <td>27.1830</td>\n",
              "      <td>15.3035</td>\n",
              "      <td>11.8835</td>\n",
              "      <td>28.4130</td>\n",
              "      <td>9.8265</td>\n",
              "      <td>23.0295</td>\n",
              "      <td>7.3620</td>\n",
              "      <td>13.6975</td>\n",
              "      <td>25.6885</td>\n",
              "      <td>27.9370</td>\n",
              "      <td>25.8285</td>\n",
              "      <td>27.2545</td>\n",
              "      <td>27.3865</td>\n",
              "      <td>8.9820</td>\n",
              "      <td>9.3750</td>\n",
              "      <td>25.7990</td>\n",
              "      <td>27.4660</td>\n",
              "      <td>13.2985</td>\n",
              "      <td>21.5595</td>\n",
              "      <td>27.9705</td>\n",
              "      <td>11.8980</td>\n",
              "      <td>23.2905</td>\n",
              "      <td>25.5630</td>\n",
              "      <td>27.1525</td>\n",
              "      <td>12.1930</td>\n",
              "      <td>27.8005</td>\n",
              "      <td>25.3945</td>\n",
              "      <td>20.4330</td>\n",
              "      <td>27.1050</td>\n",
              "      <td>25.0250</td>\n",
              "      <td>-3.3165</td>\n",
              "      <td>24.8025</td>\n",
              "      <td>...</td>\n",
              "      <td>22.7555</td>\n",
              "      <td>1.6740</td>\n",
              "      <td>14.4405</td>\n",
              "      <td>13.6930</td>\n",
              "      <td>27.5730</td>\n",
              "      <td>28.9775</td>\n",
              "      <td>26.7355</td>\n",
              "      <td>-8.6625</td>\n",
              "      <td>19.4395</td>\n",
              "      <td>3.4350</td>\n",
              "      <td>7.3845</td>\n",
              "      <td>20.3870</td>\n",
              "      <td>23.4490</td>\n",
              "      <td>6.2085</td>\n",
              "      <td>23.0040</td>\n",
              "      <td>27.1200</td>\n",
              "      <td>26.2090</td>\n",
              "      <td>27.3300</td>\n",
              "      <td>23.6760</td>\n",
              "      <td>27.1180</td>\n",
              "      <td>21.4475</td>\n",
              "      <td>13.6315</td>\n",
              "      <td>17.2380</td>\n",
              "      <td>27.1220</td>\n",
              "      <td>23.4935</td>\n",
              "      <td>10.3895</td>\n",
              "      <td>29.7170</td>\n",
              "      <td>8.0815</td>\n",
              "      <td>8.1955</td>\n",
              "      <td>8.5330</td>\n",
              "      <td>17.8770</td>\n",
              "      <td>14.6835</td>\n",
              "      <td>25.5875</td>\n",
              "      <td>25.2840</td>\n",
              "      <td>27.1525</td>\n",
              "      <td>24.2500</td>\n",
              "      <td>27.4595</td>\n",
              "      <td>21.5855</td>\n",
              "      <td>22.4170</td>\n",
              "      <td>6.5450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>13.8930</td>\n",
              "      <td>25.8935</td>\n",
              "      <td>13.1795</td>\n",
              "      <td>23.7635</td>\n",
              "      <td>26.9025</td>\n",
              "      <td>11.6925</td>\n",
              "      <td>22.5965</td>\n",
              "      <td>27.6000</td>\n",
              "      <td>27.4565</td>\n",
              "      <td>16.3900</td>\n",
              "      <td>9.7750</td>\n",
              "      <td>29.1925</td>\n",
              "      <td>9.6880</td>\n",
              "      <td>22.9055</td>\n",
              "      <td>8.4815</td>\n",
              "      <td>11.7310</td>\n",
              "      <td>25.7310</td>\n",
              "      <td>27.3475</td>\n",
              "      <td>26.2830</td>\n",
              "      <td>27.7290</td>\n",
              "      <td>27.6520</td>\n",
              "      <td>7.2300</td>\n",
              "      <td>11.3630</td>\n",
              "      <td>26.8950</td>\n",
              "      <td>27.6090</td>\n",
              "      <td>14.2560</td>\n",
              "      <td>22.1190</td>\n",
              "      <td>28.5240</td>\n",
              "      <td>11.8085</td>\n",
              "      <td>23.5525</td>\n",
              "      <td>25.8270</td>\n",
              "      <td>27.2885</td>\n",
              "      <td>11.6535</td>\n",
              "      <td>28.6125</td>\n",
              "      <td>25.8425</td>\n",
              "      <td>20.5755</td>\n",
              "      <td>27.4275</td>\n",
              "      <td>25.0570</td>\n",
              "      <td>-4.2240</td>\n",
              "      <td>24.6290</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0605</td>\n",
              "      <td>1.9295</td>\n",
              "      <td>13.8730</td>\n",
              "      <td>14.3375</td>\n",
              "      <td>28.1780</td>\n",
              "      <td>29.3565</td>\n",
              "      <td>27.1070</td>\n",
              "      <td>-4.7220</td>\n",
              "      <td>19.6335</td>\n",
              "      <td>2.4900</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>19.6995</td>\n",
              "      <td>22.9190</td>\n",
              "      <td>4.7415</td>\n",
              "      <td>23.4630</td>\n",
              "      <td>27.4645</td>\n",
              "      <td>26.6770</td>\n",
              "      <td>27.5015</td>\n",
              "      <td>22.7445</td>\n",
              "      <td>27.3370</td>\n",
              "      <td>19.8120</td>\n",
              "      <td>12.9035</td>\n",
              "      <td>14.4070</td>\n",
              "      <td>27.2505</td>\n",
              "      <td>23.8455</td>\n",
              "      <td>9.6355</td>\n",
              "      <td>29.1640</td>\n",
              "      <td>9.7480</td>\n",
              "      <td>9.9650</td>\n",
              "      <td>8.3530</td>\n",
              "      <td>17.9630</td>\n",
              "      <td>12.2385</td>\n",
              "      <td>26.0265</td>\n",
              "      <td>25.1260</td>\n",
              "      <td>27.2885</td>\n",
              "      <td>23.0090</td>\n",
              "      <td>27.5515</td>\n",
              "      <td>22.1360</td>\n",
              "      <td>22.4860</td>\n",
              "      <td>5.5765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>17.4285</td>\n",
              "      <td>26.1595</td>\n",
              "      <td>12.6825</td>\n",
              "      <td>25.4575</td>\n",
              "      <td>27.3260</td>\n",
              "      <td>11.4400</td>\n",
              "      <td>23.0795</td>\n",
              "      <td>27.8365</td>\n",
              "      <td>27.6700</td>\n",
              "      <td>14.9110</td>\n",
              "      <td>10.3800</td>\n",
              "      <td>28.9830</td>\n",
              "      <td>9.2920</td>\n",
              "      <td>22.8570</td>\n",
              "      <td>6.5825</td>\n",
              "      <td>12.4060</td>\n",
              "      <td>25.5405</td>\n",
              "      <td>28.6650</td>\n",
              "      <td>25.9590</td>\n",
              "      <td>27.9750</td>\n",
              "      <td>27.8335</td>\n",
              "      <td>6.5175</td>\n",
              "      <td>9.9740</td>\n",
              "      <td>26.1025</td>\n",
              "      <td>27.9585</td>\n",
              "      <td>14.1200</td>\n",
              "      <td>22.3840</td>\n",
              "      <td>28.5635</td>\n",
              "      <td>10.4040</td>\n",
              "      <td>24.6665</td>\n",
              "      <td>26.1220</td>\n",
              "      <td>27.5365</td>\n",
              "      <td>11.0505</td>\n",
              "      <td>28.7360</td>\n",
              "      <td>25.9420</td>\n",
              "      <td>21.0325</td>\n",
              "      <td>27.7620</td>\n",
              "      <td>25.0005</td>\n",
              "      <td>-0.5750</td>\n",
              "      <td>25.5675</td>\n",
              "      <td>...</td>\n",
              "      <td>22.8585</td>\n",
              "      <td>2.9730</td>\n",
              "      <td>12.0380</td>\n",
              "      <td>13.9145</td>\n",
              "      <td>28.0610</td>\n",
              "      <td>29.3295</td>\n",
              "      <td>27.2295</td>\n",
              "      <td>-6.9995</td>\n",
              "      <td>20.1955</td>\n",
              "      <td>2.8540</td>\n",
              "      <td>7.6390</td>\n",
              "      <td>20.6380</td>\n",
              "      <td>22.6125</td>\n",
              "      <td>7.2945</td>\n",
              "      <td>23.6685</td>\n",
              "      <td>27.3915</td>\n",
              "      <td>26.8810</td>\n",
              "      <td>27.7085</td>\n",
              "      <td>23.7675</td>\n",
              "      <td>27.5575</td>\n",
              "      <td>21.0155</td>\n",
              "      <td>12.6085</td>\n",
              "      <td>17.4685</td>\n",
              "      <td>27.3330</td>\n",
              "      <td>23.8330</td>\n",
              "      <td>8.8855</td>\n",
              "      <td>30.5700</td>\n",
              "      <td>8.9375</td>\n",
              "      <td>9.0460</td>\n",
              "      <td>10.2315</td>\n",
              "      <td>17.1540</td>\n",
              "      <td>15.0755</td>\n",
              "      <td>25.9715</td>\n",
              "      <td>24.8810</td>\n",
              "      <td>27.5365</td>\n",
              "      <td>24.2365</td>\n",
              "      <td>28.2950</td>\n",
              "      <td>22.6880</td>\n",
              "      <td>23.3040</td>\n",
              "      <td>4.7550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>16.0240</td>\n",
              "      <td>25.6220</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>24.1415</td>\n",
              "      <td>27.0205</td>\n",
              "      <td>13.4625</td>\n",
              "      <td>22.7390</td>\n",
              "      <td>27.5875</td>\n",
              "      <td>27.4115</td>\n",
              "      <td>15.3845</td>\n",
              "      <td>9.1040</td>\n",
              "      <td>28.6525</td>\n",
              "      <td>10.1260</td>\n",
              "      <td>22.3575</td>\n",
              "      <td>8.3240</td>\n",
              "      <td>11.1355</td>\n",
              "      <td>26.3965</td>\n",
              "      <td>27.6540</td>\n",
              "      <td>25.9400</td>\n",
              "      <td>27.2925</td>\n",
              "      <td>27.5385</td>\n",
              "      <td>7.4190</td>\n",
              "      <td>12.5040</td>\n",
              "      <td>26.1965</td>\n",
              "      <td>27.5335</td>\n",
              "      <td>13.3205</td>\n",
              "      <td>22.0800</td>\n",
              "      <td>28.0250</td>\n",
              "      <td>11.5420</td>\n",
              "      <td>23.8825</td>\n",
              "      <td>25.6010</td>\n",
              "      <td>27.1510</td>\n",
              "      <td>9.8560</td>\n",
              "      <td>28.5590</td>\n",
              "      <td>25.2830</td>\n",
              "      <td>20.5615</td>\n",
              "      <td>27.1375</td>\n",
              "      <td>24.3175</td>\n",
              "      <td>-3.8630</td>\n",
              "      <td>25.0240</td>\n",
              "      <td>...</td>\n",
              "      <td>22.7890</td>\n",
              "      <td>2.6125</td>\n",
              "      <td>12.4460</td>\n",
              "      <td>16.0565</td>\n",
              "      <td>27.9235</td>\n",
              "      <td>28.2635</td>\n",
              "      <td>26.9860</td>\n",
              "      <td>-6.0160</td>\n",
              "      <td>19.9855</td>\n",
              "      <td>5.0380</td>\n",
              "      <td>9.3225</td>\n",
              "      <td>18.5920</td>\n",
              "      <td>23.4055</td>\n",
              "      <td>6.8305</td>\n",
              "      <td>23.3210</td>\n",
              "      <td>26.5930</td>\n",
              "      <td>26.1860</td>\n",
              "      <td>27.3240</td>\n",
              "      <td>24.5635</td>\n",
              "      <td>26.9090</td>\n",
              "      <td>20.7080</td>\n",
              "      <td>11.0750</td>\n",
              "      <td>16.6760</td>\n",
              "      <td>27.3290</td>\n",
              "      <td>23.4685</td>\n",
              "      <td>8.3405</td>\n",
              "      <td>29.8950</td>\n",
              "      <td>10.9015</td>\n",
              "      <td>11.1730</td>\n",
              "      <td>9.5360</td>\n",
              "      <td>17.8235</td>\n",
              "      <td>14.6060</td>\n",
              "      <td>25.6440</td>\n",
              "      <td>24.1880</td>\n",
              "      <td>27.1510</td>\n",
              "      <td>24.2690</td>\n",
              "      <td>27.8980</td>\n",
              "      <td>22.0270</td>\n",
              "      <td>22.9420</td>\n",
              "      <td>7.4990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>15.6755</td>\n",
              "      <td>25.5670</td>\n",
              "      <td>13.9060</td>\n",
              "      <td>24.5915</td>\n",
              "      <td>27.1620</td>\n",
              "      <td>11.5065</td>\n",
              "      <td>22.3530</td>\n",
              "      <td>27.8255</td>\n",
              "      <td>27.6260</td>\n",
              "      <td>15.4870</td>\n",
              "      <td>12.6345</td>\n",
              "      <td>28.9155</td>\n",
              "      <td>9.9735</td>\n",
              "      <td>22.8970</td>\n",
              "      <td>7.3080</td>\n",
              "      <td>15.1500</td>\n",
              "      <td>25.4820</td>\n",
              "      <td>28.3010</td>\n",
              "      <td>26.2315</td>\n",
              "      <td>27.1255</td>\n",
              "      <td>27.6910</td>\n",
              "      <td>7.7895</td>\n",
              "      <td>9.5395</td>\n",
              "      <td>26.1350</td>\n",
              "      <td>27.1935</td>\n",
              "      <td>12.7990</td>\n",
              "      <td>22.4505</td>\n",
              "      <td>28.3755</td>\n",
              "      <td>11.9515</td>\n",
              "      <td>23.3960</td>\n",
              "      <td>25.6640</td>\n",
              "      <td>27.5920</td>\n",
              "      <td>13.5420</td>\n",
              "      <td>28.4720</td>\n",
              "      <td>25.6835</td>\n",
              "      <td>20.7700</td>\n",
              "      <td>27.7305</td>\n",
              "      <td>24.4420</td>\n",
              "      <td>-3.5450</td>\n",
              "      <td>24.9555</td>\n",
              "      <td>...</td>\n",
              "      <td>23.3000</td>\n",
              "      <td>1.5655</td>\n",
              "      <td>13.6410</td>\n",
              "      <td>13.1825</td>\n",
              "      <td>28.2240</td>\n",
              "      <td>28.7735</td>\n",
              "      <td>26.8695</td>\n",
              "      <td>-5.0175</td>\n",
              "      <td>19.5850</td>\n",
              "      <td>1.8185</td>\n",
              "      <td>7.7160</td>\n",
              "      <td>20.7445</td>\n",
              "      <td>23.2380</td>\n",
              "      <td>6.8280</td>\n",
              "      <td>23.5930</td>\n",
              "      <td>27.5620</td>\n",
              "      <td>26.4145</td>\n",
              "      <td>27.1935</td>\n",
              "      <td>23.6735</td>\n",
              "      <td>27.1215</td>\n",
              "      <td>22.1020</td>\n",
              "      <td>14.4445</td>\n",
              "      <td>18.6450</td>\n",
              "      <td>27.3080</td>\n",
              "      <td>23.5005</td>\n",
              "      <td>11.2685</td>\n",
              "      <td>29.8950</td>\n",
              "      <td>8.3920</td>\n",
              "      <td>8.4340</td>\n",
              "      <td>9.9420</td>\n",
              "      <td>17.9600</td>\n",
              "      <td>16.6915</td>\n",
              "      <td>25.7590</td>\n",
              "      <td>25.5770</td>\n",
              "      <td>27.5920</td>\n",
              "      <td>23.6175</td>\n",
              "      <td>27.8575</td>\n",
              "      <td>22.4530</td>\n",
              "      <td>23.0455</td>\n",
              "      <td>6.2540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>18.1000</td>\n",
              "      <td>25.7865</td>\n",
              "      <td>15.9465</td>\n",
              "      <td>26.3620</td>\n",
              "      <td>27.6665</td>\n",
              "      <td>10.9400</td>\n",
              "      <td>22.5180</td>\n",
              "      <td>27.1730</td>\n",
              "      <td>27.1535</td>\n",
              "      <td>13.7915</td>\n",
              "      <td>12.8550</td>\n",
              "      <td>28.3895</td>\n",
              "      <td>12.8950</td>\n",
              "      <td>21.3410</td>\n",
              "      <td>9.3810</td>\n",
              "      <td>14.8345</td>\n",
              "      <td>25.9760</td>\n",
              "      <td>28.7880</td>\n",
              "      <td>26.5410</td>\n",
              "      <td>28.0495</td>\n",
              "      <td>27.3275</td>\n",
              "      <td>11.8220</td>\n",
              "      <td>10.2010</td>\n",
              "      <td>26.7590</td>\n",
              "      <td>28.1590</td>\n",
              "      <td>14.2370</td>\n",
              "      <td>20.4045</td>\n",
              "      <td>27.8670</td>\n",
              "      <td>14.1520</td>\n",
              "      <td>20.5910</td>\n",
              "      <td>25.1850</td>\n",
              "      <td>27.4930</td>\n",
              "      <td>15.1925</td>\n",
              "      <td>29.3415</td>\n",
              "      <td>26.3585</td>\n",
              "      <td>20.7565</td>\n",
              "      <td>28.2365</td>\n",
              "      <td>25.3965</td>\n",
              "      <td>2.9260</td>\n",
              "      <td>24.1845</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0220</td>\n",
              "      <td>2.0640</td>\n",
              "      <td>14.1655</td>\n",
              "      <td>13.1960</td>\n",
              "      <td>28.3585</td>\n",
              "      <td>29.0810</td>\n",
              "      <td>26.9610</td>\n",
              "      <td>-6.7665</td>\n",
              "      <td>18.3590</td>\n",
              "      <td>5.7840</td>\n",
              "      <td>8.3850</td>\n",
              "      <td>20.5535</td>\n",
              "      <td>22.8910</td>\n",
              "      <td>8.2600</td>\n",
              "      <td>22.6990</td>\n",
              "      <td>27.9445</td>\n",
              "      <td>26.5110</td>\n",
              "      <td>27.7640</td>\n",
              "      <td>24.7140</td>\n",
              "      <td>27.2690</td>\n",
              "      <td>22.3255</td>\n",
              "      <td>14.8120</td>\n",
              "      <td>19.6655</td>\n",
              "      <td>27.5460</td>\n",
              "      <td>23.5870</td>\n",
              "      <td>14.3245</td>\n",
              "      <td>30.9620</td>\n",
              "      <td>8.1970</td>\n",
              "      <td>8.2305</td>\n",
              "      <td>14.0730</td>\n",
              "      <td>16.2660</td>\n",
              "      <td>17.9390</td>\n",
              "      <td>25.7170</td>\n",
              "      <td>26.4565</td>\n",
              "      <td>27.4930</td>\n",
              "      <td>24.2745</td>\n",
              "      <td>30.0215</td>\n",
              "      <td>21.2985</td>\n",
              "      <td>20.4505</td>\n",
              "      <td>6.0140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>44 rows × 242 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "Country  Afghanistan   Africa  Albania  ...   Zambia  Zimbabwe   Åland\n",
              "date                                    ...                           \n",
              "1970         16.1990  25.2360  12.4230  ...  22.1330   23.2560  6.9340\n",
              "1971         14.9340  24.7755  11.6735  ...  21.8265   22.7055  4.4870\n",
              "1972         13.8725  25.1230  11.2175  ...  21.9800   22.3980  5.6200\n",
              "1973         14.9130  25.3445  11.8370  ...  22.1090   22.5815  3.5795\n",
              "1974         13.6160  24.8260  11.3210  ...  21.6005   21.3255  5.4550\n",
              "1975         13.5130  24.8045  12.3595  ...  21.8635   21.7910  6.0170\n",
              "1976         14.1615  24.8225  12.6080  ...  21.6980   21.9880  4.3855\n",
              "1977         15.6575  25.0345  11.9870  ...  22.1670   22.2910  5.4745\n",
              "1978         15.2580  24.9135  11.5490  ...  22.1010   22.6210  5.7475\n",
              "1979         16.2990  25.3705  11.7765  ...  22.2165   22.7160  4.6730\n",
              "1980         16.3110  25.2955  11.9060  ...  22.4310   22.2040  4.5410\n",
              "1981         14.4390  25.2910  13.3060  ...  22.3740   21.6195  4.8730\n",
              "1982         15.0705  25.1225  12.4050  ...  22.7425   22.3540  6.4350\n",
              "1983         13.6025  25.4250  12.6770  ...  23.0990   23.3410  5.1200\n",
              "1984         14.4185  25.2940  12.7785  ...  22.5665   23.3255  6.7930\n",
              "1985         14.8955  24.9255  12.6220  ...  22.4485   22.4450  3.8720\n",
              "1986         15.1815  25.1730  13.3720  ...  22.2760   22.4890  6.4775\n",
              "1987         13.6260  25.5695  12.5730  ...  23.1640   23.3280  4.4655\n",
              "1988         15.2810  25.4000  12.3825  ...  22.6545   22.8105  3.9515\n",
              "1989         14.6015  25.1320  12.9095  ...  22.4170   22.5005  5.6370\n",
              "1990         14.4935  25.6785  13.4490  ...  22.7115   22.8805  6.1945\n",
              "1991         14.3920  25.2885  11.5885  ...  22.3795   23.3265  5.4660\n",
              "1992         13.7520  25.4245  13.5850  ...  23.2185   24.1030  3.2440\n",
              "1993         14.0685  25.6620  13.6500  ...  22.0050   22.6745  4.7270\n",
              "1994         13.6150  25.2650  13.2390  ...  22.1780   22.5795  5.2915\n",
              "1995         14.4835  25.5005  11.8750  ...  22.7600   23.4275  4.4450\n",
              "1996         14.5665  25.3650  11.7315  ...  22.3725   22.6680  5.0130\n",
              "1997         15.2480  25.4135  10.7790  ...  22.3355   22.6220  4.1280\n",
              "1998         15.7670  25.9285  13.4790  ...  22.7895   22.9400  4.5975\n",
              "1999         15.5875  25.5770  13.5485  ...  22.0465   22.3720  6.0360\n",
              "2000         16.8560  25.4540  13.9625  ...  22.2105   22.7690  7.8450\n",
              "2001         16.3620  25.8030  13.7490  ...  22.1860   22.7205  5.6560\n",
              "2002         16.4970  25.7410  12.5335  ...  23.1025   23.1010  3.9725\n",
              "2003         15.4800  26.0325  12.1450  ...  22.9690   22.8095  4.9515\n",
              "2004         14.6700  25.7840  13.6610  ...  22.4920   22.1475  5.8705\n",
              "2005         15.5190  26.0680  12.4485  ...  22.9420   22.9180  6.4785\n",
              "2006         16.6300  25.9285  13.6685  ...  22.2425   22.0520  6.5160\n",
              "2007         15.4785  25.6990  13.1770  ...  22.2380   22.9095  6.2955\n",
              "2008         16.3810  25.6660  13.2985  ...  21.5855   22.4170  6.5450\n",
              "2009         13.8930  25.8935  13.1795  ...  22.1360   22.4860  5.5765\n",
              "2010         17.4285  26.1595  12.6825  ...  22.6880   23.3040  4.7550\n",
              "2011         16.0240  25.6220  12.4440  ...  22.0270   22.9420  7.4990\n",
              "2012         15.6755  25.5670  13.9060  ...  22.4530   23.0455  6.2540\n",
              "2013         18.1000  25.7865  15.9465  ...  21.2985   20.4505  6.0140\n",
              "\n",
              "[44 rows x 242 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "jDa-8hWP-Cct",
        "outputId": "3a92e1b2-fe26-4676-82a2-f28f77d930aa"
      },
      "source": [
        "df_t_pivot.boxplot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feff4067650>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "u3dj6pGv-H1G",
        "outputId": "15236d33-b9d6-4e7e-bdfb-9112b754d1cf"
      },
      "source": [
        "df_t_pivot.T.sample(2).T.boxplot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feff2d7c110>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nr0h6JdS--yB"
      },
      "source": [
        "## 1.4. Preprocesamiento de los datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tkNEsFG-hJt",
        "outputId": "80daf80c-cedd-4442-e34e-ac59d3b32cde"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "API_AG.LND.AGRI.K2_DS2_en_csv_v2_716226.csv\tMeteorite_Landings.csv\n",
            "API_AG.LND.FRST.K2_DS2_en_csv_v2_716262.csv\tpoblacion.csv\n",
            "API_EG.USE.ELEC.KH.PC_DS2_en_csv_v2_715482.csv\ttest.csv\n",
            "API_EN.ATM.CO2E.KT_DS2_en_csv_v2_713263.csv\ttest.hdf\n",
            "API_SP.POP.TOTL_DS2_en_csv_v2_713131.csv\ttest.json\n",
            "covid_19_data.csv\t\t\t\ttest.parquet\n",
            "GlobalLandTemperaturesByCountry.csv\t\ttest.pkl\n",
            "london_merged.csv\t\t\t\ttest.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2m6avwU7_ige"
      },
      "source": [
        "df_agri = pd.read_csv('API_AG.LND.AGRI.K2_DS2_en_csv_v2_716226.csv',header=2)\n",
        "df_fore = pd.read_csv('API_AG.LND.FRST.K2_DS2_en_csv_v2_716262.csv',header=2)\n",
        "df_elec = pd.read_csv('API_EG.USE.ELEC.KH.PC_DS2_en_csv_v2_715482.csv',header=2)\n",
        "df_co2e = pd.read_csv('API_EN.ATM.CO2E.KT_DS2_en_csv_v2_713263.csv',header=2)\n",
        "df_popu = pd.read_csv('API_SP.POP.TOTL_DS2_en_csv_v2_713131.csv',header=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "v7ZfpN2w_ult",
        "outputId": "dc57aee5-4edb-414f-e190-2ff015a71a78"
      },
      "source": [
        "df_popu"
      ],
      "execution_count": null,
      "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>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>1960</th>\n",
              "      <th>1961</th>\n",
              "      <th>1962</th>\n",
              "      <th>1963</th>\n",
              "      <th>1964</th>\n",
              "      <th>1965</th>\n",
              "      <th>1966</th>\n",
              "      <th>1967</th>\n",
              "      <th>1968</th>\n",
              "      <th>1969</th>\n",
              "      <th>1970</th>\n",
              "      <th>1971</th>\n",
              "      <th>1972</th>\n",
              "      <th>1973</th>\n",
              "      <th>1974</th>\n",
              "      <th>1975</th>\n",
              "      <th>1976</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>1987</th>\n",
              "      <th>1988</th>\n",
              "      <th>1989</th>\n",
              "      <th>1990</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>Unnamed: 64</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>54211.0</td>\n",
              "      <td>55438.0</td>\n",
              "      <td>56225.0</td>\n",
              "      <td>56695.0</td>\n",
              "      <td>57032.0</td>\n",
              "      <td>57360.0</td>\n",
              "      <td>57715.0</td>\n",
              "      <td>58055.0</td>\n",
              "      <td>58386.0</td>\n",
              "      <td>58726.0</td>\n",
              "      <td>59063.0</td>\n",
              "      <td>59440.0</td>\n",
              "      <td>59840.0</td>\n",
              "      <td>60243.0</td>\n",
              "      <td>60528.0</td>\n",
              "      <td>60657.0</td>\n",
              "      <td>60586.0</td>\n",
              "      <td>60366.0</td>\n",
              "      <td>60103.0</td>\n",
              "      <td>59980.0</td>\n",
              "      <td>60096.0</td>\n",
              "      <td>60567.0</td>\n",
              "      <td>61345.0</td>\n",
              "      <td>62201.0</td>\n",
              "      <td>62836.0</td>\n",
              "      <td>63026.0</td>\n",
              "      <td>62644.0</td>\n",
              "      <td>61833.0</td>\n",
              "      <td>61079.0</td>\n",
              "      <td>61032.0</td>\n",
              "      <td>62149.0</td>\n",
              "      <td>64622.0</td>\n",
              "      <td>68235.0</td>\n",
              "      <td>72504.0</td>\n",
              "      <td>76700.0</td>\n",
              "      <td>80324.0</td>\n",
              "      <td>83200.0</td>\n",
              "      <td>85451.0</td>\n",
              "      <td>87277.0</td>\n",
              "      <td>89005.0</td>\n",
              "      <td>90853.0</td>\n",
              "      <td>92898.0</td>\n",
              "      <td>94992.0</td>\n",
              "      <td>97017.0</td>\n",
              "      <td>98737.0</td>\n",
              "      <td>100031.0</td>\n",
              "      <td>100834.0</td>\n",
              "      <td>101222.0</td>\n",
              "      <td>101358.0</td>\n",
              "      <td>101455.0</td>\n",
              "      <td>101669.0</td>\n",
              "      <td>102046.0</td>\n",
              "      <td>102560.0</td>\n",
              "      <td>103159.0</td>\n",
              "      <td>103774.0</td>\n",
              "      <td>104341.0</td>\n",
              "      <td>104872.0</td>\n",
              "      <td>105366.0</td>\n",
              "      <td>105845.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>8996973.0</td>\n",
              "      <td>9169410.0</td>\n",
              "      <td>9351441.0</td>\n",
              "      <td>9543205.0</td>\n",
              "      <td>9744781.0</td>\n",
              "      <td>9956320.0</td>\n",
              "      <td>10174836.0</td>\n",
              "      <td>10399926.0</td>\n",
              "      <td>10637063.0</td>\n",
              "      <td>10893776.0</td>\n",
              "      <td>11173642.0</td>\n",
              "      <td>11475445.0</td>\n",
              "      <td>11791215.0</td>\n",
              "      <td>12108963.0</td>\n",
              "      <td>12412950.0</td>\n",
              "      <td>12689160.0</td>\n",
              "      <td>12943093.0</td>\n",
              "      <td>13171306.0</td>\n",
              "      <td>13341198.0</td>\n",
              "      <td>13411056.0</td>\n",
              "      <td>13356511.0</td>\n",
              "      <td>13171673.0</td>\n",
              "      <td>12882528.0</td>\n",
              "      <td>12537730.0</td>\n",
              "      <td>12204292.0</td>\n",
              "      <td>11938208.0</td>\n",
              "      <td>11736179.0</td>\n",
              "      <td>11604534.0</td>\n",
              "      <td>11618005.0</td>\n",
              "      <td>11868877.0</td>\n",
              "      <td>12412308.0</td>\n",
              "      <td>13299017.0</td>\n",
              "      <td>14485546.0</td>\n",
              "      <td>15816603.0</td>\n",
              "      <td>17075727.0</td>\n",
              "      <td>18110657.0</td>\n",
              "      <td>18853437.0</td>\n",
              "      <td>19357126.0</td>\n",
              "      <td>19737765.0</td>\n",
              "      <td>20170844.0</td>\n",
              "      <td>20779953.0</td>\n",
              "      <td>21606988.0</td>\n",
              "      <td>22600770.0</td>\n",
              "      <td>23680871.0</td>\n",
              "      <td>24726684.0</td>\n",
              "      <td>25654277.0</td>\n",
              "      <td>26433049.0</td>\n",
              "      <td>27100536.0</td>\n",
              "      <td>27722276.0</td>\n",
              "      <td>28394813.0</td>\n",
              "      <td>29185507.0</td>\n",
              "      <td>30117413.0</td>\n",
              "      <td>31161376.0</td>\n",
              "      <td>32269589.0</td>\n",
              "      <td>33370794.0</td>\n",
              "      <td>34413603.0</td>\n",
              "      <td>35383128.0</td>\n",
              "      <td>36296400.0</td>\n",
              "      <td>37172386.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Angola</td>\n",
              "      <td>AGO</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>5454933.0</td>\n",
              "      <td>5531472.0</td>\n",
              "      <td>5608539.0</td>\n",
              "      <td>5679458.0</td>\n",
              "      <td>5735044.0</td>\n",
              "      <td>5770570.0</td>\n",
              "      <td>5781214.0</td>\n",
              "      <td>5774243.0</td>\n",
              "      <td>5771652.0</td>\n",
              "      <td>5803254.0</td>\n",
              "      <td>5890365.0</td>\n",
              "      <td>6040777.0</td>\n",
              "      <td>6248552.0</td>\n",
              "      <td>6496962.0</td>\n",
              "      <td>6761380.0</td>\n",
              "      <td>7024000.0</td>\n",
              "      <td>7279509.0</td>\n",
              "      <td>7533735.0</td>\n",
              "      <td>7790707.0</td>\n",
              "      <td>8058067.0</td>\n",
              "      <td>8341289.0</td>\n",
              "      <td>8640446.0</td>\n",
              "      <td>8952950.0</td>\n",
              "      <td>9278096.0</td>\n",
              "      <td>9614754.0</td>\n",
              "      <td>9961997.0</td>\n",
              "      <td>10320111.0</td>\n",
              "      <td>10689250.0</td>\n",
              "      <td>11068050.0</td>\n",
              "      <td>11454777.0</td>\n",
              "      <td>11848386.0</td>\n",
              "      <td>12248901.0</td>\n",
              "      <td>12657366.0</td>\n",
              "      <td>13075049.0</td>\n",
              "      <td>13503747.0</td>\n",
              "      <td>13945206.0</td>\n",
              "      <td>14400719.0</td>\n",
              "      <td>14871570.0</td>\n",
              "      <td>15359601.0</td>\n",
              "      <td>15866869.0</td>\n",
              "      <td>16395473.0</td>\n",
              "      <td>16945753.0</td>\n",
              "      <td>17519417.0</td>\n",
              "      <td>18121479.0</td>\n",
              "      <td>18758145.0</td>\n",
              "      <td>19433602.0</td>\n",
              "      <td>20149901.0</td>\n",
              "      <td>20905363.0</td>\n",
              "      <td>21695634.0</td>\n",
              "      <td>22514281.0</td>\n",
              "      <td>23356246.0</td>\n",
              "      <td>24220661.0</td>\n",
              "      <td>25107931.0</td>\n",
              "      <td>26015780.0</td>\n",
              "      <td>26941779.0</td>\n",
              "      <td>27884381.0</td>\n",
              "      <td>28842484.0</td>\n",
              "      <td>29816748.0</td>\n",
              "      <td>30809762.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>1608800.0</td>\n",
              "      <td>1659800.0</td>\n",
              "      <td>1711319.0</td>\n",
              "      <td>1762621.0</td>\n",
              "      <td>1814135.0</td>\n",
              "      <td>1864791.0</td>\n",
              "      <td>1914573.0</td>\n",
              "      <td>1965598.0</td>\n",
              "      <td>2022272.0</td>\n",
              "      <td>2081695.0</td>\n",
              "      <td>2135479.0</td>\n",
              "      <td>2187853.0</td>\n",
              "      <td>2243126.0</td>\n",
              "      <td>2296752.0</td>\n",
              "      <td>2350124.0</td>\n",
              "      <td>2404831.0</td>\n",
              "      <td>2458526.0</td>\n",
              "      <td>2513546.0</td>\n",
              "      <td>2566266.0</td>\n",
              "      <td>2617832.0</td>\n",
              "      <td>2671997.0</td>\n",
              "      <td>2726056.0</td>\n",
              "      <td>2784278.0</td>\n",
              "      <td>2843960.0</td>\n",
              "      <td>2904429.0</td>\n",
              "      <td>2964762.0</td>\n",
              "      <td>3022635.0</td>\n",
              "      <td>3083605.0</td>\n",
              "      <td>3142336.0</td>\n",
              "      <td>3227943.0</td>\n",
              "      <td>3286542.0</td>\n",
              "      <td>3266790.0</td>\n",
              "      <td>3247039.0</td>\n",
              "      <td>3227287.0</td>\n",
              "      <td>3207536.0</td>\n",
              "      <td>3187784.0</td>\n",
              "      <td>3168033.0</td>\n",
              "      <td>3148281.0</td>\n",
              "      <td>3128530.0</td>\n",
              "      <td>3108778.0</td>\n",
              "      <td>3089027.0</td>\n",
              "      <td>3060173.0</td>\n",
              "      <td>3051010.0</td>\n",
              "      <td>3039616.0</td>\n",
              "      <td>3026939.0</td>\n",
              "      <td>3011487.0</td>\n",
              "      <td>2992547.0</td>\n",
              "      <td>2970017.0</td>\n",
              "      <td>2947314.0</td>\n",
              "      <td>2927519.0</td>\n",
              "      <td>2913021.0</td>\n",
              "      <td>2905195.0</td>\n",
              "      <td>2900401.0</td>\n",
              "      <td>2895092.0</td>\n",
              "      <td>2889104.0</td>\n",
              "      <td>2880703.0</td>\n",
              "      <td>2876101.0</td>\n",
              "      <td>2873457.0</td>\n",
              "      <td>2866376.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>AND</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>13411.0</td>\n",
              "      <td>14375.0</td>\n",
              "      <td>15370.0</td>\n",
              "      <td>16412.0</td>\n",
              "      <td>17469.0</td>\n",
              "      <td>18549.0</td>\n",
              "      <td>19647.0</td>\n",
              "      <td>20758.0</td>\n",
              "      <td>21890.0</td>\n",
              "      <td>23058.0</td>\n",
              "      <td>24276.0</td>\n",
              "      <td>25559.0</td>\n",
              "      <td>26892.0</td>\n",
              "      <td>28232.0</td>\n",
              "      <td>29520.0</td>\n",
              "      <td>30705.0</td>\n",
              "      <td>31777.0</td>\n",
              "      <td>32771.0</td>\n",
              "      <td>33737.0</td>\n",
              "      <td>34818.0</td>\n",
              "      <td>36067.0</td>\n",
              "      <td>37500.0</td>\n",
              "      <td>39114.0</td>\n",
              "      <td>40867.0</td>\n",
              "      <td>42706.0</td>\n",
              "      <td>44600.0</td>\n",
              "      <td>46517.0</td>\n",
              "      <td>48455.0</td>\n",
              "      <td>50434.0</td>\n",
              "      <td>52448.0</td>\n",
              "      <td>54509.0</td>\n",
              "      <td>56671.0</td>\n",
              "      <td>58888.0</td>\n",
              "      <td>60971.0</td>\n",
              "      <td>62677.0</td>\n",
              "      <td>63850.0</td>\n",
              "      <td>64360.0</td>\n",
              "      <td>64327.0</td>\n",
              "      <td>64142.0</td>\n",
              "      <td>64370.0</td>\n",
              "      <td>65390.0</td>\n",
              "      <td>67341.0</td>\n",
              "      <td>70049.0</td>\n",
              "      <td>73182.0</td>\n",
              "      <td>76244.0</td>\n",
              "      <td>78867.0</td>\n",
              "      <td>80993.0</td>\n",
              "      <td>82684.0</td>\n",
              "      <td>83862.0</td>\n",
              "      <td>84463.0</td>\n",
              "      <td>84449.0</td>\n",
              "      <td>83747.0</td>\n",
              "      <td>82427.0</td>\n",
              "      <td>80774.0</td>\n",
              "      <td>79213.0</td>\n",
              "      <td>78011.0</td>\n",
              "      <td>77297.0</td>\n",
              "      <td>77001.0</td>\n",
              "      <td>77006.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>XKX</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>947000.0</td>\n",
              "      <td>966000.0</td>\n",
              "      <td>994000.0</td>\n",
              "      <td>1022000.0</td>\n",
              "      <td>1050000.0</td>\n",
              "      <td>1078000.0</td>\n",
              "      <td>1106000.0</td>\n",
              "      <td>1135000.0</td>\n",
              "      <td>1163000.0</td>\n",
              "      <td>1191000.0</td>\n",
              "      <td>1219000.0</td>\n",
              "      <td>1247000.0</td>\n",
              "      <td>1278000.0</td>\n",
              "      <td>1308000.0</td>\n",
              "      <td>1339000.0</td>\n",
              "      <td>1369000.0</td>\n",
              "      <td>1400000.0</td>\n",
              "      <td>1430000.0</td>\n",
              "      <td>1460000.0</td>\n",
              "      <td>1491000.0</td>\n",
              "      <td>1521000.0</td>\n",
              "      <td>1552000.0</td>\n",
              "      <td>1582000.0</td>\n",
              "      <td>1614000.0</td>\n",
              "      <td>1647000.0</td>\n",
              "      <td>1682000.0</td>\n",
              "      <td>1717000.0</td>\n",
              "      <td>1753000.0</td>\n",
              "      <td>1791000.0</td>\n",
              "      <td>1827000.0</td>\n",
              "      <td>1862000.0</td>\n",
              "      <td>1898000.0</td>\n",
              "      <td>1932000.0</td>\n",
              "      <td>1965000.0</td>\n",
              "      <td>1997000.0</td>\n",
              "      <td>2029000.0</td>\n",
              "      <td>2059000.0</td>\n",
              "      <td>2086000.0</td>\n",
              "      <td>1966000.0</td>\n",
              "      <td>1762000.0</td>\n",
              "      <td>1700000.0</td>\n",
              "      <td>1701154.0</td>\n",
              "      <td>1702310.0</td>\n",
              "      <td>1703466.0</td>\n",
              "      <td>1704622.0</td>\n",
              "      <td>1705780.0</td>\n",
              "      <td>1719536.0</td>\n",
              "      <td>1733404.0</td>\n",
              "      <td>1747383.0</td>\n",
              "      <td>1761474.0</td>\n",
              "      <td>1775680.0</td>\n",
              "      <td>1791000.0</td>\n",
              "      <td>1805200.0</td>\n",
              "      <td>1824100.0</td>\n",
              "      <td>1821800.0</td>\n",
              "      <td>1801800.0</td>\n",
              "      <td>1816200.0</td>\n",
              "      <td>1830700.0</td>\n",
              "      <td>1845300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\n",
              "      <td>Yemen, Rep.</td>\n",
              "      <td>YEM</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>5315355.0</td>\n",
              "      <td>5393036.0</td>\n",
              "      <td>5473671.0</td>\n",
              "      <td>5556766.0</td>\n",
              "      <td>5641597.0</td>\n",
              "      <td>5727751.0</td>\n",
              "      <td>5816247.0</td>\n",
              "      <td>5907874.0</td>\n",
              "      <td>6001852.0</td>\n",
              "      <td>6097035.0</td>\n",
              "      <td>6193384.0</td>\n",
              "      <td>6290365.0</td>\n",
              "      <td>6390574.0</td>\n",
              "      <td>6500816.0</td>\n",
              "      <td>6629999.0</td>\n",
              "      <td>6784695.0</td>\n",
              "      <td>6967941.0</td>\n",
              "      <td>7178675.0</td>\n",
              "      <td>7414158.0</td>\n",
              "      <td>7669694.0</td>\n",
              "      <td>7941898.0</td>\n",
              "      <td>8231910.0</td>\n",
              "      <td>8541605.0</td>\n",
              "      <td>8869370.0</td>\n",
              "      <td>9213084.0</td>\n",
              "      <td>9572175.0</td>\n",
              "      <td>9941109.0</td>\n",
              "      <td>10322043.0</td>\n",
              "      <td>10730862.0</td>\n",
              "      <td>11189177.0</td>\n",
              "      <td>11709993.0</td>\n",
              "      <td>12302124.0</td>\n",
              "      <td>12954155.0</td>\n",
              "      <td>13634076.0</td>\n",
              "      <td>14297613.0</td>\n",
              "      <td>14913315.0</td>\n",
              "      <td>15469274.0</td>\n",
              "      <td>15975668.0</td>\n",
              "      <td>16450310.0</td>\n",
              "      <td>16921149.0</td>\n",
              "      <td>17409072.0</td>\n",
              "      <td>17918373.0</td>\n",
              "      <td>18443691.0</td>\n",
              "      <td>18985000.0</td>\n",
              "      <td>19540098.0</td>\n",
              "      <td>20107409.0</td>\n",
              "      <td>20687646.0</td>\n",
              "      <td>21282515.0</td>\n",
              "      <td>21892146.0</td>\n",
              "      <td>22516460.0</td>\n",
              "      <td>23154855.0</td>\n",
              "      <td>23807588.0</td>\n",
              "      <td>24473178.0</td>\n",
              "      <td>25147109.0</td>\n",
              "      <td>25823485.0</td>\n",
              "      <td>26497889.0</td>\n",
              "      <td>27168210.0</td>\n",
              "      <td>27834821.0</td>\n",
              "      <td>28498687.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>ZAF</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>17099840.0</td>\n",
              "      <td>17524533.0</td>\n",
              "      <td>17965725.0</td>\n",
              "      <td>18423161.0</td>\n",
              "      <td>18896307.0</td>\n",
              "      <td>19384841.0</td>\n",
              "      <td>19888250.0</td>\n",
              "      <td>20406864.0</td>\n",
              "      <td>20942145.0</td>\n",
              "      <td>21496075.0</td>\n",
              "      <td>22069776.0</td>\n",
              "      <td>22665271.0</td>\n",
              "      <td>23281508.0</td>\n",
              "      <td>23913099.0</td>\n",
              "      <td>24552540.0</td>\n",
              "      <td>25195187.0</td>\n",
              "      <td>25836888.0</td>\n",
              "      <td>26480913.0</td>\n",
              "      <td>27138965.0</td>\n",
              "      <td>27827320.0</td>\n",
              "      <td>28556769.0</td>\n",
              "      <td>29333103.0</td>\n",
              "      <td>30150448.0</td>\n",
              "      <td>30993758.0</td>\n",
              "      <td>31841593.0</td>\n",
              "      <td>32678874.0</td>\n",
              "      <td>33495953.0</td>\n",
              "      <td>34297727.0</td>\n",
              "      <td>35100909.0</td>\n",
              "      <td>35930050.0</td>\n",
              "      <td>36800509.0</td>\n",
              "      <td>37718950.0</td>\n",
              "      <td>38672607.0</td>\n",
              "      <td>39633750.0</td>\n",
              "      <td>40564059.0</td>\n",
              "      <td>41435758.0</td>\n",
              "      <td>42241011.0</td>\n",
              "      <td>42987461.0</td>\n",
              "      <td>43682260.0</td>\n",
              "      <td>44338543.0</td>\n",
              "      <td>44967708.0</td>\n",
              "      <td>45571274.0</td>\n",
              "      <td>46150913.0</td>\n",
              "      <td>46719196.0</td>\n",
              "      <td>47291610.0</td>\n",
              "      <td>47880601.0</td>\n",
              "      <td>48489459.0</td>\n",
              "      <td>49119759.0</td>\n",
              "      <td>49779471.0</td>\n",
              "      <td>50477011.0</td>\n",
              "      <td>51216964.0</td>\n",
              "      <td>52004172.0</td>\n",
              "      <td>52834005.0</td>\n",
              "      <td>53689236.0</td>\n",
              "      <td>54545991.0</td>\n",
              "      <td>55386367.0</td>\n",
              "      <td>56203654.0</td>\n",
              "      <td>57000451.0</td>\n",
              "      <td>57779622.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>ZMB</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>3070776.0</td>\n",
              "      <td>3164329.0</td>\n",
              "      <td>3260650.0</td>\n",
              "      <td>3360104.0</td>\n",
              "      <td>3463213.0</td>\n",
              "      <td>3570464.0</td>\n",
              "      <td>3681955.0</td>\n",
              "      <td>3797873.0</td>\n",
              "      <td>3918872.0</td>\n",
              "      <td>4045740.0</td>\n",
              "      <td>4179067.0</td>\n",
              "      <td>4319224.0</td>\n",
              "      <td>4466174.0</td>\n",
              "      <td>4619546.0</td>\n",
              "      <td>4778724.0</td>\n",
              "      <td>4943283.0</td>\n",
              "      <td>5112823.0</td>\n",
              "      <td>5287548.0</td>\n",
              "      <td>5468262.0</td>\n",
              "      <td>5656139.0</td>\n",
              "      <td>5851825.0</td>\n",
              "      <td>6055366.0</td>\n",
              "      <td>6265864.0</td>\n",
              "      <td>6481916.0</td>\n",
              "      <td>6701540.0</td>\n",
              "      <td>6923149.0</td>\n",
              "      <td>7146969.0</td>\n",
              "      <td>7372837.0</td>\n",
              "      <td>7598275.0</td>\n",
              "      <td>7820205.0</td>\n",
              "      <td>8036845.0</td>\n",
              "      <td>8246656.0</td>\n",
              "      <td>8451347.0</td>\n",
              "      <td>8656486.0</td>\n",
              "      <td>8869740.0</td>\n",
              "      <td>9096607.0</td>\n",
              "      <td>9339733.0</td>\n",
              "      <td>9597609.0</td>\n",
              "      <td>9866476.0</td>\n",
              "      <td>10140561.0</td>\n",
              "      <td>10415944.0</td>\n",
              "      <td>10692193.0</td>\n",
              "      <td>10971698.0</td>\n",
              "      <td>11256743.0</td>\n",
              "      <td>11550642.0</td>\n",
              "      <td>11856247.0</td>\n",
              "      <td>12173514.0</td>\n",
              "      <td>12502958.0</td>\n",
              "      <td>12848530.0</td>\n",
              "      <td>13215139.0</td>\n",
              "      <td>13605984.0</td>\n",
              "      <td>14023193.0</td>\n",
              "      <td>14465121.0</td>\n",
              "      <td>14926504.0</td>\n",
              "      <td>15399753.0</td>\n",
              "      <td>15879361.0</td>\n",
              "      <td>16363507.0</td>\n",
              "      <td>16853688.0</td>\n",
              "      <td>17351822.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>Population, total</td>\n",
              "      <td>SP.POP.TOTL</td>\n",
              "      <td>3776681.0</td>\n",
              "      <td>3905034.0</td>\n",
              "      <td>4039201.0</td>\n",
              "      <td>4178726.0</td>\n",
              "      <td>4322861.0</td>\n",
              "      <td>4471177.0</td>\n",
              "      <td>4623351.0</td>\n",
              "      <td>4779827.0</td>\n",
              "      <td>4941906.0</td>\n",
              "      <td>5111337.0</td>\n",
              "      <td>5289303.0</td>\n",
              "      <td>5476982.0</td>\n",
              "      <td>5673911.0</td>\n",
              "      <td>5877726.0</td>\n",
              "      <td>6085074.0</td>\n",
              "      <td>6293875.0</td>\n",
              "      <td>6502569.0</td>\n",
              "      <td>6712827.0</td>\n",
              "      <td>6929664.0</td>\n",
              "      <td>7160023.0</td>\n",
              "      <td>7408624.0</td>\n",
              "      <td>7675591.0</td>\n",
              "      <td>7958241.0</td>\n",
              "      <td>8254747.0</td>\n",
              "      <td>8562249.0</td>\n",
              "      <td>8877489.0</td>\n",
              "      <td>9200149.0</td>\n",
              "      <td>9527203.0</td>\n",
              "      <td>9849125.0</td>\n",
              "      <td>10153852.0</td>\n",
              "      <td>10432421.0</td>\n",
              "      <td>10680995.0</td>\n",
              "      <td>10900502.0</td>\n",
              "      <td>11092766.0</td>\n",
              "      <td>11261744.0</td>\n",
              "      <td>11410714.0</td>\n",
              "      <td>11541217.0</td>\n",
              "      <td>11653242.0</td>\n",
              "      <td>11747072.0</td>\n",
              "      <td>11822719.0</td>\n",
              "      <td>11881477.0</td>\n",
              "      <td>11923914.0</td>\n",
              "      <td>11954290.0</td>\n",
              "      <td>11982224.0</td>\n",
              "      <td>12019912.0</td>\n",
              "      <td>12076699.0</td>\n",
              "      <td>12155491.0</td>\n",
              "      <td>12255922.0</td>\n",
              "      <td>12379549.0</td>\n",
              "      <td>12526968.0</td>\n",
              "      <td>12697723.0</td>\n",
              "      <td>12894316.0</td>\n",
              "      <td>13115131.0</td>\n",
              "      <td>13350356.0</td>\n",
              "      <td>13586681.0</td>\n",
              "      <td>13814629.0</td>\n",
              "      <td>14030390.0</td>\n",
              "      <td>14236745.0</td>\n",
              "      <td>14439018.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 65 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Country Name Country Code  ... 2019 Unnamed: 64\n",
              "0           Aruba          ABW  ...  NaN         NaN\n",
              "1     Afghanistan          AFG  ...  NaN         NaN\n",
              "2          Angola          AGO  ...  NaN         NaN\n",
              "3         Albania          ALB  ...  NaN         NaN\n",
              "4         Andorra          AND  ...  NaN         NaN\n",
              "..            ...          ...  ...  ...         ...\n",
              "259        Kosovo          XKX  ...  NaN         NaN\n",
              "260   Yemen, Rep.          YEM  ...  NaN         NaN\n",
              "261  South Africa          ZAF  ...  NaN         NaN\n",
              "262        Zambia          ZMB  ...  NaN         NaN\n",
              "263      Zimbabwe          ZWE  ...  NaN         NaN\n",
              "\n",
              "[264 rows x 65 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoBRBWVI_5qF",
        "outputId": "746c5130-fca0-4c6d-9f93-0fc4cc57081b"
      },
      "source": [
        "cols = ['Country Name','Country Code'] + list(map(str,range(1971,2015)))\n",
        "cols[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Country Name', 'Country Code', '1971', '1972', '1973']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "0A-AAfW3ARGA",
        "outputId": "0e5d721c-4f70-43b4-850b-4a55817bad4e"
      },
      "source": [
        "df_agri.loc[:,cols].melt(id_vars=['Country Name','Country Code']).rename(\n",
        "    columns={'variable':'date',\n",
        "             'Country Name':'Country',\n",
        "             'Country Code':'name',\n",
        "             'value':'agriculture'})"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>name</th>\n",
              "      <th>date</th>\n",
              "      <th>agriculture</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>1971</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1971</td>\n",
              "      <td>380360.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Angola</td>\n",
              "      <td>AGO</td>\n",
              "      <td>1971</td>\n",
              "      <td>574000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>1971</td>\n",
              "      <td>12000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>AND</td>\n",
              "      <td>1971</td>\n",
              "      <td>250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11611</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>XKX</td>\n",
              "      <td>2014</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11612</th>\n",
              "      <td>Yemen, Rep.</td>\n",
              "      <td>YEM</td>\n",
              "      <td>2014</td>\n",
              "      <td>235460.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11613</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>ZAF</td>\n",
              "      <td>2014</td>\n",
              "      <td>968410.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11614</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>ZMB</td>\n",
              "      <td>2014</td>\n",
              "      <td>238360.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11615</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2014</td>\n",
              "      <td>162000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11616 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Country name  date  agriculture\n",
              "0             Aruba  ABW  1971         20.0\n",
              "1       Afghanistan  AFG  1971     380360.0\n",
              "2            Angola  AGO  1971     574000.0\n",
              "3           Albania  ALB  1971      12000.0\n",
              "4           Andorra  AND  1971        250.0\n",
              "...             ...  ...   ...          ...\n",
              "11611        Kosovo  XKX  2014          NaN\n",
              "11612   Yemen, Rep.  YEM  2014     235460.0\n",
              "11613  South Africa  ZAF  2014     968410.0\n",
              "11614        Zambia  ZMB  2014     238360.0\n",
              "11615      Zimbabwe  ZWE  2014     162000.0\n",
              "\n",
              "[11616 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgaxv70RAtRl"
      },
      "source": [
        "def fun_format(df, col = 'agriculture'):\n",
        "  return df.loc[:,cols].melt(id_vars=['Country Name','Country Code']).rename(\n",
        "      columns={'variable':'date',\n",
        "              'Country Name':'Country',\n",
        "              'Country Code':'name',\n",
        "              'value':col})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "HNM1yaKpAx8V",
        "outputId": "52211e38-5c32-4daf-e4e4-c9d4e1e9ae1b"
      },
      "source": [
        "df_agri = fun_format(df_agri, col = 'agriculture')\n",
        "df_fore = fun_format(df_fore, col = 'forest')\n",
        "df_elec = fun_format(df_elec, col = 'electricprod')\n",
        "df_co2e = fun_format(df_co2e, col = 'co2')\n",
        "df_popu = fun_format(df_popu, col = 'population')\n",
        "df_popu"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>name</th>\n",
              "      <th>date</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>1971</td>\n",
              "      <td>59440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>1971</td>\n",
              "      <td>11475445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Angola</td>\n",
              "      <td>AGO</td>\n",
              "      <td>1971</td>\n",
              "      <td>6040777.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>1971</td>\n",
              "      <td>2187853.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>AND</td>\n",
              "      <td>1971</td>\n",
              "      <td>25559.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11611</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>XKX</td>\n",
              "      <td>2014</td>\n",
              "      <td>1821800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11612</th>\n",
              "      <td>Yemen, Rep.</td>\n",
              "      <td>YEM</td>\n",
              "      <td>2014</td>\n",
              "      <td>25823485.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11613</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>ZAF</td>\n",
              "      <td>2014</td>\n",
              "      <td>54545991.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11614</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>ZMB</td>\n",
              "      <td>2014</td>\n",
              "      <td>15399753.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11615</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>2014</td>\n",
              "      <td>13586681.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11616 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Country name  date  population\n",
              "0             Aruba  ABW  1971     59440.0\n",
              "1       Afghanistan  AFG  1971  11475445.0\n",
              "2            Angola  AGO  1971   6040777.0\n",
              "3           Albania  ALB  1971   2187853.0\n",
              "4           Andorra  AND  1971     25559.0\n",
              "...             ...  ...   ...         ...\n",
              "11611        Kosovo  XKX  2014   1821800.0\n",
              "11612   Yemen, Rep.  YEM  2014  25823485.0\n",
              "11613  South Africa  ZAF  2014  54545991.0\n",
              "11614        Zambia  ZMB  2014  15399753.0\n",
              "11615      Zimbabwe  ZWE  2014  13586681.0\n",
              "\n",
              "[11616 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "M9aC5F35AzNV",
        "outputId": "92785600-1b5b-474a-a902-1e151180ce2f"
      },
      "source": [
        "df_t_med"
      ],
      "execution_count": null,
      "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",
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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>Country</th>\n",
              "      <th>year</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1970-12-31</td>\n",
              "      <td>16.1990</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1971-12-31</td>\n",
              "      <td>14.9340</td>\n",
              "      <td>1971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1972-12-31</td>\n",
              "      <td>13.8725</td>\n",
              "      <td>1972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1973-12-31</td>\n",
              "      <td>14.9130</td>\n",
              "      <td>1973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>1974-12-31</td>\n",
              "      <td>13.6160</td>\n",
              "      <td>1974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10687</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2009-12-31</td>\n",
              "      <td>5.5765</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10688</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2010-12-31</td>\n",
              "      <td>4.7550</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10689</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2011-12-31</td>\n",
              "      <td>7.4990</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10690</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2012-12-31</td>\n",
              "      <td>6.2540</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10691</th>\n",
              "      <td>Åland</td>\n",
              "      <td>2013-12-31</td>\n",
              "      <td>6.0140</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10692 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Country       year  temperature  date\n",
              "0      Afghanistan 1970-12-31      16.1990  1970\n",
              "1      Afghanistan 1971-12-31      14.9340  1971\n",
              "2      Afghanistan 1972-12-31      13.8725  1972\n",
              "3      Afghanistan 1973-12-31      14.9130  1973\n",
              "4      Afghanistan 1974-12-31      13.6160  1974\n",
              "...            ...        ...          ...   ...\n",
              "10687        Åland 2009-12-31       5.5765  2009\n",
              "10688        Åland 2010-12-31       4.7550  2010\n",
              "10689        Åland 2011-12-31       7.4990  2011\n",
              "10690        Åland 2012-12-31       6.2540  2012\n",
              "10691        Åland 2013-12-31       6.0140  2013\n",
              "\n",
              "[10692 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mcXYJxIBGek",
        "outputId": "e3cbda23-ba6a-4637-a219-15e8e7fa5f06"
      },
      "source": [
        "df_popu['date'] = df_popu['date'].astype(float)\n",
        "df_fore['date'] = df_fore['date'].astype(float)\n",
        "df_agri['date'] = df_agri['date'].astype(float)\n",
        "df_elec['date'] = df_elec['date'].astype(float)\n",
        "df_co2e['date'] = df_co2e['date'].astype(float)\n",
        "df_popu.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11616 entries, 0 to 11615\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Country     11616 non-null  object \n",
            " 1   name        11616 non-null  object \n",
            " 2   date        11616 non-null  float64\n",
            " 3   population  11501 non-null  float64\n",
            "dtypes: float64(2), object(2)\n",
            "memory usage: 363.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSupB0QYBLTl"
      },
      "source": [
        "df_merge = pd.merge(df_t_med[['Country','temperature','date']],\n",
        "                    df_popu, on = ['Country','date'], how = 'inner')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "d7Dev89_BPt9",
        "outputId": "836ca644-9297-42e7-a2b4-1a7baf1db77b"
      },
      "source": [
        "df_merge"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "      <th>name</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>14.9340</td>\n",
              "      <td>1971</td>\n",
              "      <td>AFG</td>\n",
              "      <td>11475445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.8725</td>\n",
              "      <td>1972</td>\n",
              "      <td>AFG</td>\n",
              "      <td>11791215.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>14.9130</td>\n",
              "      <td>1973</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12108963.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.6160</td>\n",
              "      <td>1974</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12412950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.5130</td>\n",
              "      <td>1975</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12689160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7219</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.4860</td>\n",
              "      <td>2009</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12526968.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7220</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.3040</td>\n",
              "      <td>2010</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12697723.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7221</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.9420</td>\n",
              "      <td>2011</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12894316.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7222</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.0455</td>\n",
              "      <td>2012</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13115131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7223</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>20.4505</td>\n",
              "      <td>2013</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13350356.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7224 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Country  temperature  date name  population\n",
              "0     Afghanistan      14.9340  1971  AFG  11475445.0\n",
              "1     Afghanistan      13.8725  1972  AFG  11791215.0\n",
              "2     Afghanistan      14.9130  1973  AFG  12108963.0\n",
              "3     Afghanistan      13.6160  1974  AFG  12412950.0\n",
              "4     Afghanistan      13.5130  1975  AFG  12689160.0\n",
              "...           ...          ...   ...  ...         ...\n",
              "7219     Zimbabwe      22.4860  2009  ZWE  12526968.0\n",
              "7220     Zimbabwe      23.3040  2010  ZWE  12697723.0\n",
              "7221     Zimbabwe      22.9420  2011  ZWE  12894316.0\n",
              "7222     Zimbabwe      23.0455  2012  ZWE  13115131.0\n",
              "7223     Zimbabwe      20.4505  2013  ZWE  13350356.0\n",
              "\n",
              "[7224 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "WPrH8bAXBQru",
        "outputId": "f5ee7028-15a5-4ecd-a3b1-953f6988ba6f"
      },
      "source": [
        "df_merge = pd.merge(df_merge,\n",
        "                    df_co2e,\n",
        "                    on = ['Country','name','date'],\n",
        "                    how = 'inner')\n",
        "\n",
        "df_merge = pd.merge(df_merge,\n",
        "                    df_elec,\n",
        "                    on = ['Country','name','date'],\n",
        "                    how = 'inner')\n",
        "\n",
        "df_merge = pd.merge(df_merge,\n",
        "                    df_agri,\n",
        "                    on = ['Country','name','date'],\n",
        "                    how = 'inner')\n",
        "\n",
        "df_merge = pd.merge(df_merge,\n",
        "                    df_fore,\n",
        "                    on = ['Country','name','date'],\n",
        "                    how = 'inner')\n",
        "df_merge"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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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>Country</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "      <th>name</th>\n",
              "      <th>population</th>\n",
              "      <th>co2</th>\n",
              "      <th>electricprod</th>\n",
              "      <th>agriculture</th>\n",
              "      <th>forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>14.9340</td>\n",
              "      <td>1971</td>\n",
              "      <td>AFG</td>\n",
              "      <td>11475445.0</td>\n",
              "      <td>1895.839</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380360.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.8725</td>\n",
              "      <td>1972</td>\n",
              "      <td>AFG</td>\n",
              "      <td>11791215.0</td>\n",
              "      <td>1532.806</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380460.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>14.9130</td>\n",
              "      <td>1973</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12108963.0</td>\n",
              "      <td>1639.149</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380480.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.6160</td>\n",
              "      <td>1974</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12412950.0</td>\n",
              "      <td>1917.841</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380480.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>13.5130</td>\n",
              "      <td>1975</td>\n",
              "      <td>AFG</td>\n",
              "      <td>12689160.0</td>\n",
              "      <td>2126.860</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380480.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7219</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.4860</td>\n",
              "      <td>2009</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12526968.0</td>\n",
              "      <td>5603.176</td>\n",
              "      <td>585.456912</td>\n",
              "      <td>163000.0</td>\n",
              "      <td>159510.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7220</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.3040</td>\n",
              "      <td>2010</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12697723.0</td>\n",
              "      <td>7777.707</td>\n",
              "      <td>606.644199</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>156240.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7221</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.9420</td>\n",
              "      <td>2011</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12894316.0</td>\n",
              "      <td>9563.536</td>\n",
              "      <td>636.249337</td>\n",
              "      <td>164000.0</td>\n",
              "      <td>153115.996094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7222</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.0455</td>\n",
              "      <td>2012</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13115131.0</td>\n",
              "      <td>7792.375</td>\n",
              "      <td>608.762505</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>149992.001953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7223</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>20.4505</td>\n",
              "      <td>2013</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13350356.0</td>\n",
              "      <td>11675.728</td>\n",
              "      <td>628.747278</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>146867.998047</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7224 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Country  temperature  date  ... electricprod  agriculture         forest\n",
              "0     Afghanistan      14.9340  1971  ...          NaN     380360.0            NaN\n",
              "1     Afghanistan      13.8725  1972  ...          NaN     380460.0            NaN\n",
              "2     Afghanistan      14.9130  1973  ...          NaN     380480.0            NaN\n",
              "3     Afghanistan      13.6160  1974  ...          NaN     380480.0            NaN\n",
              "4     Afghanistan      13.5130  1975  ...          NaN     380480.0            NaN\n",
              "...           ...          ...   ...  ...          ...          ...            ...\n",
              "7219     Zimbabwe      22.4860  2009  ...   585.456912     163000.0  159510.000000\n",
              "7220     Zimbabwe      23.3040  2010  ...   606.644199     162000.0  156240.000000\n",
              "7221     Zimbabwe      22.9420  2011  ...   636.249337     164000.0  153115.996094\n",
              "7222     Zimbabwe      23.0455  2012  ...   608.762505     162000.0  149992.001953\n",
              "7223     Zimbabwe      20.4505  2013  ...   628.747278     162000.0  146867.998047\n",
              "\n",
              "[7224 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "NTwEqt3rBULO",
        "outputId": "33562009-2d3b-45c9-c084-539a98d161f7"
      },
      "source": [
        "df_merge.dropna()"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "      <th>name</th>\n",
              "      <th>population</th>\n",
              "      <th>co2</th>\n",
              "      <th>electricprod</th>\n",
              "      <th>agriculture</th>\n",
              "      <th>forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.4490</td>\n",
              "      <td>1990</td>\n",
              "      <td>ALB</td>\n",
              "      <td>3286542.0</td>\n",
              "      <td>5515.168</td>\n",
              "      <td>552.252185</td>\n",
              "      <td>11210.0</td>\n",
              "      <td>7887.999878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Albania</td>\n",
              "      <td>11.5885</td>\n",
              "      <td>1991</td>\n",
              "      <td>ALB</td>\n",
              "      <td>3266790.0</td>\n",
              "      <td>4286.723</td>\n",
              "      <td>418.453589</td>\n",
              "      <td>11270.0</td>\n",
              "      <td>7868.499756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.5850</td>\n",
              "      <td>1992</td>\n",
              "      <td>ALB</td>\n",
              "      <td>3247039.0</td>\n",
              "      <td>2515.562</td>\n",
              "      <td>453.644074</td>\n",
              "      <td>11270.0</td>\n",
              "      <td>7849.000244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.6500</td>\n",
              "      <td>1993</td>\n",
              "      <td>ALB</td>\n",
              "      <td>3227287.0</td>\n",
              "      <td>2335.879</td>\n",
              "      <td>536.363825</td>\n",
              "      <td>11260.0</td>\n",
              "      <td>7829.500122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Albania</td>\n",
              "      <td>13.2390</td>\n",
              "      <td>1994</td>\n",
              "      <td>ALB</td>\n",
              "      <td>3207536.0</td>\n",
              "      <td>1925.175</td>\n",
              "      <td>596.407959</td>\n",
              "      <td>11260.0</td>\n",
              "      <td>7810.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7219</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.4860</td>\n",
              "      <td>2009</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12526968.0</td>\n",
              "      <td>5603.176</td>\n",
              "      <td>585.456912</td>\n",
              "      <td>163000.0</td>\n",
              "      <td>159510.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7220</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.3040</td>\n",
              "      <td>2010</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12697723.0</td>\n",
              "      <td>7777.707</td>\n",
              "      <td>606.644199</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>156240.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7221</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>22.9420</td>\n",
              "      <td>2011</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>12894316.0</td>\n",
              "      <td>9563.536</td>\n",
              "      <td>636.249337</td>\n",
              "      <td>164000.0</td>\n",
              "      <td>153115.996094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7222</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>23.0455</td>\n",
              "      <td>2012</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13115131.0</td>\n",
              "      <td>7792.375</td>\n",
              "      <td>608.762505</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>149992.001953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7223</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>20.4505</td>\n",
              "      <td>2013</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>13350356.0</td>\n",
              "      <td>11675.728</td>\n",
              "      <td>628.747278</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>146867.998047</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2735 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Country  temperature  date  ... electricprod  agriculture         forest\n",
              "62     Albania      13.4490  1990  ...   552.252185      11210.0    7887.999878\n",
              "63     Albania      11.5885  1991  ...   418.453589      11270.0    7868.499756\n",
              "64     Albania      13.5850  1992  ...   453.644074      11270.0    7849.000244\n",
              "65     Albania      13.6500  1993  ...   536.363825      11260.0    7829.500122\n",
              "66     Albania      13.2390  1994  ...   596.407959      11260.0    7810.000000\n",
              "...        ...          ...   ...  ...          ...          ...            ...\n",
              "7219  Zimbabwe      22.4860  2009  ...   585.456912     163000.0  159510.000000\n",
              "7220  Zimbabwe      23.3040  2010  ...   606.644199     162000.0  156240.000000\n",
              "7221  Zimbabwe      22.9420  2011  ...   636.249337     164000.0  153115.996094\n",
              "7222  Zimbabwe      23.0455  2012  ...   608.762505     162000.0  149992.001953\n",
              "7223  Zimbabwe      20.4505  2013  ...   628.747278     162000.0  146867.998047\n",
              "\n",
              "[2735 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2pB18plCnIc"
      },
      "source": [
        "## 1.5. Data visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjRtPHfMClml"
      },
      "source": [
        "df_climate = df_merge.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjwlhXV1DnUD"
      },
      "source": [
        "pd.set_option('display.float_format', '{:,.1f}'.format)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCJ_MYBLDZEF",
        "outputId": "b8789d7c-8a01-4bef-84c8-f7274f551a63"
      },
      "source": [
        "df_climate.groupby('Country')['co2'].median().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "North America   5,886,299.6\n",
              "United States   5,382,123.7\n",
              "China           3,668,917.8\n",
              "Japan           1,202,082.9\n",
              "India           1,047,705.9\n",
              "                    ...    \n",
              "Haiti               1,652.0\n",
              "Mozambique          1,584.1\n",
              "Togo                1,285.3\n",
              "Niger                 784.7\n",
              "Eritrea               614.2\n",
              "Name: co2, Length: 120, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gv377q9uDiG9",
        "outputId": "385b0038-ca5e-43cb-c0d2-7ba7bbcf6d70"
      },
      "source": [
        "first_10 =df_climate.groupby('Country')['co2'].median().sort_values(ascending=False).head(10).index\n",
        "first_10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['North America', 'United States', 'China', 'Japan', 'India', 'Germany',\n",
              "       'United Kingdom', 'Canada', 'Italy', 'Mexico'],\n",
              "      dtype='object', name='Country')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "thWjd6IHD8m1",
        "outputId": "5a567a64-8a81-458b-da08-3b4f6c6d4af3"
      },
      "source": [
        "df_max_co2 = df_climate[df_climate['Country'].isin(['United States', 'China', 'Japan', 'India', 'Germany',\n",
        "       'United Kingdom', 'Canada', 'Italy', 'Mexico'])]\n",
        "df_max_co2"
      ],
      "execution_count": null,
      "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>Country</th>\n",
              "      <th>temperature</th>\n",
              "      <th>date</th>\n",
              "      <th>name</th>\n",
              "      <th>population</th>\n",
              "      <th>co2</th>\n",
              "      <th>electricprod</th>\n",
              "      <th>agriculture</th>\n",
              "      <th>forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>Canada</td>\n",
              "      <td>-4.6</td>\n",
              "      <td>1990</td>\n",
              "      <td>CAN</td>\n",
              "      <td>27,691,138.0</td>\n",
              "      <td>435,181.2</td>\n",
              "      <td>16,167.4</td>\n",
              "      <td>677,680.0</td>\n",
              "      <td>3,482,730.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1267</th>\n",
              "      <td>Canada</td>\n",
              "      <td>-4.2</td>\n",
              "      <td>1991</td>\n",
              "      <td>CAN</td>\n",
              "      <td>28,037,420.0</td>\n",
              "      <td>426,780.1</td>\n",
              "      <td>16,181.6</td>\n",
              "      <td>677,530.0</td>\n",
              "      <td>3,482,259.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1268</th>\n",
              "      <td>Canada</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>1992</td>\n",
              "      <td>CAN</td>\n",
              "      <td>28,371,264.0</td>\n",
              "      <td>440,978.8</td>\n",
              "      <td>16,164.4</td>\n",
              "      <td>678,140.0</td>\n",
              "      <td>3,481,788.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1269</th>\n",
              "      <td>Canada</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>1993</td>\n",
              "      <td>CAN</td>\n",
              "      <td>28,684,764.0</td>\n",
              "      <td>445,580.8</td>\n",
              "      <td>16,320.1</td>\n",
              "      <td>678,740.0</td>\n",
              "      <td>3,481,316.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1270</th>\n",
              "      <td>Canada</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>1994</td>\n",
              "      <td>CAN</td>\n",
              "      <td>29,000,663.0</td>\n",
              "      <td>456,849.5</td>\n",
              "      <td>16,388.3</td>\n",
              "      <td>679,340.0</td>\n",
              "      <td>3,480,845.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7004</th>\n",
              "      <td>United States</td>\n",
              "      <td>8.4</td>\n",
              "      <td>2009</td>\n",
              "      <td>USA</td>\n",
              "      <td>306,771,529.0</td>\n",
              "      <td>5,263,505.5</td>\n",
              "      <td>12,913.7</td>\n",
              "      <td>4,099,606.9</td>\n",
              "      <td>3,079,274.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7005</th>\n",
              "      <td>United States</td>\n",
              "      <td>10.2</td>\n",
              "      <td>2010</td>\n",
              "      <td>USA</td>\n",
              "      <td>309,326,085.0</td>\n",
              "      <td>5,395,532.1</td>\n",
              "      <td>13,394.9</td>\n",
              "      <td>4,084,261.9</td>\n",
              "      <td>3,087,200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7006</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.5</td>\n",
              "      <td>2011</td>\n",
              "      <td>USA</td>\n",
              "      <td>311,580,009.0</td>\n",
              "      <td>5,289,680.5</td>\n",
              "      <td>13,246.0</td>\n",
              "      <td>4,046,693.1</td>\n",
              "      <td>3,089,950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7007</th>\n",
              "      <td>United States</td>\n",
              "      <td>9.9</td>\n",
              "      <td>2012</td>\n",
              "      <td>USA</td>\n",
              "      <td>313,874,218.0</td>\n",
              "      <td>5,119,436.4</td>\n",
              "      <td>12,964.3</td>\n",
              "      <td>4,087,065.0</td>\n",
              "      <td>3,092,700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7008</th>\n",
              "      <td>United States</td>\n",
              "      <td>14.1</td>\n",
              "      <td>2013</td>\n",
              "      <td>USA</td>\n",
              "      <td>316,057,727.0</td>\n",
              "      <td>5,159,161.0</td>\n",
              "      <td>13,004.1</td>\n",
              "      <td>4,058,416.9</td>\n",
              "      <td>3,095,450.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Country  temperature  date  ... electricprod  agriculture      forest\n",
              "1266         Canada         -4.6  1990  ...     16,167.4    677,680.0 3,482,730.0\n",
              "1267         Canada         -4.2  1991  ...     16,181.6    677,530.0 3,482,259.1\n",
              "1268         Canada         -5.0  1992  ...     16,164.4    678,140.0 3,481,788.1\n",
              "1269         Canada         -4.0  1993  ...     16,320.1    678,740.0 3,481,316.9\n",
              "1270         Canada         -3.4  1994  ...     16,388.3    679,340.0 3,480,845.9\n",
              "...             ...          ...   ...  ...          ...          ...         ...\n",
              "7004  United States          8.4  2009  ...     12,913.7  4,099,606.9 3,079,274.1\n",
              "7005  United States         10.2  2010  ...     13,394.9  4,084,261.9 3,087,200.0\n",
              "7006  United States          9.5  2011  ...     13,246.0  4,046,693.1 3,089,950.0\n",
              "7007  United States          9.9  2012  ...     12,964.3  4,087,065.0 3,092,700.0\n",
              "7008  United States         14.1  2013  ...     13,004.1  4,058,416.9 3,095,450.0\n",
              "\n",
              "[215 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "eH-orPe9EQ11",
        "outputId": "dd33a6c6-dbcd-4c7d-ddb5-f854938c21e9"
      },
      "source": [
        "df_max_co2.set_index('Country').plot.scatter(\n",
        "    x='population',\n",
        "    y='co2',\n",
        "    c='date',\n",
        "    colormap='viridis'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefefcc4710>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tRmvE1dPEmiU",
        "outputId": "7c28e2bd-2dc6-4b70-b84f-db88dd356bea"
      },
      "source": [
        "df_climate.corr()"
      ],
      "execution_count": null,
      "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>temperature</th>\n",
              "      <th>date</th>\n",
              "      <th>population</th>\n",
              "      <th>co2</th>\n",
              "      <th>electricprod</th>\n",
              "      <th>agriculture</th>\n",
              "      <th>forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>temperature</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>-0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>co2</th>\n",
              "      <td>-0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>electricprod</th>\n",
              "      <td>-0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agriculture</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>forest</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              temperature  date  population  ...  electricprod  agriculture  forest\n",
              "temperature           1.0   0.0        -0.0  ...          -0.4         -0.1    -0.1\n",
              "date                  0.0   1.0         0.0  ...           0.1         -0.0    -0.0\n",
              "population           -0.0   0.0         1.0  ...          -0.0          0.6     0.4\n",
              "co2                  -0.2   0.0         0.6  ...           0.2          0.8     0.7\n",
              "electricprod         -0.4   0.1        -0.0  ...           1.0          0.1     0.2\n",
              "agriculture          -0.1  -0.0         0.6  ...           0.1          1.0     0.7\n",
              "forest               -0.1  -0.0         0.4  ...           0.2          0.7     1.0\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rH4TtoztE78t"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "1lrHS54-E-v1",
        "outputId": "6cdaef20-de2b-4275-9f8b-f53c6e45b868"
      },
      "source": [
        "sns.heatmap(df_climate.corr(), annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefe1048650>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "C_G5uFmrFHe1",
        "outputId": "d6e30f21-06e4-437d-ea07-31dc3db4663a"
      },
      "source": [
        "df_med = df_climate.groupby('date').median()\n",
        "df_med"
      ],
      "execution_count": null,
      "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>temperature</th>\n",
              "      <th>population</th>\n",
              "      <th>co2</th>\n",
              "      <th>electricprod</th>\n",
              "      <th>agriculture</th>\n",
              "      <th>forest</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>22.7</td>\n",
              "      <td>10,213,863.0</td>\n",
              "      <td>31,363.9</td>\n",
              "      <td>1,114.7</td>\n",
              "      <td>90,195.0</td>\n",
              "      <td>54,005.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>22.4</td>\n",
              "      <td>10,346,663.5</td>\n",
              "      <td>27,343.0</td>\n",
              "      <td>1,072.5</td>\n",
              "      <td>91,870.0</td>\n",
              "      <td>60,307.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>19.9</td>\n",
              "      <td>9,188,305.5</td>\n",
              "      <td>26,420.7</td>\n",
              "      <td>1,451.8</td>\n",
              "      <td>89,700.0</td>\n",
              "      <td>45,621.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>20.6</td>\n",
              "      <td>9,951,683.5</td>\n",
              "      <td>28,267.1</td>\n",
              "      <td>1,520.3</td>\n",
              "      <td>89,890.0</td>\n",
              "      <td>44,579.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>20.7</td>\n",
              "      <td>9,991,525.0</td>\n",
              "      <td>29,640.4</td>\n",
              "      <td>1,576.8</td>\n",
              "      <td>89,150.0</td>\n",
              "      <td>42,572.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>20.1</td>\n",
              "      <td>10,026,176.0</td>\n",
              "      <td>29,090.3</td>\n",
              "      <td>1,578.3</td>\n",
              "      <td>81,080.0</td>\n",
              "      <td>41,640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>20.2</td>\n",
              "      <td>10,063,945.0</td>\n",
              "      <td>30,670.8</td>\n",
              "      <td>1,687.1</td>\n",
              "      <td>79,870.0</td>\n",
              "      <td>41,270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>20.4</td>\n",
              "      <td>10,108,977.0</td>\n",
              "      <td>30,623.1</td>\n",
              "      <td>1,762.4</td>\n",
              "      <td>80,120.0</td>\n",
              "      <td>41,270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>21.5</td>\n",
              "      <td>10,069,000.0</td>\n",
              "      <td>31,675.5</td>\n",
              "      <td>1,807.3</td>\n",
              "      <td>80,550.0</td>\n",
              "      <td>40,834.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>21.1</td>\n",
              "      <td>10,140,561.0</td>\n",
              "      <td>31,407.9</td>\n",
              "      <td>1,746.0</td>\n",
              "      <td>84,130.0</td>\n",
              "      <td>39,917.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>20.9</td>\n",
              "      <td>10,210,971.0</td>\n",
              "      <td>30,296.8</td>\n",
              "      <td>1,897.4</td>\n",
              "      <td>80,660.0</td>\n",
              "      <td>38,380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>21.5</td>\n",
              "      <td>10,187,576.0</td>\n",
              "      <td>32,456.6</td>\n",
              "      <td>1,834.2</td>\n",
              "      <td>77,850.0</td>\n",
              "      <td>38,406.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>21.0</td>\n",
              "      <td>10,196,916.0</td>\n",
              "      <td>33,201.0</td>\n",
              "      <td>1,872.4</td>\n",
              "      <td>75,320.0</td>\n",
              "      <td>37,944.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>21.2</td>\n",
              "      <td>10,193,998.0</td>\n",
              "      <td>32,390.6</td>\n",
              "      <td>2,001.0</td>\n",
              "      <td>75,670.0</td>\n",
              "      <td>38,458.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>21.0</td>\n",
              "      <td>10,197,101.0</td>\n",
              "      <td>32,089.9</td>\n",
              "      <td>1,956.2</td>\n",
              "      <td>75,270.0</td>\n",
              "      <td>38,484.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>20.9</td>\n",
              "      <td>10,211,216.0</td>\n",
              "      <td>34,337.8</td>\n",
              "      <td>2,047.7</td>\n",
              "      <td>75,220.0</td>\n",
              "      <td>38,510.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>20.8</td>\n",
              "      <td>10,201,214.0</td>\n",
              "      <td>39,167.2</td>\n",
              "      <td>2,179.1</td>\n",
              "      <td>74,440.0</td>\n",
              "      <td>37,822.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>20.7</td>\n",
              "      <td>10,298,828.0</td>\n",
              "      <td>37,986.5</td>\n",
              "      <td>2,254.3</td>\n",
              "      <td>74,120.0</td>\n",
              "      <td>38,124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>21.4</td>\n",
              "      <td>10,384,603.0</td>\n",
              "      <td>40,403.0</td>\n",
              "      <td>2,316.6</td>\n",
              "      <td>74,450.0</td>\n",
              "      <td>38,084.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>20.6</td>\n",
              "      <td>10,443,936.0</td>\n",
              "      <td>41,103.4</td>\n",
              "      <td>2,314.7</td>\n",
              "      <td>75,344.0</td>\n",
              "      <td>37,652.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>21.0</td>\n",
              "      <td>10,474,410.0</td>\n",
              "      <td>40,054.6</td>\n",
              "      <td>2,382.1</td>\n",
              "      <td>74,890.0</td>\n",
              "      <td>37,370.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>20.6</td>\n",
              "      <td>10,526,824.0</td>\n",
              "      <td>37,788.4</td>\n",
              "      <td>2,421.4</td>\n",
              "      <td>74,633.0</td>\n",
              "      <td>38,080.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>21.7</td>\n",
              "      <td>10,514,844.0</td>\n",
              "      <td>36,428.0</td>\n",
              "      <td>2,443.6</td>\n",
              "      <td>75,069.0</td>\n",
              "      <td>38,636.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>20.4</td>\n",
              "      <td>10,542,376.0</td>\n",
              "      <td>39,599.9</td>\n",
              "      <td>2,533.3</td>\n",
              "      <td>75,130.0</td>\n",
              "      <td>38,654.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      temperature   population      co2  electricprod  agriculture   forest\n",
              "date                                                                       \n",
              "1990         22.7 10,213,863.0 31,363.9       1,114.7     90,195.0 54,005.0\n",
              "1991         22.4 10,346,663.5 27,343.0       1,072.5     91,870.0 60,307.5\n",
              "1992         19.9  9,188,305.5 26,420.7       1,451.8     89,700.0 45,621.0\n",
              "1993         20.6  9,951,683.5 28,267.1       1,520.3     89,890.0 44,579.0\n",
              "1994         20.7  9,991,525.0 29,640.4       1,576.8     89,150.0 42,572.0\n",
              "1995         20.1 10,026,176.0 29,090.3       1,578.3     81,080.0 41,640.0\n",
              "1996         20.2 10,063,945.0 30,670.8       1,687.1     79,870.0 41,270.0\n",
              "1997         20.4 10,108,977.0 30,623.1       1,762.4     80,120.0 41,270.0\n",
              "1998         21.5 10,069,000.0 31,675.5       1,807.3     80,550.0 40,834.0\n",
              "1999         21.1 10,140,561.0 31,407.9       1,746.0     84,130.0 39,917.0\n",
              "2000         20.9 10,210,971.0 30,296.8       1,897.4     80,660.0 38,380.0\n",
              "2001         21.5 10,187,576.0 32,456.6       1,834.2     77,850.0 38,406.0\n",
              "2002         21.0 10,196,916.0 33,201.0       1,872.4     75,320.0 37,944.0\n",
              "2003         21.2 10,193,998.0 32,390.6       2,001.0     75,670.0 38,458.0\n",
              "2004         21.0 10,197,101.0 32,089.9       1,956.2     75,270.0 38,484.0\n",
              "2005         20.9 10,211,216.0 34,337.8       2,047.7     75,220.0 38,510.0\n",
              "2006         20.8 10,201,214.0 39,167.2       2,179.1     74,440.0 37,822.0\n",
              "2007         20.7 10,298,828.0 37,986.5       2,254.3     74,120.0 38,124.0\n",
              "2008         21.4 10,384,603.0 40,403.0       2,316.6     74,450.0 38,084.0\n",
              "2009         20.6 10,443,936.0 41,103.4       2,314.7     75,344.0 37,652.0\n",
              "2010         21.0 10,474,410.0 40,054.6       2,382.1     74,890.0 37,370.0\n",
              "2011         20.6 10,526,824.0 37,788.4       2,421.4     74,633.0 38,080.0\n",
              "2012         21.7 10,514,844.0 36,428.0       2,443.6     75,069.0 38,636.0\n",
              "2013         20.4 10,542,376.0 39,599.9       2,533.3     75,130.0 38,654.0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "6VAx0xS0FdFU",
        "outputId": "4a2a4dbb-9a8e-479a-cff5-8d475d214013"
      },
      "source": [
        "sns.heatmap(df_med.reset_index().corr(),annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefe113e1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QyWSlGQFkLE"
      },
      "source": [
        "from pandas.plotting import scatter_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "W4pCNM7_FzZF",
        "outputId": "c3d569ba-c2e9-423f-d730-f6f8dcd91d33"
      },
      "source": [
        "scatter_matrix(df_med.reset_index(),diagonal='kde', figsize = (7,7));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 49 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4o34JV0WF2E1"
      },
      "source": [
        "from pandas.plotting import parallel_coordinates"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxbgjDBDF4Y2"
      },
      "source": [
        "df_samerica = df_climate[\n",
        "df_climate['Country'].isin(['Colombia',\n",
        "     'Argentina',\n",
        "     'Bolivia',\n",
        "     'Mexico',\n",
        "     'Peru',\n",
        "     'Chile'])\n",
        "][['Country',\n",
        " 'temperature',\n",
        " 'co2',\n",
        " 'agriculture',\n",
        " 'forest']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "M9lMzkqVF5cl",
        "outputId": "f66605af-c638-4cc3-beb4-0fd6570dfdf9"
      },
      "source": [
        "parallel_coordinates(df_samerica, 'Country', colormap = 'jet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefd8697d90>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}