Hamoye_Stage_A.ipynb

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  "metadata": {
    "colab": {
      "name": "Hamoye Stage A.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNhqak2z3akgNKlczKkmCDD",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CinnamonXI/Hamoye-Tasks/blob/master/Hamoye_Stage_A.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WbtraqD1KS-",
        "colab_type": "text"
      },
      "source": [
        "Hamoye Stage A Assessment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWhJ0n0n1Td3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7T5dUJSa1_jJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#collecting the dataset and converting to dataframe\n",
        "fuel_data = pd.read_csv('https://bit.ly/HDSC-StageOneDataset')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUyx9xcz24oO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "15b3817a-9ace-4ae6-be82-8555e92d1c2a"
      },
      "source": [
        "#getting a preview of the data\n",
        "fuel_data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>record_id</th>\n",
              "      <th>utility_id_ferc1</th>\n",
              "      <th>report_year</th>\n",
              "      <th>plant_name_ferc1</th>\n",
              "      <th>fuel_type_code_pudl</th>\n",
              "      <th>fuel_unit</th>\n",
              "      <th>fuel_qty_burned</th>\n",
              "      <th>fuel_mmbtu_per_unit</th>\n",
              "      <th>fuel_cost_per_unit_burned</th>\n",
              "      <th>fuel_cost_per_unit_delivered</th>\n",
              "      <th>fuel_cost_per_mmbtu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>f1_fuel_1994_12_1_0_7</td>\n",
              "      <td>1</td>\n",
              "      <td>1994</td>\n",
              "      <td>rockport</td>\n",
              "      <td>coal</td>\n",
              "      <td>ton</td>\n",
              "      <td>5377489.0</td>\n",
              "      <td>16.590</td>\n",
              "      <td>18.59</td>\n",
              "      <td>18.53</td>\n",
              "      <td>1.121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>f1_fuel_1994_12_1_0_10</td>\n",
              "      <td>1</td>\n",
              "      <td>1994</td>\n",
              "      <td>rockport total plant</td>\n",
              "      <td>coal</td>\n",
              "      <td>ton</td>\n",
              "      <td>10486945.0</td>\n",
              "      <td>16.592</td>\n",
              "      <td>18.58</td>\n",
              "      <td>18.53</td>\n",
              "      <td>1.120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f1_fuel_1994_12_2_0_1</td>\n",
              "      <td>2</td>\n",
              "      <td>1994</td>\n",
              "      <td>gorgas</td>\n",
              "      <td>coal</td>\n",
              "      <td>ton</td>\n",
              "      <td>2978683.0</td>\n",
              "      <td>24.130</td>\n",
              "      <td>39.72</td>\n",
              "      <td>38.12</td>\n",
              "      <td>1.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>f1_fuel_1994_12_2_0_7</td>\n",
              "      <td>2</td>\n",
              "      <td>1994</td>\n",
              "      <td>barry</td>\n",
              "      <td>coal</td>\n",
              "      <td>ton</td>\n",
              "      <td>3739484.0</td>\n",
              "      <td>23.950</td>\n",
              "      <td>47.21</td>\n",
              "      <td>45.99</td>\n",
              "      <td>1.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>f1_fuel_1994_12_2_0_10</td>\n",
              "      <td>2</td>\n",
              "      <td>1994</td>\n",
              "      <td>chickasaw</td>\n",
              "      <td>gas</td>\n",
              "      <td>mcf</td>\n",
              "      <td>40533.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.77</td>\n",
              "      <td>2.77</td>\n",
              "      <td>2.570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                record_id  ...  fuel_cost_per_mmbtu\n",
              "0   f1_fuel_1994_12_1_0_7  ...                1.121\n",
              "1  f1_fuel_1994_12_1_0_10  ...                1.120\n",
              "2   f1_fuel_1994_12_2_0_1  ...                1.650\n",
              "3   f1_fuel_1994_12_2_0_7  ...                1.970\n",
              "4  f1_fuel_1994_12_2_0_10  ...                2.570\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ech1qEYG43EP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "d627a7a3-0f07-4dac-8204-eb9f178b6b01"
      },
      "source": [
        "#getting information about the columns and number of non null values\n",
        "fuel_data.info()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 29523 entries, 0 to 29522\n",
            "Data columns (total 11 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   record_id                     29523 non-null  object \n",
            " 1   utility_id_ferc1              29523 non-null  int64  \n",
            " 2   report_year                   29523 non-null  int64  \n",
            " 3   plant_name_ferc1              29523 non-null  object \n",
            " 4   fuel_type_code_pudl           29523 non-null  object \n",
            " 5   fuel_unit                     29343 non-null  object \n",
            " 6   fuel_qty_burned               29523 non-null  float64\n",
            " 7   fuel_mmbtu_per_unit           29523 non-null  float64\n",
            " 8   fuel_cost_per_unit_burned     29523 non-null  float64\n",
            " 9   fuel_cost_per_unit_delivered  29523 non-null  float64\n",
            " 10  fuel_cost_per_mmbtu           29523 non-null  float64\n",
            "dtypes: float64(5), int64(2), object(4)\n",
            "memory usage: 2.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxTZ3W3z5kSf",
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          "height": 410
        },
        "outputId": "7ea2e0d8-a660-4c95-961e-7de30b77e84c"
      },
      "source": [
        "fuel_data.describe(include='all')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>record_id</th>\n",
              "      <th>utility_id_ferc1</th>\n",
              "      <th>report_year</th>\n",
              "      <th>plant_name_ferc1</th>\n",
              "      <th>fuel_type_code_pudl</th>\n",
              "      <th>fuel_unit</th>\n",
              "      <th>fuel_qty_burned</th>\n",
              "      <th>fuel_mmbtu_per_unit</th>\n",
              "      <th>fuel_cost_per_unit_burned</th>\n",
              "      <th>fuel_cost_per_unit_delivered</th>\n",
              "      <th>fuel_cost_per_mmbtu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29523</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523</td>\n",
              "      <td>29523</td>\n",
              "      <td>29343</td>\n",
              "      <td>2.952300e+04</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>2.952300e+04</td>\n",
              "      <td>29523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>29523</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2315</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>f1_fuel_2007_12_45_3_7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>big stone</td>\n",
              "      <td>gas</td>\n",
              "      <td>mcf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>156</td>\n",
              "      <td>11486</td>\n",
              "      <td>11354</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>118.601836</td>\n",
              "      <td>2005.806050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.622119e+06</td>\n",
              "      <td>8.492111</td>\n",
              "      <td>208.649031</td>\n",
              "      <td>9.175704e+02</td>\n",
              "      <td>19.304354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.178353</td>\n",
              "      <td>7.025483</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.118004e+06</td>\n",
              "      <td>10.600220</td>\n",
              "      <td>2854.490090</td>\n",
              "      <td>6.877593e+04</td>\n",
              "      <td>2091.540939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>-276.080000</td>\n",
              "      <td>-8.749370e+02</td>\n",
              "      <td>-41.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.381700e+04</td>\n",
              "      <td>1.024000</td>\n",
              "      <td>5.207000</td>\n",
              "      <td>3.778500e+00</td>\n",
              "      <td>1.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.533220e+05</td>\n",
              "      <td>5.762694</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.737100e+01</td>\n",
              "      <td>4.127000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.424034e+06</td>\n",
              "      <td>17.006000</td>\n",
              "      <td>47.113000</td>\n",
              "      <td>4.213700e+01</td>\n",
              "      <td>7.745000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>514.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.558942e+08</td>\n",
              "      <td>341.260000</td>\n",
              "      <td>139358.000000</td>\n",
              "      <td>7.964521e+06</td>\n",
              "      <td>359278.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     record_id  ...  fuel_cost_per_mmbtu\n",
              "count                    29523  ...         29523.000000\n",
              "unique                   29523  ...                  NaN\n",
              "top     f1_fuel_2007_12_45_3_7  ...                  NaN\n",
              "freq                         1  ...                  NaN\n",
              "mean                       NaN  ...            19.304354\n",
              "std                        NaN  ...          2091.540939\n",
              "min                        NaN  ...           -41.501000\n",
              "25%                        NaN  ...             1.940000\n",
              "50%                        NaN  ...             4.127000\n",
              "75%                        NaN  ...             7.745000\n",
              "max                        NaN  ...        359278.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niuKrtxf0NoY",
        "colab_type": "text"
      },
      "source": [
        "Question three"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czfLgH4h0QIE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "3c7f563b-3f21-49b1-aaba-95518d0e80b1"
      },
      "source": [
        "fuel_data.groupby('fuel_type_code_pudl').fuel_cost_per_unit_burned.mean()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fuel_type_code_pudl\n",
              "coal         67.421830\n",
              "gas          13.659397\n",
              "nuclear    4955.157002\n",
              "oil         168.877086\n",
              "other        18.253856\n",
              "waste        19.518122\n",
              "Name: fuel_cost_per_unit_burned, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEShTsWL0RDg",
        "colab_type": "text"
      },
      "source": [
        "Question four"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kq755gqw0dTK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "ee24c9a4-5a44-490d-99c4-e6f513dbe025"
      },
      "source": [
        "fuel_data.describe(include='all')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>record_id</th>\n",
              "      <th>utility_id_ferc1</th>\n",
              "      <th>report_year</th>\n",
              "      <th>plant_name_ferc1</th>\n",
              "      <th>fuel_type_code_pudl</th>\n",
              "      <th>fuel_unit</th>\n",
              "      <th>fuel_qty_burned</th>\n",
              "      <th>fuel_mmbtu_per_unit</th>\n",
              "      <th>fuel_cost_per_unit_burned</th>\n",
              "      <th>fuel_cost_per_unit_delivered</th>\n",
              "      <th>fuel_cost_per_mmbtu</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29523</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523</td>\n",
              "      <td>29523</td>\n",
              "      <td>29343</td>\n",
              "      <td>2.952300e+04</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>29523.000000</td>\n",
              "      <td>2.952300e+04</td>\n",
              "      <td>29523.000000</td>\n",
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              "      <th>unique</th>\n",
              "      <td>29523</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2315</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>f1_fuel_2007_12_45_3_7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>big stone</td>\n",
              "      <td>gas</td>\n",
              "      <td>mcf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>156</td>\n",
              "      <td>11486</td>\n",
              "      <td>11354</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>118.601836</td>\n",
              "      <td>2005.806050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.622119e+06</td>\n",
              "      <td>8.492111</td>\n",
              "      <td>208.649031</td>\n",
              "      <td>9.175704e+02</td>\n",
              "      <td>19.304354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>74.178353</td>\n",
              "      <td>7.025483</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.118004e+06</td>\n",
              "      <td>10.600220</td>\n",
              "      <td>2854.490090</td>\n",
              "      <td>6.877593e+04</td>\n",
              "      <td>2091.540939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>-276.080000</td>\n",
              "      <td>-8.749370e+02</td>\n",
              "      <td>-41.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.381700e+04</td>\n",
              "      <td>1.024000</td>\n",
              "      <td>5.207000</td>\n",
              "      <td>3.778500e+00</td>\n",
              "      <td>1.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.533220e+05</td>\n",
              "      <td>5.762694</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.737100e+01</td>\n",
              "      <td>4.127000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>2012.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.424034e+06</td>\n",
              "      <td>17.006000</td>\n",
              "      <td>47.113000</td>\n",
              "      <td>4.213700e+01</td>\n",
              "      <td>7.745000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>514.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.558942e+08</td>\n",
              "      <td>341.260000</td>\n",
              "      <td>139358.000000</td>\n",
              "      <td>7.964521e+06</td>\n",
              "      <td>359278.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     record_id  ...  fuel_cost_per_mmbtu\n",
              "count                    29523  ...         29523.000000\n",
              "unique                   29523  ...                  NaN\n",
              "top     f1_fuel_2007_12_45_3_7  ...                  NaN\n",
              "freq                         1  ...                  NaN\n",
              "mean                       NaN  ...            19.304354\n",
              "std                        NaN  ...          2091.540939\n",
              "min                        NaN  ...           -41.501000\n",
              "25%                        NaN  ...             1.940000\n",
              "50%                        NaN  ...             4.127000\n",
              "75%                        NaN  ...             7.745000\n",
              "max                        NaN  ...        359278.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQv6UFwr0RbL",
        "colab_type": "text"
      },
      "source": [
        "Question five"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTspblaa0hJf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f5131e78-39e0-477f-f6bb-930524d6d8ec"
      },
      "source": [
        "fuel_data.fuel_qty_burned.skew()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.851495469109503"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyoK9KK90SSp",
        "colab_type": "text"
      },
      "source": [
        "Question six"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4Ur2HuO0npL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43218196-f263-429e-8660-075264878e79"
      },
      "source": [
        "fuel_data.fuel_qty_burned.kurt()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "651.3694501337732"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfClZhGd0Udj",
        "colab_type": "text"
      },
      "source": [
        "Question six"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G39xZ94w0v5O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "d38c9bdd-494e-4503-b9a1-2f5745e1cf3e"
      },
      "source": [
        "fuel_data.isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "record_id                         0\n",
              "utility_id_ferc1                  0\n",
              "report_year                       0\n",
              "plant_name_ferc1                  0\n",
              "fuel_type_code_pudl               0\n",
              "fuel_unit                       180\n",
              "fuel_qty_burned                   0\n",
              "fuel_mmbtu_per_unit               0\n",
              "fuel_cost_per_unit_burned         0\n",
              "fuel_cost_per_unit_delivered      0\n",
              "fuel_cost_per_mmbtu               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lMJavT_9Phm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cdfa8cbc-517a-499e-b4fc-8162a4e3f6f9"
      },
      "source": [
        "(180/len(fuel_data))*100"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.609694136774718"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slzZT-KT0U5t",
        "colab_type": "text"
      },
      "source": [
        "Question seven"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_4UacvN0ziY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3fec42b8-aa8e-45cd-b0f5-a24f7fab333b"
      },
      "source": [
        "fuel_data.fuel_unit.unique()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ton', 'mcf', 'kgU', 'bbl', 'gramsU', nan, 'mwdth', 'mmbtu',\n",
              "       'mwhth', 'gal'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-fNPIyh0VV5",
        "colab_type": "text"
      },
      "source": [
        "Question eight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmenWomR03rd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "8db2b47a-ffd9-4beb-be59-708b91363e07"
      },
      "source": [
        "fuel_data.corr()"
      ],
      "execution_count": 26,
      "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>utility_id_ferc1</th>\n",
              "      <th>report_year</th>\n",
              "      <th>fuel_qty_burned</th>\n",
              "      <th>fuel_mmbtu_per_unit</th>\n",
              "      <th>fuel_cost_per_unit_burned</th>\n",
              "      <th>fuel_cost_per_unit_delivered</th>\n",
              "      <th>fuel_cost_per_mmbtu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>utility_id_ferc1</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.093323</td>\n",
              "      <td>-0.057447</td>\n",
              "      <td>-0.066946</td>\n",
              "      <td>-0.037863</td>\n",
              "      <td>-0.016414</td>\n",
              "      <td>0.006122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>report_year</th>\n",
              "      <td>0.093323</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.012952</td>\n",
              "      <td>-0.110853</td>\n",
              "      <td>0.013599</td>\n",
              "      <td>-0.014043</td>\n",
              "      <td>0.010261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_qty_burned</th>\n",
              "      <td>-0.057447</td>\n",
              "      <td>0.012952</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080946</td>\n",
              "      <td>-0.018535</td>\n",
              "      <td>-0.003551</td>\n",
              "      <td>-0.001896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_mmbtu_per_unit</th>\n",
              "      <td>-0.066946</td>\n",
              "      <td>-0.110853</td>\n",
              "      <td>-0.080946</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010034</td>\n",
              "      <td>-0.009039</td>\n",
              "      <td>-0.005884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_cost_per_unit_burned</th>\n",
              "      <td>-0.037863</td>\n",
              "      <td>0.013599</td>\n",
              "      <td>-0.018535</td>\n",
              "      <td>-0.010034</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>-0.000437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_cost_per_unit_delivered</th>\n",
              "      <td>-0.016414</td>\n",
              "      <td>-0.014043</td>\n",
              "      <td>-0.003551</td>\n",
              "      <td>-0.009039</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_cost_per_mmbtu</th>\n",
              "      <td>0.006122</td>\n",
              "      <td>0.010261</td>\n",
              "      <td>-0.001896</td>\n",
              "      <td>-0.005884</td>\n",
              "      <td>-0.000437</td>\n",
              "      <td>-0.000109</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              utility_id_ferc1  ...  fuel_cost_per_mmbtu\n",
              "utility_id_ferc1                      1.000000  ...             0.006122\n",
              "report_year                           0.093323  ...             0.010261\n",
              "fuel_qty_burned                      -0.057447  ...            -0.001896\n",
              "fuel_mmbtu_per_unit                  -0.066946  ...            -0.005884\n",
              "fuel_cost_per_unit_burned            -0.037863  ...            -0.000437\n",
              "fuel_cost_per_unit_delivered         -0.016414  ...            -0.000109\n",
              "fuel_cost_per_mmbtu                   0.006122  ...             1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aajJ7aaG_q2F",
        "colab_type": "text"
      },
      "source": [
        "Question nine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UF-kCy_n0-lb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a276a7e9-6bb0-46f3-c637-c6fe3d2c58b9"
      },
      "source": [
        "fuel1998= fuel_data[(fuel_data.fuel_type_code_pudl=='coal') & (fuel_data.report_year==1998)].fuel_cost_per_unit_burned.sum()\n",
        "print(fuel1998)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11902.597\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prqjoXrRAe_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9e89723-bbe7-4675-e9da-fd8449e30b08"
      },
      "source": [
        "fuel1994= fuel_data[(fuel_data.fuel_type_code_pudl=='coal') & (fuel_data.report_year==1994)].fuel_cost_per_unit_burned.sum()\n",
        "print(fuel1998)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11902.597\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gv5bFvs3B-l0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "01efa2c4-93b1-4aa4-916f-089198b8ae33"
      },
      "source": [
        "perc_change=((fuel1998-fuel1994)/fuel1994)*100\n",
        "perc_change"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-20.56765451826053"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSCHnnLbCd4X",
        "colab_type": "text"
      },
      "source": [
        "  Question ten\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-hTSxkGCh4B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "81ad978e-61de-4861-bd58-a55e7b28d13e"
      },
      "source": [
        "fuel_data.groupby('report_year')['fuel_cost_per_unit_delivered'].mean().plot(kind='bar')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f447a566a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fdQ_Cb7DBRP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}