hayome_internship.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of hayome internship",
      "provenance": [],
      "authorship_tag": "ABX9TyPcCfVVrH/cEWHiGhU0vA5Y",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PulkitAppz/Hamoye_Internship/blob/master/hayome_internship.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSiAAA9YoY7G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "090c6ec4-9ff7-4d3e-fbe4-230631e20bc1"
      },
      "source": [
        "import numpy as np\n",
        "arr=[6,7,8,9]\n",
        "print(type(arr))\n",
        "a = np.array(arr)\n",
        "print(type(a))  # prints <class 'numpy.ndarray'>\n",
        "print(a.shape)  # prints (4,) - a is a 1d array with 4 items\n",
        "print(a.dtype)  # prints int64\n",
        "\n",
        "# get the dimension of a with ndim \n",
        "print(a.ndim)   # prints 1\n",
        "\n",
        "b = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print(b)        # \n",
        "print(b.ndim)   # prints 2\n",
        "b.shape         # prints (2, 3) - b a 2d array with 2 rows and     3 columns\n",
        "arr = [6, 7, 8, 9]\n",
        "print(type(arr)) # prints <class 'list'>\n",
        "\n",
        "a = np.array(arr)\n",
        "print(type(a))  # prints <class 'numpy.ndarray'>\n",
        "print(a.shape)  # prints (4,) - a is a 1d array with 4 items\n",
        "print(a.dtype)  # prints int64\n",
        "\n",
        "# get the dimension of a with ndim \n",
        "print(a.ndim)   # prints 1\n",
        "\n",
        "b = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "print(b)        # prints [[1 2 3]\n",
        "print(b.ndim)   # prints 2\n",
        "b.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n",
            "<class 'numpy.ndarray'>\n",
            "(4,)\n",
            "int64\n",
            "1\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "2\n",
            "<class 'list'>\n",
            "<class 'numpy.ndarray'>\n",
            "(4,)\n",
            "int64\n",
            "1\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFf3K4MQs0ux",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjgCcFfotMoS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8e2d50b0-685d-4057-d9ad-69e16fe58b46"
      },
      "source": [
        "print(np.random.random((2, 3)) )\n",
        "# a 2x3 array of zeros\n",
        "print(np.zeros((2, 3)) )\n",
        "# a 2x3 array of ones\n",
        "print(np.empty((2, 3)) )\n",
        "# a 2x3 array of ones\n",
        "print(np.ones((2, 3)) )\n",
        "# a 3x3 identity matrix \n",
        "print(np.full((3,2),3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.48410251 0.49680761 0.52329648]\n",
            " [0.2861919  0.72670729 0.89871754]]\n",
            "[[0. 0. 0.]\n",
            " [0. 0. 0.]]\n",
            "[[0. 0. 0.]\n",
            " [0. 0. 0.]]\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]]\n",
            "[[3 3]\n",
            " [3 3]\n",
            " [3 3]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzeGujAz3N1U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "7c1e1672-ad05-4ed1-d78d-b12ac93a04e7"
      },
      "source": [
        "import pandas as pd\n",
        "days = pd.Series(['Monday', 'Tuesday', 'Wednesday'], \n",
        " index=['a', 'b', 'c'])\n",
        "days"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a       Monday\n",
              "b      Tuesday\n",
              "c    Wednesday\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-vmcbic5nxP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "de851244-df21-487d-ce2c-4daa46e5de18"
      },
      "source": [
        "df_dict2 = {'Name': ['James', 'Yemen', 'Caro', np.nan],\n",
        "           'Profession': ['Researcher', 'Artist', 'Doctor', 'Writer'],\n",
        "           'Experience': [12, np.nan, 10, 8],\n",
        "           'Height': [np.nan, 175, 180, 150]}\n",
        "\n",
        "new_df = pd.DataFrame(df_dict2)\n",
        "\n",
        "print(new_df)\n",
        "print(new_df.isnull())\n",
        "print(new_df.notnull())\n",
        "print(new_df.fillna(4))\n",
        "print(new_df.dropna())\n",
        "print(new_df.replace(to_replace =np.nan,  \n",
        "                            value =66) )\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Name  Profession  Experience  Height\n",
            "0  James  Researcher        12.0     NaN\n",
            "1  Yemen      Artist         NaN   175.0\n",
            "2   Caro      Doctor        10.0   180.0\n",
            "3    NaN      Writer         8.0   150.0\n",
            "    Name  Profession  Experience  Height\n",
            "0  False       False       False    True\n",
            "1  False       False        True   False\n",
            "2  False       False       False   False\n",
            "3   True       False       False   False\n",
            "    Name  Profession  Experience  Height\n",
            "0   True        True        True   False\n",
            "1   True        True       False    True\n",
            "2   True        True        True    True\n",
            "3  False        True        True    True\n",
            "    Name  Profession  Experience  Height\n",
            "0  James  Researcher        12.0     4.0\n",
            "1  Yemen      Artist         4.0   175.0\n",
            "2   Caro      Doctor        10.0   180.0\n",
            "3      4      Writer         8.0   150.0\n",
            "   Name Profession  Experience  Height\n",
            "2  Caro     Doctor        10.0   180.0\n",
            "    Name  Profession  Experience  Height\n",
            "0  James  Researcher        12.0    66.0\n",
            "1  Yemen      Artist        66.0   175.0\n",
            "2   Caro      Doctor        10.0   180.0\n",
            "3     66      Writer         8.0   150.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MT7DYCQjCh_d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7e096537-3011-422b-895f-dc8b266737e3"
      },
      "source": [
        "import pandas as pd\n",
        "import os \n",
        "from google.colab import drive\n",
        "print(os. getcwd())\n",
        "print(os.listdir())\n",
        "#csv_df = pd.read_csv('sample_file')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "['.config', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOzcnj95M8kH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "2445368e-ac6a-470e-c46d-5219529dac75"
      },
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEBR4uYSMpcc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "ace9f005-11f7-411a-d7ba-dca1c9efa705"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzARTFIBNQ-1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "outputId": "306b048d-a00d-4841-dd5b-ae0698d01164"
      },
      "source": [
        "os.chdir('/content/gdrive/My Drive/Colab Notebooks')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-9a4c2ba4b7d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/gdrive/My Drive/Colab Notebooks'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/gdrive/My Drive/Colab Notebooks'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XW3g82mKNj8i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "fccc0d1d-1da3-456b-b1dd-1497c808b0f5"
      },
      "source": [
        "print(os.listdir())\n",
        "csv_df = pd.read_csv('sample_file.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'drive', 'sample_data']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-fbed5c41472f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcsv_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sample_file.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File sample_file.csv does not exist: 'sample_file.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlUfYmBLH_PN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bdd6f573-210c-4763-f3ea-b003ff3e850e"
      },
      "source": [
        "csv_df.iat[0,0]=2\n",
        "print(csv_df.iat[0,0])\n",
        "csv_df.to_csv('sample_file.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZdBZmlqLUdy6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "c6830904-184a-487f-8068-7e84960f7f83"
      },
      "source": [
        "import pandas as pd\n",
        "url='https://github.com/WalePhenomenon/climate_change/blob/master/fuel_ferc1.csv?raw=true'\n",
        "fuel_data = pd.read_csv(url, error_bad_lines=False)\n",
        "fuel_data.describe(include='all')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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_1998_12_121_0_15</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_1998_12_121_0_15  ...                  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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1RIsNa5WomI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "69ce298d-780e-4b59-ca9f-ff8541177c53"
      },
      "source": [
        "print(fuel_data.groupby('fuel_unit')['fuel_unit'].last())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fuel_unit\n",
            "bbl          bbl\n",
            "gal          gal\n",
            "gramsU    gramsU\n",
            "kgU          kgU\n",
            "mcf          mcf\n",
            "mmbtu      mmbtu\n",
            "mwdth      mwdth\n",
            "mwhth      mwhth\n",
            "ton          ton\n",
            "Name: fuel_unit, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uu7e8p2D4E_S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac09e6cd-5303-4f12-cddb-07e6b22ed9de"
      },
      "source": [
        "\n",
        "fuel_df1 = fuel_data.iloc[0:19000].reset_index(drop=True)\n",
        "fuel_df2 = fuel_data.iloc[19000:].reset_index(drop=True)\n",
        "\n",
        "#check that the length of both dataframes sum to the expected length\n",
        "assert len(fuel_data) == (len(fuel_df1) + len(fuel_df2))\n",
        "print(len(fuel_data) == (len(fuel_df1) + len(fuel_df2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2s3NXZM5Mmc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "e415ae5a-8093-4e82-c40d-843acaa71685"
      },
      "source": [
        "print(pd.merge(fuel_df1, fuel_df2, how=\"inner\"))\n",
        "print(pd.merge(fuel_df1, fuel_df2, how=\"outer\"))\n",
        "print(pd.merge(fuel_df1, fuel_df2, how=\"left\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [record_id, utility_id_ferc1, report_year, plant_name_ferc1, fuel_type_code_pudl, fuel_unit, fuel_qty_burned, fuel_mmbtu_per_unit, fuel_cost_per_unit_burned, fuel_cost_per_unit_delivered, fuel_cost_per_mmbtu]\n",
            "Index: []\n",
            "                     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",
            "29518  f1_fuel_2018_12_12_0_13  ...                9.030\n",
            "29519   f1_fuel_2018_12_12_1_1  ...                6.950\n",
            "29520  f1_fuel_2018_12_12_1_10  ...                8.990\n",
            "29521  f1_fuel_2018_12_12_1_13  ...                1.110\n",
            "29522  f1_fuel_2018_12_12_1_14  ...               11.680\n",
            "\n",
            "[29523 rows x 11 columns]\n",
            "                      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",
            "18995   f1_fuel_2009_12_182_1_9  ...                4.711\n",
            "18996  f1_fuel_2009_12_182_1_10  ...               14.634\n",
            "18997  f1_fuel_2009_12_182_1_13  ...                1.107\n",
            "18998  f1_fuel_2009_12_182_1_14  ...               15.102\n",
            "18999    f1_fuel_2009_12_79_0_1  ...                1.727\n",
            "\n",
            "[19000 rows x 11 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPDjfB1z71CQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "8a74aef3-5c5f-4f33-daf5-b4d1e579e78d"
      },
      "source": [
        "fuel_data.duplicated()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "29518    False\n",
              "29519    False\n",
              "29520    False\n",
              "29521    False\n",
              "29522    False\n",
              "Length: 29523, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SUbnbimteZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "outputId": "f2082355-7e5b-40e4-eac8-abd661d99959"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(7,4))\n",
        "plt.xticks(rotation=90)\n",
        "fuel_unit = pd.DataFrame({'unit':['BBL', 'GAL', 'GRAMSU', 'KGU', 'MCF', 'MMBTU', 'MWDTH', 'MWHTH', 'TON'],\n",
        "            'count':[7998, 84, 464, 110, 11354, 180, 95, 100, 8958]})\n",
        "sns.barplot(data=fuel_unit, x='unit', y='count')\n",
        "plt.xlabel('Fuel Unit')\n",
        "plt.show()\n",
        "\n",
        "#Because of the extreme range of the values for the fuel unit, we can plot the barchart by taking the logarithm of the y-axis as follows:\n",
        "\n",
        "\n",
        "g = sns.barplot(data=fuel_unit, x='unit', y='count')\n",
        "g.set_yscale(\"log\")\n",
        "g.set_ylim(1, 12000)\n",
        "plt.xlabel('Fuel Unit')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Fuel Unit')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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": "3kR_RJe9yr-c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f756dbaf-8fed-4231-8bc6-2a474ea725a8"
      },
      "source": [
        "sample_df = fuel_data.sample(n=50, random_state=4)\n",
        "sns.regplot(x=sample_df[\"utility_id_ferc1\"], y=sample_df[\"fuel_cost_per_mmbtu\"], fit_reg=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a59d830f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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": "ZFp8jssU0cWC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c7e6d8b9-1de5-4fbf-e7b0-5436d01b504e"
      },
      "source": [
        "#sns.boxplot(x=\"fuel_type_code_pudl\", y=\"utility_id_ferc1\",\n",
        "       #     palette=[\"b\", \"g\"], data=fuel_data)\n",
        "# KDE plot \n",
        "sns.kdeplot(sample_df['fuel_cost_per_unit_burned'], shade=True, color=\"r\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a5901c048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "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": "pqIQhxogPIDU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHjpPknTO6c7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KaDQ408O7io",
        "colab_type": "text"
      },
      "source": [
        "## ***`QUIZ QUESTION `***\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ge4WmhWN8Qw-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "7ee7ddb6-9024-41da-8027-23ac67c86950"
      },
      "source": [
        "import numpy as np\n",
        "c=np.identity(3)\n",
        "#a=np.array([1,0,0],[0,1,0],[0,0,1])\n",
        "#b=np.array[(1,0,0),(0,1,0),(0,0,1)]\n",
        "print(c,np.eye(3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]] [[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwSUBQvX9kSp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "3e354794-7ca1-49be-ddaf-4bc0a485c65b"
      },
      "source": [
        "fuel_data.groupby('fuel_type_code_pudl')['fuel_cost_per_unit_burned'].mean()\n"
      ],
      "execution_count": null,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0E41_p6gAKGj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c4427da4-2ac9-49d1-d8f1-fd18ebb0e533"
      },
      "source": [
        "print(fuel_data['fuel_mmbtu_per_unit'].std())\n",
        "print(round(fuel_data['fuel_mmbtu_per_unit'].quantile(0.75)),)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10.600220307806886\n",
            "17.0 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NmWLPcWBK-M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c0421cfa-4bfb-4ab3-b49b-9659c24f5867"
      },
      "source": [
        "print(fuel_data['fuel_qty_burned'].skew())\n",
        "print(fuel_data['fuel_qty_burned'].kurt())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15.851495469109503\n",
            "651.3694501337732\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GUoZk35Bf56",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "acfa2312-85a1-409a-9f4d-86f8513e18ed"
      },
      "source": [
        "print(fuel_data.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAHjtxvJDXzZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "739bbc6c-0eaa-4334-ce88-c392ef824a9b"
      },
      "source": [
        "print(fuel_data.groupby('report_year')['fuel_cost_per_unit_delivered'].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "report_year\n",
            "1994       63.636060\n",
            "1995       32.735269\n",
            "1996     9196.705948\n",
            "1997    11140.197239\n",
            "1998      287.154420\n",
            "1999       25.551627\n",
            "2000      985.362877\n",
            "2001       60.050396\n",
            "2002       47.594361\n",
            "2003       55.663493\n",
            "2004      139.524275\n",
            "2005       41.438184\n",
            "2006       38.657484\n",
            "2007       43.325023\n",
            "2008       58.588197\n",
            "2009      652.694163\n",
            "2010       91.862105\n",
            "2011       59.774667\n",
            "2012       60.994502\n",
            "2013      172.307591\n",
            "2014      192.737183\n",
            "2015      326.535511\n",
            "2016      103.901761\n",
            "2017       46.196861\n",
            "2018      499.269966\n",
            "Name: fuel_cost_per_unit_delivered, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPhX7_sxH_KQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "31f3bc47-0866-42ae-e312-903a67b8a16c"
      },
      "source": [
        "#CORRELATION MATRIX \n",
        "\n",
        "val = fuel_data.corr()\n",
        "copy = val['fuel_cost_per_unit_burned']\n",
        "copy.sort_values(ascending=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "utility_id_ferc1               -0.037863\n",
              "fuel_qty_burned                -0.018535\n",
              "fuel_mmbtu_per_unit            -0.010034\n",
              "fuel_cost_per_mmbtu            -0.000437\n",
              "fuel_cost_per_unit_delivered    0.011007\n",
              "report_year                     0.013599\n",
              "fuel_cost_per_unit_burned       1.000000\n",
              "Name: fuel_cost_per_unit_burned, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pw0EBpnoE8hk",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    }
  ]
}