Data_Cleaning.ipynb

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  "metadata": {
    "colab": {
      "name": "Data_Cleaning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCIPJCyLtCaQ",
        "outputId": "e0e469cf-c84e-49b4-93bf-70d97d110577"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install import-ipynb\n",
        "import import_ipynb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQqL7JqBvTws",
        "outputId": "be7cdc1a-5aea-4603-b7e9-543f40e9274e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting import-ipynb\n",
            "  Downloading import_ipynb-0.1.4-py3-none-any.whl (4.1 kB)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.7/dist-packages (from import-ipynb) (5.4.0)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.7/dist-packages (from import-ipynb) (5.5.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (57.4.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (2.6.1)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (4.8.0)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (0.8.1)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (5.1.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (1.0.18)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (0.7.5)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from IPython->import-ipynb) (4.4.2)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->IPython->import-ipynb) (0.2.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->IPython->import-ipynb) (1.15.0)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat->import-ipynb) (2.16.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from nbformat->import-ipynb) (4.3.3)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat->import-ipynb) (4.11.1)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (0.18.1)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (5.8.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (4.1.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (21.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (4.12.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat->import-ipynb) (3.8.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->IPython->import-ipynb) (0.7.0)\n",
            "Installing collected packages: import-ipynb\n",
            "Successfully installed import-ipynb-0.1.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/gdrive/MyDrive/Recession_Predictor_Project/DataCollection.ipynb /content"
      ],
      "metadata": {
        "id": "FgSVVrmA9QY3"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing all datasets from DataCollection.ipynb\n",
        "import DataCollection\n",
        "yc_df = DataCollection.yc_df\n",
        "cci_df = DataCollection.cci_df\n",
        "fedfunds_df = DataCollection.fedfunds_df\n",
        "inflation_cpi_df = DataCollection.inflation_cpi_df\n",
        "buffett_df = DataCollection.buffett_df\n",
        "unrate_df = DataCollection.unrate_df"
      ],
      "metadata": {
        "id": "OCJRBcTo9ZYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f66f13b1-0304-4634-a2fd-ea7348f2c47a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "importing Jupyter notebook from DataCollection.ipynb\n",
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import pandas as pd\n",
        "from google.colab.data_table import DataTable\n",
        "DataTable.max_columns = 30\n",
        "from functools import reduce"
      ],
      "metadata": {
        "id": "r1fYV35nKCXd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Cleaning the Datasets"
      ],
      "metadata": {
        "id": "9lvT2oHb9IGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Inspecting:\n",
        "#yield curve interest rates\n",
        "#from Jan 1990 and has DAYS and 8142x11\n",
        "#TODO: Uncomment the line below before your run it\n",
        "yc_df = yc_df.drop(['20 YR','30 YR','Extrapolation Factor','8 WEEKS BANK DISCOUNT','COUPON EQUIVALENT','52 WEEKS BANK DISCOUNT','COUPON EQUIVALENT.1','1 Mo','2 Mo'], axis = 1)\n",
        "#TODO: create a new column called year and month (using lambda). Then Use the month averages. We will be using the Month averages for each in the actual dataset. \n",
        "yc_df['Month'] = yc_df['Date'].apply(lambda x:x.split('/')[0])\n",
        "yc_df['Year'] = yc_df['Date'].apply(lambda x:x.split('/')[-1])"
      ],
      "metadata": {
        "id": "u3tJ_vVqC3HH"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Initialising the Monthly Average Columns to 0 and retaining the NaN values\n",
        "yc_df['Avg 3 Mo'] = yc_df['3 Mo'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 6 Mo'] = yc_df['6 Mo'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 1 Yr'] = yc_df['1 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 2 Yr'] = yc_df['2 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 3 Yr'] = yc_df['3 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 5 Yr'] = yc_df['5 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 7 Yr'] = yc_df['7 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 10 Yr'] = yc_df['10 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 20 Yr'] = yc_df['20 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "yc_df['Avg 30 Yr'] = yc_df['30 Yr'].apply(lambda x: float(\"NaN\") if math.isnan(x) else 0)\n",
        "#df['FirstName']=df['ID'].apply(lambda x: 'Matt' if x==103 else '')\n",
        "#yc_df['Avg 20 Yr'].apply(lambda x: print(x))"
      ],
      "metadata": {
        "id": "ae1lPL5EmsAZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NOW calculating monthly averages for ALL THE COLUMNS \n",
        "sum_3mo = 0.0\n",
        "sum_6mo = 0.0\n",
        "sum_1y = 0.0\n",
        "sum_2y = 0.0\n",
        "sum_3y = 0.0\n",
        "sum_5y = 0.0\n",
        "sum_7y = 0.0\n",
        "sum_10y = 0.0\n",
        "sum_20y = 0.0\n",
        "sum_30y = 0.0\n",
        "count_3mo = 0.0\n",
        "count_6mo = 0.0\n",
        "count_1y = 0.0\n",
        "count_2y = 0.0\n",
        "count_3y = 0.0\n",
        "count_5y = 0.0\n",
        "count_7y = 0.0\n",
        "count_10y = 0.0\n",
        "count_20y = 0.0\n",
        "count_30y = 0.0\n",
        "for row in range(len(yc_df.index)-1):\n",
        "\n",
        "  #Monthly averages for the 3 Months column\n",
        "  if math.isnan(yc_df['3 Mo'][row]) == False:\n",
        "      count_3mo += 1.0\n",
        "      sum_3mo += yc_df['3 Mo'][row]\n",
        "      avg_3mo = float(sum_3mo / count_3mo) #this is storing the current average\n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 3 Mo'][row] = avg_3mo\n",
        "        sum_3mo = 0.0 #resetting the sums and averages to 0 to iterate for next month\n",
        "        avg_3mo = 0.0\n",
        "        count_3mo = 0.0\n",
        "  \n",
        "  \n",
        "  #Monthly averages for the 6 Months column\n",
        "  if math.isnan(yc_df['6 Mo'][row]) == False:\n",
        "      count_6mo += 1.0\n",
        "      sum_6mo += yc_df['6 Mo'][row]\n",
        "      avg_6mo = float(sum_6mo / count_6mo) \n",
        "\n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 6 Mo'][row] = avg_6mo\n",
        "        sum_6mo = 0.0 \n",
        "        avg_6mo = 0.0\n",
        "        count_6mo = 0.0\n",
        "\n",
        "  #Monthly averages for the 1 Year column\n",
        "  if math.isnan(yc_df['1 Yr'][row]) == False:\n",
        "      count_1y += 1.0\n",
        "      sum_1y += yc_df['1 Yr'][row]\n",
        "      avg_1y = float(sum_1y / count_1y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 1 Yr'][row] = avg_1y\n",
        "        sum_1y = 0.0 \n",
        "        avg_1y = 0.0\n",
        "        count_1y = 0.0\n",
        "\n",
        "  #Monthly averages for the 2 Year column\n",
        "  if math.isnan(yc_df['2 Yr'][row]) == False:\n",
        "      count_2y += 1.0\n",
        "      sum_2y += yc_df['2 Yr'][row]\n",
        "      avg_2y = float(sum_2y / count_2y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 2 Yr'][row] = avg_2y\n",
        "        sum_2y = 0.0 \n",
        "        avg_2y = 0.0\n",
        "        count_2y = 0.0\n",
        "\n",
        "  #Monthly averages for the 3 Year column\n",
        "  if math.isnan(yc_df['3 Yr'][row]) == False:\n",
        "      count_3y += 1.0\n",
        "      sum_3y += yc_df['3 Yr'][row]\n",
        "      avg_3y = float(sum_3y / count_3y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 3 Yr'][row] = avg_3y\n",
        "        sum_3y = 0.0 \n",
        "        avg_3y = 0.0\n",
        "        count_3y = 0.0\n",
        "\n",
        "  #Monthly averages for the 5 Year column\n",
        "  if math.isnan(yc_df['5 Yr'][row]) == False:\n",
        "      count_5y += 1.0\n",
        "      sum_5y += yc_df['5 Yr'][row]\n",
        "      avg_5y = float(sum_5y / count_5y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 5 Yr'][row] = avg_5y\n",
        "        sum_5y = 0.0 \n",
        "        avg_5y = 0.0\n",
        "        count_5y = 0.0\n",
        "\n",
        "  #Monthly averages for the 7 Year column\n",
        "  if math.isnan(yc_df['7 Yr'][row]) == False:\n",
        "      count_7y += 1.0\n",
        "      sum_7y += yc_df['7 Yr'][row]\n",
        "      avg_7y = float(sum_7y / count_7y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 7 Yr'][row] = avg_7y\n",
        "        sum_7y = 0.0 \n",
        "        avg_7y = 0.0\n",
        "        count_7y = 0.0\n",
        "\n",
        "  #Monthly averages for the 10 Year column\n",
        "  if math.isnan(yc_df['10 Yr'][row]) == False:\n",
        "      count_10y += 1.0\n",
        "      sum_10y += yc_df['10 Yr'][row]\n",
        "      avg_10y = float(sum_10y / count_10y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 10 Yr'][row] = avg_10y\n",
        "        sum_10y = 0.0 \n",
        "        avg_10y = 0.0\n",
        "        count_10y = 0.0\n",
        "\n",
        "  #Monthly averages for the 20 Year column      \n",
        "  if math.isnan(yc_df['20 Yr'][row]) == False:\n",
        "      count_20y += 1.0\n",
        "      sum_20y += yc_df['20 Yr'][row]\n",
        "      avg_20y = float(sum_20y / count_20y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 20 Yr'][row] = avg_20y\n",
        "        sum_20y = 0.0 \n",
        "        avg_20y = 0.0\n",
        "        count_20y = 0.0\n",
        "\n",
        "  #Monthly averages for the 30 Year column     \n",
        "  if math.isnan(yc_df['30 Yr'][row]) == False:\n",
        "      count_30y += 1.0\n",
        "      sum_30y += yc_df['30 Yr'][row]\n",
        "      avg_30y = float(sum_30y / count_30y) \n",
        "      \n",
        "      if yc_df['Month'][row]!=yc_df['Month'][(row+1)]:\n",
        "        yc_df['Avg 30 Yr'][row] = avg_30y\n",
        "        sum_30y = 0.0 \n",
        "        avg_30y = 0.0\n",
        "        count_30y = 0.0\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOW0pY9IzSqb",
        "outputId": "3704b583-7d52-45c4-e78e-3c59e0b4dbd0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:44: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:68: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:80: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:92: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:104: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:116: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:140: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:128: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yc_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "XNIWxqtV4vTF",
        "outputId": "7c42122a-bcec-4ac1-b19b-ae5868f5224c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date  3 Mo  6 Mo  1 Yr  2 Yr  3 Yr  5 Yr  7 Yr  10 Yr  20 Yr  ...  \\\n",
              "0     01/02/1990  7.83  7.89  7.81  7.87  7.90  7.87  7.98   7.94    NaN  ...   \n",
              "1     01/03/1990  7.89  7.94  7.85  7.94  7.96  7.92  8.04   7.99    NaN  ...   \n",
              "2     01/04/1990  7.84  7.90  7.82  7.92  7.93  7.91  8.02   7.98    NaN  ...   \n",
              "3     01/05/1990  7.79  7.85  7.79  7.90  7.94  7.92  8.03   7.99    NaN  ...   \n",
              "4     01/08/1990  7.79  7.88  7.81  7.90  7.95  7.92  8.05   8.02    NaN  ...   \n",
              "...          ...   ...   ...   ...   ...   ...   ...   ...    ...    ...  ...   \n",
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              "8145  07/21/2022  2.48  3.00  3.11  3.10  3.07  3.00  2.99   2.91   3.33  ...   \n",
              "8146  07/22/2022  2.49  2.97  3.01  2.98  2.93  2.87  2.85   2.77   3.23  ...   \n",
              "\n",
              "      Avg 3 Mo Avg 6 Mo Avg 1 Yr  Avg 2 Yr  Avg 3 Yr  Avg 5 Yr  Avg 7 Yr  \\\n",
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              "8146       0.0      0.0      0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "      Avg 10 Yr  Avg 20 Yr  Avg 30 Yr  \n",
              "0           0.0        NaN        0.0  \n",
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              "      <th>Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>01/02/1990</td>\n",
              "      <td>7.83</td>\n",
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              "      <td>01/03/1990</td>\n",
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              "      <td>01/04/1990</td>\n",
              "      <td>7.84</td>\n",
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              "      <td>7.82</td>\n",
              "      <td>7.92</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.91</td>\n",
              "      <td>8.02</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01/05/1990</td>\n",
              "      <td>7.79</td>\n",
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              "      <td>7.79</td>\n",
              "      <td>7.90</td>\n",
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              "      <th>4</th>\n",
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              "      <td>7.79</td>\n",
              "      <td>7.88</td>\n",
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              "      <td>7.95</td>\n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping previous columns\n",
        "yc_df = yc_df.drop(['Date','3 Mo','6 Mo','1 Yr','2 Yr','3 Yr','5 Yr','7 Yr','10 Yr','20 Yr','30 Yr'], axis = 1)"
      ],
      "metadata": {
        "id": "dJyTyISk5YBB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the average month data by removing rows with 0s.\n",
        "yc_df = yc_df.loc[~((yc_df['Avg 3 Mo'] == 0))]"
      ],
      "metadata": {
        "id": "vVssKTZ-6kCD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yc_df = yc_df.reset_index()\n",
        "yc_df = yc_df.drop(['index'], axis = 1)"
      ],
      "metadata": {
        "id": "dsOxxPUR8Iiw"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yc_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "D5DWkFgt8iIP",
        "outputId": "29af0d42-59a5-47c7-d0b1-f8c8aa30054a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Month  Year  Avg 3 Mo  Avg 6 Mo  Avg 1 Yr  Avg 2 Yr  Avg 3 Yr  Avg 5 Yr  \\\n",
              "0      01  1990  7.898095  7.956190  7.920952  8.085238  8.132381  8.119524   \n",
              "1      02  1990  8.002105  8.121053  8.111053  8.370526  8.386842  8.424737   \n",
              "2      03  1990  8.170000  8.280000  8.350000  8.626818  8.627273  8.600455   \n",
              "3      04  1990  8.040500  8.270000  8.404500  8.724000  8.782500  8.768000   \n",
              "4      05  1990  8.006818  8.190909  8.316364  8.642273  8.692273  8.735909   \n",
              "..    ...   ...       ...       ...       ...       ...       ...       ...   \n",
              "389    02  2022  0.314737  0.638421  1.004211  1.435263  1.651053  1.811579   \n",
              "390    03  2022  0.447391  0.855652  1.335217  1.909565  2.085652  2.109130   \n",
              "391    04  2022  0.763500  1.261500  1.887000  2.538000  2.724000  2.777500   \n",
              "392    05  2022  0.992857  1.494762  2.059524  2.616667  2.790000  2.874286   \n",
              "393    06  2022  1.538571  2.165238  2.651429  2.998095  3.145714  3.190000   \n",
              "\n",
              "     Avg 7 Yr  Avg 10 Yr  Avg 20 Yr  Avg 30 Yr  \n",
              "0    8.196190   8.206667        NaN   8.258571  \n",
              "1    8.475789   8.473158        NaN   8.503684  \n",
              "2    8.648182   8.588636        NaN   8.563182  \n",
              "3    8.813000   8.785500        NaN   8.756000  \n",
              "4    8.783636   8.758182        NaN   8.731364  \n",
              "..        ...        ...        ...        ...  \n",
              "389  1.912105   1.934211   2.309474   2.246316  \n",
              "390  2.150435   2.127826   2.513478   2.406522  \n",
              "391  2.795000   2.747500   2.986500   2.812000  \n",
              "392  2.921429   2.898095   3.258571   3.065714  \n",
              "393  3.206667   3.143333   3.483810   3.245714  \n",
              "\n",
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              "      <td>8.316364</td>\n",
              "      <td>8.642273</td>\n",
              "      <td>8.692273</td>\n",
              "      <td>8.735909</td>\n",
              "      <td>8.783636</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Consumer Confidence Index Data \n",
        "#TODO: SPLIT THIS TO MONTH AND YEAR\n",
        "cci_df = cci_df.drop(['LOCATION','INDICATOR','SUBJECT','MEASURE','FREQUENCY','Flag Codes'], axis = 1)\n",
        "cci_df.rename(columns = {'Value':'CCI'}, inplace = True)\n",
        "cci_df #from Jan 1960 and has months and 749x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "Gdvb11qJDHa_",
        "outputId": "36a8ba9a-c82b-4afe-8a2a-e1539a763d5b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TIME     CCI\n",
              "0    1960-01  101.68\n",
              "1    1960-02  101.40\n",
              "2    1960-03  101.16\n",
              "3    1960-04  101.03\n",
              "4    1960-05  101.06\n",
              "..       ...     ...\n",
              "744  2022-01   97.46\n",
              "745  2022-02   97.13\n",
              "746  2022-03   96.91\n",
              "747  2022-04   96.84\n",
              "748  2022-05   96.60\n",
              "\n",
              "[749 rows x 2 columns]"
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              "          document.querySelector('#df-1d65fb6c-370b-4a67-b83e-2e3dc6ef9ed0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1d65fb6c-370b-4a67-b83e-2e3dc6ef9ed0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the 'Time' column into 'Month' and 'Year' Columns\n",
        "cci_df['Month'] = cci_df['TIME'].apply(lambda x:x.split('-')[1])\n",
        "cci_df['Year'] = cci_df['TIME'].apply(lambda x:x.split('-')[0])\n",
        "cci_df = cci_df.drop(['TIME'], axis = 1)"
      ],
      "metadata": {
        "id": "9FMZKxFowqBH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cci_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "L1lN197Gxcmt",
        "outputId": "0532b3bc-7d3a-4fa4-be2e-fe90591e7b00"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CCI Month  Year\n",
              "0    101.68    01  1960\n",
              "1    101.40    02  1960\n",
              "2    101.16    03  1960\n",
              "3    101.03    04  1960\n",
              "4    101.06    05  1960\n",
              "..      ...   ...   ...\n",
              "744   97.46    01  2022\n",
              "745   97.13    02  2022\n",
              "746   96.91    03  2022\n",
              "747   96.84    04  2022\n",
              "748   96.60    05  2022\n",
              "\n",
              "[749 rows x 3 columns]"
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              "      <td>101.16</td>\n",
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              "      <td>1960</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>101.03</td>\n",
              "      <td>04</td>\n",
              "      <td>1960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101.06</td>\n",
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              "      <th>748</th>\n",
              "      <td>96.60</td>\n",
              "      <td>05</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Effective Federal Funds Rate Data\n",
        "fedfunds_df #from July 1954 and has months and 816x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "vm60XzV_DNaJ",
        "outputId": "04bdc41c-cd51-48e0-a0b7-011da1a0d28c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           DATE  FEDFUNDS\n",
              "0    1954-07-01      0.80\n",
              "1    1954-08-01      1.22\n",
              "2    1954-09-01      1.07\n",
              "3    1954-10-01      0.85\n",
              "4    1954-11-01      0.83\n",
              "..          ...       ...\n",
              "811  2022-02-01      0.08\n",
              "812  2022-03-01      0.20\n",
              "813  2022-04-01      0.33\n",
              "814  2022-05-01      0.77\n",
              "815  2022-06-01      1.21\n",
              "\n",
              "[816 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c739814e-305b-4eab-972d-015a07d8a11f\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>FEDFUNDS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1954-07-01</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1954-08-01</td>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1954-09-01</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1954-10-01</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1954-11-01</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>811</th>\n",
              "      <td>2022-02-01</td>\n",
              "      <td>0.08</td>\n",
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              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>2022-04-01</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>2022-05-01</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>815</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>816 rows × 2 columns</p>\n",
              "</div>\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-c739814e-305b-4eab-972d-015a07d8a11f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the 'DATE' column into 'Month' and 'Year' Columns\n",
        "fedfunds_df['Month'] = fedfunds_df['DATE'].apply(lambda x:x.split('-')[1])\n",
        "fedfunds_df['Year'] = fedfunds_df['DATE'].apply(lambda x:x.split('-')[0])\n",
        "fedfunds_df = fedfunds_df.drop(['DATE'], axis = 1)"
      ],
      "metadata": {
        "id": "LANFkTlixowd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fedfunds_df"
      ],
      "metadata": {
        "colab": {
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          "height": 419
        },
        "id": "nbs2YHrOx5Dr",
        "outputId": "d8fdb961-12c9-48fc-de39-420eee109b82"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     FEDFUNDS Month  Year\n",
              "0        0.80    07  1954\n",
              "1        1.22    08  1954\n",
              "2        1.07    09  1954\n",
              "3        0.85    10  1954\n",
              "4        0.83    11  1954\n",
              "..        ...   ...   ...\n",
              "811      0.08    02  2022\n",
              "812      0.20    03  2022\n",
              "813      0.33    04  2022\n",
              "814      0.77    05  2022\n",
              "815      1.21    06  2022\n",
              "\n",
              "[816 rows x 3 columns]"
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              "      <th>FEDFUNDS</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.80</td>\n",
              "      <td>07</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.22</td>\n",
              "      <td>08</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.07</td>\n",
              "      <td>09</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.85</td>\n",
              "      <td>10</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.83</td>\n",
              "      <td>11</td>\n",
              "      <td>1954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>811</th>\n",
              "      <td>0.08</td>\n",
              "      <td>02</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>0.20</td>\n",
              "      <td>03</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>0.33</td>\n",
              "      <td>04</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>0.77</td>\n",
              "      <td>05</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>815</th>\n",
              "      <td>1.21</td>\n",
              "      <td>06</td>\n",
              "      <td>2022</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>816 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-327423f5-fb01-4881-9e6e-5648b012352f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-327423f5-fb01-4881-9e6e-5648b012352f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Inflation Rate/Consumer Price Index Data\n",
        "#from Jan 1947 and has months and 76x13\n",
        "inflation_cpi_df = inflation_cpi_df.drop(['Annual','HALF1','HALF2'], axis = 1) \n",
        "months = {'Jan':1,'Feb':2,'Mar':3,'Apr':4,'May':5,'Jun':6,'Jul':7,'Aug':8,'Sep':9,'Oct':10,'Nov':11,'Dec':12}\n",
        "inflation_cpi_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "1PIQ7cqtDQb_",
        "outputId": "71c0bd71-8d7b-442a-9583-d57de79be55e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year      Jan      Feb      Mar      Apr      May      Jun      Jul  \\\n",
              "0   1947.0   21.480   21.620   22.000   22.000   21.950   22.080   22.230   \n",
              "1   1948.0   23.680   23.670   23.500   23.820   24.010   24.150   24.400   \n",
              "2   1949.0   24.010   23.910   23.910   23.920   23.910   23.920   23.700   \n",
              "3   1950.0   23.510   23.610   23.640   23.650   23.770   23.880   24.070   \n",
              "4   1951.0   25.380   25.830   25.880   25.920   25.990   25.930   25.910   \n",
              "..     ...      ...      ...      ...      ...      ...      ...      ...   \n",
              "71  2018.0  248.743  249.439  249.581  250.146  250.779  251.118  251.323   \n",
              "72  2019.0  252.470  253.135  254.273  255.163  255.325  255.361  255.900   \n",
              "73  2020.0  258.682  259.007  258.165  256.094  255.944  257.217  258.543   \n",
              "74  2021.0  262.200  263.346  265.028  266.727  268.599  270.955  272.184   \n",
              "75  2022.0  281.933  284.182  287.708  288.663  291.474      NaN      NaN   \n",
              "\n",
              "        Aug      Sep      Oct      Nov      Dec  \n",
              "0    22.400   22.840   22.910   23.060   23.410  \n",
              "1    24.430   24.360   24.310   24.160   24.050  \n",
              "2    23.700   23.750   23.670   23.700   23.610  \n",
              "3    24.200   24.340   24.500   24.600   24.980  \n",
              "4    25.860   26.030   26.160   26.320   26.470  \n",
              "..      ...      ...      ...      ...      ...  \n",
              "71  251.749  252.239  252.862  252.657  252.551  \n",
              "72  256.179  256.596  257.305  257.788  258.263  \n",
              "73  259.580  260.190  260.352  260.721  261.564  \n",
              "74  273.092  274.214  276.590  278.524  280.126  \n",
              "75      NaN      NaN      NaN      NaN      NaN  \n",
              "\n",
              "[76 rows x 13 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Jan</th>\n",
              "      <th>Feb</th>\n",
              "      <th>Mar</th>\n",
              "      <th>Apr</th>\n",
              "      <th>May</th>\n",
              "      <th>Jun</th>\n",
              "      <th>Jul</th>\n",
              "      <th>Aug</th>\n",
              "      <th>Sep</th>\n",
              "      <th>Oct</th>\n",
              "      <th>Nov</th>\n",
              "      <th>Dec</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1947.0</td>\n",
              "      <td>21.480</td>\n",
              "      <td>21.620</td>\n",
              "      <td>22.000</td>\n",
              "      <td>22.000</td>\n",
              "      <td>21.950</td>\n",
              "      <td>22.080</td>\n",
              "      <td>22.230</td>\n",
              "      <td>22.400</td>\n",
              "      <td>22.840</td>\n",
              "      <td>22.910</td>\n",
              "      <td>23.060</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1948.0</td>\n",
              "      <td>23.680</td>\n",
              "      <td>23.670</td>\n",
              "      <td>23.500</td>\n",
              "      <td>23.820</td>\n",
              "      <td>24.010</td>\n",
              "      <td>24.150</td>\n",
              "      <td>24.400</td>\n",
              "      <td>24.430</td>\n",
              "      <td>24.360</td>\n",
              "      <td>24.310</td>\n",
              "      <td>24.160</td>\n",
              "      <td>24.050</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949.0</td>\n",
              "      <td>24.010</td>\n",
              "      <td>23.910</td>\n",
              "      <td>23.910</td>\n",
              "      <td>23.920</td>\n",
              "      <td>23.910</td>\n",
              "      <td>23.920</td>\n",
              "      <td>23.700</td>\n",
              "      <td>23.700</td>\n",
              "      <td>23.750</td>\n",
              "      <td>23.670</td>\n",
              "      <td>23.700</td>\n",
              "      <td>23.610</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1950.0</td>\n",
              "      <td>23.510</td>\n",
              "      <td>23.610</td>\n",
              "      <td>23.640</td>\n",
              "      <td>23.650</td>\n",
              "      <td>23.770</td>\n",
              "      <td>23.880</td>\n",
              "      <td>24.070</td>\n",
              "      <td>24.200</td>\n",
              "      <td>24.340</td>\n",
              "      <td>24.500</td>\n",
              "      <td>24.600</td>\n",
              "      <td>24.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1951.0</td>\n",
              "      <td>25.380</td>\n",
              "      <td>25.830</td>\n",
              "      <td>25.880</td>\n",
              "      <td>25.920</td>\n",
              "      <td>25.990</td>\n",
              "      <td>25.930</td>\n",
              "      <td>25.910</td>\n",
              "      <td>25.860</td>\n",
              "      <td>26.030</td>\n",
              "      <td>26.160</td>\n",
              "      <td>26.320</td>\n",
              "      <td>26.470</td>\n",
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>2018.0</td>\n",
              "      <td>248.743</td>\n",
              "      <td>249.439</td>\n",
              "      <td>249.581</td>\n",
              "      <td>250.146</td>\n",
              "      <td>250.779</td>\n",
              "      <td>251.118</td>\n",
              "      <td>251.323</td>\n",
              "      <td>251.749</td>\n",
              "      <td>252.239</td>\n",
              "      <td>252.862</td>\n",
              "      <td>252.657</td>\n",
              "      <td>252.551</td>\n",
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              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2019.0</td>\n",
              "      <td>252.470</td>\n",
              "      <td>253.135</td>\n",
              "      <td>254.273</td>\n",
              "      <td>255.163</td>\n",
              "      <td>255.325</td>\n",
              "      <td>255.361</td>\n",
              "      <td>255.900</td>\n",
              "      <td>256.179</td>\n",
              "      <td>256.596</td>\n",
              "      <td>257.305</td>\n",
              "      <td>257.788</td>\n",
              "      <td>258.263</td>\n",
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              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>258.682</td>\n",
              "      <td>259.007</td>\n",
              "      <td>258.165</td>\n",
              "      <td>256.094</td>\n",
              "      <td>255.944</td>\n",
              "      <td>257.217</td>\n",
              "      <td>258.543</td>\n",
              "      <td>259.580</td>\n",
              "      <td>260.190</td>\n",
              "      <td>260.352</td>\n",
              "      <td>260.721</td>\n",
              "      <td>261.564</td>\n",
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              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>2021.0</td>\n",
              "      <td>262.200</td>\n",
              "      <td>263.346</td>\n",
              "      <td>265.028</td>\n",
              "      <td>266.727</td>\n",
              "      <td>268.599</td>\n",
              "      <td>270.955</td>\n",
              "      <td>272.184</td>\n",
              "      <td>273.092</td>\n",
              "      <td>274.214</td>\n",
              "      <td>276.590</td>\n",
              "      <td>278.524</td>\n",
              "      <td>280.126</td>\n",
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              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2022.0</td>\n",
              "      <td>281.933</td>\n",
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              "      <td>288.663</td>\n",
              "      <td>291.474</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "<p>76 rows × 13 columns</p>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inflation_cpi_df = inflation_cpi_df.melt(id_vars=['Year'], var_name='Month', value_name='Value')\n"
      ],
      "metadata": {
        "id": "lfECfmgO0lDU"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inflation_cpi_df['Month'] = inflation_cpi_df['Month'].apply(lambda x: months[x])"
      ],
      "metadata": {
        "id": "_pXLrzpL-F8a"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "inflation_cpi_df"
      ],
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        "id": "CM2Gt891_9k3",
        "outputId": "11511689-7d31-4e9c-ecb4-e91b2ed75d5e"
      },
      "execution_count": 23,
      "outputs": [
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              "       Year  Month    Value\n",
              "0    1947.0      1   21.480\n",
              "1    1948.0      1   23.680\n",
              "2    1949.0      1   24.010\n",
              "3    1950.0      1   23.510\n",
              "4    1951.0      1   25.380\n",
              "..      ...    ...      ...\n",
              "907  2018.0     12  252.551\n",
              "908  2019.0     12  258.263\n",
              "909  2020.0     12  261.564\n",
              "910  2021.0     12  280.126\n",
              "911  2022.0     12      NaN\n",
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        "inflation_cpi_df = inflation_cpi_df.sort_values(by=['Year', 'Month'])\n",
        "inflation_cpi_df = inflation_cpi_df.reset_index()\n",
        "inflation_cpi_df = inflation_cpi_df.drop(['index'], axis = 1)\n",
        "inflation_cpi_df.rename(columns = {'Value':'Inflation/CPI'}, inplace = True)"
      ],
      "metadata": {
        "id": "BYsctJ1jBDEE"
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      "execution_count": 24,
      "outputs": []
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      "execution_count": 25,
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              "       Year  Month  Inflation/CPI\n",
              "0    1947.0      1          21.48\n",
              "1    1947.0      2          21.62\n",
              "2    1947.0      3          22.00\n",
              "3    1947.0      4          22.00\n",
              "4    1947.0      5          21.95\n",
              "..      ...    ...            ...\n",
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              "908  2022.0      9            NaN\n",
              "909  2022.0     10            NaN\n",
              "910  2022.0     11            NaN\n",
              "911  2022.0     12            NaN\n",
              "\n",
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    {
      "cell_type": "code",
      "source": [
        "#Market Capitalization to GDP Ratio Data (Buffett Indicator)\n",
        "buffett_df #from Jan 1971 and has every 3 months and 205x2"
      ],
      "metadata": {
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        "id": "DvdGCoEiDTB2",
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      },
      "execution_count": 26,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "           DATE  WILL5000INDFC_NBD20071001_GDP_NBD20071001\n",
              "0    1971-01-01                                   0.254315\n",
              "1    1971-04-01                                   0.262771\n",
              "2    1971-07-01                                   0.252699\n",
              "3    1971-10-01                                   0.250019\n",
              "4    1972-01-01                                   0.269344\n",
              "..          ...                                        ...\n",
              "200  2021-01-01                                   2.357019\n",
              "201  2021-04-01                                   2.450278\n",
              "202  2021-07-01                                   2.528974\n",
              "203  2021-10-01                                   2.532661\n",
              "204  2022-01-01                                   2.371155\n",
              "\n",
              "[205 rows x 2 columns]"
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              "      <th>0</th>\n",
              "      <td>1971-01-01</td>\n",
              "      <td>0.254315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1971-04-01</td>\n",
              "      <td>0.262771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1971-07-01</td>\n",
              "      <td>0.252699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1971-10-01</td>\n",
              "      <td>0.250019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1972-01-01</td>\n",
              "      <td>0.269344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2021-01-01</td>\n",
              "      <td>2.357019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>2.450278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>2.528974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>2.532661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>2022-01-01</td>\n",
              "      <td>2.371155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the 'DATE' column into 'Month' and 'Year' Columns\n",
        "buffett_df['Month'] = buffett_df['DATE'].apply(lambda x:x.split('-')[1])\n",
        "buffett_df['Year'] = buffett_df['DATE'].apply(lambda x:x.split('-')[0])\n",
        "buffett_df = buffett_df.drop(['DATE'], axis = 1)\n",
        "buffett_df.rename(columns = {'WILL5000INDFC_NBD20071001_GDP_NBD20071001':'Market_Cap_to_GDP'}, inplace = True)"
      ],
      "metadata": {
        "id": "nIVxUR1S61ru"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "buffett_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "ZTB2zb-J8MSg",
        "outputId": "421c0926-2ef9-4c07-bda1-7806d13999ec"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Market_Cap_to_GDP Month  Year\n",
              "0             0.254315    01  1971\n",
              "1             0.262771    04  1971\n",
              "2             0.252699    07  1971\n",
              "3             0.250019    10  1971\n",
              "4             0.269344    01  1972\n",
              "..                 ...   ...   ...\n",
              "200           2.357019    01  2021\n",
              "201           2.450278    04  2021\n",
              "202           2.528974    07  2021\n",
              "203           2.532661    10  2021\n",
              "204           2.371155    01  2022\n",
              "\n",
              "[205 rows x 3 columns]"
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              "      <th>Market_Cap_to_GDP</th>\n",
              "      <th>Month</th>\n",
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              "      <td>1971</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.250019</td>\n",
              "      <td>10</td>\n",
              "      <td>1971</td>\n",
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              "    <tr>\n",
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              "      <td>04</td>\n",
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              "      <th>202</th>\n",
              "      <td>2.528974</td>\n",
              "      <td>07</td>\n",
              "      <td>2021</td>\n",
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              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>2.532661</td>\n",
              "      <td>10</td>\n",
              "      <td>2021</td>\n",
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              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>2.371155</td>\n",
              "      <td>01</td>\n",
              "      <td>2022</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "          const element = document.querySelector('#df-590a2579-5be0-4e5f-bbbb-cbc70193290b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Unemployment Rate Data\n",
        "unrate_df #from Jan 1948 and has months and 894x2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "4pyJVJV8DXW_",
        "outputId": "0475dd32-bfab-4aaa-fed8-5a58d060cbd4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           DATE  UNRATE\n",
              "0    1948-01-01     3.4\n",
              "1    1948-02-01     3.8\n",
              "2    1948-03-01     4.0\n",
              "3    1948-04-01     3.9\n",
              "4    1948-05-01     3.5\n",
              "..          ...     ...\n",
              "889  2022-02-01     3.8\n",
              "890  2022-03-01     3.6\n",
              "891  2022-04-01     3.6\n",
              "892  2022-05-01     3.6\n",
              "893  2022-06-01     3.6\n",
              "\n",
              "[894 rows x 2 columns]"
            ],
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>1948-04-01</td>\n",
              "      <td>3.9</td>\n",
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              "      <th>4</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting the 'DATE' column into 'Month' and 'Year' Columns\n",
        "unrate_df['Month'] = unrate_df['DATE'].apply(lambda x:x.split('-')[1])\n",
        "unrate_df['Year'] = unrate_df['DATE'].apply(lambda x:x.split('-')[0])\n",
        "unrate_df = unrate_df.drop(['DATE'], axis = 1)"
      ],
      "metadata": {
        "id": "naXPowCH8a0C"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unrate_df"
      ],
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        "colab": {
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        "outputId": "7bf6f1c2-bf9f-41f5-f9f0-400fe8266f64"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     UNRATE Month  Year\n",
              "0       3.4    01  1948\n",
              "1       3.8    02  1948\n",
              "2       4.0    03  1948\n",
              "3       3.9    04  1948\n",
              "4       3.5    05  1948\n",
              "..      ...   ...   ...\n",
              "889     3.8    02  2022\n",
              "890     3.6    03  2022\n",
              "891     3.6    04  2022\n",
              "892     3.6    05  2022\n",
              "893     3.6    06  2022\n",
              "\n",
              "[894 rows x 3 columns]"
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              "<p>894 rows × 3 columns</p>\n",
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          },
          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "#Merging the datasets\n"
      ],
      "metadata": {
        "id": "mJvdEHOh80mD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting all 'Month' and 'Year' Columns to type int from string\n",
        "inflation_cpi_df['Year']=inflation_cpi_df['Year'].astype(int)\n",
        "inflation_cpi_df['Month']=inflation_cpi_df['Month'].astype(int)\n",
        "unrate_df['Year']=unrate_df['Year'].astype(int)\n",
        "unrate_df['Month']=unrate_df['Month'].astype(int)\n",
        "yc_df['Year']=yc_df['Year'].astype(int)\n",
        "yc_df['Month']=yc_df['Month'].astype(int)\n",
        "cci_df['Year']=cci_df['Year'].astype(int)\n",
        "cci_df['Month']=cci_df['Month'].astype(int)\n",
        "fedfunds_df['Year']=fedfunds_df['Year'].astype(int)\n",
        "fedfunds_df['Month']=fedfunds_df['Month'].astype(int)\n",
        "buffett_df['Year']=buffett_df['Year'].astype(int)\n",
        "buffett_df['Month']=buffett_df['Month'].astype(int)"
      ],
      "metadata": {
        "id": "iQcvJvCeEN8u"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_frames = [inflation_cpi_df,unrate_df,fedfunds_df, cci_df,buffett_df,yc_df]\n",
        "df = reduce(lambda left,right: pd.merge(left,right,on=['Month','Year'], how='outer'), data_frames)\n"
      ],
      "metadata": {
        "id": "HHaSAje-Fbvo"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mCx7rUeuGuxr"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####According to Forbes (https://www.forbes.com/sites/billconerly/2022/05/12/the-yield-curve-predicts-recession-sometimes-but-not-today/?sh=7dd995365443), \"We can measure the yield curve by comparing any two maturities, but the most common measure compares the 10-year Treasury bond to the 3-month Treasury bill. \"\n",
        "##Thus I will be removing all other yield curve data except the data that corresponds to the 10-year Treasury bond and the 3-month Treasury bill."
      ],
      "metadata": {
        "id": "8Fn3XI0ZnT3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Avg 6 Mo','Avg 1 Yr','Avg 2 Yr','Avg 3 Yr','Avg 5 Yr','Avg 7 Yr','Avg 20 Yr','Avg 30 Yr'], axis = 1)\n"
      ],
      "metadata": {
        "id": "O637T7VJn6jT"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling Buffet Indicator, which has values after 3 month gaps. Giving those '3 month gaps' the value of the month prior to that gap.\n",
        "for i in range(288, len(df)): #because all indices less than 288 are NaN values for the Buffett Indicator. \n",
        "  if math.isnan(df['Market_Cap_to_GDP'][i]):\n",
        "    df['Market_Cap_to_GDP'][i] = df['Market_Cap_to_GDP'][i-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UegoSGxAi2Ka",
        "outputId": "d7f94fff-ab25-40c5-fcac-2bc51d87063f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 419
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        "id": "He5esD_OlaNA",
        "outputId": "b496e2a3-26cd-48fc-dc6c-3d73f7a46b7f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year  Month  Inflation/CPI  UNRATE  FEDFUNDS  CCI  Market_Cap_to_GDP  \\\n",
              "0    1947      1          21.48     NaN       NaN  NaN                NaN   \n",
              "1    1947      2          21.62     NaN       NaN  NaN                NaN   \n",
              "2    1947      3          22.00     NaN       NaN  NaN                NaN   \n",
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              "..    ...    ...            ...     ...       ...  ...                ...   \n",
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              "913  2022     10            NaN     NaN       NaN  NaN           2.371155   \n",
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              "915  2022     12            NaN     NaN       NaN  NaN           2.371155   \n",
              "\n",
              "     Avg 3 Mo  Avg 10 Yr  \n",
              "0         NaN        NaN  \n",
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              "3         NaN        NaN  \n",
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              "..        ...        ...  \n",
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              "912       NaN        NaN  \n",
              "913       NaN        NaN  \n",
              "914       NaN        NaN  \n",
              "915       NaN        NaN  \n",
              "\n",
              "[916 rows x 9 columns]"
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    {
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        "#Using only the difference between the 10-year Treasury bond and the 3-month Treasury bill as only that is important.\n",
        "df['Yield_Curve_Diff'] = df.apply(lambda x: x['Avg 3 Mo'] - x['Avg 10 Yr'], axis=1)\n",
        "df = df.drop(['Avg 3 Mo','Avg 10 Yr'], axis = 1)"
      ],
      "metadata": {
        "id": "lHuO4ESEeyz_"
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      "execution_count": 47,
      "outputs": []
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        "id": "NibxCHRcgVr8",
        "outputId": "734e5a52-39ab-4582-e825-2df19b704cf6"
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      "execution_count": 48,
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              "     Year  Month  Inflation/CPI  UNRATE  FEDFUNDS  CCI  Market_Cap_to_GDP  \\\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Handling NaN Values "
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      "metadata": {
        "id": "G45QJm5zbu38"
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    {
      "cell_type": "markdown",
      "source": [
        ""
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    {
      "cell_type": "code",
      "source": [
        "final_df=df.dropna().reset_index(drop=True)\n",
        "final_df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "zOv91o9JlVOJ",
        "outputId": "e08d7fc9-56fd-45ef-d644-91382d6d489b"
      },
      "execution_count": 49,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year  Month  Inflation/CPI  UNRATE  FEDFUNDS     CCI  Market_Cap_to_GDP  \\\n",
              "0    1990      1        127.500     5.4      8.23  100.63           0.384150   \n",
              "1    1990      2        128.000     5.3      8.24  100.61           0.384150   \n",
              "2    1990      3        128.600     5.2      8.28  100.68           0.384150   \n",
              "3    1990      4        128.900     5.4      8.26  100.74           0.392923   \n",
              "4    1990      5        129.100     5.4      8.18  100.60           0.392923   \n",
              "..    ...    ...            ...     ...       ...     ...                ...   \n",
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              "388  2022      5        291.474     3.6      0.77   96.60           2.371155   \n",
              "\n",
              "     Yield_Curve_Diff  \n",
              "0           -0.308571  \n",
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              "..                ...  \n",
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              "385         -1.619474  \n",
              "386         -1.680435  \n",
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              "      <th>UNRATE</th>\n",
              "      <th>FEDFUNDS</th>\n",
              "      <th>CCI</th>\n",
              "      <th>Market_Cap_to_GDP</th>\n",
              "      <th>Yield_Curve_Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1990</td>\n",
              "      <td>1</td>\n",
              "      <td>127.500</td>\n",
              "      <td>5.4</td>\n",
              "      <td>8.23</td>\n",
              "      <td>100.63</td>\n",
              "      <td>0.384150</td>\n",
              "      <td>-0.308571</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1990</td>\n",
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              "      <td>5.3</td>\n",
              "      <td>8.24</td>\n",
              "      <td>100.61</td>\n",
              "      <td>0.384150</td>\n",
              "      <td>-0.471053</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1990</td>\n",
              "      <td>3</td>\n",
              "      <td>128.600</td>\n",
              "      <td>5.2</td>\n",
              "      <td>8.28</td>\n",
              "      <td>100.68</td>\n",
              "      <td>0.384150</td>\n",
              "      <td>-0.418636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1990</td>\n",
              "      <td>4</td>\n",
              "      <td>128.900</td>\n",
              "      <td>5.4</td>\n",
              "      <td>8.26</td>\n",
              "      <td>100.74</td>\n",
              "      <td>0.392923</td>\n",
              "      <td>-0.745000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1990</td>\n",
              "      <td>5</td>\n",
              "      <td>129.100</td>\n",
              "      <td>5.4</td>\n",
              "      <td>8.18</td>\n",
              "      <td>100.60</td>\n",
              "      <td>0.392923</td>\n",
              "      <td>-0.751364</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>384</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>281.933</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>97.46</td>\n",
              "      <td>2.371155</td>\n",
              "      <td>-1.618500</td>\n",
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              "    <tr>\n",
              "      <th>385</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
              "      <td>284.182</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0.08</td>\n",
              "      <td>97.13</td>\n",
              "      <td>2.371155</td>\n",
              "      <td>-1.619474</td>\n",
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              "    <tr>\n",
              "      <th>386</th>\n",
              "      <td>2022</td>\n",
              "      <td>3</td>\n",
              "      <td>287.708</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.20</td>\n",
              "      <td>96.91</td>\n",
              "      <td>2.371155</td>\n",
              "      <td>-1.680435</td>\n",
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              "    <tr>\n",
              "      <th>387</th>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>288.663</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.33</td>\n",
              "      <td>96.84</td>\n",
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              "      <td>-1.984000</td>\n",
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              "      <td>3.6</td>\n",
              "      <td>0.77</td>\n",
              "      <td>96.60</td>\n",
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              "      <td>-1.905238</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>389 rows × 8 columns</p>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a column for labels for Recession in the US\n",
        "#1 indicates recession was there, 0 indicates was not there\n",
        "#Source: Wikipedia: https://en.wikipedia.org/wiki/List_of_recessions_in_the_United_States \n",
        "\n",
        "final_df['Recession_US'] = final_df.apply(lambda x: 1 if ((x['Year'] == 1990)and(x['Month']>=7)) or ((x['Year'] == 1991)and (x['Month']<=3)) or\n",
        "((x['Year']==2001)and (x['Month']>=3)and (x['Month']<=11)) or\n",
        "((x['Year']==2007)and (x['Month']==12)) or \n",
        "(x['Year']==2008) or \n",
        "((x['Year']==2009)and (x['Month']<=6)) or\n",
        "((x['Year']==2020)and (x['Month']>=2)and(x['Month']<=4))\n",
        "else 0, axis=1)\n"
      ],
      "metadata": {
        "id": "a7Ae0kK4tGyF"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "Wa5jorCT1hLq",
        "outputId": "b65ce842-34ed-4e6d-dd6e-51a344649b32"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year  Month  Inflation/CPI  UNRATE  FEDFUNDS     CCI  Market_Cap_to_GDP  \\\n",
              "0    1990      1        127.500     5.4      8.23  100.63           0.384150   \n",
              "1    1990      2        128.000     5.3      8.24  100.61           0.384150   \n",
              "2    1990      3        128.600     5.2      8.28  100.68           0.384150   \n",
              "3    1990      4        128.900     5.4      8.26  100.74           0.392923   \n",
              "4    1990      5        129.100     5.4      8.18  100.60           0.392923   \n",
              "..    ...    ...            ...     ...       ...     ...                ...   \n",
              "384  2022      1        281.933     4.0      0.08   97.46           2.371155   \n",
              "385  2022      2        284.182     3.8      0.08   97.13           2.371155   \n",
              "386  2022      3        287.708     3.6      0.20   96.91           2.371155   \n",
              "387  2022      4        288.663     3.6      0.33   96.84           2.371155   \n",
              "388  2022      5        291.474     3.6      0.77   96.60           2.371155   \n",
              "\n",
              "     Yield_Curve_Diff  Recession_US  \n",
              "0           -0.308571             0  \n",
              "1           -0.471053             0  \n",
              "2           -0.418636             0  \n",
              "3           -0.745000             0  \n",
              "4           -0.751364             0  \n",
              "..                ...           ...  \n",
              "384         -1.618500             0  \n",
              "385         -1.619474             0  \n",
              "386         -1.680435             0  \n",
              "387         -1.984000             0  \n",
              "388         -1.905238             0  \n",
              "\n",
              "[389 rows x 9 columns]"
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e36f2231-990e-4a20-87f5-9b19fd85f46e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e36f2231-990e-4a20-87f5-9b19fd85f46e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    }
  ]
}