data/data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path  \n",
    "import streamlit as st\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "interestrate = pd.read_csv(\"DFF(Federal Funds Effective Rate).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1827 entries, 0 to 1826\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DATE    1827 non-null   object \n",
      " 1   DFF     1827 non-null   float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 28.7+ KB\n"
     ]
    }
   ],
   "source": [
    "interestrate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>DFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1827.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.070022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.887853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DFF\n",
       "count  1827.000000\n",
       "mean      1.070022\n",
       "std       0.887853\n",
       "min       0.040000\n",
       "25%       0.090000\n",
       "50%       1.160000\n",
       "75%       1.905000\n",
       "max       2.450000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interestrate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>DATE</th>\n",
       "      <th>DFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1817</th>\n",
       "      <td>2022-02-19</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>2022-02-20</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1819</th>\n",
       "      <td>2022-02-21</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>2022-02-23</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>2022-02-26</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>2022-02-27</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE   DFF\n",
       "1817  2022-02-19  0.08\n",
       "1818  2022-02-20  0.08\n",
       "1819  2022-02-21  0.08\n",
       "1820  2022-02-22  0.08\n",
       "1821  2022-02-23  0.08\n",
       "1822  2022-02-24  0.08\n",
       "1823  2022-02-25  0.08\n",
       "1824  2022-02-26  0.08\n",
       "1825  2022-02-27  0.08\n",
       "1826  2022-02-28  0.08"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interestrate.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "interestrate.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "interestrate.index = pd.to_datetime(interestrate.index)\n",
    "interestrate_new = interestrate.resample('Q').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interestrate_new.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>DFF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>0.779375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>0.947363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>1.153696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>1.204674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>1.447000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>1.736923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>1.926196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>2.220109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>2.401778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2.397363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>2.192174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1.645652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>1.254945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>0.058791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>0.092609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>0.088804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>0.076889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0.068242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>0.079457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>0.079661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DFF\n",
       "DATE                \n",
       "2017-03-31  0.779375\n",
       "2017-06-30  0.947363\n",
       "2017-09-30  1.153696\n",
       "2017-12-31  1.204674\n",
       "2018-03-31  1.447000\n",
       "2018-06-30  1.736923\n",
       "2018-09-30  1.926196\n",
       "2018-12-31  2.220109\n",
       "2019-03-31  2.401778\n",
       "2019-06-30  2.397363\n",
       "2019-09-30  2.192174\n",
       "2019-12-31  1.645652\n",
       "2020-03-31  1.254945\n",
       "2020-06-30  0.058791\n",
       "2020-09-30  0.092609\n",
       "2020-12-31  0.088804\n",
       "2021-03-31  0.076889\n",
       "2021-06-30  0.068242\n",
       "2021-09-30  0.090000\n",
       "2021-12-31  0.079457\n",
       "2022-03-31  0.079661"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interestrate_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "income = pd.read_csv(\"MEDIANINCOME(HOUSEHOLD).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of           DATE  MEHOINUSA672N\n",
       "0   1984-01-01          53337\n",
       "1   1985-01-01          54334\n",
       "2   1986-01-01          56291\n",
       "3   1987-01-01          56964\n",
       "4   1988-01-01          57433\n",
       "5   1989-01-01          58425\n",
       "6   1990-01-01          57677\n",
       "7   1991-01-01          55992\n",
       "8   1992-01-01          55559\n",
       "9   1993-01-01          55263\n",
       "10  1994-01-01          55905\n",
       "11  1995-01-01          57655\n",
       "12  1996-01-01          58494\n",
       "13  1997-01-01          59697\n",
       "14  1998-01-01          61891\n",
       "15  1999-01-01          63423\n",
       "16  2000-01-01          63292\n",
       "17  2001-01-01          61889\n",
       "18  2002-01-01          61190\n",
       "19  2003-01-01          61113\n",
       "20  2004-01-01          60901\n",
       "21  2005-01-01          61553\n",
       "22  2006-01-01          62033\n",
       "23  2007-01-01          62865\n",
       "24  2008-01-01          60624\n",
       "25  2009-01-01          60200\n",
       "26  2010-01-01          58627\n",
       "27  2011-01-01          57732\n",
       "28  2012-01-01          57623\n",
       "29  2013-01-01          59640\n",
       "30  2014-01-01          58725\n",
       "31  2015-01-01          61748\n",
       "32  2016-01-01          63683\n",
       "33  2017-01-01          64557\n",
       "34  2018-01-01          65127\n",
       "35  2019-01-01          69560\n",
       "36  2020-01-01          67521>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>MEHOINUSA672N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59960.621622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3617.661394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>53337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59697.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69560.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MEHOINUSA672N\n",
       "count      37.000000\n",
       "mean    59960.621622\n",
       "std      3617.661394\n",
       "min     53337.000000\n",
       "25%     57623.000000\n",
       "50%     59697.000000\n",
       "75%     61891.000000\n",
       "max     69560.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_20 = income.tail(21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_20.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/incomenew.csv')  \n",
    "# income_20.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "income_20.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>MEHOINUSA672N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>63292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>61889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>61190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>61113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>60901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>61553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>62033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>62865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>60624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>60200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>58627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>57732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>57623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>59640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>58725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>61748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>63683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>64557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>65127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>69560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>67521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MEHOINUSA672N\n",
       "DATE                     \n",
       "2000-01-01          63292\n",
       "2001-01-01          61889\n",
       "2002-01-01          61190\n",
       "2003-01-01          61113\n",
       "2004-01-01          60901\n",
       "2005-01-01          61553\n",
       "2006-01-01          62033\n",
       "2007-01-01          62865\n",
       "2008-01-01          60624\n",
       "2009-01-01          60200\n",
       "2010-01-01          58627\n",
       "2011-01-01          57732\n",
       "2012-01-01          57623\n",
       "2013-01-01          59640\n",
       "2014-01-01          58725\n",
       "2015-01-01          61748\n",
       "2016-01-01          63683\n",
       "2017-01-01          64557\n",
       "2018-01-01          65127\n",
       "2019-01-01          69560\n",
       "2020-01-01          67521"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage = pd.read_csv('MORTGAGE30US.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of             DATE  MORTGAGE30US\n",
       "0     1971-04-02          7.33\n",
       "1     1971-04-09          7.31\n",
       "2     1971-04-16          7.31\n",
       "3     1971-04-23          7.31\n",
       "4     1971-04-30          7.29\n",
       "...          ...           ...\n",
       "2652  2022-01-27          3.55\n",
       "2653  2022-02-03          3.55\n",
       "2654  2022-02-10          3.69\n",
       "2655  2022-02-17          3.92\n",
       "2656  2022-02-24          3.89\n",
       "\n",
       "[2657 rows x 2 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgage.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2657.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.789827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.291404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.630000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MORTGAGE30US\n",
       "count   2657.000000\n",
       "mean       7.789827\n",
       "std        3.291404\n",
       "min        2.650000\n",
       "25%        5.080000\n",
       "50%        7.450000\n",
       "75%        9.590000\n",
       "max       18.630000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgage.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1971-04-02</td>\n",
       "      <td>7.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971-04-09</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1971-04-16</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1971-04-23</td>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1971-04-30</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>2022-02-10</td>\n",
       "      <td>3.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2655</th>\n",
       "      <td>2022-02-17</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>2022-02-24</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2657 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  MORTGAGE30US\n",
       "0     1971-04-02          7.33\n",
       "1     1971-04-09          7.31\n",
       "2     1971-04-16          7.31\n",
       "3     1971-04-23          7.31\n",
       "4     1971-04-30          7.29\n",
       "...          ...           ...\n",
       "2652  2022-01-27          3.55\n",
       "2653  2022-02-03          3.55\n",
       "2654  2022-02-10          3.69\n",
       "2655  2022-02-17          3.92\n",
       "2656  2022-02-24          3.89\n",
       "\n",
       "[2657 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage = mortgage.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage.index = pd.to_datetime(mortgage.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "quaterly_mort = mortgage.resample('Q').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage_cleaned= quaterly_mort.tail(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage_cleaned = mortgage_cleaned.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index  MORTGAGE30US\n",
       "DATE                           \n",
       "2002-06-30  False         False\n",
       "2002-09-30  False         False\n",
       "2002-12-31  False         False\n",
       "2003-03-31  False         False\n",
       "2003-06-30  False         False\n",
       "...           ...           ...\n",
       "2021-03-31  False         False\n",
       "2021-06-30  False         False\n",
       "2021-09-30  False         False\n",
       "2021-12-31  False         False\n",
       "2022-03-31  False         False\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgage_cleaned.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mortgage_cleaned.drop('index', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/mortgage_cleaned.csv')  \n",
    "# mortgage_cleaned.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mortgage_cleaned.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = pd.read_csv('POPULATIONTHM.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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>POPTHM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>757.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>255589.434610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47063.449383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>175818.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>214538.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250285.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>298170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>332684.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              POPTHM\n",
       "count     757.000000\n",
       "mean   255589.434610\n",
       "std     47063.449383\n",
       "min    175818.000000\n",
       "25%    214538.000000\n",
       "50%    250285.000000\n",
       "75%    298170.000000\n",
       "max    332684.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 757 entries, 0 to 756\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DATE    757 non-null    object \n",
      " 1   POPTHM  757 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 12.0+ KB\n"
     ]
    }
   ],
   "source": [
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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>POPTHM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>175818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>176044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>176274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>176503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>176723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>332403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>332513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>332598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>332640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>332684.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              POPTHM\n",
       "DATE                \n",
       "1959-01-01  175818.0\n",
       "1959-02-01  176044.0\n",
       "1959-03-01  176274.0\n",
       "1959-04-01  176503.0\n",
       "1959-05-01  176723.0\n",
       "...              ...\n",
       "2021-09-01  332403.0\n",
       "2021-10-01  332513.0\n",
       "2021-11-01  332598.0\n",
       "2021-12-01  332640.0\n",
       "2022-01-01  332684.0\n",
       "\n",
       "[757 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "population.index = pd.to_datetime(population.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POPTHM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>175818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>176044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>176274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>176503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>176723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>332403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>332513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>332598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>332640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>332684.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              POPTHM\n",
       "DATE                \n",
       "1959-01-01  175818.0\n",
       "1959-02-01  176044.0\n",
       "1959-03-01  176274.0\n",
       "1959-04-01  176503.0\n",
       "1959-05-01  176723.0\n",
       "...              ...\n",
       "2021-09-01  332403.0\n",
       "2021-10-01  332513.0\n",
       "2021-11-01  332598.0\n",
       "2021-12-01  332640.0\n",
       "2022-01-01  332684.0\n",
       "\n",
       "[757 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_q = population.resample('Q').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_q_cleaned = population_q.tail(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_q_cleaned = population_q_cleaned.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POPTHM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>287574.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>288302.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>289006.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>289609.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>290252.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>331948.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>332021.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>332296.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>332583.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>332684.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               POPTHM\n",
       "DATE                 \n",
       "2002-06-30  287574.00\n",
       "2002-09-30  288302.67\n",
       "2002-12-31  289006.67\n",
       "2003-03-31  289609.00\n",
       "2003-06-30  290252.67\n",
       "...               ...\n",
       "2021-03-31  331948.67\n",
       "2021-06-30  332021.00\n",
       "2021-09-30  332296.67\n",
       "2021-12-31  332583.67\n",
       "2022-03-31  332684.00\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_q_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_q_cleaned['POPTHM'] = population_q_cleaned['POPTHM'].apply(lambda x: x*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POPTHM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>287574000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>288302670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>289006670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>289609000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>290252670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>331948670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>332021000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>332296670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>332583670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>332684000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 POPTHM\n",
       "DATE                   \n",
       "2002-06-30  287574000.0\n",
       "2002-09-30  288302670.0\n",
       "2002-12-31  289006670.0\n",
       "2003-03-31  289609000.0\n",
       "2003-06-30  290252670.0\n",
       "...                 ...\n",
       "2021-03-31  331948670.0\n",
       "2021-06-30  332021000.0\n",
       "2021-09-30  332296670.0\n",
       "2021-12-31  332583670.0\n",
       "2022-03-31  332684000.0\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_q_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/population_q_cleaned.csv')  \n",
    "# population_q_cleaned.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "population_q_cleaned.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp = pd.read_csv('UNEMPRATE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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>UNRATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.758943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.697299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           UNRATE\n",
       "count  889.000000\n",
       "mean     5.758943\n",
       "std      1.697299\n",
       "min      2.500000\n",
       "25%      4.500000\n",
       "50%      5.600000\n",
       "75%      6.800000\n",
       "max     14.700000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 889 entries, 0 to 888\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DATE    889 non-null    object \n",
      " 1   UNRATE  889 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 14.0+ KB\n"
     ]
    }
   ],
   "source": [
    "unemp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp.index = pd.to_datetime(unemp.index)\n",
    "unemp_q = unemp.resample('Q').mean()\n",
    "unemp_q = unemp_q.tail(80)\n",
    "unemp_q = unemp_q.round(decimals=2)\n",
    "# filepath = Path('cleaned/unemp.csv')  \n",
    "# unemp_q.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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>UNRATE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>5.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>5.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            UNRATE\n",
       "DATE              \n",
       "2002-06-30    5.83\n",
       "2002-09-30    5.73\n",
       "2002-12-31    5.87\n",
       "2003-03-31    5.87\n",
       "2003-06-30    6.13\n",
       "...            ...\n",
       "2021-03-31    6.20\n",
       "2021-06-30    5.90\n",
       "2021-09-30    5.10\n",
       "2021-12-31    4.23\n",
       "2022-03-31    4.00\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemp_q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unemp_q.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "debt = pd.read_csv('public_debt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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>GFDEBTN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.428303e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.831763e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.662216e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.908656e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.414766e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.942955e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.961722e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            GFDEBTN\n",
       "count  8.800000e+01\n",
       "mean   1.428303e+07\n",
       "std    6.831763e+06\n",
       "min    5.662216e+06\n",
       "25%    7.908656e+06\n",
       "50%    1.414766e+07\n",
       "75%    1.942955e+07\n",
       "max    2.961722e+07"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 88 entries, 0 to 87\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   DATE     88 non-null     object \n",
      " 1   GFDEBTN  88 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "debt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "debt.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "debt.index = pd.to_datetime(debt.index)\n",
    "debt_q = debt.resample('Q').mean()\n",
    "debt_q = debt_q.tail(80)\n",
    "debt_q = debt_q.round(decimals=2)\n",
    "# filepath = Path('cleaned/unemp.csv')  \n",
    "# unemp_q.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/debt_q.csv')  \n",
    "# debt_q.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pheQVlfHytaeQ0iYu1CWFNQW6iISdsopKXl2ynb99uJ4tew7w+A/TGdi5TajLCnsKdBEJG6Xllby8KJO/fbSezL1FDOzUhicnj+S0/imhLi0iVBvoZtYNeAboADjwmLv/+bBtDPgzcC5wAJji7ouDX66IRKv12QVc+9wi1mcXMKxbIndfOIjx/VMa3WjPuqjJFXo58HN3X2xmrYFFZva+u686ZJtzgL6Bx0nAI4H/iohU690VO7n1paU0b9qEJyanK8iPU7WB7u5ZQFbg+X4zWw10AQ4N9IuAZ9zdgc/NLNHMOgVeKyKNjLvz/BdbeW/lLtK6JTKmT3vSuiV+YxRnRaXzwPtrePjDDQzrlsijPxhBp4T4EFUd+WrVhm5mqcBwYP5hq7oA2w75OTOw7GuBbmZTgakA3bt3r2WpIhIJissq+PVrK3h5USZdEuOZuy6HP/97HXGxTUjrlkiLZk356tp71/5iVmzPZ8LIbtx14SDiYtUlsS5qHOhm1gp4BbjF3fOP583c/THgMYD09HQ/nn2ISPjavq+Iac8tYllmHjef3pebT+/L/uJy5m/KZd6GXJZl7qOwpATHca+6Bdy9lwxhwihd4AVDjQLdzGKpCvPp7j7rCJtsB7od8nPXwDIRaQQqKp1/rd7F7bOWU1JeyeOT0jlzYAcAElrEctagjpw1qGOIq4x+NenlYsATwGp3f+Aom70B3GBmM6n6MjRP7eci0W9jTgEvLcpk1uJMduWX0Du5JY9NSqe3JsoKiZpcoY8BfggsN7OMwLLbge4A7v4o8DZVXRbXU9Vt8aqgVyoiIbe7oIRFW/ayaMte5m/MZWlmHjFNjHEnJHPXBV05fUCHRj19bajVpJfLJ8Ax+w8FerdcH6yiRCS8/GvVLu5990vWZxcA0CymCUO6JvCrc/pzyfAuGpIfJjRSVESOKq+ojHveXMUrizPp16E1vzqnP+k92jK4S4J6pIQhBbqIHNGctTn86pVlZO8v4cbxfbhxfF81p4Q5BbqIfMP0+Vu449UV9ElpxawfnMiwbomhLklqQIEuIl+zbc8Bfjd7NWP7tufxSelqWokg+vtJRA5yd375yjJimhj3fW+owjzCKNBF5KCZC7Yxb0Mut587gM6JmlMl0ijQRQSAHfuK+P1bqzmldxITR3Wr/gUSdhToIoK7c8ery6modO69ZKimro1Q+lJUJMrsyi/m3RU7KS6roKyikrIKp7yy8qjbG8bughI+XJPDnRcMpHtSiwasVoJJgS4SJSoqnenzt/CHd9dQUFL+tXUxTeyIw72dqqtzgDMGpDB5dGq91yn1R4EuEgVW7cjntleXs3TbPsb2bc+dFwykc2I8TZs0ITbG1ITSSCjQRSKUu7N4615eXJDJy4szadsilj9PSOPCYZ0V4I2UAl0kQpSUV5BfVM7eA6X8a/UuXl6UycacQuJjY5g4qhu3ntWPxBbNQl2mhJACXSSMLdm6l5+/tJTte4soKf/6F5sjU9ty7bd7c+6QTrRqrn/KokAXCVvvrdzJTTOXkNy6OVPGpNImLpY2cU1pHRfLsG6J9GzfMtQlSphRoIuEoWc+28xdb6xkSJcEnpgykvatmoe6JIkACnSRMFJZ6dz3zy/5+5yNnDEghb9MHE6LZvpnKjWjT4pImDhQWs7PXljKuyt38oOTu3P3hYOJaaLeKlJzCnSRMJC59wDXPLOINTvz+fV5A7j6Wz3V9VBqTYEuEmKLtuzhJ88uoqSskiemjOS0fimhLkkilAJdpJ7MXraD/3n7S4rLKkiIj6VNfCwJ8bE0P+Q2bpUOc9fm0DkxjplT0+mT0jqEFUukU6CLBFluQQn/9foK3l6+kyFdEhjSNYG8ojLyi8rYe6CU0sP6k581qAO/u3iwBgVJnSnQRYLorWVZ/NfrKygoLucX3+nHT07tRdMYzVItDUOBLhIEBSXl/Oa1Fcxasp0hXRL442XD6NdRzSfSsBToInW0dNs+bp65hK17DnDz6X25YXwfYnVVLiGgQBc5TpWVzuMfb+T+f64hpXVzZk4dzaie7UJdljRiCnSRWtpfXMarS7bz7GdbWJddwDmDO3LvJUNJaBEb6tKkkVOgi9TQ+uwCnp63mVmLMyksrWBo1wTNPy5hRYEuUo3VWfk89MF63l6RRWxME84f2olJo1NJ65YY6tJEvkaBLnIUK7bn8Zd/r+O9Vbto1bwp143rzY/G9CRJMx9KmFKgixymstJ5ZM4G/vTeGlo2b8rNp/flqjGpGvgjYU+BLnKIPYWl/PSFDOaszeHCYZ357cWDSYjXl50SGaoNdDN7EjgfyHb3wUdYPw54HdgUWDTL3e8JYo0iDWLRlj3c8PwScgtK+f13B3PFqO76slMiSk2u0P8BPAQ8c4xtPnb384NSkUgIPD9/K795fQWdE+OZdd0pDO6SEOqSRGqt2kB397lmltoAtYg0uIpK5953VvP4x5sY1y+Zv0wcTps4NbFIZApWG/poM1sK7ABudfeVQdqvSL05UFrOLTMzeG/VLiaN7sFvzh+oibQkogUj0BcDPdy9wMzOBV4D+h5pQzObCkwF6N69exDeWuT4ZOcXc/XTC1m5I487LxjIVWN6hrokkTqr8+WIu+e7e0Hg+dtArJm1P8q2j7l7urunJycn1/WtRY7L+uz9fPdv89iQU8Djk9IV5hI16nyFbmYdgV3u7mY2iqpfErl1rkykHnyxaQ8/fnoBzZrG8MLU0Qzpqi8/JXrUpNviDGAc0N7MMoE7gVgAd38UuBSYZmblQBEwwd293ioWOU5vLcvipy9m0LVtPE9fNYpu7VqEuiSRoKpJL5eJ1ax/iKpujSJho7yiku37iti0u5DNuwv5cud+Xli4jRO7t+XxSem0balRnxJ9NFJUokregTL+MW8zT83bxL4DZQeXt2wWwyXDu/L77w4mLjYmhBWK1B8FukSF3QUlPPHJJp79bAsFJeWcMSCFswZ2JLV9S1LbtyC5VXON+pSop0CXiPfPlTu5ZWYGxeUVnDekE9ef1ocBndqEuiyRBqdAl4j2woKt3DZrOUO7JvKn7w+jd3KrUJckEjIKdIlI7s6jczZy37tfcuoJyTz6gxG0aKaPszRu+hcgEaey0vmfwPwrFw7rzB8vG0azphqyL6JAl4iyu6CEX768jH9/mc2UU1L5zfkDadJEX3aKgAJdIshHa7K59aVl5BeXcecFA5lySqp6rogcQoEuYa+4rIJ73/mSf8zbTL8OrXnux6Po31G9WEQOp0CXsLYzr5gfP7OAFdvzuWpMKr88u78GBokchQJdwtaK7Xlc/fQCCksqeGJyOqcP6BDqkkTCmgJdwtJXg4XatWzGy9PUxCJSEwp0CSvuzuMfb+R/3vmSYV0TeWzSiaS0jgt1WSIRQYEuYaOy0vndW6t58tNNnDe0E3+6bJjay0VqQYEuYaGsopJfvLSU1zJ2cNWYVP7rPPUvF6ktBbqE3IHScqY9t5g5a3P4xXf6cd243upfLnIcFOgSUjn7S5j67EKWbtvHvZcMYcIo3Txc5Hgp0CVklmzdy7TnFrOvqJS/XXkiZw/uGOqSRCKaAl1CYsYXW7nz9ZV0SGjOK9NOYVBn3axZpK4U6NKgSsoruOuNlcz4Yhtj+7bnrxOHk9hC9/cUCQYFujSY9dkF3DRjCauy8pk2rje3ntWPGPVkEQkaBbrUO3fnhQXbuPvNVcTFNuH/JqVzxkAN4xcJNgW61Ku8A2Xc9uoy3l6+kzF9knjg+2l0aKORnyL1QYEu9Wbdrv1c/fRCduwr4rZz+nPN2F4aLCRSjxToUi8++HIXN83IIL5ZDC9eO5oR3duGuiSRqKdAl6Bydx6bu5F73/2SQZ3b8PikdDolxIe6LJFGQYEuQVNSXsFts5Yza/F2zhvaiT9eOoz4ZppcS6ShKNAlKHILSvjJs4tYuGUvPz3jBG46vY/mYxFpYAp0qbN1u/bzo6cXkJ1fwkNXDOf8oZ1DXZJIo6RAlzqZszaHG6YvpnlsDC/8ZDRp3RJDXZJIo6VAl+M2f2MuP/rHAvqmtOKJKSPpkqgvP0VCSYEux2V3QQk3zVxC93YtePHa0bSJiw11SSKNXpNQFyCRp7LS+ekLGew9UMZDVwxXmIuEiWoD3cyeNLNsM1txlPVmZn8xs/VmtszMRgS/TAknj8zZwMfrdnPnBQM17a1IGKnJFfo/gLOPsf4coG/gMRV4pO5lSbiavzGXP723hguGdeYK3V1IJKxUG+juPhfYc4xNLgKe8SqfA4lm1ilYBUr4yNlf1W7eI6kl//3dwepnLhJmgtGG3gXYdsjPmYFl32BmU81soZktzMnJCcJbS0PJKypj8pNfkFdU1W7eWu3mImGnQb8UdffH3D3d3dOTk5Mb8q2lDgpLypny1Besy97P33+YrnZzkTAVjG6L24Fuh/zcNbBMokBxWQXXPLOQZZl5PHzFcL59gn4Ri4SrYFyhvwFMCvR2ORnIc/esIOxXQqysopLrpy9m3oZc7r90KGcP1lcjIuGs2it0M5sBjAPam1kmcCcQC+DujwJvA+cC64EDwFX1VazUnz2FpcxanMmqHflk5RWzK7+YrLxiisoq+N3Fg7lkRNdQlygi1ag20N19YjXrHbg+aBVJg3F3Fm/dx3Ofb+Gt5VmUllfSOSGOTonxDOjchtP6pzCqZzu+M6hjqEsVkRrQ0P9GqLisgjeX7uCpTzezKiufVs2bMmFkN648qQf9OrYOdXkicpwU6I1I9v5ipn++lenzt7C7oJR+HVrz398dwkVpnWnZXB8FkUinf8VRzN1ZnbWfuetymLs2hwWb91Be6ZzeP4UfjenJ6N5JGhwkEkUU6BHO3Vm5I5+3l2exJfcAxWUVFJdXUFRawdY9RewuKAGgf8fW/GhMTyaM6k7P9i1DXLWI1AcFegT6KsTfWp7FW8uy2LrnADFNjB5JLYiPjSEuNob4ZjF8q08SY/q059QTkunQJi7UZYtIPVOgR5B1u/bz5tIdvLksi027C4lpYpzSO4nrT+vNWQM70rZls1CXKCIhpEAPQ+7OrvwS1u7az9pd+1mzcz9LM/exdlcBTQxO7pXENWN7cfbgjrRTiItIgAI9xLLyinhpYSbLMvPI2V9Mzv4ScgpKKKvwg9u0b9WcAZ1ac+VJPThnSEdSWqv5RES+SYEeAuUVlXy0JocZX2zlwzXZVDqc0KEVHdrE0TulFSmt4+icGEfflNac0KEVSa2ah7pkEYkACvQGVFhSzswF23ji443syCsmpXVzpo3rzeXp3eme1CLU5YlIhFOgN4A9haU8PW8zT3+2mX0HyjipZzvuvHAQ4/unEBuj27qKSHAo0IPI3ckrKmPT7kJWZeWzckfVY3VWPqXllZw5sAPXfrs3J/ZoG+pSRSQKKdDrYH12AW9kbCcjM48d+4rYsa+IA6UVB9e3iWvKwM5tmDy6B5eld+OEDponRUTqjwK9lrL3F/Pm0ixeW7Kd5dvzaGIwqHMCfVNa8e0TkumcGE+3tvEM6NSGrm3jNbReRBqMAr0G3J0l2/bxj0838/byLMornSFdEvj1eQO4cFhnUjQKU0TCgAL9KNydzL1FfL4xl+c+38LSzDxaxzVl8impTBzVjT4paj4RkfCiQA8oq6hk0Za9fLYhl6WZ+1iemUduYSkAvZNb8tuLB3PJ8C6aZlZEwlajTqfMvQeYu3Y3c9Zm8+n6XApKymli0DelNeP7pzC0WyJpXRMZ3KWN2sJFJOw1qkDfd6CUT9fn8sn63czbsJstuQcA6JwQxwXDOvPtE5IZ0yeJ1nGxIa5URKT2GkWgL8vcxz/mbWb20ixKKypp1bwpJ/dqx+TRqXyrb3v6prTSFbiIRLyoDfQd+4qYt6HqC82Mbfto0SyGy0d24+LhXRjWNYGmGqEpIlEmagI9K6+It5fvZPGWvSzeupesvGIAerZvyZ0XDOR7J3aljZpSRCSKRXSguzvzNuTy7GdbeH/1LioqnS6J8aSntmNE90RO7NGWwZ0TaNJEzSkiEv0iJtB3F5SwblcBO/OLyMorZmdeMZ+s383GnELatojlx2N7csWo7vRI0v0yRaRxCutAzysq458rd/JGxg7mbdhN5X/u+UCbuKb079iGG8f34ZzBnYiLjQldoSIiYSCsAn1vYSnLt+exfHseS7buZe7a3ZRWVNIjqQU3nNaHk3ol0Skhjo4JcbRoFlali4iEXEhTce2u/XyxaQ8LNu9h0Za9ZO4tOriuR1ILrjy5OxelVfVKUbdCEZFjC1mgr8rK56wH5wKQ3Lo5I1Pb8sOTezCkSwKDuiSQEK8eKSIitRGyQG8TF8sfLh3KqNR29EhqoStwEZE6Clmgd20bz/fTu4Xq7UVEoo6GS4qIRAkFuohIlFCgi4hEiRoFupmdbWZrzGy9mf3qCOunmFmOmWUEHj8OfqkiInIs1X4pamYxwMPAmUAmsMDM3nD3VYdt+oK731APNYqISA3U5Ap9FLDe3Te6eykwE7iofssSEZHaqkmgdwG2HfJzZmDZ4b5nZsvM7GUzO2J/RDObamYLzWxhTk7OcZQrIiJHE6wvRd8EUt19KPA+8PSRNnL3x9w93d3Tk5OTg/TWIiICNRtYtB049Iq7a2DZQe6ee8iP/wf8obqdLlq0KN/M1tWkyGNIAPLquI/2wO4wqCOa9qFzGvx9RMs5DYcaILLPZ4+jrnH3Yz6oCv2NQE+gGbAUGHTYNp0Oef5d4PMa7Pex6rZpoH0sDJM6omkfOqc6p2FbQzSdz8Mf1V6hu3u5md0A/BOIAZ5095Vmdk/gpLwB3GRmFwLlwB5gSnX7paqZpq6CsY9gCJdjCZd9BEO4HEu47CMYwuFYwqGGYAm7Y7HAb4lGy8wWunt6qOuIJjqnwadzGlzRej41UhQeC3UBUUjnNPh0ToMrKs9no79CFxGJFrpCFxGJEgp0EZEoEXWBbmbdzOxDM1tlZivN7ObA8nZm9r6ZrQv8t21guZnZXwITjy0zsxGH7a+NmWWa2UOhOJ5wEMxzamb3mdmKwOPyUB1TqB3HOe1vZp+ZWYmZ3XqE/cWY2RIzm93QxxIOgnk+zezmwOdzpZndEoLDOW5RF+hUdZ38ubsPBE4GrjezgcCvgH+7e1/g34GfAc4B+gYeU4FHDtvfb4G5DVF4GAvKOTWz84ARQBpwEnCrmbVpwOMIJ7U9p3uAm4A/HmV/NwOr67fksBaU82lmg4FrqJrDahhwvpn1aZhDqLuoC3R3z3L3xYHn+6n6kHehakKxr6YkeBq4OPD8IuAZr/I5kGhmnQDM7ESgA/Bewx1B+AniOR0IzHX3cncvBJYBZzfckYSP2p5Td8929wVA2eH7MrOuwHlUjdJulIJ4PgcA8939gLuXA3OAS+r/CIIj6gL9UGaWCgwH5gMd3D0rsGonVUENR5l8zMyaAH8CvvHnbWNWl3NK1Sjjs82shZm1B07j69NKNEo1PKfH8r/A/wMq66O+SFPH87kCGGtmSWbWAjiXCPqMhuwm0fXNzFoBrwC3uHu+mR1c5+5uZtX117wOeNvdMw99bWNW13Pq7u+Z2UhgHpADfAZU1GPJYa+u59TMzgey3X2RmY2rz1ojQRA+o6vN7D6q/iovBDKIoM9oVF6hm1ksVf9Tp7v7rMDiXYc0pXQCsgPLjzb52GjgBjPbTFU72yQzu7cByg9LQTqnuPvv3T3N3c8EDFjbEPWHo1qe06MZA1wY+JzOBMab2XP1VHJYC9L5xN2fcPcT3f1UYC8R9BmNukC3ql/JTwCr3f2BQ1a9AUwOPJ8MvH7I8kmBnhknA3mB9rgr3b27u6dS1ezyjLt/4/Z7jUGwzmmgJ0ZSYJ9DgaE00u8njuOcHpG73+buXQOf0wnAB+7+g3ooOawF63wG9pUS+G93qtrPnw9utfUomDN9hcMD+BbgVH3hlhF4nAskUfUt9zrgX0C7wPZG1S32NgDLgfQj7HMK8FCojy3SzykQB6wKPD4H0kJ9bBF0TjtS9V1EPrAv8LzNYfscB8wO9bFF+vkEPg58RpcCp4f62Grz0NB/EZEoEXVNLiIijZUCXUQkSijQRUSihAJdRCRKKNBFRKKEAl0aBTOrMLOMwAx6S83s54HpHQ7d5jUz+zzw/DuB7TPMrMDM1gSeP2Nm48ws75D1GWZ2RmiOTOQ/1G1RGgUzK3D3VoHnKVQNFvnU3e8MLEukqs98AXCeu2885LUfAbe6+8LAz+MCP5/fgIcgUi1doUuj4+7ZVE3re4P9Z7KPS6i6A/tMqkZcikQcBbo0SoEr8BggJbBoIjAj8JhYg12MPazJpXc9lSpSY1E726JITZlZB6puxvGJu7uZlZnZYHdfcYyXfawmFwk3ukKXRsnMelE1LWo28H2gLbApMGthKjW7ShcJKwp0aXTMLBl4lKoJ15yq8D7b3VO9atbCE1E7ukQgNblIYxFvZhlALFX3n3wWeCBwd5seVM3+CIC7bwp0SzzJ3ecfZX9jA/v7yu/c/eV6qVykhtRtUUQkSqjJRUQkSijQRUSihAJdRCRKKNBFRKKEAl1EJEoo0EVEooQCXUQkSvx//p0phbHuJ/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "debt_q.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-16 13:34:09.872 \n",
      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
      "  command:\n",
      "\n",
      "    streamlit run /Users/rishit/miniconda3/lib/python3.8/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Multi-family dwelling unit', 'Single Family dewlling unit', 'Others'\n",
    "sizes = [37.75, 208.36, 10.67]\n",
    "explode = (0, 0.1, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "st.pyplot(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "snp = pd.read_csv('CSUSHPISA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "snp.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "snp.index = pd.to_datetime(snp.index)\n",
    "snp = snp.resample('Q').mean()\n",
    "snp = snp.tail(80)\n",
    "snp = snp.round(decimals=2)\n",
    "# filepath = Path('cleaned/snp.csv')  \n",
    "# snp.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "permit = pd.read_csv('PERMIT1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "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>PERMIT1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>745.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>891.397315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>284.568161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>337.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>694.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>861.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1066.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1798.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PERMIT1\n",
       "count   745.000000\n",
       "mean    891.397315\n",
       "std     284.568161\n",
       "min     337.000000\n",
       "25%     694.000000\n",
       "50%     861.000000\n",
       "75%    1066.000000\n",
       "max    1798.000000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 745 entries, 0 to 744\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   DATE     745 non-null    object \n",
      " 1   PERMIT1  745 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 11.8+ KB\n"
     ]
    }
   ],
   "source": [
    "permit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "permit.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "permit.index = pd.to_datetime(permit.index)\n",
    "permit = permit.resample('Q').mean()\n",
    "permit = permit.tail(80)\n",
    "permit = permit.round(decimals=2)\n",
    "# filepath = Path('cleaned/permit.csv')  \n",
    "# permit.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "permit.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Labor Shortage(%)':[14, 33, 40]}\n",
    "chart_data = pd.DataFrame(data,index=[\"2010\", \"2020\", \"2030\"])\n",
    "st.bar_chart(chart_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh = pd.read_csv('HNFSEPUSSA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 709 entries, 0 to 708\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   DATE        709 non-null    object \n",
      " 1   HNFSEPUSSA  709 non-null    float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 11.2+ KB\n"
     ]
    }
   ],
   "source": [
    "newh.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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>HNFSEPUSSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>709.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>310.754584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>84.097395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>252.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>308.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>358.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>572.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HNFSEPUSSA\n",
       "count  709.000000\n",
       "mean   310.754584\n",
       "std     84.097395\n",
       "min    142.000000\n",
       "25%    252.000000\n",
       "50%    308.000000\n",
       "75%    358.000000\n",
       "max    572.000000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh.set_index('DATE', inplace=True)\n",
    "newh.index = pd.to_datetime(newh.index)\n",
    "newh = newh.resample('Q').mean()\n",
    "newh = newh.tail(80)\n",
    "newh = newh.round(decimals=2)\n",
    "# filepath = Path('cleaned/newh.csv')  \n",
    "# newh.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "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>HNFSEPUSSA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>326.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>337.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>340.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>342.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>304.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>331.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>373.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>392.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>406.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HNFSEPUSSA\n",
       "DATE                  \n",
       "2002-06-30      326.33\n",
       "2002-09-30      333.33\n",
       "2002-12-31      337.33\n",
       "2003-03-31      340.00\n",
       "2003-06-30      342.00\n",
       "...                ...\n",
       "2021-03-31      304.33\n",
       "2021-06-30      331.67\n",
       "2021-09-30      373.67\n",
       "2021-12-31      392.00\n",
       "2022-03-31      406.00\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newh.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = pd.read_excel('fredgraph.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd.index = pd.to_datetime(dd.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "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>HOUSTCB1FQ_HOUSTOB1FQ</th>\n",
       "      <th>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>1.265060</td>\n",
       "      <td>8.674615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>1.111111</td>\n",
       "      <td>9.699615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>1.166667</td>\n",
       "      <td>9.021923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01</th>\n",
       "      <td>1.164835</td>\n",
       "      <td>9.072308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>1.212766</td>\n",
       "      <td>8.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.679231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.291667</td>\n",
       "      <td>3.380385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>2.344828</td>\n",
       "      <td>2.852963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.942400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>2.281250</td>\n",
       "      <td>2.971852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HOUSTCB1FQ_HOUSTOB1FQ  MORTGAGE30US\n",
       "DATE                                           \n",
       "1974-01-01               1.265060      8.674615\n",
       "1974-07-01               1.111111      9.699615\n",
       "1975-01-01               1.166667      9.021923\n",
       "1975-07-01               1.164835      9.072308\n",
       "1976-01-01               1.212766      8.825000\n",
       "...                           ...           ...\n",
       "2019-07-01               2.333333      3.679231\n",
       "2020-01-01               2.291667      3.380385\n",
       "2020-07-01               2.344828      2.852963\n",
       "2021-01-01               2.333333      2.942400\n",
       "2021-07-01               2.281250      2.971852\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = dd.tail(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "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>HOUSTCB1FQ_HOUSTOB1FQ</th>\n",
       "      <th>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>1.507692</td>\n",
       "      <td>6.892308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-01</th>\n",
       "      <td>1.612903</td>\n",
       "      <td>6.182308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1.627119</td>\n",
       "      <td>5.673846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-07-01</th>\n",
       "      <td>1.632353</td>\n",
       "      <td>5.974444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>1.462687</td>\n",
       "      <td>5.862000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-07-01</th>\n",
       "      <td>1.629032</td>\n",
       "      <td>5.818148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>1.475410</td>\n",
       "      <td>5.738462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-07-01</th>\n",
       "      <td>1.536232</td>\n",
       "      <td>5.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>1.491803</td>\n",
       "      <td>6.424615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>1.684211</td>\n",
       "      <td>6.401923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>6.286538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>1.384615</td>\n",
       "      <td>6.388077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>5.979615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>1.457143</td>\n",
       "      <td>6.072963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>1.583333</td>\n",
       "      <td>5.035200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>1.807692</td>\n",
       "      <td>5.037778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.653846</td>\n",
       "      <td>4.957600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>4.441852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1.545455</td>\n",
       "      <td>4.748846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>1.680000</td>\n",
       "      <td>4.146923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>1.863636</td>\n",
       "      <td>3.858462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>3.456538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.909091</td>\n",
       "      <td>3.584615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>2.227273</td>\n",
       "      <td>4.366538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.296154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>2.222222</td>\n",
       "      <td>4.046296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>2.181818</td>\n",
       "      <td>3.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>2.259259</td>\n",
       "      <td>3.925185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.590909</td>\n",
       "      <td>3.663846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.644231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.076538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>2.137931</td>\n",
       "      <td>3.903077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>2.115385</td>\n",
       "      <td>4.413462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>2.370370</td>\n",
       "      <td>4.675769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.347826</td>\n",
       "      <td>4.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.679231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.291667</td>\n",
       "      <td>3.380385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>2.344828</td>\n",
       "      <td>2.852963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>2.942400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>2.281250</td>\n",
       "      <td>2.971852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            HOUSTCB1FQ_HOUSTOB1FQ  MORTGAGE30US\n",
       "DATE                                           \n",
       "2002-01-01               1.507692      6.892308\n",
       "2002-07-01               1.612903      6.182308\n",
       "2003-01-01               1.627119      5.673846\n",
       "2003-07-01               1.632353      5.974444\n",
       "2004-01-01               1.462687      5.862000\n",
       "2004-07-01               1.629032      5.818148\n",
       "2005-01-01               1.475410      5.738462\n",
       "2005-07-01               1.536232      5.995000\n",
       "2006-01-01               1.491803      6.424615\n",
       "2006-07-01               1.684211      6.401923\n",
       "2007-01-01               1.500000      6.286538\n",
       "2007-07-01               1.384615      6.388077\n",
       "2008-01-01               1.333333      5.979615\n",
       "2008-07-01               1.457143      6.072963\n",
       "2009-01-01               1.583333      5.035200\n",
       "2009-07-01               1.807692      5.037778\n",
       "2010-01-01               1.653846      4.957600\n",
       "2010-07-01               1.500000      4.441852\n",
       "2011-01-01               1.545455      4.748846\n",
       "2011-07-01               1.680000      4.146923\n",
       "2012-01-01               1.863636      3.858462\n",
       "2012-07-01               1.750000      3.456538\n",
       "2013-01-01               1.909091      3.584615\n",
       "2013-07-01               2.227273      4.366538\n",
       "2014-01-01               2.000000      4.296154\n",
       "2014-07-01               2.222222      4.046296\n",
       "2015-01-01               2.181818      3.770000\n",
       "2015-07-01               2.259259      3.925185\n",
       "2016-01-01               2.590909      3.663846\n",
       "2016-07-01               2.333333      3.644231\n",
       "2017-01-01               2.000000      4.076538\n",
       "2017-07-01               2.137931      3.903077\n",
       "2018-01-01               2.115385      4.413462\n",
       "2018-07-01               2.370370      4.675769\n",
       "2019-01-01               2.347826      4.192308\n",
       "2019-07-01               2.333333      3.679231\n",
       "2020-01-01               2.291667      3.380385\n",
       "2020-07-01               2.344828      2.852963\n",
       "2021-01-01               2.333333      2.942400\n",
       "2021-07-01               2.281250      2.971852"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = dd.round(decimals=2)\n",
    "# filepath = Path('cleaned/dd.csv')  \n",
    "# dd.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOUSTCB1FQ_HOUSTOB1FQ</th>\n",
       "      <th>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HOUSTCB1FQ_HOUSTOB1FQ</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.804178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORTGAGE30US</th>\n",
       "      <td>-0.804178</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       HOUSTCB1FQ_HOUSTOB1FQ  MORTGAGE30US\n",
       "HOUSTCB1FQ_HOUSTOB1FQ               1.000000     -0.804178\n",
       "MORTGAGE30US                       -0.804178      1.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlatin analysis\n",
    "dd.corr(method = 'pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdsp = pd.read_csv('MDSP.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>MDSP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>4.375714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>4.484928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-07-01</td>\n",
       "      <td>4.493386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-10-01</td>\n",
       "      <td>4.512131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981-01-01</td>\n",
       "      <td>4.596082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>3.873989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>3.959033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3.506884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>3.789223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3.807581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE      MDSP\n",
       "0    1980-01-01  4.375714\n",
       "1    1980-04-01  4.484928\n",
       "2    1980-07-01  4.493386\n",
       "3    1980-10-01  4.512131\n",
       "4    1981-01-01  4.596082\n",
       "..          ...       ...\n",
       "162  2020-07-01  3.873989\n",
       "163  2020-10-01  3.959033\n",
       "164  2021-01-01  3.506884\n",
       "165  2021-04-01  3.789223\n",
       "166  2021-07-01  3.807581\n",
       "\n",
       "[167 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdsp.set_index('DATE', inplace=True)\n",
    "mdsp.index = pd.to_datetime(mdsp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDSP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-10-01</th>\n",
       "      <td>5.929021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>5.811716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>5.775532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-01</th>\n",
       "      <td>5.808071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-10-01</th>\n",
       "      <td>5.831791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>3.873989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>3.959033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>3.506884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>3.789223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>3.807581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MDSP\n",
       "DATE                \n",
       "2001-10-01  5.929021\n",
       "2002-01-01  5.811716\n",
       "2002-04-01  5.775532\n",
       "2002-07-01  5.808071\n",
       "2002-10-01  5.831791\n",
       "...              ...\n",
       "2020-07-01  3.873989\n",
       "2020-10-01  3.959033\n",
       "2021-01-01  3.506884\n",
       "2021-04-01  3.789223\n",
       "2021-07-01  3.807581\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsp.tail(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdsp = mdsp.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>MDSP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-07-01</th>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-10-01</th>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>3.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>3.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MDSP\n",
       "DATE            \n",
       "1980-01-01  4.38\n",
       "1980-04-01  4.48\n",
       "1980-07-01  4.49\n",
       "1980-10-01  4.51\n",
       "1981-01-01  4.60\n",
       "...          ...\n",
       "2020-07-01  3.87\n",
       "2020-10-01  3.96\n",
       "2021-01-01  3.51\n",
       "2021-04-01  3.79\n",
       "2021-07-01  3.81\n",
       "\n",
       "[167 rows x 1 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdsp = mdsp.tail(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/debt_inc.csv')  \n",
    "# mdsp.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'go' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-96-197409d058b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m10.44\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.58\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.22\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Use `hole` to create a donut-like pie chart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhole\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.3\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[0m\u001b[1;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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;31mNameError\u001b[0m: name 'go' is not defined"
     ]
    }
   ],
   "source": [
    "# labels = ['Mortgage','Student','Others']\n",
    "# values = [10.44, 1.58, 3.22]\n",
    "# # Use `hole` to create a donut-like pie chart\n",
    "# fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "pops = pd.read_csv('pops.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "pops.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "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>CNP16OV_TTLHH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>2.03024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1.98775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>1.99063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1.98753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>1.99426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>1.99468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>2.00041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>1.99866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>2.00190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>2.01228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>2.02343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1.99803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>2.00922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>2.00622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>2.01208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>2.01306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.01510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2.02084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>2.02052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.01569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2.02668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CNP16OV_TTLHH\n",
       "Date                     \n",
       "2000-01-01        2.03024\n",
       "2001-01-01        1.98775\n",
       "2002-01-01        1.99063\n",
       "2003-01-01        1.98753\n",
       "2004-01-01        1.99426\n",
       "2005-01-01        1.99468\n",
       "2006-01-01        2.00041\n",
       "2007-01-01        1.99866\n",
       "2008-01-01        2.00190\n",
       "2009-01-01        2.01228\n",
       "2010-01-01        2.02343\n",
       "2011-01-01        1.99803\n",
       "2012-01-01        2.00922\n",
       "2013-01-01        2.00622\n",
       "2014-01-01        2.01208\n",
       "2015-01-01        2.01306\n",
       "2016-01-01        2.01510\n",
       "2017-01-01        2.02084\n",
       "2018-01-01        2.02052\n",
       "2019-01-01        2.01569\n",
       "2020-01-01        2.02668"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pops"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "filepath = Path('cleaned/pops.csv')  \n",
    "pops.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh  = pd.read_csv('HSN1F.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh.set_index(\"DATE\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSN1F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02-01</th>\n",
       "      <td>464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03-01</th>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04-01</th>\n",
       "      <td>605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05-01</th>\n",
       "      <td>586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>801.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HSN1F\n",
       "DATE             \n",
       "1963-01-01  591.0\n",
       "1963-02-01  464.0\n",
       "1963-03-01  461.0\n",
       "1963-04-01  605.0\n",
       "1963-05-01  586.0\n",
       "...           ...\n",
       "2021-09-01  725.0\n",
       "2021-10-01  667.0\n",
       "2021-11-01  749.0\n",
       "2021-12-01  839.0\n",
       "2022-01-01  801.0\n",
       "\n",
       "[709 rows x 1 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh.index = pd.to_datetime(newh.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSN1F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02-01</th>\n",
       "      <td>464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03-01</th>\n",
       "      <td>461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04-01</th>\n",
       "      <td>605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05-01</th>\n",
       "      <td>586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>801.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            HSN1F\n",
       "DATE             \n",
       "1963-01-01  591.0\n",
       "1963-02-01  464.0\n",
       "1963-03-01  461.0\n",
       "1963-04-01  605.0\n",
       "1963-05-01  586.0\n",
       "...           ...\n",
       "2021-09-01  725.0\n",
       "2021-10-01  667.0\n",
       "2021-11-01  749.0\n",
       "2021-12-01  839.0\n",
       "2022-01-01  801.0\n",
       "\n",
       "[709 rows x 1 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh = newh.resample('Q').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSN1F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-03-31</th>\n",
       "      <td>505.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-06-30</th>\n",
       "      <td>572.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-09-30</th>\n",
       "      <td>608.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-12-31</th>\n",
       "      <td>553.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-31</th>\n",
       "      <td>573.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>896.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>737.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>751.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>801.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 HSN1F\n",
       "DATE                  \n",
       "1963-03-31  505.333333\n",
       "1963-06-30  572.333333\n",
       "1963-09-30  608.333333\n",
       "1963-12-31  553.333333\n",
       "1964-03-31  573.333333\n",
       "...                ...\n",
       "2021-03-31  896.333333\n",
       "2021-06-30  737.333333\n",
       "2021-09-30  699.000000\n",
       "2021-12-31  751.666667\n",
       "2022-03-31  801.000000\n",
       "\n",
       "[237 rows x 1 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh = newh.tail(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "newh = newh.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>HSN1F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-06-30</th>\n",
       "      <td>957.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-30</th>\n",
       "      <td>1004.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>1026.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03-31</th>\n",
       "      <td>978.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-30</th>\n",
       "      <td>1094.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>896.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>737.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>699.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>751.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>801.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              HSN1F\n",
       "DATE               \n",
       "2002-06-30   957.00\n",
       "2002-09-30  1004.67\n",
       "2002-12-31  1026.00\n",
       "2003-03-31   978.00\n",
       "2003-06-30  1094.33\n",
       "...             ...\n",
       "2021-03-31   896.33\n",
       "2021-06-30   737.33\n",
       "2021-09-30   699.00\n",
       "2021-12-31   751.67\n",
       "2022-03-31   801.00\n",
       "\n",
       "[80 rows x 1 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/newh.csv')  \n",
    "# newh.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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