data/data_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data resampling to similar time frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "income = pd.read_csv('./cleaned/incomenew.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "income.set_index('DATE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "income.index = pd.to_datetime(income.index)\n",
    "income = income.resample('A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>MEHOINUSA672N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-12-31</th>\n",
       "      <td>61889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>61190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>61113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>60901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>61553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>62033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>62865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>60624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>60200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>58627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>57732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>57623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>59640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>58725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>61748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>63683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>64557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>65127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>69560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>67521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MEHOINUSA672N\n",
       "DATE                     \n",
       "2001-12-31          61889\n",
       "2002-12-31          61190\n",
       "2003-12-31          61113\n",
       "2004-12-31          60901\n",
       "2005-12-31          61553\n",
       "2006-12-31          62033\n",
       "2007-12-31          62865\n",
       "2008-12-31          60624\n",
       "2009-12-31          60200\n",
       "2010-12-31          58627\n",
       "2011-12-31          57732\n",
       "2012-12-31          57623\n",
       "2013-12-31          59640\n",
       "2014-12-31          58725\n",
       "2015-12-31          61748\n",
       "2016-12-31          63683\n",
       "2017-12-31          64557\n",
       "2018-12-31          65127\n",
       "2019-12-31          69560\n",
       "2020-12-31          67521"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = Path('cleaned/annual/income.csv')  \n",
    "# income.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 7,
     "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.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp = pd.read_csv('./cleaned/unemp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 9,
     "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.set_index(\"DATE\", inplace=True)\n",
    "unemp.index = pd.to_datetime(unemp.index)\n",
    "unemp = unemp.resample('A').mean()\n",
    "# filepath = Path('cleaned/annual/unemployment.csv')  \n",
    "# unemp.to_csv(filepath, index=True)\n",
    "unemp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mort = pd.read_csv(\"./cleaned/mortgage_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "mort.set_index(\"DATE\", inplace=True)\n",
    "mort.index = pd.to_datetime(mort.index)\n",
    "mort = mort.resample('A').mean()\n",
    "# filepath = Path('cleaned/annual/mortgage_rate.csv')  \n",
    "# mort.to_csv(filepath, index=True)\n",
    "mort.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>MORTGAGE30US</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-12-31</th>\n",
       "      <td>6.393333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>5.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>5.835000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>5.867500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>6.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>6.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>6.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>5.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>4.702500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>4.447500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>3.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>3.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>4.172500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>3.847500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>3.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>3.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>4.542500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>3.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>3.117500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>2.957500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MORTGAGE30US\n",
       "DATE                    \n",
       "2002-12-31      6.393333\n",
       "2003-12-31      5.825000\n",
       "2004-12-31      5.835000\n",
       "2005-12-31      5.867500\n",
       "2006-12-31      6.412500\n",
       "2007-12-31      6.335000\n",
       "2008-12-31      6.030000\n",
       "2009-12-31      5.037500\n",
       "2010-12-31      4.702500\n",
       "2011-12-31      4.447500\n",
       "2012-12-31      3.655000\n",
       "2013-12-31      3.975000\n",
       "2014-12-31      4.172500\n",
       "2015-12-31      3.847500\n",
       "2016-12-31      3.655000\n",
       "2017-12-31      3.987500\n",
       "2018-12-31      4.542500\n",
       "2019-12-31      3.935000\n",
       "2020-12-31      3.117500\n",
       "2021-12-31      2.957500\n",
       "2022-12-31      3.600000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.read_csv('./cleaned/population_q_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "pop.set_index(\"DATE\", inplace=True)\n",
    "pop.index = pd.to_datetime(pop.index)\n",
    "pop = pop.resample('A').mean()\n",
    "# filepath = Path('cleaned/annual/totpop.csv')  \n",
    "# pop.to_csv(filepath, index=True)\n",
    "pop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "permits = pd.read_csv('./cleaned/permit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "permits.set_index(\"DATE\", inplace=True)\n",
    "permits.index = pd.to_datetime(permits.index)\n",
    "permits = permits.resample('A').mean()\n",
    "# filepath = Path('cleaned/annual/totpermits.csv')  \n",
    "# permits.to_csv(filepath, index=True)\n",
    "permits.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "      <th>HSN1F</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>2002-12-31</th>\n",
       "      <td>1339.0000</td>\n",
       "      <td>995.8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>1461.0850</td>\n",
       "      <td>1090.6650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-31</th>\n",
       "      <td>1604.2525</td>\n",
       "      <td>1200.8325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>1684.4975</td>\n",
       "      <td>1278.9150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>1380.4175</td>\n",
       "      <td>1049.3325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>971.5825</td>\n",
       "      <td>768.6675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>567.4175</td>\n",
       "      <td>482.1650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>439.5850</td>\n",
       "      <td>374.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>448.0000</td>\n",
       "      <td>321.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>420.0000</td>\n",
       "      <td>306.4150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>519.6675</td>\n",
       "      <td>368.0825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>619.0825</td>\n",
       "      <td>429.8325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>639.8325</td>\n",
       "      <td>440.2525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>694.5825</td>\n",
       "      <td>502.5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>750.6675</td>\n",
       "      <td>560.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>823.0000</td>\n",
       "      <td>615.9150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>853.7475</td>\n",
       "      <td>613.8350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>863.5000</td>\n",
       "      <td>683.1675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>988.1650</td>\n",
       "      <td>827.5825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>1116.8325</td>\n",
       "      <td>771.0825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>1213.0000</td>\n",
       "      <td>801.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PERMIT1      HSN1F\n",
       "DATE                            \n",
       "2002-12-31  1339.0000   995.8900\n",
       "2003-12-31  1461.0850  1090.6650\n",
       "2004-12-31  1604.2525  1200.8325\n",
       "2005-12-31  1684.4975  1278.9150\n",
       "2006-12-31  1380.4175  1049.3325\n",
       "2007-12-31   971.5825   768.6675\n",
       "2008-12-31   567.4175   482.1650\n",
       "2009-12-31   439.5850   374.2500\n",
       "2010-12-31   448.0000   321.2500\n",
       "2011-12-31   420.0000   306.4150\n",
       "2012-12-31   519.6675   368.0825\n",
       "2013-12-31   619.0825   429.8325\n",
       "2014-12-31   639.8325   440.2525\n",
       "2015-12-31   694.5825   502.5850\n",
       "2016-12-31   750.6675   560.5000\n",
       "2017-12-31   823.0000   615.9150\n",
       "2018-12-31   853.7475   613.8350\n",
       "2019-12-31   863.5000   683.1675\n",
       "2020-12-31   988.1650   827.5825\n",
       "2021-12-31  1116.8325   771.0825\n",
       "2022-12-31  1213.0000   801.0000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dispinc = pd.read_csv('./cleaned/debt_inc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "dispinc.set_index(\"DATE\", inplace=True)\n",
    "dispinc.index = pd.to_datetime(dispinc.index)\n",
    "dispinc = dispinc.resample('A').mean()\n",
    "# filepath = Path('cleaned/annual/totdispinc.csv')  \n",
    "# dispinc.to_csv(filepath, index=True)\n",
    "dispinc.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.read_csv('CSUSHPISA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "index.set_index('DATE', inplace=True)\n",
    "index.index = pd.to_datetime(index.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = index.resample('A').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = index.tail(21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = Path('cleaned/annual/index.csv')  \n",
    "index.to_csv(filepath, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 36,
     "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": [
    "index.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}