{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy import inspect\n",
    "import ETL_config\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Treasury Securities CSVs into DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
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       "\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>Series Description</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 1-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 3-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 6-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 2-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 7-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 20-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 30-year   constant maturity, quoted on investment basis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unit:</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "      <td>Percent:_Per_Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Multiplier:</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Currency:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unique Identifier:</td>\n",
       "      <td>H15/H15/RIFLGFCM01_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCM03_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCM06_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY01_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY02_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY03_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY05_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY07_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY10_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY20_N.B</td>\n",
       "      <td>H15/H15/RIFLGFCY30_N.B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Time Period</td>\n",
       "      <td>RIFLGFCM01_N.B</td>\n",
       "      <td>RIFLGFCM03_N.B</td>\n",
       "      <td>RIFLGFCM06_N.B</td>\n",
       "      <td>RIFLGFCY01_N.B</td>\n",
       "      <td>RIFLGFCY02_N.B</td>\n",
       "      <td>RIFLGFCY03_N.B</td>\n",
       "      <td>RIFLGFCY05_N.B</td>\n",
       "      <td>RIFLGFCY07_N.B</td>\n",
       "      <td>RIFLGFCY10_N.B</td>\n",
       "      <td>RIFLGFCY20_N.B</td>\n",
       "      <td>RIFLGFCY30_N.B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Series Description  \\\n",
       "0                Unit:   \n",
       "1          Multiplier:   \n",
       "2            Currency:   \n",
       "3  Unique Identifier:    \n",
       "4          Time Period   \n",
       "5           1962-01-02   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 1-month   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                    \n",
       "1                                                  1                                                    \n",
       "2                                                NaN                                                    \n",
       "3                             H15/H15/RIFLGFCM01_N.B                                                    \n",
       "4                                     RIFLGFCM01_N.B                                                    \n",
       "5                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 3-month   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                    \n",
       "1                                                  1                                                    \n",
       "2                                                NaN                                                    \n",
       "3                             H15/H15/RIFLGFCM03_N.B                                                    \n",
       "4                                     RIFLGFCM03_N.B                                                    \n",
       "5                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 6-month   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                    \n",
       "1                                                  1                                                    \n",
       "2                                                NaN                                                    \n",
       "3                             H15/H15/RIFLGFCM06_N.B                                                    \n",
       "4                                     RIFLGFCM06_N.B                                                    \n",
       "5                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY01_N.B                                                   \n",
       "4                                     RIFLGFCY01_N.B                                                   \n",
       "5                                               3.22                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 2-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY02_N.B                                                   \n",
       "4                                     RIFLGFCY02_N.B                                                   \n",
       "5                                                NaN                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY03_N.B                                                   \n",
       "4                                     RIFLGFCY03_N.B                                                   \n",
       "5                                               3.70                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY05_N.B                                                   \n",
       "4                                     RIFLGFCY05_N.B                                                   \n",
       "5                                               3.88                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 7-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY07_N.B                                                   \n",
       "4                                     RIFLGFCY07_N.B                                                   \n",
       "5                                                NaN                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                    \n",
       "1                                                  1                                                    \n",
       "2                                                NaN                                                    \n",
       "3                             H15/H15/RIFLGFCY10_N.B                                                    \n",
       "4                                     RIFLGFCY10_N.B                                                    \n",
       "5                                               4.06                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 20-year   constant maturity, quoted on investment basis  \\\n",
       "0                                  Percent:_Per_Year                                                    \n",
       "1                                                  1                                                    \n",
       "2                                                NaN                                                    \n",
       "3                             H15/H15/RIFLGFCY20_N.B                                                    \n",
       "4                                     RIFLGFCY20_N.B                                                    \n",
       "5                                               4.07                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 30-year   constant maturity, quoted on investment basis  \n",
       "0                                  Percent:_Per_Year                                                   \n",
       "1                                                  1                                                   \n",
       "2                                                NaN                                                   \n",
       "3                             H15/H15/RIFLGFCY30_N.B                                                   \n",
       "4                                     RIFLGFCY30_N.B                                                   \n",
       "5                                                NaN                                                   "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# got from datasource https://www.federalreserve.gov/releases/h15/\n",
    "frb_file = \"Resources/FRB_H15.csv\"\n",
    "frb_df = pd.read_csv(frb_file)\n",
    "frb_df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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",
       "    .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>Series Description</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 1-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 3-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 6-month   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 2-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 7-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 20-year   constant maturity, quoted on investment basis</th>\n",
       "      <th>Market yield on U.S. Treasury securities at 30-year   constant maturity, quoted on investment basis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.06</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>4.06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Series Description  \\\n",
       "0         1962-01-02   \n",
       "1         1962-01-03   \n",
       "2         1962-01-04   \n",
       "3         1962-01-05   \n",
       "4         1962-01-08   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 1-month   constant maturity, quoted on investment basis  \\\n",
       "0                                                NaN                                                    \n",
       "1                                                NaN                                                    \n",
       "2                                                NaN                                                    \n",
       "3                                                NaN                                                    \n",
       "4                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 3-month   constant maturity, quoted on investment basis  \\\n",
       "0                                                NaN                                                    \n",
       "1                                                NaN                                                    \n",
       "2                                                NaN                                                    \n",
       "3                                                NaN                                                    \n",
       "4                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 6-month   constant maturity, quoted on investment basis  \\\n",
       "0                                                NaN                                                    \n",
       "1                                                NaN                                                    \n",
       "2                                                NaN                                                    \n",
       "3                                                NaN                                                    \n",
       "4                                                NaN                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis  \\\n",
       "0                                               3.22                                                   \n",
       "1                                               3.24                                                   \n",
       "2                                               3.24                                                   \n",
       "3                                               3.26                                                   \n",
       "4                                               3.31                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 2-year   constant maturity, quoted on investment basis  \\\n",
       "0                                                NaN                                                   \n",
       "1                                                NaN                                                   \n",
       "2                                                NaN                                                   \n",
       "3                                                NaN                                                   \n",
       "4                                                NaN                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis  \\\n",
       "0                                               3.70                                                   \n",
       "1                                               3.70                                                   \n",
       "2                                               3.69                                                   \n",
       "3                                               3.71                                                   \n",
       "4                                               3.71                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis  \\\n",
       "0                                               3.88                                                   \n",
       "1                                               3.87                                                   \n",
       "2                                               3.86                                                   \n",
       "3                                               3.89                                                   \n",
       "4                                               3.91                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 7-year   constant maturity, quoted on investment basis  \\\n",
       "0                                                NaN                                                   \n",
       "1                                                NaN                                                   \n",
       "2                                                NaN                                                   \n",
       "3                                                NaN                                                   \n",
       "4                                                NaN                                                   \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis  \\\n",
       "0                                               4.06                                                    \n",
       "1                                               4.03                                                    \n",
       "2                                               3.99                                                    \n",
       "3                                               4.02                                                    \n",
       "4                                               4.03                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 20-year   constant maturity, quoted on investment basis  \\\n",
       "0                                               4.07                                                    \n",
       "1                                               4.07                                                    \n",
       "2                                               4.06                                                    \n",
       "3                                               4.07                                                    \n",
       "4                                               4.08                                                    \n",
       "\n",
       "  Market yield on U.S. Treasury securities at 30-year   constant maturity, quoted on investment basis  \n",
       "0                                                NaN                                                   \n",
       "1                                                NaN                                                   \n",
       "2                                                NaN                                                   \n",
       "3                                                NaN                                                   \n",
       "4                                                NaN                                                   "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop some row without data\n",
    "frb_df.set_index('Series Description',inplace=True)\n",
    "newfrb_df=frb_df.drop({'Time Period','Unit:','Multiplier:','Currency:','Unique Identifier: '},axis='rows')\n",
    "newfrb_df.reset_index(inplace=True)\n",
    "newfrb_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform premise DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Series Description',\n",
       "       'Market yield on U.S. Treasury securities at 1-month   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 3-month   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 6-month   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 2-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 7-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 20-year   constant maturity, quoted on investment basis',\n",
       "       'Market yield on U.S. Treasury securities at 30-year   constant maturity, quoted on investment basis'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newfrb_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>data_date</th>\n",
       "      <th>treasury_securities_1y</th>\n",
       "      <th>treasury_securities_3y</th>\n",
       "      <th>treasury_securities_5y</th>\n",
       "      <th>treasury_securities_10y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>2022-10-17</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.24</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15860</th>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15861</th>\n",
       "      <td>2022-10-19</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15862</th>\n",
       "      <td>2022-10-20</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15863</th>\n",
       "      <td>2022-10-21</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        data_date treasury_securities_1y treasury_securities_3y  \\\n",
       "15859  2022-10-17                   4.50                   4.45   \n",
       "15860  2022-10-18                   4.50                   4.43   \n",
       "15861  2022-10-19                   4.60                   4.56   \n",
       "15862  2022-10-20                   4.66                   4.66   \n",
       "15863  2022-10-21                   4.58                   4.52   \n",
       "\n",
       "      treasury_securities_5y treasury_securities_10y  \n",
       "15859                   4.24                    4.02  \n",
       "15860                   4.21                    4.01  \n",
       "15861                   4.35                    4.14  \n",
       "15862                   4.45                    4.24  \n",
       "15863                   4.34                    4.21  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treasury_securities_df = newfrb_df[[\n",
    "     'Series Description',\n",
    "     'Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis',\n",
    "     'Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis', \n",
    "     'Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis',\n",
    "     'Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis']].copy()\n",
    "treasury_securities_df=treasury_securities_df.rename(columns={\n",
    "     'Series Description':'data_date','Market yield on U.S. Treasury securities at 1-year   constant maturity, quoted on investment basis':'treasury_securities_1y',\n",
    "     'Market yield on U.S. Treasury securities at 3-year   constant maturity, quoted on investment basis':'treasury_securities_3y',\n",
    "     'Market yield on U.S. Treasury securities at 5-year   constant maturity, quoted on investment basis':'treasury_securities_5y',\n",
    "     'Market yield on U.S. Treasury securities at 10-year   constant maturity, quoted on investment basis':'treasury_securities_10y'})\n",
    "treasury_securities_df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_date                  object\n",
       "treasury_securities_1y     object\n",
       "treasury_securities_3y     object\n",
       "treasury_securities_5y     object\n",
       "treasury_securities_10y    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data type\n",
    "treasury_securities_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_date                  datetime64[ns]\n",
       "treasury_securities_1y            float64\n",
       "treasury_securities_3y            float64\n",
       "treasury_securities_5y            float64\n",
       "treasury_securities_10y           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the data type of columns\n",
    "# Delete the row without data, value is 'ND'\n",
    "treasury_securities_df=treasury_securities_df.drop(treasury_securities_df[treasury_securities_df['treasury_securities_1y']=='ND'].index)\n",
    "\n",
    "# Correct data type\n",
    "treasury_securities_df=treasury_securities_df.astype({'data_date':'datetime64[ns]',\n",
    "        'treasury_securities_1y':'float',\n",
    "        'treasury_securities_3y':'float',\n",
    "        'treasury_securities_5y':'float',\n",
    "        'treasury_securities_10y':'float',\n",
    "        }.copy()\n",
    "    ) \n",
    "treasury_securities_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>data_date</th>\n",
       "      <th>treasury_securities_1y</th>\n",
       "      <th>treasury_securities_3y</th>\n",
       "      <th>treasury_securities_5y</th>\n",
       "      <th>treasury_securities_10y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15863</th>\n",
       "      <td>2022-10-21</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15862</th>\n",
       "      <td>2022-10-20</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15861</th>\n",
       "      <td>2022-10-19</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15860</th>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>2022-10-17</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.24</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>3.31</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.91</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-01-05</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.71</td>\n",
       "      <td>3.89</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-01-04</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.87</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.70</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15188 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       data_date  treasury_securities_1y  treasury_securities_3y  \\\n",
       "15863 2022-10-21                    4.58                    4.52   \n",
       "15862 2022-10-20                    4.66                    4.66   \n",
       "15861 2022-10-19                    4.60                    4.56   \n",
       "15860 2022-10-18                    4.50                    4.43   \n",
       "15859 2022-10-17                    4.50                    4.45   \n",
       "...          ...                     ...                     ...   \n",
       "4     1962-01-08                    3.31                    3.71   \n",
       "3     1962-01-05                    3.26                    3.71   \n",
       "2     1962-01-04                    3.24                    3.69   \n",
       "1     1962-01-03                    3.24                    3.70   \n",
       "0     1962-01-02                    3.22                    3.70   \n",
       "\n",
       "       treasury_securities_5y  treasury_securities_10y  \n",
       "15863                    4.34                     4.21  \n",
       "15862                    4.45                     4.24  \n",
       "15861                    4.35                     4.14  \n",
       "15860                    4.21                     4.01  \n",
       "15859                    4.24                     4.02  \n",
       "...                       ...                      ...  \n",
       "4                        3.91                     4.03  \n",
       "3                        3.89                     4.02  \n",
       "2                        3.86                     3.99  \n",
       "1                        3.87                     4.03  \n",
       "0                        3.88                     4.06  \n",
       "\n",
       "[15188 rows x 5 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treasury_securities_df=treasury_securities_df.sort_values('data_date',ascending=False)\n",
    "treasury_securities_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Unemployment Rate CSVs into DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Area Type</th>\n",
       "      <th>Area Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Seasonally Adjusted</th>\n",
       "      <th>Not Seasonally Adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State</td>\n",
       "      <td>California</td>\n",
       "      <td>01/01/1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>January</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>State</td>\n",
       "      <td>California</td>\n",
       "      <td>02/01/1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>February</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>State</td>\n",
       "      <td>California</td>\n",
       "      <td>03/01/1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>March</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>State</td>\n",
       "      <td>California</td>\n",
       "      <td>04/01/1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>April</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>State</td>\n",
       "      <td>California</td>\n",
       "      <td>05/01/1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>May</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Area Type   Area Name        Date  Year     Month  Seasonally Adjusted   \\\n",
       "0     State  California  01/01/1976  1976   January                 0.092   \n",
       "1     State  California  02/01/1976  1976  February                 0.092   \n",
       "2     State  California  03/01/1976  1976     March                 0.091   \n",
       "3     State  California  04/01/1976  1976     April                 0.091   \n",
       "4     State  California  05/01/1976  1976       May                 0.090   \n",
       "\n",
       "   Not Seasonally Adjusted  \n",
       "0                    0.104  \n",
       "1                    0.101  \n",
       "2                    0.094  \n",
       "3                    0.088  \n",
       "4                    0.079  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# got from datasource: https://catalog.data.gov/dataset/civilian-unemployment-rate-for-us-and-california\n",
    "data_gov_file = \"Resources/Civilian_Unemployment_Rate_for_US_and_California.csv\"\n",
    "data_gov_df = pd.read_csv(data_gov_file)\n",
    "data_gov_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform Unemployment DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Area Type', 'Area Name', 'Date', 'Year', 'Month',\n",
       "       'Seasonally Adjusted ', 'Not Seasonally Adjusted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Delete the State unemployment data\n",
    "new_data_gov_df=data_gov_df.drop(data_gov_df[data_gov_df['Area Type']=='State'].index)\n",
    "new_data_gov_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
       "\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>Seasonally Adjusted</th>\n",
       "      <th>Not Seasonally Adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>01/01/1948</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>02/01/1948</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>03/01/1948</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>04/01/1948</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>05/01/1948</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Seasonally Adjusted   Not Seasonally Adjusted\n",
       "561  01/01/1948                 0.034                    0.040\n",
       "562  02/01/1948                 0.038                    0.047\n",
       "563  03/01/1948                 0.040                    0.045\n",
       "564  04/01/1948                 0.039                    0.040\n",
       "565  05/01/1948                 0.035                    0.034"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df = new_data_gov_df[[\n",
    "     'Date',\n",
    "     'Seasonally Adjusted ',\n",
    "     'Not Seasonally Adjusted'\n",
    "     ]].copy()\n",
    "\n",
    "unemployment_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>data_date</th>\n",
       "      <th>rate_seasonally_adjusted</th>\n",
       "      <th>rate_not_seasonally_adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>01/01/1948</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>02/01/1948</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>03/01/1948</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>04/01/1948</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>05/01/1948</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_date  rate_seasonally_adjusted  rate_not_seasonally_adjusted\n",
       "561  01/01/1948                       3.4                           4.0\n",
       "562  02/01/1948                       3.8                           4.7\n",
       "563  03/01/1948                       4.0                           4.5\n",
       "564  04/01/1948                       3.9                           4.0\n",
       "565  05/01/1948                       3.5                           3.4"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df=unemployment_df.rename(columns={'Date':'data_date','Seasonally Adjusted ':'rate_seasonally_adjusted','Not Seasonally Adjusted':'rate_not_seasonally_adjusted'})\n",
    "unemployment_df['rate_seasonally_adjusted']=unemployment_df['rate_seasonally_adjusted'].map(lambda x:x*100)\n",
    "unemployment_df['rate_not_seasonally_adjusted']=unemployment_df['rate_not_seasonally_adjusted'].map(lambda x:x*100)\n",
    "unemployment_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_date                        object\n",
       "rate_seasonally_adjusted        float64\n",
       "rate_not_seasonally_adjusted    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_date                       datetime64[ns]\n",
       "rate_seasonally_adjusted               float64\n",
       "rate_not_seasonally_adjusted           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df=unemployment_df.astype({'data_date':'datetime64[ns]',\n",
    "        'rate_seasonally_adjusted':'float',\n",
    "        'rate_not_seasonally_adjusted':'float'\n",
    "        }.copy()\n",
    "    ) \n",
    "unemployment_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create database connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "protocol = 'postgresql'\n",
    "password=ETL_config.password\n",
    "username=ETL_config.username\n",
    "host = 'localhost'\n",
    "port = 5432\n",
    "database_name = 'rate_db'\n",
    "rds_connection_string = f'{protocol}://{username}:{password}@{host}:{port}/{database_name}'\n",
    "engine = create_engine(rds_connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['treasury_securities', 'unemployment_rate']"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm tables\n",
    "insp = inspect(engine)\n",
    "insp.get_table_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load DataFrames into database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treasury_securities_df.to_sql(name='treasury_securities', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "897"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unemployment_df.to_sql(name='unemployment_rate', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>data_date</th>\n",
       "      <th>treasury_securities_1y</th>\n",
       "      <th>treasury_securities_3y</th>\n",
       "      <th>treasury_securities_5y</th>\n",
       "      <th>treasury_securities_10y</th>\n",
       "      <th>rate_seasonally_adjusted</th>\n",
       "      <th>rate_not_seasonally_adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.66</td>\n",
       "      <td>2.60</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.94</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.55</td>\n",
       "      <td>2.39</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data_date  treasury_securities_1y  treasury_securities_3y  \\\n",
       "0  2022-09-01                    3.51                    3.54   \n",
       "1  2022-08-01                    2.98                    2.82   \n",
       "2  2022-07-01                    2.79                    2.85   \n",
       "3  2022-06-01                    2.16                    2.84   \n",
       "4  2022-04-01                    1.72                    2.61   \n",
       "\n",
       "   treasury_securities_5y  treasury_securities_10y  rate_seasonally_adjusted  \\\n",
       "0                    3.39                     3.26                       3.5   \n",
       "1                    2.66                     2.60                       3.7   \n",
       "2                    2.88                     2.88                       3.5   \n",
       "3                    2.94                     2.94                       3.6   \n",
       "4                    2.55                     2.39                       3.6   \n",
       "\n",
       "   rate_not_seasonally_adjusted  \n",
       "0                           3.3  \n",
       "1                           3.8  \n",
       "2                           3.8  \n",
       "3                           3.8  \n",
       "4                           3.3  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_join = r\"\"\" SELECT ts.data_date, ts.treasury_securities_1y, ts.treasury_securities_3y, ts.treasury_securities_5y, ts.treasury_securities_10y, ur.rate_seasonally_adjusted, ur.rate_not_seasonally_adjusted\n",
    "FROM treasury_securities ts\n",
    "INNER JOIN unemployment_rate ur\n",
    "ON ts.data_date = ur.data_date \"\"\"\n",
    "\n",
    "full_df=pd.read_sql_query(sql_join, con=engine)\n",
    "full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a plot to find some interesting things\n",
    "full_df=full_df.rename(columns={\n",
    "    'data_date':'Date',\n",
    "    'treasury_securities_1y':'Rate of 1 Year Treasury Securities',\n",
    "    'treasury_securities_3y':'Rate of 3 Years Treasury Securities',\n",
    "    'treasury_securities_5y':'Rate of 5 Years Treasury Securities',\n",
    "    'treasury_securities_10y':'Rate of 10 Years Treasury Securities',\n",
    "    'rate_seasonally_adjusted':'Unemployment Rate Seasonally Adjusted',\n",
    "    'rate_not_seasonally_adjusted':'Unemployment Rate Not Seasonally Adjusted',\n",
    "    })\n",
    "\n",
    "full_df.plot(x='Date', y=['Rate of 1 Year Treasury Securities', 'Unemployment Rate Not Seasonally Adjusted'],\n",
    "    figsize=(12,6),ylabel='Rate %',xlabel='Date',title=' U.S. Rate of 1 year Treasury Securities vs Civilian Unemployment Rate')\n",
    "plt.savefig(\"Output/Rate_of_Treasury_Securities_and_Unemployment.png\")\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "0d50397dabfa9a816f0b2bb17b02cf6912088a0836263e0b871ec0c8e7b3e328"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}