{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93c38816",
   "metadata": {},
   "source": [
    "# Economic Indicators\n",
    "\n",
    "_What we learn from history is that people don't learn from history_ - Warren Buffett \n",
    "\n",
    "Economic data is fundamental to financial analysis, policymaking, and investment strategies. However, many economic indicators are subject to revisions, meaning initial estimates may change over time as more accurate data becomes available. Understanding these revisions is crucial for interpreting past economic conditions, refining forecasting models, and making informed decisions. We explore retrieving data from online sources such as the Federal Reserve Economic Data (FRED), its archival counterpart (ALFRED), and key derived datasets such as FRED-MD and FRED-QD. Additionally, we examine the impact of data revisions on critical economic indicators like Total Nonfarm Payrolls (PAYEMS), and methods for detecting outliers in historical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca23887b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By: Terence Lim, 2020-2025 (terence-lim.github.io)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "import textwrap\n",
    "from finds.readers import Alfred, fred_md, fred_qd\n",
    "from finds.utils import plot_date, plot_groupbar\n",
    "from finds.recipes import is_outlier\n",
    "from datetime import datetime\n",
    "from pprint import pprint\n",
    "from secret import credentials\n",
    "VERBOSE = 0\n",
    "# %matplotlib qt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f420d36",
   "metadata": {},
   "source": [
    "## FRED\n",
    "\n",
    "Federal Reserve Economic Data (FRED) is a widely used online database maintained by the Federal Reserve Bank of St. Louis, providing access to hundreds of thousands of economic data series from national and international sources. Users can retrieve data via the website, an Excel add-in, or API calls.  \n",
    "\n",
    "## Retrieving data from websites\n",
    "\n",
    "Economic data can be retrieved from the web through several methods:  \n",
    "\n",
    "1. **Downloading structured files** – Many websites provide data in formats like CSV, Excel, or JSON, making it easy to import into analytical tools.  \n",
    "2. **Web scraping** – Extracting information directly from web pages by identifying specific HTML tags or text patterns.  \n",
    "3. **Using APIs** – Some platforms, including FRED, offer APIs that allow developers to automate data retrieval via structured queries. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021b38a7",
   "metadata": {},
   "source": [
    "### Download structured files       \n",
    "\n",
    "Many economic data providers allow users to download pre-structured files containing historical and current data. These files often include metadata, timestamps, and adjustment information.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f332383",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This URL is the location of the FRED-MD csv file from the St Louis FRED\n",
    "url = 'https://www.stlouisfed.org/-/media/project/frbstl/stlouisfed/research/fred-md/monthly/current.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "30ef7573",
   "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>sasdate</th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPLx</th>\n",
       "      <th>RETAILx</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>DNDGRG3M086SBEA</th>\n",
       "      <th>DSERRG3M086SBEA</th>\n",
       "      <th>CES0600000008</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
       "      <th>UMCSENTx</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Transform:</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000</td>\n",
       "      <td>6.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/1959</td>\n",
       "      <td>2583.560</td>\n",
       "      <td>2426.0</td>\n",
       "      <td>15.188</td>\n",
       "      <td>2.766768e+05</td>\n",
       "      <td>18235.77392</td>\n",
       "      <td>21.9616</td>\n",
       "      <td>23.3868</td>\n",
       "      <td>22.2620</td>\n",
       "      <td>31.6664</td>\n",
       "      <td>...</td>\n",
       "      <td>18.294</td>\n",
       "      <td>10.152</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>84.2043</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/1/1959</td>\n",
       "      <td>2593.596</td>\n",
       "      <td>2434.8</td>\n",
       "      <td>15.346</td>\n",
       "      <td>2.787140e+05</td>\n",
       "      <td>18369.56308</td>\n",
       "      <td>22.3917</td>\n",
       "      <td>23.7024</td>\n",
       "      <td>22.4549</td>\n",
       "      <td>31.8987</td>\n",
       "      <td>...</td>\n",
       "      <td>18.302</td>\n",
       "      <td>10.167</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6476.00</td>\n",
       "      <td>12298.00</td>\n",
       "      <td>83.5280</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/1/1959</td>\n",
       "      <td>2610.396</td>\n",
       "      <td>2452.7</td>\n",
       "      <td>15.491</td>\n",
       "      <td>2.777753e+05</td>\n",
       "      <td>18523.05762</td>\n",
       "      <td>22.7142</td>\n",
       "      <td>23.8459</td>\n",
       "      <td>22.5651</td>\n",
       "      <td>31.8987</td>\n",
       "      <td>...</td>\n",
       "      <td>18.289</td>\n",
       "      <td>10.185</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6508.00</td>\n",
       "      <td>12349.00</td>\n",
       "      <td>81.6405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/1/1959</td>\n",
       "      <td>2627.446</td>\n",
       "      <td>2470.0</td>\n",
       "      <td>15.435</td>\n",
       "      <td>2.833627e+05</td>\n",
       "      <td>18534.46600</td>\n",
       "      <td>23.1981</td>\n",
       "      <td>24.1903</td>\n",
       "      <td>22.8957</td>\n",
       "      <td>32.4019</td>\n",
       "      <td>...</td>\n",
       "      <td>18.300</td>\n",
       "      <td>10.221</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6620.00</td>\n",
       "      <td>12484.00</td>\n",
       "      <td>81.8099</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>8/1/2024</td>\n",
       "      <td>20007.209</td>\n",
       "      <td>16322.1</td>\n",
       "      <td>121.052</td>\n",
       "      <td>1.530317e+06</td>\n",
       "      <td>710038.00000</td>\n",
       "      <td>103.0135</td>\n",
       "      <td>100.9825</td>\n",
       "      <td>100.9803</td>\n",
       "      <td>102.2118</td>\n",
       "      <td>...</td>\n",
       "      <td>119.653</td>\n",
       "      <td>128.291</td>\n",
       "      <td>31.26</td>\n",
       "      <td>35.81</td>\n",
       "      <td>27.97</td>\n",
       "      <td>67.9</td>\n",
       "      <td>551667.22</td>\n",
       "      <td>933066.90</td>\n",
       "      <td>5327.6461</td>\n",
       "      <td>19.6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>9/1/2024</td>\n",
       "      <td>20044.142</td>\n",
       "      <td>16333.7</td>\n",
       "      <td>121.690</td>\n",
       "      <td>1.541305e+06</td>\n",
       "      <td>716388.00000</td>\n",
       "      <td>102.5969</td>\n",
       "      <td>100.3826</td>\n",
       "      <td>100.0630</td>\n",
       "      <td>101.9696</td>\n",
       "      <td>...</td>\n",
       "      <td>119.220</td>\n",
       "      <td>128.682</td>\n",
       "      <td>31.44</td>\n",
       "      <td>36.00</td>\n",
       "      <td>28.11</td>\n",
       "      <td>70.1</td>\n",
       "      <td>553347.06</td>\n",
       "      <td>934283.59</td>\n",
       "      <td>5368.5818</td>\n",
       "      <td>17.6597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>10/1/2024</td>\n",
       "      <td>20128.752</td>\n",
       "      <td>16397.9</td>\n",
       "      <td>121.948</td>\n",
       "      <td>1.539382e+06</td>\n",
       "      <td>720393.00000</td>\n",
       "      <td>102.0854</td>\n",
       "      <td>99.5434</td>\n",
       "      <td>98.9267</td>\n",
       "      <td>101.3127</td>\n",
       "      <td>...</td>\n",
       "      <td>119.064</td>\n",
       "      <td>129.169</td>\n",
       "      <td>31.55</td>\n",
       "      <td>36.22</td>\n",
       "      <td>28.14</td>\n",
       "      <td>70.5</td>\n",
       "      <td>554377.25</td>\n",
       "      <td>937299.96</td>\n",
       "      <td>5407.3304</td>\n",
       "      <td>19.9478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>11/1/2024</td>\n",
       "      <td>20161.687</td>\n",
       "      <td>16432.8</td>\n",
       "      <td>122.519</td>\n",
       "      <td>1.544190e+06</td>\n",
       "      <td>725925.00000</td>\n",
       "      <td>102.2549</td>\n",
       "      <td>99.8216</td>\n",
       "      <td>99.4970</td>\n",
       "      <td>101.7893</td>\n",
       "      <td>...</td>\n",
       "      <td>119.112</td>\n",
       "      <td>129.375</td>\n",
       "      <td>31.61</td>\n",
       "      <td>36.21</td>\n",
       "      <td>28.29</td>\n",
       "      <td>71.8</td>\n",
       "      <td>555000.61</td>\n",
       "      <td>938899.31</td>\n",
       "      <td>5382.4019</td>\n",
       "      <td>15.9822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>12/1/2024</td>\n",
       "      <td>20184.060</td>\n",
       "      <td>16457.8</td>\n",
       "      <td>123.013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>729191.00000</td>\n",
       "      <td>103.1942</td>\n",
       "      <td>100.5351</td>\n",
       "      <td>100.1302</td>\n",
       "      <td>102.2582</td>\n",
       "      <td>...</td>\n",
       "      <td>119.689</td>\n",
       "      <td>129.760</td>\n",
       "      <td>31.73</td>\n",
       "      <td>36.44</td>\n",
       "      <td>28.34</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5370.6184</td>\n",
       "      <td>15.6997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sasdate        RPI  W875RX1  DPCERA3M086SBEA     CMRMTSPLx  \\\n",
       "0    Transform:      5.000      5.0            5.000  5.000000e+00   \n",
       "1      1/1/1959   2583.560   2426.0           15.188  2.766768e+05   \n",
       "2      2/1/1959   2593.596   2434.8           15.346  2.787140e+05   \n",
       "3      3/1/1959   2610.396   2452.7           15.491  2.777753e+05   \n",
       "4      4/1/1959   2627.446   2470.0           15.435  2.833627e+05   \n",
       "..          ...        ...      ...              ...           ...   \n",
       "788    8/1/2024  20007.209  16322.1          121.052  1.530317e+06   \n",
       "789    9/1/2024  20044.142  16333.7          121.690  1.541305e+06   \n",
       "790   10/1/2024  20128.752  16397.9          121.948  1.539382e+06   \n",
       "791   11/1/2024  20161.687  16432.8          122.519  1.544190e+06   \n",
       "792   12/1/2024  20184.060  16457.8          123.013           NaN   \n",
       "\n",
       "          RETAILx    INDPRO   IPFPNSS   IPFINAL   IPCONGD  ...  \\\n",
       "0         5.00000    5.0000    5.0000    5.0000    5.0000  ...   \n",
       "1     18235.77392   21.9616   23.3868   22.2620   31.6664  ...   \n",
       "2     18369.56308   22.3917   23.7024   22.4549   31.8987  ...   \n",
       "3     18523.05762   22.7142   23.8459   22.5651   31.8987  ...   \n",
       "4     18534.46600   23.1981   24.1903   22.8957   32.4019  ...   \n",
       "..            ...       ...       ...       ...       ...  ...   \n",
       "788  710038.00000  103.0135  100.9825  100.9803  102.2118  ...   \n",
       "789  716388.00000  102.5969  100.3826  100.0630  101.9696  ...   \n",
       "790  720393.00000  102.0854   99.5434   98.9267  101.3127  ...   \n",
       "791  725925.00000  102.2549   99.8216   99.4970  101.7893  ...   \n",
       "792  729191.00000  103.1942  100.5351  100.1302  102.2582  ...   \n",
       "\n",
       "     DNDGRG3M086SBEA  DSERRG3M086SBEA  CES0600000008  CES2000000008  \\\n",
       "0              6.000            6.000           6.00           6.00   \n",
       "1             18.294           10.152           2.13           2.45   \n",
       "2             18.302           10.167           2.14           2.46   \n",
       "3             18.289           10.185           2.15           2.45   \n",
       "4             18.300           10.221           2.16           2.47   \n",
       "..               ...              ...            ...            ...   \n",
       "788          119.653          128.291          31.26          35.81   \n",
       "789          119.220          128.682          31.44          36.00   \n",
       "790          119.064          129.169          31.55          36.22   \n",
       "791          119.112          129.375          31.61          36.21   \n",
       "792          119.689          129.760          31.73          36.44   \n",
       "\n",
       "     CES3000000008  UMCSENTx  DTCOLNVHFNM   DTCTHFNM     INVEST  VIXCLSx  \n",
       "0             6.00       2.0         6.00       6.00     6.0000   1.0000  \n",
       "1             2.04       NaN      6476.00   12298.00    84.2043      NaN  \n",
       "2             2.05       NaN      6476.00   12298.00    83.5280      NaN  \n",
       "3             2.07       NaN      6508.00   12349.00    81.6405      NaN  \n",
       "4             2.08       NaN      6620.00   12484.00    81.8099      NaN  \n",
       "..             ...       ...          ...        ...        ...      ...  \n",
       "788          27.97      67.9    551667.22  933066.90  5327.6461  19.6750  \n",
       "789          28.11      70.1    553347.06  934283.59  5368.5818  17.6597  \n",
       "790          28.14      70.5    554377.25  937299.96  5407.3304  19.9478  \n",
       "791          28.29      71.8    555000.61  938899.31  5382.4019  15.9822  \n",
       "792          28.34      74.0          NaN        NaN  5370.6184  15.6997  \n",
       "\n",
       "[793 rows x 127 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas has several built-in readers for csv, xml, json, excel and even html files          \n",
    "df = pd.read_csv(url, header=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db65a278",
   "metadata": {},
   "source": [
    "\n",
    "### Web scraping\n",
    "\n",
    "Web scraping involves extracting data from unstructured web pages by identifying patterns in the HTML structure. This method is useful when structured data files are unavailable, but it requires compliance with website policies.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "336c92a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL that displays the most popular series in the FRED economic data web site\n",
    "url = f\"https://fred.stlouisfed.org/tags/series?ob=pv&pageID=1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be575083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use requests package to retrieve the web page\n",
    "import requests\n",
    "data = requests.get(url)\n",
    "data    # a response code of 200 indicates the request has succeeded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac8b4ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'<!DOCTYPE html>\\n<html lang=\"en\">\\n<head>\\n    <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\\n    <meta charset=\"utf-8\">\\n                <title>Economic Data Series by Tag | FRED | St. Louis Fed</'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the content is just a byte-string that you can parse with Python string (or other) methods\n",
    "data.content[:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "899f3ce3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s=\"series-title pager-series-title-gtm\" href=\"/series/T10Y2Y\" id=\"titleLink\" style=\"font-size:1.2em; padding-bottom: 2px\">10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity</a></h3>\n",
      "</div>\n",
      "<div class=\"display-results-popularity-bar d-none d-sm-block col-sm-2\">\n",
      "<span aria-label=\"popularity 100% popular\" class=\"popularity-bar-span-parent\" data-target=\"popularity-bar-span-T10Y2Y\" tabindex=\"0\" title=\"100% popular\">\n",
      "<span aria-hidden=\"true\" class=\"popularity_bar\" style=\"padding-top: 3px; padding-left:60px;\"> </span> <span aria-hidden=\"true\" class=\"popularity_bar_background\" id=\"popularity-bar-span-T10Y2Y\"> </span></span>\n",
      "</div>\n",
      "</td>\n",
      "</tr>\n",
      "<tr class=\"series-pager-attr\">\n",
      "<td colspan=\"2\">\n",
      "<div class=\"series-meta series-group-meta\">\n",
      "<span class=\"attributes\">Percent, Not Seasonally Adjusted</span>\n",
      "<br class=\"clear\"/>\n",
      "</div>\n",
      "<div class=\"series-meta\">\n",
      "<input aria-labelledby=\"unitLinkT10Y2Y\" class=\"pager-item-checkbox pager-check-series-gtm\" name=\"sids[0]\" type=\"checkbox\" v\n"
     ]
    }
   ],
   "source": [
    "# use the BeautifulSoup package to parse html formats\n",
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(data.content, 'lxml')\n",
    "\n",
    "# based on this snippet, we want to extract the href property of the series-title class tag\n",
    "print(soup.decode()[39000:40000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a2555b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a class=\"series-title pager-series-title-gtm\" href=\"/series/T10Y2Y\" id=\"titleLink\" style=\"font-size:1.2em; padding-bottom: 2px\">10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity</a>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify all the tags whose class starts with 'series-title'\n",
    "tags = soup.findAll(name='a', attrs={'class': 'series-title'})\n",
    "tags[0]   # show first tag found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ba23be6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T10Y2Y'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract desired substring (which is a data series mnemonic) from the href property\n",
    "details = [tag.get('href').split('/')[-1] for tag in tags]  # only want substring after last '/'\n",
    "details[0]  # show first mnemonic string found"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30454d5",
   "metadata": {},
   "source": [
    "### Using APIs\n",
    "\n",
    "APIs (Application Programming Interfaces) enable direct communication with data servers, allowing for real-time data retrieval. Many economic research institutions, including the St Louis Fed, offer APIs to access macroeconomic data programmatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82e9b10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# an API call is simply a URL string containing your parameters for the request\n",
    "url = \"{root}?series_id={series_id}&file_type={file_type}&api_key={api_key}\".format(\n",
    "    root=\"https://api.stlouisfed.org/fred/series\", # base url of the API call\n",
    "    series_id=details[0],                          # mnemonic of the data series to retrieve\n",
    "    file_type='json',                              # request data be returned in json format\n",
    "    api_key=credentials['fred']['api_key'])        # private api key (obtain from FRED for free)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54d6f09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'{\"realtime_start\":\"2025-02-28\",\"realtime_end\":\"2025-02-28\",\"seriess\":[{\"id\":\"T10Y2Y\",\"realtime_start\":\"2025-02-28\",\"realtime_end\":\"2025-02-28\",\"title\":\"10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity\",\"observation_start\":\"1976-06-01\",\"observation_end\":\"2025-02-28\",\"frequency\":\"Daily\",\"frequency_short\":\"D\",\"units\":\"Percent\",\"units_short\":\"%\",\"seasonal_adjustment\":\"Not Seasonally Adjusted\",\"seasonal_adjustment_short\":\"NSA\",\"last_updated\":\"2025-02-28 16:02:07-06\",\"popularity\":100,\"notes\":\"Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department (https:\\\\/\\\\/www.treasury.gov\\\\/resource-center\\\\/data-chart-center\\\\/interest-rates\\\\/Pages\\\\/TextView.aspx?data=yield).\\\\r\\\\nSeries is calculated as the spread between 10-Year Treasury Constant Maturity (BC_10YEAR) and 2-Year Treasury Constant Maturity (BC_2YEAR). Both underlying series are published at the U.S. Treasury Department (https:\\\\/\\\\/www.treasury.gov\\\\/resource-center\\\\/data-chart-center\\\\/interest-rates\\\\/Pages\\\\/TextView.aspx?data=yield).\"}]}'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the API call to retrieve the data\n",
    "data = requests.get(url)\n",
    "data.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "41965924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'realtime_start': '2025-02-28',\n",
       " 'realtime_end': '2025-02-28',\n",
       " 'seriess': [{'id': 'T10Y2Y',\n",
       "   'realtime_start': '2025-02-28',\n",
       "   'realtime_end': '2025-02-28',\n",
       "   'title': '10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity',\n",
       "   'observation_start': '1976-06-01',\n",
       "   'observation_end': '2025-02-28',\n",
       "   'frequency': 'Daily',\n",
       "   'frequency_short': 'D',\n",
       "   'units': 'Percent',\n",
       "   'units_short': '%',\n",
       "   'seasonal_adjustment': 'Not Seasonally Adjusted',\n",
       "   'seasonal_adjustment_short': 'NSA',\n",
       "   'last_updated': '2025-02-28 16:02:07-06',\n",
       "   'popularity': 100,\n",
       "   'notes': 'Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department (https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield).\\r\\nSeries is calculated as the spread between 10-Year Treasury Constant Maturity (BC_10YEAR) and 2-Year Treasury Constant Maturity (BC_2YEAR). Both underlying series are published at the U.S. Treasury Department (https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield).'}]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the json package to convert byte-string data content\n",
    "import json\n",
    "v = json.loads(data.content)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a90968e2",
   "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>id</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>popularity</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T10Y2Y</td>\n",
       "      <td>2025-02-28</td>\n",
       "      <td>2025-02-28</td>\n",
       "      <td>10-Year Treasury Constant Maturity Minus 2-Yea...</td>\n",
       "      <td>1976-06-01</td>\n",
       "      <td>2025-02-28</td>\n",
       "      <td>Daily</td>\n",
       "      <td>D</td>\n",
       "      <td>Percent</td>\n",
       "      <td>%</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2025-02-28 16:02:07-06</td>\n",
       "      <td>100</td>\n",
       "      <td>Starting with the update on June 21, 2019, the...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id realtime_start realtime_end  \\\n",
       "0  T10Y2Y     2025-02-28   2025-02-28   \n",
       "\n",
       "                                               title observation_start  \\\n",
       "0  10-Year Treasury Constant Maturity Minus 2-Yea...        1976-06-01   \n",
       "\n",
       "  observation_end frequency frequency_short    units units_short  \\\n",
       "0      2025-02-28     Daily               D  Percent           %   \n",
       "\n",
       "       seasonal_adjustment seasonal_adjustment_short            last_updated  \\\n",
       "0  Not Seasonally Adjusted                       NSA  2025-02-28 16:02:07-06   \n",
       "\n",
       "   popularity                                              notes  \n",
       "0         100  Starting with the update on June 21, 2019, the...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas can create a DataFrame directly from a dict data structure\n",
    "df = DataFrame(v['seriess'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bcb032",
   "metadata": {},
   "source": [
    "\n",
    "### ALFRED (Archival FRED)\n",
    "\n",
    "ALFRED extends FRED’s functionality by preserving historical versions of economic data. This allows researchers to track how data revisions impact economic narratives over time.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be22ec63",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = int(datetime.today().strftime('%Y%m%d'))\n",
    "alf = Alfred(api_key=credentials['fred']['api_key'], verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8871881",
   "metadata": {},
   "source": [
    "### Popular FRED series\n",
    "\n",
    "FRED organizes its data into **categories, frequencies, and seasonal adjustments**. Some of the most frequently accessed series include employment figures, inflation metrics, and GDP growth rates. A current list of the most popular FRED series can be found [here](https://fred.stlouisfed.org/tags/series?ob=pv&pageID=1).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a329b24f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Popular Series in FRED, retrieved 20250302\n"
     ]
    },
    {
     "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>title</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T10Y2Y</th>\n",
       "      <td>10-Year Treasury Constant Maturity Minus 2-Yea...</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORTGAGE30US</th>\n",
       "      <td>30-Year Fixed Rate Mortgage Average in the Uni...</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <td>Federal Funds Effective Rate</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2SL</th>\n",
       "      <td>M2</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRPONTSYD</th>\n",
       "      <td>Overnight Reverse Repurchase Agreements: Treas...</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPIAUCSL</th>\n",
       "      <td>Consumer Price Index for All Urban Consumers: ...</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE</th>\n",
       "      <td>Unemployment Rate</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALCL</th>\n",
       "      <td>Assets: Total Assets: Total Assets (Less Elimi...</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10Y3M</th>\n",
       "      <td>10-Year Treasury Constant Maturity Minus 3-Mon...</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>Gross Domestic Product</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDPC1</th>\n",
       "      <td>Real Gross Domestic Product</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DGS10</th>\n",
       "      <td>Market Yield on U.S. Treasury Securities at 10...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAMLH0A0HYM2</th>\n",
       "      <td>ICE BofA US High Yield Index Option-Adjusted S...</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSPUS</th>\n",
       "      <td>Median Sales Price of Houses Sold for the Unit...</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSUSHPINSA</th>\n",
       "      <td>S&amp;P CoreLogic Case-Shiller U.S. National Home ...</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T10YIE</th>\n",
       "      <td>10-Year Breakeven Inflation Rate</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPCPITOTLZGUSA</th>\n",
       "      <td>Inflation, consumer prices for the United States</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M1SL</th>\n",
       "      <td>M1</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            title  popularity\n",
       "T10Y2Y          10-Year Treasury Constant Maturity Minus 2-Yea...         100\n",
       "MORTGAGE30US    30-Year Fixed Rate Mortgage Average in the Uni...          99\n",
       "FEDFUNDS                             Federal Funds Effective Rate          98\n",
       "M2SL                                                           M2          93\n",
       "RRPONTSYD       Overnight Reverse Repurchase Agreements: Treas...          95\n",
       "CPIAUCSL        Consumer Price Index for All Urban Consumers: ...          95\n",
       "UNRATE                                          Unemployment Rate          95\n",
       "WALCL           Assets: Total Assets: Total Assets (Less Elimi...          94\n",
       "T10Y3M          10-Year Treasury Constant Maturity Minus 3-Mon...          94\n",
       "GDP                                        Gross Domestic Product          93\n",
       "GDPC1                                 Real Gross Domestic Product          92\n",
       "DGS10           Market Yield on U.S. Treasury Securities at 10...          92\n",
       "BAMLH0A0HYM2    ICE BofA US High Yield Index Option-Adjusted S...          92\n",
       "MSPUS           Median Sales Price of Houses Sold for the Unit...          90\n",
       "CSUSHPINSA      S&P CoreLogic Case-Shiller U.S. National Home ...          88\n",
       "T10YIE                           10-Year Breakeven Inflation Rate          89\n",
       "FPCPITOTLZGUSA   Inflation, consumer prices for the United States          85\n",
       "M1SL                                                           M1          84"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scrape FRED most popular page\n",
    "popular = {}\n",
    "titles = Alfred.popular(1)  \n",
    "for title in titles:\n",
    "    series = alf.request_series(title)   # requests 'series' FRED api\n",
    "    if not series.empty:\n",
    "        popular[title] = series.iloc[-1][['title', 'popularity']]\n",
    "print(f\"Most Popular Series in FRED, retrieved {today}\")\n",
    "DataFrame.from_dict(popular, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea47c659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'FRED Most Popular series (retrieved 20250302)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot popular series\n",
    "fig, axes = plt.subplots(ncols=3, nrows=5, figsize=(10, 12), layout='constrained')\n",
    "for cn, (ax, title) in enumerate(zip(np.ravel(axes), titles[:15])):\n",
    "    series = alf(title)\n",
    "    plot_date(series, ax=ax, title=f\"{cn+1}.  {title}\", xlabel=alf.header(title)[:70],\n",
    "              fontsize=6, ls='-', cn=cn, nbins=4)\n",
    "plt.suptitle(f\"FRED Most Popular series (retrieved {today})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ed8f53",
   "metadata": {},
   "source": [
    "### FRED series categories\n",
    "\n",
    "One of the most closely watched FRED series is `Total Nonfarm Payroll Employment (PAYEMS)`, a key labor market indicator. This series belongs to broader employment-related categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15ca52fd",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>parent_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PAYEMS</th>\n",
       "      <td>32305</td>\n",
       "      <td>Total Nonfarm</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id           name parent_id\n",
       "PAYEMS  32305  Total Nonfarm        11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve grandparent,  parent and siblings of series                               \n",
    "series_id, freq = 'PAYEMS', 'M'\n",
    "category = alf.categories(series_id).iloc[0]\n",
    "grand_category = alf.get_category(category['parent_id'])\n",
    "parent_category = alf.get_category(category['id'])\n",
    "category.to_frame().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a155987e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Super category 11: Current Employment Statistics (Establishment Survey)\n",
      "The establishment survey provides data on employment, hours, and\n",
      "earnings by industry.  Numerous conceptual and methodological\n",
      "differences between the current population (household) and\n",
      "establishment surveys result in important distinctions in the\n",
      "employment estimates derived from the surveys. Among these are:   The\n",
      "household survey includes agricultural workers, the self- employed,\n",
      "unpaid family workers, and private household workers among the\n",
      "employed. These groups are excluded from the establishment survey.\n",
      "The household survey includes people on unpaid leave among the\n",
      "employed. The establishment survey does not.   The household survey is\n",
      "limited to workers 16 years of age and older. The establishment survey\n",
      "is not limited by age.   The household survey has no duplication of\n",
      "individuals, because individuals are counted only once, even if they\n",
      "hold more than one job. In the establishment survey, employees working\n",
      "at more than one job and thus appearing on more than one payroll are\n",
      "counted separately for each appearance.   For more information, visit\n",
      "http://www.bls.gov/news.release/empsit.tn.htm.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Super category {grand_category['id']}: {grand_category['name']}\")\n",
    "if 'notes' in grand_category:\n",
    "    print(textwrap.fill(grand_category['notes']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38ce3496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parent categories:\n",
      " 32305: Total Nonfarm  (children=0, series=5)\n",
      " 32306: Total Private  (children=0, series=27)\n",
      " 32307: Goods-Producing  (children=0, series=27)\n",
      " 32326: Service-Providing  (children=0, series=1)\n",
      " 32308: Private Service-Providing  (children=0, series=27)\n",
      " 32309: Mining and Logging  (children=0, series=39)\n",
      " 32310: Construction  (children=0, series=41)\n",
      " 32311: Manufacturing  (children=0, series=31)\n",
      " 32312: Durable Goods  (children=0, series=63)\n",
      " 32313: Nondurable Goods  (children=0, series=55)\n",
      " 32314: Trade, Transportation, and Utilities  (children=0, series=27)\n",
      " 32315: Wholesale Trade  (children=0, series=33)\n",
      " 32316: Retail Trade  (children=0, series=55)\n",
      " 32317: Transportation and Warehousing  (children=0, series=47)\n",
      " 32318: Utilities  (children=0, series=27)\n",
      " 32319: Information  (children=0, series=39)\n",
      " 32320: Financial Activities  (children=0, series=51)\n",
      " 32321: Professional and Business Services  (children=0, series=55)\n",
      " 32322: Education and Health Services  (children=0, series=51)\n",
      " 32323: Leisure and Hospitality  (children=0, series=41)\n",
      " 32324: Other Services  (children=0, series=33)\n",
      " 32325: Government  (children=0, series=23)\n"
     ]
    }
   ],
   "source": [
    "print(\"Parent categories:\")\n",
    "for child in grand_category['children']:\n",
    "    node = alf.get_category(child['id'])\n",
    "    if node:\n",
    "        print(f\" {node['id']}: {node['name']} \"\n",
    "              f\" (children={len(node['children'])}, series={len(node['series'])})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cb51e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sibling series:\n",
      "  CES0000000010: Women Employees, Total Nonfarm Seasonally Adjusted (popularity=4)\n",
      "  CES0000000039: Women Employees-To-All Employees Ratio: Total Nonfarm Seasonally Adjusted (popularity=16)\n",
      "  CEU0000000010: Women Employees, Total Nonfarm Not Seasonally Adjusted (popularity=1)\n",
      "  PAYEMS: All Employees, Total Nonfarm Seasonally Adjusted (popularity=83)\n",
      "  PAYNSA: All Employees, Total Nonfarm Not Seasonally Adjusted (popularity=47)\n"
     ]
    }
   ],
   "source": [
    "print(\"Sibling series:\")\n",
    "for child in parent_category['series']:\n",
    "    if child['id'] == series_id:\n",
    "        node = child\n",
    "    print(f\"  {child['id']}: {child['title']} {child['seasonal_adjustment']}\"\n",
    "          f\" (popularity={child['popularity']})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b67a122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PAYEMS: All Employees, Total Nonfarm Seasonally Adjusted  (1939-01-01-2025-01-01)\n",
      "\n",
      "All Employees: Total Nonfarm, commonly known as Total Nonfarm Payroll,\n",
      "is a measure of the number of U.S. workers in the economy that\n",
      "excludes proprietors, private household employees, unpaid volunteers,\n",
      "farm employees, and the unincorporated self-employed. This measure\n",
      "accounts for approximately 80 percent of the workers who contribute to\n",
      "Gross Domestic Product (GDP).  This measure provides useful insights\n",
      "into the current economic situation because it can represent the\n",
      "number of jobs added or lost in an economy. Increases in employment\n",
      "might indicate that businesses are hiring which might also suggest\n",
      "that businesses are growing. Additionally, those who are newly\n",
      "employed have increased their personal incomes, which means (all else\n",
      "constant) their disposable incomes have also increased, thus fostering\n",
      "further economic expansion.  Generally, the U.S. labor force and\n",
      "levels of employment and unemployment are subject to fluctuations due\n",
      "to seasonal changes in weather, major holidays, and the opening and\n",
      "closing of schools. The Bureau of Labor Statistics (BLS) adjusts the\n",
      "data to offset the seasonal effects to show non-seasonal changes: for\n",
      "example, women's participation in the labor force; or a general\n",
      "decline in the number of employees, a possible indication of a\n",
      "downturn in the economy. To closely examine seasonal and non-seasonal\n",
      "changes, the BLS releases two monthly statistical measures: the\n",
      "seasonally adjusted All Employees: Total Nonfarm (PAYEMS) and All\n",
      "Employees: Total Nonfarm (PAYNSA), which is not seasonally adjusted.\n",
      "The series comes from the 'Current Employment Statistics\n",
      "(Establishment Survey).'  The source code is: CES0000000001\n"
     ]
    }
   ],
   "source": [
    "print(f\"{node['id']}: {node['title']} {node['seasonal_adjustment']}\",\n",
    "      f\" ({node['observation_start']}-{node['observation_end']})\")\n",
    "print()\n",
    "print(textwrap.fill(node['notes']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3abf1c",
   "metadata": {},
   "source": [
    "## Revisions and vintage dates\n",
    "\n",
    "Economic data revisions occur as new information becomes available, improving the accuracy of initial estimates. The Bureau of Labor Statistics (BLS), for instance, releases an initial estimate of Total Nonfarm Payroll Employment (PAYEMS) on the first Friday of each month. However, this figure is a very rough estimate, which is then revised in subsequent months as more firm-level data is collected.  \n",
    "\n",
    "These revisions can be significant, sometimes altering economic assessments. ALFRED, the archival FRED tool, allows users to compare initial estimates with later revisions.  For the monthly values of PAYEMS in 2023, we examine the total amount of changes at each subsequent revision.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b1c2a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Employees, Total Nonfarm (retrieved 20250302):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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>PAYEMS</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20230131</th>\n",
       "      <td>154780</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230228</th>\n",
       "      <td>155086</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230331</th>\n",
       "      <td>155171</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230430</th>\n",
       "      <td>155387</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230531</th>\n",
       "      <td>155614</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230630</th>\n",
       "      <td>155871</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230731</th>\n",
       "      <td>156019</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230831</th>\n",
       "      <td>156176</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230930</th>\n",
       "      <td>156334</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231031</th>\n",
       "      <td>156520</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231130</th>\n",
       "      <td>156661</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231231</th>\n",
       "      <td>156930</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PAYEMS  realtime_start  realtime_end\n",
       "date                                          \n",
       "20230131  154780        20250207      99991231\n",
       "20230228  155086        20250207      99991231\n",
       "20230331  155171        20250207      99991231\n",
       "20230430  155387        20250207      99991231\n",
       "20230531  155614        20250207      99991231\n",
       "20230630  155871        20250207      99991231\n",
       "20230731  156019        20250207      99991231\n",
       "20230831  156176        20250207      99991231\n",
       "20230930  156334        20250207      99991231\n",
       "20231031  156520        20250207      99991231\n",
       "20231130  156661        20250207      99991231\n",
       "20231231  156930        20250207      99991231"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, end = 20230101, 20231231\n",
    "data = {}\n",
    "print(f\"{alf.header(series_id)} (retrieved {today}):\")\n",
    "latest = alf(series_id, start=start, end=end, freq=freq, realtime=True)\n",
    "latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a9efbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First Release:\n"
     ]
    },
    {
     "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>PAYEMS</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20230131</th>\n",
       "      <td>155073</td>\n",
       "      <td>20230203</td>\n",
       "      <td>20230309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230228</th>\n",
       "      <td>155350</td>\n",
       "      <td>20230310</td>\n",
       "      <td>20230406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230331</th>\n",
       "      <td>155569</td>\n",
       "      <td>20230407</td>\n",
       "      <td>20230504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230430</th>\n",
       "      <td>155673</td>\n",
       "      <td>20230505</td>\n",
       "      <td>20230601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230531</th>\n",
       "      <td>156105</td>\n",
       "      <td>20230602</td>\n",
       "      <td>20230706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230630</th>\n",
       "      <td>156204</td>\n",
       "      <td>20230707</td>\n",
       "      <td>20230803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230731</th>\n",
       "      <td>156342</td>\n",
       "      <td>20230804</td>\n",
       "      <td>20230831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230831</th>\n",
       "      <td>156419</td>\n",
       "      <td>20230901</td>\n",
       "      <td>20231005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230930</th>\n",
       "      <td>156874</td>\n",
       "      <td>20231006</td>\n",
       "      <td>20231102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231031</th>\n",
       "      <td>156923</td>\n",
       "      <td>20231103</td>\n",
       "      <td>20231207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231130</th>\n",
       "      <td>157087</td>\n",
       "      <td>20231208</td>\n",
       "      <td>20240104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231231</th>\n",
       "      <td>157232</td>\n",
       "      <td>20240105</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PAYEMS  realtime_start  realtime_end\n",
       "date                                          \n",
       "20230131  155073        20230203      20230309\n",
       "20230228  155350        20230310      20230406\n",
       "20230331  155569        20230407      20230504\n",
       "20230430  155673        20230505      20230601\n",
       "20230531  156105        20230602      20230706\n",
       "20230630  156204        20230707      20230803\n",
       "20230731  156342        20230804      20230831\n",
       "20230831  156419        20230901      20231005\n",
       "20230930  156874        20231006      20231102\n",
       "20231031  156923        20231103      20231207\n",
       "20231130  157087        20231208      20240104\n",
       "20231231  157232        20240105      20240201"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"First Release:\")\n",
    "data[0] = alf(series_id, release=1, start=start, end=end, freq=freq, realtime=True)\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0ecb2116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second Release:\n"
     ]
    },
    {
     "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>PAYEMS</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20230131</th>\n",
       "      <td>155039</td>\n",
       "      <td>20230310</td>\n",
       "      <td>20230406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230228</th>\n",
       "      <td>155333</td>\n",
       "      <td>20230407</td>\n",
       "      <td>20230504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230331</th>\n",
       "      <td>155420</td>\n",
       "      <td>20230505</td>\n",
       "      <td>20230601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230430</th>\n",
       "      <td>155766</td>\n",
       "      <td>20230602</td>\n",
       "      <td>20230706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230531</th>\n",
       "      <td>155995</td>\n",
       "      <td>20230707</td>\n",
       "      <td>20230803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230630</th>\n",
       "      <td>156155</td>\n",
       "      <td>20230804</td>\n",
       "      <td>20230831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230731</th>\n",
       "      <td>156232</td>\n",
       "      <td>20230901</td>\n",
       "      <td>20231005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230831</th>\n",
       "      <td>156538</td>\n",
       "      <td>20231006</td>\n",
       "      <td>20231102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230930</th>\n",
       "      <td>156773</td>\n",
       "      <td>20231103</td>\n",
       "      <td>20231207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231031</th>\n",
       "      <td>156888</td>\n",
       "      <td>20231208</td>\n",
       "      <td>20240104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231130</th>\n",
       "      <td>157016</td>\n",
       "      <td>20240105</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231231</th>\n",
       "      <td>157347</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20240307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PAYEMS  realtime_start  realtime_end\n",
       "date                                          \n",
       "20230131  155039        20230310      20230406\n",
       "20230228  155333        20230407      20230504\n",
       "20230331  155420        20230505      20230601\n",
       "20230430  155766        20230602      20230706\n",
       "20230531  155995        20230707      20230803\n",
       "20230630  156155        20230804      20230831\n",
       "20230731  156232        20230901      20231005\n",
       "20230831  156538        20231006      20231102\n",
       "20230930  156773        20231103      20231207\n",
       "20231031  156888        20231208      20240104\n",
       "20231130  157016        20240105      20240201\n",
       "20231231  157347        20240202      20240307"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Second Release:\")\n",
    "data[1] = alf(series_id, release=2, start=start, end=end, freq=freq, realtime=True)\n",
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "77d83d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third Release:\n"
     ]
    },
    {
     "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",
       "        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>PAYEMS</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20230131</th>\n",
       "      <td>155007</td>\n",
       "      <td>20230407</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230228</th>\n",
       "      <td>155255</td>\n",
       "      <td>20230505</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230331</th>\n",
       "      <td>155472</td>\n",
       "      <td>20230602</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230430</th>\n",
       "      <td>155689</td>\n",
       "      <td>20230707</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230531</th>\n",
       "      <td>155970</td>\n",
       "      <td>20230804</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230630</th>\n",
       "      <td>156075</td>\n",
       "      <td>20230901</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230731</th>\n",
       "      <td>156311</td>\n",
       "      <td>20231006</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230831</th>\n",
       "      <td>156476</td>\n",
       "      <td>20231103</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230930</th>\n",
       "      <td>156738</td>\n",
       "      <td>20231208</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231031</th>\n",
       "      <td>156843</td>\n",
       "      <td>20240105</td>\n",
       "      <td>20240201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231130</th>\n",
       "      <td>157014</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231231</th>\n",
       "      <td>157304</td>\n",
       "      <td>20240308</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PAYEMS  realtime_start  realtime_end\n",
       "date                                          \n",
       "20230131  155007        20230407      20240201\n",
       "20230228  155255        20230505      20240201\n",
       "20230331  155472        20230602      20240201\n",
       "20230430  155689        20230707      20240201\n",
       "20230531  155970        20230804      20240201\n",
       "20230630  156075        20230901      20240201\n",
       "20230731  156311        20231006      20240201\n",
       "20230831  156476        20231103      20240201\n",
       "20230930  156738        20231208      20240201\n",
       "20231031  156843        20240105      20240201\n",
       "20231130  157014        20240202      20250206\n",
       "20231231  157304        20240308      20250206"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Third Release:\")\n",
    "data[2] = alf(series_id, release=3, start=start, end=end, freq=freq, realtime=True)\n",
    "data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d22e9937",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fourth Release:\n"
     ]
    },
    {
     "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>PAYEMS</th>\n",
       "      <th>realtime_start</th>\n",
       "      <th>realtime_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20230131</th>\n",
       "      <td>154773</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230228</th>\n",
       "      <td>155060</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230331</th>\n",
       "      <td>155206</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230430</th>\n",
       "      <td>155484</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230531</th>\n",
       "      <td>155787</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230630</th>\n",
       "      <td>156027</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230731</th>\n",
       "      <td>156211</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230831</th>\n",
       "      <td>156421</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20230930</th>\n",
       "      <td>156667</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231031</th>\n",
       "      <td>156832</td>\n",
       "      <td>20240202</td>\n",
       "      <td>20250206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231130</th>\n",
       "      <td>156661</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20231231</th>\n",
       "      <td>156930</td>\n",
       "      <td>20250207</td>\n",
       "      <td>99991231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PAYEMS  realtime_start  realtime_end\n",
       "date                                          \n",
       "20230131  154773        20240202      20250206\n",
       "20230228  155060        20240202      20250206\n",
       "20230331  155206        20240202      20250206\n",
       "20230430  155484        20240202      20250206\n",
       "20230531  155787        20240202      20250206\n",
       "20230630  156027        20240202      20250206\n",
       "20230731  156211        20240202      20250206\n",
       "20230831  156421        20240202      20250206\n",
       "20230930  156667        20240202      20250206\n",
       "20231031  156832        20240202      20250206\n",
       "20231130  156661        20250207      99991231\n",
       "20231231  156930        20250207      99991231"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Fourth Release:\")\n",
    "data[3] = alf(series_id, release=4, start=start, end=end, freq=freq, realtime=True)\n",
    "data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41b85a8a",
   "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>Total revisions ('000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Revision 1</th>\n",
       "      <td>-349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revision 2</th>\n",
       "      <td>-348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revision 3</th>\n",
       "      <td>-2095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total revisions ('000)\n",
       "Revision 1                    -349\n",
       "Revision 2                    -348\n",
       "Revision 3                   -2095"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([(data[i][series_id] - data[i-1][series_id]).rename(f\"Revision {i}\")\n",
    "                for i in range(1, len(data))], axis=1)\n",
    "labels = pd.concat([data[i]['realtime_start'].rename(f\"Revision {i}\")\n",
    "                    for i in range(1, len(data))], axis=1).fillna(0).astype(int)\n",
    "DataFrame(df.sum(axis=0).rename(\"Total revisions ('000)\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a3fe299",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df = pd.concat([data[i][series_id].rename(f\"Revision {i}\")\n",
    "#                for i in range(1, len(data))], axis=1)\n",
    "#labels = pd.concat([data[i]['realtime_start'].rename(f\"Revision {i}\")\n",
    "#                    for i in range(1, len(data))], axis=1).fillna(0).astype(int)\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "plot_groupbar(df, labels=labels, ax=ax)\n",
    "plt.legend()\n",
    "plt.ylabel(f'Change in {series_id}')\n",
    "plt.title(f'Revisions and vintage dates of {series_id}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9baefff4",
   "metadata": {},
   "source": [
    "### FRED-MD and FRED-QD\n",
    "\n",
    "FRED-MD (Monthly Database) and FRED-QD (Quarterly Database) are curated datasets that streamline access to macroeconomic indicators. These datasets mimic the coverage of macroeconomic datasets used in the research literature and are updated in real-time, relieving users from the task of incorporating data changes and revisions. Historical monthly snap-shots of the datasets are also available."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c23c82",
   "metadata": {},
   "source": [
    "### Release dates\n",
    "\n",
    "The timing of data releases is crucial for market participants and policymakers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "476d2c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FRED-MD vintage: monthly/current.csv\n"
     ]
    }
   ],
   "source": [
    "md_df, md_transform = fred_md()\n",
    "end = md_df.index[-1]\n",
    "out = {}\n",
    "for i, title in enumerate(md_df.columns):\n",
    "    out[title] = alf(series_id=title,\n",
    "                     release=1,\n",
    "                     start=end, # within 4 days of monthend\n",
    "                     end=end,\n",
    "                     realtime=True)\n",
    "    if title.startswith('S&P'):  # stock market data available same day close\n",
    "        out[title] = Series({end: end}, name='realtime_start').to_frame()\n",
    "    elif title in alf.splice_:   # these series were renamed or spliced\n",
    "        if isinstance(Alfred.splice_[title], str):  # if renamed\n",
    "            out[title] = alf(series_id=Alfred.splice_[title],\n",
    "                             release=1,\n",
    "                             start=end-4,  # within 4 days of monthend\n",
    "                             end=end,\n",
    "                             realtime=True)\n",
    "        else:  # if FRED-MD series was spliced\n",
    "            out[title] = pd.concat([alf(series_id=sub,\n",
    "                                        reglease=1,\n",
    "                                        start=end-4,  # within 4 days of monthend\n",
    "                                        end=end,\n",
    "                                        realtime=True)\n",
    "                                    for sub in Alfred.splice_[title][1:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bc3d484e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# date convention of Consumer Sentiment\n",
    "df = alf('UMCSENT', release=1, realtime=True)\n",
    "out['UMCSENT'] = df[df['realtime_start'] > end - 4].iloc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1ead8b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weekly averages of Claims\n",
    "df = alf('ICNSA', release=1, realtime=True)\n",
    "out['CLAIMS'] = df[df['realtime_start'] > end - 4].iloc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb9b9c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot release dates of series in FRED-MD\n",
    "release = Series({k: str(min(v['realtime_start'])) if v is not None and len(v)\n",
    "                  else None  for k,v in out.items()}).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "27b79eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(clear=True, num=1, figsize=(13, 5))\n",
    "ax.plot(pd.to_datetime(release, errors='coerce'))\n",
    "ax.axvline(release[~release.isnull()].index[-1], c='r')\n",
    "ax.set_title(f\"Current ({end}) FRED-MD series, retrieved {today}\")\n",
    "ax.set_ylabel('First Release Date')\n",
    "ax.set_xticks(np.arange(len(release)))\n",
    "ax.set_xticklabels(release.index, rotation=90, fontsize='xx-small')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "df6e6cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recent values available to update missing in current FRED-MD\n",
      "date\n",
      "20241031    1538666.0\n",
      "20241130    1544822.0\n",
      "20241231    1555153.0\n",
      "Name: CMRMTSPL, dtype: float64\n",
      "date\n",
      "20241031    7839\n",
      "20241130    8156\n",
      "20241231    7600\n",
      "Name: HWI, dtype: int64\n",
      "date\n",
      "20241130    1.145345\n",
      "20241231    1.103689\n",
      "20250131         NaN\n",
      "Name: HWIURATIO, dtype: float64\n",
      "date\n",
      "20241031    248120.0\n",
      "20241130    248160.0\n",
      "20241231    248851.0\n",
      "Name: ACOGNO, dtype: float64\n",
      "date\n",
      "20241031    2585582.0\n",
      "20241130    2588757.0\n",
      "20241231    2584314.0\n",
      "Name: BUSINV, dtype: float64\n",
      "date\n",
      "20241031    1.37\n",
      "20241130    1.37\n",
      "20241231    1.35\n",
      "Name: ISRATIO, dtype: float64\n",
      "date\n",
      "20241031    3736897.53\n",
      "20241130    3745366.76\n",
      "20241231    3763355.59\n",
      "Name: NONREVSL, dtype: float64\n",
      "date\n",
      "20241130    149.697308\n",
      "20241231    149.793644\n",
      "20250131           NaN\n",
      "Name: CONSPI, dtype: float64\n",
      "date\n",
      "20241231    37.90\n",
      "20250131    37.66\n",
      "20250228    37.53\n",
      "Name: S&P PE ratio, dtype: float64\n",
      "date\n",
      "20241031    554951.25\n",
      "20241130    556075.09\n",
      "20241231    558854.68\n",
      "Name: DTCOLNVHFNM, dtype: float64\n",
      "date\n",
      "20241031    938525.34\n",
      "20241130    941204.79\n",
      "20241231    946489.00\n",
      "Name: DTCTHFNM, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check if recently released data available to update latest FRED-MD                         \n",
    "md_missing = md_df.iloc[-1]\n",
    "md_missing = md_missing[md_missing.isnull()]\n",
    "print(\"Recent values available to update missing in current FRED-MD\")\n",
    "for series_id in md_missing.index:\n",
    "    print(alf.splice(series_id).iloc[-3:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "43b34fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(W875RX1, Real personal income excluding current transfer receipts)                              20250131\n",
       "(ACOGNO, Manufacturers' New Orders: Consumer Goods)                                              20250204\n",
       "(HWI, Help Wanted Index for United States)                                                       20250204\n",
       "(NONREVSL, Nonrevolving Consumer Credit Owned and Securitized)                                   20250207\n",
       "(CONSPI, Nonrevolving consumer credit to Personal Income)                                        20250207\n",
       "(BUSINV, Total Business Inventories)                                                             20250214\n",
       "(ISRATIO, Total Business: Inventories to Sales Ratio)                                            20250214\n",
       "(CMRMTSPL, Real Manufacturing and Trade Industries Sales)                                        20250228\n",
       "(DTCOLNVHFNM, Consumer Motor Vehicle Loans Owned by Finance Companies, Level)                    20250228\n",
       "(DTCTHFNM, Total Consumer Loans and Leases Owned and Securitized by Finance Companies, Level)    20250228\n",
       "(COMPAPFF, 3-Month Commercial Paper Minus FEDFUNDS)                                                  None\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find any missing series observations, if any, now available to update current FRED-MD\n",
    "Series(release.values, index=[(s, alf.header(s)) for s in release.index])\\\n",
    "    .tail(len(md_missing))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9bc42c",
   "metadata": {},
   "source": [
    "## Outliers\n",
    "\n",
    "1. **Interquartile Range (IQR) Approach** – Filters data within median ± 10 times the interquartile range to minimize extreme values.  \n",
    "\n",
    "2. **Tukey’s Rule** – Proposed by John Tukey, this method classifies data points as \"outliers\" if they fall beyond 1.5 times the interquartile range (IQR) of the first or third quartile, that is outside of [Q1 - 1.5(Q3-Q1), Q3 + 1.5(Q3-Q1)], and as **\"far out\"** if beyond 3 times the IQR.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "59abddf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "19390228    0.005898\n",
       "19390331    0.005962\n",
       "19390430   -0.006162\n",
       "19390531    0.006789\n",
       "19390630    0.006678\n",
       "              ...   \n",
       "20240930    0.001517\n",
       "20241031    0.000278\n",
       "20241130    0.001647\n",
       "20241231    0.001934\n",
       "20250131    0.000899\n",
       "Name: PAYEMS, Length: 1032, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payems = alf('PAYEMS', freq=freq, realtime=True, diff=1, log=1).dropna().iloc[:,0]\n",
    "payems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b84b1196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers fraction detected by tukey: 0.0969\n",
      "Outliers fraction detected by farout: 0.0329\n",
      "Outliers fraction detected by iq10: 0.0029\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date\n",
       "19450930   -0.049622\n",
       "20200430   -0.145794\n",
       "20200630    0.034217\n",
       "Name: PAYEMS, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for method in ['tukey', 'farout', 'iq10']:\n",
    "    print(f\"Outliers fraction detected by {method}:\", np.mean(is_outlier(payems, method=method)).round(4))\n",
    "payems.iloc[is_outlier(payems, method='iq10')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839bc605",
   "metadata": {},
   "source": [
    "__Box-and-whiskers plot__\n",
    "\n",
    "A box plot shows the quartiles of the data while the whiskers extend to show the rest of the distribution, except for points that are determined to be “outliers”, which are more than some multiple of the inter-quartile range (IQR) beyond the first and third quartiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7e4413cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PAYEMS'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.boxplot(payems, ax=ax, orient='h', whis=3) # whiskers at 3xIQR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555d5001",
   "metadata": {},
   "source": [
    "**Referenes:**\n",
    "\n",
    "https://fred.stlouisfed.org/\n",
    "\n",
    "https://www.stlouisfed.org/research/economists/mccracken/fred-databases\n",
    "\n",
    "McCracken, M. W., & Ng, S. (2016). FRED-MD: A Monthly Database for Macroeconomic Research. Journal of Business & Economic Statistics, 34(4), 574–589.\n",
    "\n",
    "McCracken, M.W., Ng, S., 2020. FRED-QD: A Quarterly Database for\n",
    "Macroeconomic Research, Federal Reserve Bank of St. Louis Working Paper 2020-\n",
    "005\n",
    "\n",
    "Katrina Stierholz, 2018, Economic Data Revisions: What They Are and Where to Find Them\n",
    "https://journals.ala.org/index.php/dttp/article/view/6383/8404"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "env3.11",
   "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": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}