[1]M1_clean_and_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "# Nowcasting Consumer Expenditure:\n",
    "\n",
    "### Load, Clean and Preprocess Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### **Introduction: Uncovering Reliable Proxies for Consumer Spending Behaviour**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**1.1. Introduction: The Problem (Why)**\n",
    "\n",
    "</div>\n",
    "\n",
    "The current quarterly GDP reports lag in reflecting the dynamic changes in the economy, impacting decision-makers who rely on timely economic data. This project is devised to mitigate this issue by identifying high-frequency, readily updated data proxies that offer quicker insights into consumer expenditure patterns.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**1.2. Project Scope and Objectives (What)**\n",
    "\n",
    "</div>\n",
    "The project's primary objective is to systematically identify, harmonise, and validate high-frequency data sources as proxies for real-time tracking of consumer expenditure in the United States. The goal is to refine these proxies to provide more immediate data on consumer spending habits, thus bridging the gap caused by the delayed reporting of official GDP figures.\n",
    "\n",
    "**Key Questions:**\n",
    "\n",
    "- Which high-frequency data sources can serve as accurate proxies for consumer spending?\n",
    "- How can we validate these proxies against established measures of consumer expenditure?\n",
    "- What techniques can we employ to ensure these proxies offer immediate and reliable insights into current consumer spending trends?\n",
    "- How will we address potential discrepancies between different data sources in terms of scale, units, or reporting standards?\n",
    "- Are there any unforeseen challenges in harmonizing data frequencies (monthly vs. quarterly) that could impact the accuracy of our analysis?\n",
    "- How can we ensure the economic relevance of our findings, beyond statistical correlations?\n",
    "- What contingency plans do we have for dealing with data anomalies or irregularities that might skew our analysis?\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**1.3. Methodology**\n",
    "\n",
    "</div>\n",
    "We used three notebooks to run the full analysis. `[1]M1_clean_andPreprocess.ipynb`, `[2]M1_Exploratory_Data_Analysis.ipynb`, `[3]M1_Analysis_for_Model_fitting.ipynb`. \n",
    "\n",
    "The methodology is designed to focus on data preparation and validation:\n",
    "\n",
    "- **Clean and Preprocess Data**: This section is load, clean and preprocess the data into a single data fram ensuring data integrity for analytical steps to follow.\n",
    "- **Exploratory Data Analysis (EDA)**: To understand the characteristics and quality of the high-frequency monthly indicators and their initial relationships to consumer spending.\n",
    "- **Proxy Validation**: To establish a correlation with established measures of consumer spending through statistical analysis, ensuring that the proxies are reliable and relevant.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**1.4. Assumptions**\n",
    "\n",
    "</div>\n",
    "\n",
    "**Data Quality and Relevance:** We operate under the assumption that the high-frequency data from FRED and other sources accurately reflect current economic trends and consumer sentiments. However, there is an inherent risk of data bias or inaccuracy, which could impact the reliability of our findings.\n",
    "\n",
    "**Predictive Power and Relevance:** While we aim to identify effective proxies for consumer expenditure, there's a risk that these proxies may not fully capture the complexities of consumer behaviour or may not adapt swiftly to sudden economic shifts.\n",
    "\n",
    "**External Factors:** The project also assumes a stable economic environment. Sudden external shocks (like global events or policy changes) could significantly affect consumer behaviour, potentially reducing the predictive accuracy of our proxies.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### About the Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Primary Dataset Description**\n",
    "\n",
    "</div>\n",
    "\n",
    "**Short Description:** The primary dataset is \"Table 1.1.5. Gross Domestic Product\" from the U.S. Bureau of Economic Analysis. It comprises seasonally adjusted quarterly U.S. Gross Domestic Product (GDP) rates in billions of dollars.\n",
    "\n",
    "**Relevance:** The dataset's detailed information on U.S. GDP over several years is integral to the project's goal of nowcasting consumption. The data's granularity and time-series nature will allow for comprehensive analysis and identification of trends, making it pivotal for the project's success.\n",
    "\n",
    "**Data frequency:** The data reflecting the economic output of the United States is crucial for analyzing economic trends and growth patterns. The presentation of data is done quarterly by the GDP component.\n",
    "\n",
    "**Location:** Available at [U.S. Bureau of Economic Analysis](https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey&_gl=1*j1lvlb*_ga*MTk0MDMyMjk0MC4xNzA1NDk1NTk4*_ga_J4698JNNFT*MTcwNTQ5NTU5OC4xLjEuMTcwNTQ5NzA2MC42MC4wLjA.#eyJhcHBpZCI6MTksInN0ZXBzIjpbMSwyLDMsM10sImRhdGEiOltbImNhdGVnb3JpZXMiLCJTdXJ2ZXkiXSxbIk5JUEFfVGFibGVfTGlzdCIsIjUiXSxbIkZpcnN0X1llYXIiLCIxOTQ3Il0sWyJMYXN0X1llYXIiLCIyMDIzIl0sWyJTY2FsZSIsIi05Il0sWyJTZXJpZXMiLCJRIl1dfQ==). ([BEA](https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey&_gl=1*j1lvlb*_ga*MTk0MDMyMjk0MC4xNzA1NDk1NTk4*_ga_J4698JNNFT*MTcwNTQ5NTU5OC4xLjEuMTcwNTQ5NzA2MC42MC4wLjA.#eyJhcHBpZCI6MTksInN0ZXBzIjpbMSwyLDMsM10sImRhdGEiOltbImNhdGVnb3JpZXMiLCJTdXJ2ZXkiXSxbIk5JUEFfVGFibGVfTGlzdCIsIjUiXSxbIkZpcnN0X1llYXIiLCIxOTQ3Il0sWyJMYXN0X1llYXIiLCIyMDIzIl0sWyJTY2FsZSIsIi05Il0sWyJTZXJpZXMiLCJRIl1dfQ==))\n",
    "\n",
    "**Format:** CSV\n",
    "\n",
    "**Access Method:** The dataset is readily available and can be easily accessed and downloaded directly from the U.S. Bureau of Economic Analysis website.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Secondary Datasets**\n",
    "\n",
    "</div>\n",
    "\n",
    "**Federal Reserve Economic Data (FRED)**\n",
    "\n",
    "**Short Description:** This dataset is sourced from the Federal Reserve Bank of St. Louis's FRED macroeconomic database. It contains a variety of economic data points available at monthly intervals, with a particular focus on US GDP data. The data covers consumer spending indicators, a crucial component of the Gross Domestic Product (GDP).\n",
    "\n",
    "**Relevance**: Complements the primary dataset with additional economic indicators, useful for cross-referencing and correlation analysis.\n",
    "\n",
    "**Data frequency:** The monthly frequency of this dataset provides a more detailed temporal resolution than the primary dataset, which may reveal more immediate economic trends. This granularity will be useful in identifying more immediate proxies for nowcasting.\n",
    "\n",
    "**Estimated Size**: 0.6MB\n",
    "\n",
    "**Location**: https://research.stlouisfed.org/econ/mccracken/fred-databases/\n",
    "\n",
    "**Format**: CSV.\n",
    "\n",
    "**Access Method**: Direct download.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Setup Environment and import libraries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Environment Setup Instructions**\n",
    "\n",
    "</div>\n",
    "\n",
    "To ensure a smooth experience running these notebooks, please follow the steps below to set up your environment. These instructions are tailored for users intending to download the folder and run the notebooks locally.\n",
    "\n",
    "**Step 1: Download the Notebook Folder**\n",
    "\n",
    "Begin by downloading the folder containing the notebooks you wish to run. Save it to a convenient location on your computer.\n",
    "\n",
    "**Step 2: Create and Activate a Virtual Environment**\n",
    "\n",
    "A virtual environment is crucial for managing the dependencies required by different projects separately. To create and activate a virtual environment, follow these steps:\n",
    "\n",
    "1. Open your terminal or command prompt.\n",
    "2. Navigate to the directory where you've downloaded the notebook folder.\n",
    "3. Run the following command to create a virtual environment named `myenv`:\n",
    "   - `python -m venv myenv`\n",
    "   - macOS: `source myenv/bin/activate`\n",
    "   - windows: `myenv\\Scripts\\activate`\n",
    "4. With your virtual environment activated, install the required Python packages by running:\n",
    "    `pip install --upgrade pip`\n",
    "    `pip install -r requirements.txt`\n",
    "    `pip install jupyter`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Implementation Method**\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "To enhance the clarity and organization of this Jupyter Notebook, we've modularized the codebase. Specifically:\n",
    "\n",
    "- Complex function and visualization scripts are stored in the `utils` directory. This organization helps keep the notebook streamlined, focusing on analysis and insights.\n",
    "- Visualization-related code is specifically located within the `visualisations` folder, accessible via a `plot_X_visualisation.py` file.\n",
    "\n",
    "**Quick Import Example:**\n",
    "\n",
    "To integrate our modular visualization functions seamlessly, you can import them as shown below:\n",
    "\n",
    "```python\n",
    "from visualisation.plot_chart import plot_chart\n",
    "\n",
    "plot_chart(data)\n",
    "```\n",
    "\n",
    "**Advantages of Modularization:**\n",
    "\n",
    "This modular approach offers several benefits:\n",
    "\n",
    "- **Improved Organization:** Separates complex scripts from the notebook, making both the codebase and notebook easier to navigate and maintain.\n",
    "- **Reusability:** Encourages the reuse of visualization tools across projects, streamlining development and ensuring visual consistency.\n",
    "- **Better Collaboration:** Facilitates teamwork by providing a common repository of tools and functions, simplifying collaboration.\n",
    "\n",
    "By adopting this structure, we aim to keep the notebook focused and user-friendly while maintaining a robust and adaptable codebase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/30/bhymp02j1fb47cmvs80_1b4m0000gn/T/ipykernel_86921/2344078838.py:8: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "# ------- Standard Library Imports -------\n",
    "import warnings\n",
    "from pprint import pprint\n",
    "\n",
    "# from typing import List\n",
    "\n",
    "# ------- Third-Party Library Imports -------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Remove warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Set the display options\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.width\", None)\n",
    "pd.set_option(\"display.max_colwidth\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Load and preprocesses BEA Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"./data/bea/bea_usgdp.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_gdp_data(file_path):\n",
    "    \"\"\"\n",
    "    Loads and preprocesses the GDP data from a CSV file.\n",
    "    Args:file_path (str): The path to the CSV file containing GDP data.\n",
    "    Returns:pandas.DataFrame: Preprocessed GDP data.\n",
    "    \"\"\"\n",
    "\n",
    "    # Load the data with specified rows to skip and number of rows to read\n",
    "    pce_df = pd.read_csv(file_path, skiprows=3, nrows=28)\n",
    "\n",
    "    # Drop the first column (unnecessary or identifier column)\n",
    "    pce_df.drop(pce_df.columns[0], axis=1, inplace=True)\n",
    "\n",
    "    # Rename the first column as 'description'\n",
    "    pce_df.rename(columns={pce_df.columns[0]: \"description\"}, inplace=True)\n",
    "\n",
    "    # Remove any characters after (and including) the \".\" in column names\n",
    "    pce_df.columns = pce_df.columns.str.replace(r\"\\..*\", \"\", regex=True)\n",
    "\n",
    "    # Concatenate the column names with the first row values, handling NaNs\n",
    "    pce_df.columns = pce_df.columns + \" \" + pce_df.iloc[0].fillna(\"\")\n",
    "\n",
    "    # Drop the first row as it's now part of the column names\n",
    "    pce_df.drop(pce_df.index[0], inplace=True)\n",
    "\n",
    "    # Reset the index of the DataFrame\n",
    "    pce_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "    # Correct any trailing space issues in the 'description' column name\n",
    "    pce_df.rename(columns=lambda x: x.strip(), inplace=True)\n",
    "\n",
    "    return pce_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_structured_description(pce_df):\n",
    "    \"\"\"\n",
    "    Process a DataFrame to create a structured description column.\n",
    "    This function takes a DataFrame with a 'description' column and adds structure to it\n",
    "    based on indentation levels, indicating hierarchical relationships.\n",
    "    \"\"\"\n",
    "\n",
    "    # Function to determine the indentation level (number of leading spaces)\n",
    "    def indentation_level(s):\n",
    "        \"\"\"Return the number of leading spaces in a string, indicating the indentation level.\"\"\"\n",
    "        return len(s) - len(s.lstrip())\n",
    "\n",
    "    # Apply the function to find indentation levels\n",
    "    pce_df[\"indentation\"] = pce_df[\"description\"].apply(indentation_level)\n",
    "\n",
    "    # Initialize an empty list to store the new structured names\n",
    "    structured_names = []\n",
    "    current_parent = \"\"\n",
    "    current_subparent = \"\"\n",
    "\n",
    "    # Iterate through the DataFrame to construct the hierarchical names\n",
    "    for index, row in pce_df.iterrows():\n",
    "        if row[\"indentation\"] == 0:\n",
    "            name = row[\"description\"].strip()\n",
    "            current_parent = name\n",
    "        elif row[\"indentation\"] == 4:\n",
    "            name = f\"{current_parent} : {row['description'].strip()}\"\n",
    "            current_subparent = row[\"description\"].strip()\n",
    "        elif row[\"indentation\"] == 8:\n",
    "            name = (\n",
    "                f\"{current_parent} : {current_subparent} : {row['description'].strip()}\"\n",
    "            )\n",
    "        else:\n",
    "            name = row[\"description\"].strip()\n",
    "\n",
    "        structured_names.append(name)\n",
    "\n",
    "    # Assigning the structured names to the 'description' column\n",
    "    pce_df[\"description\"] = structured_names\n",
    "\n",
    "    # Dropping the 'indentation' column as it's no longer needed\n",
    "    pce_df.drop(\"indentation\", axis=1, inplace=True)\n",
    "    \n",
    "    # Use regex to remove leading and trailing \":\", \" \", or combinations thereof\n",
    "    pce_df[\"description\"] = pce_df[\"description\"].str.replace(r'^[:\\s]+|[:\\s]+$', '', regex=True)\n",
    "\n",
    "    return pce_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and preprocess the data\n",
    "pce_df = load_and_preprocess_gdp_data(file_path)\n",
    "\n",
    "# create hierarchy for PCE data\n",
    "pce_df = create_structured_description(pce_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gross domestic product', 'Personal consumption expenditures',\n",
       "       'Personal consumption expenditures : Goods',\n",
       "       'Personal consumption expenditures : Goods : Durable goods',\n",
       "       'Personal consumption expenditures : Goods : Nondurable goods',\n",
       "       'Personal consumption expenditures : Services',\n",
       "       'Gross private domestic investment',\n",
       "       'Gross private domestic investment : Fixed investment',\n",
       "       'Gross private domestic investment : Fixed investment : Nonresidential',\n",
       "       'Structures', 'Equipment', 'Intellectual property products',\n",
       "       'Gross private domestic investment : Fixed investment : Residential',\n",
       "       'Gross private domestic investment : Change in private inventories',\n",
       "       'Net exports of goods and services',\n",
       "       'Net exports of goods and services : Exports',\n",
       "       'Net exports of goods and services : Exports : Goods',\n",
       "       'Net exports of goods and services : Exports : Services',\n",
       "       'Net exports of goods and services : Imports',\n",
       "       'Net exports of goods and services : Imports : Goods',\n",
       "       'Net exports of goods and services : Imports : Services',\n",
       "       'Government consumption expenditures and gross investment',\n",
       "       'Government consumption expenditures and gross investment : Federal',\n",
       "       'Government consumption expenditures and gross investment : Federal : National defense',\n",
       "       'Government consumption expenditures and gross investment : Federal : Nondefense',\n",
       "       'Government consumption expenditures and gross investment : State and local'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pce_df.description.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract only \"Personal consumption expenditures\"\n",
    "pce_df = pce_df[pce_df.description == \"Personal consumption expenditures\"]\n",
    "\n",
    "# rename \"Personal consumption expenditures\" to \"PCE\"\n",
    "pce_df[\"description\"] = pce_df[\"description\"].str.replace(\n",
    "    \"Personal consumption expenditures\", \"PCE\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_date_formats(pce_df):\n",
    "    # Step 1: Extract only non-date columns\n",
    "    non_date_columns = pce_df.columns[:1]\n",
    "\n",
    "    # Step 2: Extract and transform date columns\n",
    "    date_columns = pce_df.columns[1:]  # Non-Date columns start from the 2nd column\n",
    "\n",
    "    # Function to convert quarter to the format 'YYYYQX'\n",
    "    def quarter_to_yyyyqx(q):\n",
    "        year, quarter = q.split(\" Q\")\n",
    "        return f\"{year}Q{quarter}\"\n",
    "\n",
    "    # Apply this function to each of the date columns\n",
    "    transformed_date_columns = [quarter_to_yyyyqx(col) for col in date_columns]\n",
    "\n",
    "    # Step 3: Combine the columns back together\n",
    "    pce_df.columns = list(non_date_columns) + transformed_date_columns\n",
    "\n",
    "    # Transpose the dataset for easier manipulation (columns become rows)\n",
    "    pce_df = pce_df.set_index(\"description\").transpose()\n",
    "\n",
    "    # Convert all columns to numeric\n",
    "    for col in pce_df.columns:\n",
    "        pce_df[col] = pd.to_numeric(pce_df[col], errors=\"coerce\")\n",
    "\n",
    "    return pce_df\n",
    "\n",
    "\n",
    "# Transform the date formats\n",
    "pce_df = transform_date_formats(pce_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "description    PCE\n",
      "1947Q1       156.2\n",
      "1947Q2       160.0\n",
      "1947Q3       163.5\n",
      "1947Q4       167.7\n",
      "1948Q1       170.4\n",
      "description      PCE\n",
      "2022Q4       17917.0\n",
      "2023Q1       18269.6\n",
      "2023Q2       18419.0\n",
      "2023Q3       18679.5\n",
      "2023Q4       18888.1\n"
     ]
    }
   ],
   "source": [
    "#show head and tail 5 rows\n",
    "print(pce_df.head(5))\n",
    "print(pce_df.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data\n",
    "pce_df.to_csv(\"./results/bea/bea_pce_original.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section focuses on loading the Personal Consumption Expenditures (PCE) data sourced from the Bureau of Economic Analysis (BEA). Our objective is to preprocess and analyze the data to uncover insights into trends in personal consumption within the United States.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Data Source**\n",
    "\n",
    "</div>\n",
    "\n",
    "The PCE data is retrieved from a CSV file provided by the BEA:\n",
    "\n",
    "- **File Path:** `./data/bea/bea_usgdp.csv`\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Data Preparation and Preprocessing**\n",
    "\n",
    "</div>\n",
    "\n",
    "To ensure the data's quality and usability for analysis, we perform several preprocessing steps using custom utilities from the `utils.bea_load_data` module. These steps include:\n",
    "\n",
    "1. **Loading and Preprocessing the Data**: The initial step involves loading the GDP data and applying preliminary preprocessing to clean and structure the dataset for analysis.\n",
    "\n",
    "2. **Creating a Hierarchy for PCE Data**: To better understand the data, we create a hierarchical structure for GDP components and subcomponents that categorizes the information, making it easier to navigate and analyze.\n",
    "\n",
    "3. **Creating Short Descriptions for PCE Data**: Short descriptions are generated for each data point to provide concise summaries, enhancing readability and understanding.\n",
    "\n",
    "4. **Extracting PCE Data**: We focus our analysis specifically on PCE data by filtering the dataset to include only entries with 'PCE' in their short descriptions.\n",
    "\n",
    "5. **Transforming Date Formats**: The date formats are standardized to facilitate time series analysis and ensure consistency across the dataset.\n",
    "\n",
    "6. **Saving the Processed Data**: After preprocessing, the refined PCE dataset is saved to a CSV file for further analysis or reporting:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Statistical Analysis and Visualization of PCE Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this section is to inspect the Personal Consumption Expenditures (PCE) data to understand its distribution, identify outliers, and analyze its trend over time. This involves calculating the median, interquartile range (IQR), detecting outliers, and examining the rate of change across different periods.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Rate of Change**:\n",
    "\n",
    "</div>\n",
    "\n",
    "To analyze trends, the rate of change is a crucial metric. It is calculated using the formula:\n",
    "\n",
    "$$\n",
    "\\text{Rate of Change} = \\left( \\frac{\\text{Current Value} - \\text{Previous Value}}{\\text{Previous Value}} \\right) \\times 100\\%\n",
    "$$\n",
    "\n",
    "This calculation is efficiently performed using the `pct_change()` function in pandas, followed by multiplying the result by 100 to express it as a percentage.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Plotting the Data**\n",
    "\n",
    "</div>\n",
    "For a comprehensive analysis, we plot the data focusing on the extended range, IQR, and outliers. A custom function `analyze_and_plot` is defined to standardize datetime indices, compute relevant statistics, and visualize the results.\n",
    "\n",
    "The `analyze_and_plot` function is stored in a subfolder `utils/visualisation` for better organization. It is then imported and executed by importing the file `from utils.visualisation import analyze_and_plot` and calling it with `analyze_and_plot(pce_df, 'PCE')`\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Function Overview**\n",
    "\n",
    "</div>\n",
    "This function includes steps to standardize the datetime index, accommodating different date formats and ensuring consistency for time series analysis. It is designed to handle quarterly data and convert it to a standard datetime format for ease of analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from visualisations.analyze_and_plot import analyze_and_plot\n",
    "\n",
    "# Call the function from utils/visualisations and store the result in result\n",
    "analyze_and_plot(pce_df, \"PCE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Implementation**\n",
    "\n",
    "</div>\n",
    "\n",
    "By executing the above steps, we gain valuable insights into the PCE data's behavior over time, its variability, and any potential outliers that may influence further analysis.\n",
    "\n",
    "Our analysis of Personal Consumption Expenditures (PCE) data reveals notable periods of outliers and volatility, alongside consistent trends within the Interquartile Range (IQR) from 1950 to the present. These observations offer insights into consumer spending behavior under varying economic conditions:\n",
    "\n",
    "- **1950s:** The post-World War II economic boom led to a significant rise in consumer spending, with PCE outliers reflecting the era’s prosperity and confidence.\n",
    "- **1980:** Economic policies to combat the early 1980s recession caused fluctuations in PCE, mirroring the impacts of high inflation and unemployment on consumer spending.\n",
    "- **2008:** The financial crisis resulted in a sharp PCE decline, highlighting the effect of economic downturns on consumer confidence and expenditure.\n",
    "- **COVID-19 Pandemic:** The pandemic's onset disrupted PCE patterns drastically, with immediate impacts from lockdowns and shifts in consumer priorities.\n",
    "\n",
    "Outside these periods, the PCE line graph generally remains within the IQR, indicating relative stability in consumer spending amidst the evolving economic landscape. This stability, punctuated by periods of significant deviation, underscores the dynamic interplay between consumer behavior and macroeconomic factors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Loading the FRED data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section of the analysis, we are incorporating secondary datasets from the Federal Reserve Economic Data (FRED) to enrich our examination of Personal Consumption Expenditures (PCE). The FRED datasets are pivotal for comparing a wide array of economic variables against PCE, facilitating a comprehensive macroeconomic analysis.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Dataset Overview**\n",
    "\n",
    "</div>\n",
    "\n",
    "Federal Reserve Bank of St. Louis's FRED database. The dataset includes 123 monthly economic indicators, focusing on US GDP and consumer spending variables—key components of GDP. This dataset complements our primary PCE data by providing additional economic indicators for a multifaceted analysis. Monthly updates offer detailed insights into short-term economic trends and consumer behavior.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Data Loading Procedure**\n",
    "\n",
    "</div>\n",
    "\n",
    "The `load_fredmd_data` function is designed to perform the following actions:\n",
    "\n",
    "1. **Download Data:** Retrieves the latest version `current` of the FRED-MD dataset based on the specified `vintage`, ensuring our analysis is grounded on the most current data.\n",
    "2. **Preprocess Data:** Processes the dataset by extracting transformation codes, mapping short names to descriptions, and setting observation dates as the index, which is crucial for time-series analysis.\n",
    "3. **Save Processed Data:** The preprocessed dataset is then saved for subsequent analysis, with the observation date index facilitating easy access to specific time periods.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_fredmd_data(vintage):\n",
    "    \"\"\"\n",
    "    Loads and processes the FRED-MD data.\n",
    "    \"\"\"\n",
    "    # Define the base URL for the FRED-MD dataset\n",
    "    base_url = \"https://files.stlouisfed.org/files/htdocs/fred-md\"\n",
    "\n",
    "    # Load the dataset for the specified 'vintage', dropping rows that are entirely NA\n",
    "    fred_orig = pd.read_csv(f\"{base_url}/monthly/{vintage}.csv\")#.dropna(how=\"all\")\n",
    "\n",
    "    # Drop the first row (containing transformation info) from the dataset\n",
    "    fred_orig = fred_orig.iloc[1:]\n",
    "\n",
    "    # Convert 'sasdate' column to a PeriodIndex with monthly frequency for time-series analysis\n",
    "    fred_orig.index = pd.PeriodIndex(fred_orig.sasdate.tolist(), freq=\"M\")\n",
    "\n",
    "    # Remove the 'sasdate' column as it's now set as the index\n",
    "    fred_orig.drop(\"sasdate\", axis=1, inplace=True)\n",
    "\n",
    "    # Return the processed data and the transformation information\n",
    "    return fred_orig\n",
    "\n",
    "\n",
    "# Load data for the current vintage and unpack into original data and transformation info\n",
    "fred = load_fredmd_data(\"current\")\n",
    "\n",
    "# Save the original data\n",
    "fred.to_csv(\"./results/fred/fred_monthly_orig.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Implementation**\n",
    "\n",
    "</div>\n",
    "\n",
    "After loading the data, we further enhance the dataset's usability by mapping column names to their descriptive titles using the `map_column_names` function. This step significantly improves the readability and interpretability of the data for in-depth analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Real Personal Income</th>\n",
       "      <th>Real personal income ex transfer receipts</th>\n",
       "      <th>Real personal consumption expenditures</th>\n",
       "      <th>Real Manu. and Trade Industries Sales</th>\n",
       "      <th>Retail and Food Services Sales</th>\n",
       "      <th>IP Index</th>\n",
       "      <th>IP: Final Products and Nonindustrial Supplies</th>\n",
       "      <th>IP: Final Products (Market Group)</th>\n",
       "      <th>IP: Consumer Goods</th>\n",
       "      <th>IP: Durable Consumer Goods</th>\n",
       "      <th>IP: Nondurable Consumer Goods</th>\n",
       "      <th>IP: Business Equipment</th>\n",
       "      <th>IP: Materials</th>\n",
       "      <th>IP: Durable Materials</th>\n",
       "      <th>IP: Nondurable Materials</th>\n",
       "      <th>IP: Manufacturing (SIC)</th>\n",
       "      <th>IPB51222S</th>\n",
       "      <th>IP: Fuels</th>\n",
       "      <th>Capacity Utilization: Manufacturing</th>\n",
       "      <th>Help-Wanted Index for United States</th>\n",
       "      <th>Ratio of Help Wanted/No. Unemployed</th>\n",
       "      <th>Civilian Labor Force</th>\n",
       "      <th>Civilian Employment</th>\n",
       "      <th>Civilian Unemployment Rate</th>\n",
       "      <th>Average Duration of Unemployment (Weeks)</th>\n",
       "      <th>Civilians Unemployed - Less Than 5 Weeks</th>\n",
       "      <th>Civilians Unemployed for 5-14 Weeks</th>\n",
       "      <th>Civilians Unemployed - 15 Weeks &amp; Over</th>\n",
       "      <th>Civilians Unemployed for 15-26 Weeks</th>\n",
       "      <th>Civilians Unemployed for 27 Weeks and Over</th>\n",
       "      <th>Initial Claims</th>\n",
       "      <th>All Employees: Total nonfarm</th>\n",
       "      <th>All Employees: Goods-Producing Industries</th>\n",
       "      <th>All Employees: Mining and Logging: Mining</th>\n",
       "      <th>All Employees: Construction</th>\n",
       "      <th>All Employees: Manufacturing</th>\n",
       "      <th>All Employees: Durable goods</th>\n",
       "      <th>All Employees: Nondurable goods</th>\n",
       "      <th>All Employees: Service-Providing Industries</th>\n",
       "      <th>All Employees: Trade, Transportation &amp; Utilities</th>\n",
       "      <th>All Employees: Wholesale Trade</th>\n",
       "      <th>All Employees: Retail Trade</th>\n",
       "      <th>All Employees: Financial Activities</th>\n",
       "      <th>All Employees: Government</th>\n",
       "      <th>Avg Weekly Hours : Goods-Producing</th>\n",
       "      <th>Avg Weekly Overtime Hours : Manufacturing</th>\n",
       "      <th>Avg Weekly Hours : Manufacturing</th>\n",
       "      <th>Housing Starts: Total New Privately Owned</th>\n",
       "      <th>Housing Starts, Northeast</th>\n",
       "      <th>Housing Starts, Midwest</th>\n",
       "      <th>Housing Starts, South</th>\n",
       "      <th>Housing Starts, West</th>\n",
       "      <th>New Private Housing Permits (SAAR)</th>\n",
       "      <th>New Private Housing Permits, Northeast (SAAR)</th>\n",
       "      <th>New Private Housing Permits, Midwest (SAAR)</th>\n",
       "      <th>New Private Housing Permits, South (SAAR)</th>\n",
       "      <th>New Private Housing Permits, West (SAAR)</th>\n",
       "      <th>New Orders for Consumer Goods</th>\n",
       "      <th>New Orders for Durable Goods</th>\n",
       "      <th>New Orders for Nondefense Capital Goods</th>\n",
       "      <th>Un lled Orders for Durable Goods</th>\n",
       "      <th>Total Business Inventories</th>\n",
       "      <th>Total Business: Inventories to Sales Ratio</th>\n",
       "      <th>M1 Money Stock</th>\n",
       "      <th>M2 Money Stock</th>\n",
       "      <th>Real M2 Money Stock</th>\n",
       "      <th>BOGMBASE</th>\n",
       "      <th>Total Reserves of Depository Institutions</th>\n",
       "      <th>Reserves Of Depository Institutions</th>\n",
       "      <th>Commercial and Industrial Loans</th>\n",
       "      <th>Real Estate Loans at All Commercial Banks</th>\n",
       "      <th>Total Nonrevolving Credit</th>\n",
       "      <th>Nonrevolving consumer credit to Personal Income</th>\n",
       "      <th>S&amp;P s Common Stock Price Index: Composite</th>\n",
       "      <th>S&amp;P s Common Stock Price Index: Industrials</th>\n",
       "      <th>S&amp;P s Composite Common Stock: Dividend Yield</th>\n",
       "      <th>S&amp;P s Composite Common Stock: Price-Earnings Ratio</th>\n",
       "      <th>E?ective Federal Funds Rate</th>\n",
       "      <th>3-Month AA Financial Commercial Paper Rate</th>\n",
       "      <th>3-Month Treasury Bill:</th>\n",
       "      <th>6-Month Treasury Bill:</th>\n",
       "      <th>1-Year Treasury Rate</th>\n",
       "      <th>5-Year Treasury Rate</th>\n",
       "      <th>10-Year Treasury Rate</th>\n",
       "      <th>Moody s Seasoned Aaa Corporate Bond Yield</th>\n",
       "      <th>Moody s Seasoned Baa Corporate Bond Yield</th>\n",
       "      <th>3-Month Commercial Paper Minus FEDFUNDS</th>\n",
       "      <th>3-Month Treasury C Minus FEDFUNDS</th>\n",
       "      <th>6-Month Treasury C Minus FEDFUNDS</th>\n",
       "      <th>1-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>5-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>10-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>Moody s Aaa Corporate Bond Minus FEDFUNDS</th>\n",
       "      <th>Moody s Baa Corporate Bond Minus FEDFUNDS</th>\n",
       "      <th>TWEXAFEGSMTHx</th>\n",
       "      <th>Switzerland / U.S. Foreign Exchange Rate</th>\n",
       "      <th>Japan / U.S. Foreign Exchange Rate</th>\n",
       "      <th>U.S. / U.K. Foreign Exchange Rate</th>\n",
       "      <th>Canada / U.S. Foreign Exchange Rate</th>\n",
       "      <th>PPI: Finished Goods</th>\n",
       "      <th>PPI: Finished Consumer Goods</th>\n",
       "      <th>PPI: Intermediate Materials</th>\n",
       "      <th>PPI: Crude Materials</th>\n",
       "      <th>Crude Oil, spliced WTI and Cushing</th>\n",
       "      <th>PPI: Metals and metal products:</th>\n",
       "      <th>CPI : All Items</th>\n",
       "      <th>CPI : Apparel</th>\n",
       "      <th>CPI : Transportation</th>\n",
       "      <th>CPI : Medical Care</th>\n",
       "      <th>CPI : Commodities</th>\n",
       "      <th>CPI : Durables</th>\n",
       "      <th>CPI : Services</th>\n",
       "      <th>CPI : All Items Less Food</th>\n",
       "      <th>CPI : All items less shelter</th>\n",
       "      <th>CPI : All items less medical care</th>\n",
       "      <th>Personal Cons. Expend.: Chain Index</th>\n",
       "      <th>Personal Cons. Exp: Durable goods</th>\n",
       "      <th>Personal Cons. Exp: Nondurable goods</th>\n",
       "      <th>Personal Cons. Exp: Services</th>\n",
       "      <th>Avg Hourly Earnings : Goods-Producing</th>\n",
       "      <th>Avg Hourly Earnings : Construction</th>\n",
       "      <th>Avg Hourly Earnings : Manufacturing</th>\n",
       "      <th>Consumer Sentiment Index</th>\n",
       "      <th>Consumer Motor Vehicle Loans Outstanding</th>\n",
       "      <th>Total Consumer Loans and Leases Outstanding</th>\n",
       "      <th>Securities in Bank Credit at All Commercial Banks</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-08</th>\n",
       "      <td>19098.603</td>\n",
       "      <td>15707.7</td>\n",
       "      <td>116.171</td>\n",
       "      <td>1497717.0</td>\n",
       "      <td>699540.0</td>\n",
       "      <td>103.2411</td>\n",
       "      <td>101.4468</td>\n",
       "      <td>101.9571</td>\n",
       "      <td>102.5513</td>\n",
       "      <td>107.1786</td>\n",
       "      <td>101.2416</td>\n",
       "      <td>96.9910</td>\n",
       "      <td>105.4323</td>\n",
       "      <td>97.8894</td>\n",
       "      <td>96.0607</td>\n",
       "      <td>99.5222</td>\n",
       "      <td>110.8632</td>\n",
       "      <td>90.0112</td>\n",
       "      <td>77.7278</td>\n",
       "      <td>9497</td>\n",
       "      <td>1.497950</td>\n",
       "      <td>167840</td>\n",
       "      <td>161500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2224</td>\n",
       "      <td>1913</td>\n",
       "      <td>2296</td>\n",
       "      <td>970</td>\n",
       "      <td>1326</td>\n",
       "      <td>237750</td>\n",
       "      <td>156476</td>\n",
       "      <td>21622</td>\n",
       "      <td>597.6</td>\n",
       "      <td>7997</td>\n",
       "      <td>12981</td>\n",
       "      <td>8137</td>\n",
       "      <td>4844</td>\n",
       "      <td>134854</td>\n",
       "      <td>28842</td>\n",
       "      <td>6065.3</td>\n",
       "      <td>15532.1</td>\n",
       "      <td>9153</td>\n",
       "      <td>22790</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>40.8</td>\n",
       "      <td>1305</td>\n",
       "      <td>128</td>\n",
       "      <td>160</td>\n",
       "      <td>732</td>\n",
       "      <td>285</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>252413.0</td>\n",
       "      <td>283917.0</td>\n",
       "      <td>85849.0</td>\n",
       "      <td>1335197.0</td>\n",
       "      <td>2548843.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>18303.5</td>\n",
       "      <td>20825.6</td>\n",
       "      <td>6799.8</td>\n",
       "      <td>5559100</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>2971200</td>\n",
       "      <td>2764.0963</td>\n",
       "      <td>5483.8976</td>\n",
       "      <td>3681.31604</td>\n",
       "      <td>0.159560</td>\n",
       "      <td>4457.36</td>\n",
       "      <td>6383.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.44</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.37</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.17</td>\n",
       "      <td>4.95</td>\n",
       "      <td>6.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.69</td>\n",
       "      <td>115.0546</td>\n",
       "      <td>0.8783</td>\n",
       "      <td>144.7804</td>\n",
       "      <td>1.2706</td>\n",
       "      <td>1.3478</td>\n",
       "      <td>257.192</td>\n",
       "      <td>275.952</td>\n",
       "      <td>258.693</td>\n",
       "      <td>253.963</td>\n",
       "      <td>81.39</td>\n",
       "      <td>246.357</td>\n",
       "      <td>306.269</td>\n",
       "      <td>131.835</td>\n",
       "      <td>272.143</td>\n",
       "      <td>546.954</td>\n",
       "      <td>225.311</td>\n",
       "      <td>126.743</td>\n",
       "      <td>385.778</td>\n",
       "      <td>303.579</td>\n",
       "      <td>279.774</td>\n",
       "      <td>294.629</td>\n",
       "      <td>120.803</td>\n",
       "      <td>107.261</td>\n",
       "      <td>120.012</td>\n",
       "      <td>123.370</td>\n",
       "      <td>29.74</td>\n",
       "      <td>34.45</td>\n",
       "      <td>26.50</td>\n",
       "      <td>69.4</td>\n",
       "      <td>504406.44</td>\n",
       "      <td>910966.86</td>\n",
       "      <td>5111.5017</td>\n",
       "      <td>15.7822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09</th>\n",
       "      <td>19088.877</td>\n",
       "      <td>15719.1</td>\n",
       "      <td>116.594</td>\n",
       "      <td>1507530.0</td>\n",
       "      <td>705304.0</td>\n",
       "      <td>103.3346</td>\n",
       "      <td>101.2042</td>\n",
       "      <td>101.5822</td>\n",
       "      <td>102.3594</td>\n",
       "      <td>108.0793</td>\n",
       "      <td>100.7622</td>\n",
       "      <td>95.9351</td>\n",
       "      <td>105.9495</td>\n",
       "      <td>98.3527</td>\n",
       "      <td>97.0218</td>\n",
       "      <td>99.6277</td>\n",
       "      <td>108.7412</td>\n",
       "      <td>90.5250</td>\n",
       "      <td>77.7261</td>\n",
       "      <td>9350</td>\n",
       "      <td>1.473137</td>\n",
       "      <td>167897</td>\n",
       "      <td>161550</td>\n",
       "      <td>3.8</td>\n",
       "      <td>21.4</td>\n",
       "      <td>2053</td>\n",
       "      <td>2043</td>\n",
       "      <td>2288</td>\n",
       "      <td>985</td>\n",
       "      <td>1303</td>\n",
       "      <td>210800</td>\n",
       "      <td>156738</td>\n",
       "      <td>21643</td>\n",
       "      <td>597.5</td>\n",
       "      <td>8006</td>\n",
       "      <td>12992</td>\n",
       "      <td>8147</td>\n",
       "      <td>4845</td>\n",
       "      <td>135095</td>\n",
       "      <td>28873</td>\n",
       "      <td>6080.9</td>\n",
       "      <td>15533.8</td>\n",
       "      <td>9153</td>\n",
       "      <td>22853</td>\n",
       "      <td>40.5</td>\n",
       "      <td>3.6</td>\n",
       "      <td>40.7</td>\n",
       "      <td>1356</td>\n",
       "      <td>84</td>\n",
       "      <td>192</td>\n",
       "      <td>784</td>\n",
       "      <td>296</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>818.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>252436.0</td>\n",
       "      <td>295365.0</td>\n",
       "      <td>101003.0</td>\n",
       "      <td>1352860.0</td>\n",
       "      <td>2553086.0</td>\n",
       "      <td>1.36</td>\n",
       "      <td>18171.7</td>\n",
       "      <td>20755.4</td>\n",
       "      <td>6750.1</td>\n",
       "      <td>5567300</td>\n",
       "      <td>3239.6</td>\n",
       "      <td>3017400</td>\n",
       "      <td>2765.4185</td>\n",
       "      <td>5498.2932</td>\n",
       "      <td>3687.80456</td>\n",
       "      <td>0.159311</td>\n",
       "      <td>4409.10</td>\n",
       "      <td>6323.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.31</td>\n",
       "      <td>5.44</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.38</td>\n",
       "      <td>5.13</td>\n",
       "      <td>6.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.83</td>\n",
       "      <td>117.0418</td>\n",
       "      <td>0.8996</td>\n",
       "      <td>147.8450</td>\n",
       "      <td>1.2381</td>\n",
       "      <td>1.3531</td>\n",
       "      <td>259.616</td>\n",
       "      <td>279.261</td>\n",
       "      <td>260.179</td>\n",
       "      <td>262.335</td>\n",
       "      <td>89.43</td>\n",
       "      <td>251.022</td>\n",
       "      <td>307.481</td>\n",
       "      <td>130.833</td>\n",
       "      <td>273.006</td>\n",
       "      <td>548.079</td>\n",
       "      <td>225.542</td>\n",
       "      <td>126.290</td>\n",
       "      <td>387.981</td>\n",
       "      <td>304.857</td>\n",
       "      <td>280.506</td>\n",
       "      <td>295.843</td>\n",
       "      <td>121.267</td>\n",
       "      <td>107.154</td>\n",
       "      <td>120.395</td>\n",
       "      <td>123.976</td>\n",
       "      <td>29.90</td>\n",
       "      <td>34.54</td>\n",
       "      <td>26.64</td>\n",
       "      <td>67.9</td>\n",
       "      <td>508808.61</td>\n",
       "      <td>913938.95</td>\n",
       "      <td>5074.6667</td>\n",
       "      <td>15.0424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10</th>\n",
       "      <td>19134.088</td>\n",
       "      <td>15766.4</td>\n",
       "      <td>116.583</td>\n",
       "      <td>1505904.0</td>\n",
       "      <td>703528.0</td>\n",
       "      <td>102.4592</td>\n",
       "      <td>100.4656</td>\n",
       "      <td>100.5505</td>\n",
       "      <td>101.1135</td>\n",
       "      <td>102.1752</td>\n",
       "      <td>100.7416</td>\n",
       "      <td>95.0661</td>\n",
       "      <td>104.9027</td>\n",
       "      <td>96.6090</td>\n",
       "      <td>96.7225</td>\n",
       "      <td>98.8026</td>\n",
       "      <td>105.8353</td>\n",
       "      <td>90.3818</td>\n",
       "      <td>76.9992</td>\n",
       "      <td>8852</td>\n",
       "      <td>1.373894</td>\n",
       "      <td>167723</td>\n",
       "      <td>161280</td>\n",
       "      <td>3.8</td>\n",
       "      <td>21.6</td>\n",
       "      <td>2269</td>\n",
       "      <td>1836</td>\n",
       "      <td>2370</td>\n",
       "      <td>1079</td>\n",
       "      <td>1291</td>\n",
       "      <td>210750</td>\n",
       "      <td>156843</td>\n",
       "      <td>21631</td>\n",
       "      <td>596.4</td>\n",
       "      <td>8033</td>\n",
       "      <td>12954</td>\n",
       "      <td>8110</td>\n",
       "      <td>4844</td>\n",
       "      <td>135212</td>\n",
       "      <td>28871</td>\n",
       "      <td>6089.0</td>\n",
       "      <td>15550.3</td>\n",
       "      <td>9145</td>\n",
       "      <td>22914</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.7</td>\n",
       "      <td>1376</td>\n",
       "      <td>79</td>\n",
       "      <td>215</td>\n",
       "      <td>727</td>\n",
       "      <td>355</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>244652.0</td>\n",
       "      <td>280217.0</td>\n",
       "      <td>86621.0</td>\n",
       "      <td>1357730.0</td>\n",
       "      <td>2550320.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>18080.9</td>\n",
       "      <td>20725.7</td>\n",
       "      <td>6737.5</td>\n",
       "      <td>5601300</td>\n",
       "      <td>3276.5</td>\n",
       "      <td>3104600</td>\n",
       "      <td>2779.4523</td>\n",
       "      <td>5499.5336</td>\n",
       "      <td>3690.65220</td>\n",
       "      <td>0.159015</td>\n",
       "      <td>4269.40</td>\n",
       "      <td>6149.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.42</td>\n",
       "      <td>4.77</td>\n",
       "      <td>4.80</td>\n",
       "      <td>5.61</td>\n",
       "      <td>6.63</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.30</td>\n",
       "      <td>118.3751</td>\n",
       "      <td>0.9035</td>\n",
       "      <td>149.5933</td>\n",
       "      <td>1.2175</td>\n",
       "      <td>1.3712</td>\n",
       "      <td>254.829</td>\n",
       "      <td>272.340</td>\n",
       "      <td>257.484</td>\n",
       "      <td>257.031</td>\n",
       "      <td>85.64</td>\n",
       "      <td>245.972</td>\n",
       "      <td>307.619</td>\n",
       "      <td>130.960</td>\n",
       "      <td>270.454</td>\n",
       "      <td>549.927</td>\n",
       "      <td>224.543</td>\n",
       "      <td>125.660</td>\n",
       "      <td>389.314</td>\n",
       "      <td>304.875</td>\n",
       "      <td>280.201</td>\n",
       "      <td>295.902</td>\n",
       "      <td>121.299</td>\n",
       "      <td>106.855</td>\n",
       "      <td>120.011</td>\n",
       "      <td>124.214</td>\n",
       "      <td>29.98</td>\n",
       "      <td>34.64</td>\n",
       "      <td>26.70</td>\n",
       "      <td>63.8</td>\n",
       "      <td>512056.53</td>\n",
       "      <td>916941.34</td>\n",
       "      <td>5015.7795</td>\n",
       "      <td>19.0462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11</th>\n",
       "      <td>19220.584</td>\n",
       "      <td>15863.0</td>\n",
       "      <td>117.115</td>\n",
       "      <td>1518957.0</td>\n",
       "      <td>705981.0</td>\n",
       "      <td>102.4301</td>\n",
       "      <td>100.5157</td>\n",
       "      <td>100.8247</td>\n",
       "      <td>101.1938</td>\n",
       "      <td>105.3427</td>\n",
       "      <td>100.0119</td>\n",
       "      <td>96.0276</td>\n",
       "      <td>104.7739</td>\n",
       "      <td>97.4843</td>\n",
       "      <td>96.1894</td>\n",
       "      <td>99.0386</td>\n",
       "      <td>106.7216</td>\n",
       "      <td>89.4254</td>\n",
       "      <td>77.1002</td>\n",
       "      <td>8925</td>\n",
       "      <td>1.425263</td>\n",
       "      <td>168127</td>\n",
       "      <td>161866</td>\n",
       "      <td>3.7</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2069</td>\n",
       "      <td>2060</td>\n",
       "      <td>2150</td>\n",
       "      <td>931</td>\n",
       "      <td>1220</td>\n",
       "      <td>220250</td>\n",
       "      <td>157016</td>\n",
       "      <td>21661</td>\n",
       "      <td>596.2</td>\n",
       "      <td>8039</td>\n",
       "      <td>12980</td>\n",
       "      <td>8144</td>\n",
       "      <td>4836</td>\n",
       "      <td>135355</td>\n",
       "      <td>28846</td>\n",
       "      <td>6093.5</td>\n",
       "      <td>15525.9</td>\n",
       "      <td>9146</td>\n",
       "      <td>22951</td>\n",
       "      <td>40.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>40.5</td>\n",
       "      <td>1525</td>\n",
       "      <td>130</td>\n",
       "      <td>205</td>\n",
       "      <td>829</td>\n",
       "      <td>361</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>245980.0</td>\n",
       "      <td>295500.0</td>\n",
       "      <td>102454.0</td>\n",
       "      <td>1375078.0</td>\n",
       "      <td>2548858.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>18045.9</td>\n",
       "      <td>20767.5</td>\n",
       "      <td>6744.5</td>\n",
       "      <td>5731500</td>\n",
       "      <td>3403.5</td>\n",
       "      <td>3250100</td>\n",
       "      <td>2769.5164</td>\n",
       "      <td>5507.6742</td>\n",
       "      <td>3695.27091</td>\n",
       "      <td>0.158603</td>\n",
       "      <td>4460.06</td>\n",
       "      <td>6416.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.27</td>\n",
       "      <td>5.24</td>\n",
       "      <td>5.28</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5.28</td>\n",
       "      <td>6.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.84</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>116.7327</td>\n",
       "      <td>0.8902</td>\n",
       "      <td>149.6790</td>\n",
       "      <td>1.2437</td>\n",
       "      <td>1.3707</td>\n",
       "      <td>254.116</td>\n",
       "      <td>271.136</td>\n",
       "      <td>256.356</td>\n",
       "      <td>250.743</td>\n",
       "      <td>77.69</td>\n",
       "      <td>248.224</td>\n",
       "      <td>307.917</td>\n",
       "      <td>129.270</td>\n",
       "      <td>268.729</td>\n",
       "      <td>553.095</td>\n",
       "      <td>223.072</td>\n",
       "      <td>125.192</td>\n",
       "      <td>391.403</td>\n",
       "      <td>305.110</td>\n",
       "      <td>279.944</td>\n",
       "      <td>296.069</td>\n",
       "      <td>121.218</td>\n",
       "      <td>106.391</td>\n",
       "      <td>118.953</td>\n",
       "      <td>124.539</td>\n",
       "      <td>30.23</td>\n",
       "      <td>34.94</td>\n",
       "      <td>26.91</td>\n",
       "      <td>61.3</td>\n",
       "      <td>515110.84</td>\n",
       "      <td>919957.82</td>\n",
       "      <td>5003.5829</td>\n",
       "      <td>13.8563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12</th>\n",
       "      <td>19237.854</td>\n",
       "      <td>15886.5</td>\n",
       "      <td>117.749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>709890.0</td>\n",
       "      <td>102.4840</td>\n",
       "      <td>100.5301</td>\n",
       "      <td>100.9359</td>\n",
       "      <td>101.3899</td>\n",
       "      <td>106.3175</td>\n",
       "      <td>100.0037</td>\n",
       "      <td>95.8450</td>\n",
       "      <td>104.8773</td>\n",
       "      <td>96.9114</td>\n",
       "      <td>96.7414</td>\n",
       "      <td>99.0884</td>\n",
       "      <td>104.6715</td>\n",
       "      <td>91.0496</td>\n",
       "      <td>77.0566</td>\n",
       "      <td>9026</td>\n",
       "      <td>1.440013</td>\n",
       "      <td>167451</td>\n",
       "      <td>161183</td>\n",
       "      <td>3.7</td>\n",
       "      <td>22.3</td>\n",
       "      <td>2191</td>\n",
       "      <td>1791</td>\n",
       "      <td>2350</td>\n",
       "      <td>1104</td>\n",
       "      <td>1245</td>\n",
       "      <td>210600</td>\n",
       "      <td>157232</td>\n",
       "      <td>21683</td>\n",
       "      <td>595.8</td>\n",
       "      <td>8056</td>\n",
       "      <td>12986</td>\n",
       "      <td>8152</td>\n",
       "      <td>4834</td>\n",
       "      <td>135549</td>\n",
       "      <td>28846</td>\n",
       "      <td>6099.0</td>\n",
       "      <td>15543.3</td>\n",
       "      <td>9148</td>\n",
       "      <td>23003</td>\n",
       "      <td>40.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>40.4</td>\n",
       "      <td>1460</td>\n",
       "      <td>108</td>\n",
       "      <td>187</td>\n",
       "      <td>787</td>\n",
       "      <td>378</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295559.0</td>\n",
       "      <td>103044.0</td>\n",
       "      <td>1393373.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18101.4</td>\n",
       "      <td>20865.2</td>\n",
       "      <td>6755.8</td>\n",
       "      <td>5827200</td>\n",
       "      <td>3491.8</td>\n",
       "      <td>3358300</td>\n",
       "      <td>2771.7764</td>\n",
       "      <td>5521.1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4685.05</td>\n",
       "      <td>6708.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.33</td>\n",
       "      <td>5.32</td>\n",
       "      <td>5.24</td>\n",
       "      <td>5.12</td>\n",
       "      <td>4.96</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.64</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>0.31</td>\n",
       "      <td>114.6051</td>\n",
       "      <td>0.8648</td>\n",
       "      <td>143.9815</td>\n",
       "      <td>1.2657</td>\n",
       "      <td>1.3416</td>\n",
       "      <td>253.825</td>\n",
       "      <td>270.728</td>\n",
       "      <td>254.708</td>\n",
       "      <td>239.829</td>\n",
       "      <td>71.90</td>\n",
       "      <td>244.401</td>\n",
       "      <td>308.850</td>\n",
       "      <td>129.400</td>\n",
       "      <td>269.292</td>\n",
       "      <td>556.146</td>\n",
       "      <td>223.223</td>\n",
       "      <td>124.578</td>\n",
       "      <td>393.211</td>\n",
       "      <td>306.078</td>\n",
       "      <td>280.561</td>\n",
       "      <td>296.903</td>\n",
       "      <td>121.421</td>\n",
       "      <td>105.964</td>\n",
       "      <td>118.879</td>\n",
       "      <td>124.963</td>\n",
       "      <td>30.38</td>\n",
       "      <td>34.92</td>\n",
       "      <td>27.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5085.0280</td>\n",
       "      <td>12.6960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Real Personal Income  Real personal income ex transfer receipts  \\\n",
       "2023-08             19098.603                                    15707.7   \n",
       "2023-09             19088.877                                    15719.1   \n",
       "2023-10             19134.088                                    15766.4   \n",
       "2023-11             19220.584                                    15863.0   \n",
       "2023-12             19237.854                                    15886.5   \n",
       "\n",
       "         Real personal consumption expenditures  \\\n",
       "2023-08                                 116.171   \n",
       "2023-09                                 116.594   \n",
       "2023-10                                 116.583   \n",
       "2023-11                                 117.115   \n",
       "2023-12                                 117.749   \n",
       "\n",
       "         Real Manu. and Trade Industries Sales  \\\n",
       "2023-08                              1497717.0   \n",
       "2023-09                              1507530.0   \n",
       "2023-10                              1505904.0   \n",
       "2023-11                              1518957.0   \n",
       "2023-12                                    NaN   \n",
       "\n",
       "         Retail and Food Services Sales  IP Index  \\\n",
       "2023-08                        699540.0  103.2411   \n",
       "2023-09                        705304.0  103.3346   \n",
       "2023-10                        703528.0  102.4592   \n",
       "2023-11                        705981.0  102.4301   \n",
       "2023-12                        709890.0  102.4840   \n",
       "\n",
       "         IP: Final Products and Nonindustrial Supplies  \\\n",
       "2023-08                                       101.4468   \n",
       "2023-09                                       101.2042   \n",
       "2023-10                                       100.4656   \n",
       "2023-11                                       100.5157   \n",
       "2023-12                                       100.5301   \n",
       "\n",
       "         IP: Final Products (Market Group)  IP: Consumer Goods  \\\n",
       "2023-08                           101.9571            102.5513   \n",
       "2023-09                           101.5822            102.3594   \n",
       "2023-10                           100.5505            101.1135   \n",
       "2023-11                           100.8247            101.1938   \n",
       "2023-12                           100.9359            101.3899   \n",
       "\n",
       "         IP: Durable Consumer Goods  IP: Nondurable Consumer Goods  \\\n",
       "2023-08                    107.1786                       101.2416   \n",
       "2023-09                    108.0793                       100.7622   \n",
       "2023-10                    102.1752                       100.7416   \n",
       "2023-11                    105.3427                       100.0119   \n",
       "2023-12                    106.3175                       100.0037   \n",
       "\n",
       "         IP: Business Equipment  IP: Materials  IP: Durable Materials  \\\n",
       "2023-08                 96.9910       105.4323                97.8894   \n",
       "2023-09                 95.9351       105.9495                98.3527   \n",
       "2023-10                 95.0661       104.9027                96.6090   \n",
       "2023-11                 96.0276       104.7739                97.4843   \n",
       "2023-12                 95.8450       104.8773                96.9114   \n",
       "\n",
       "         IP: Nondurable Materials  IP: Manufacturing (SIC)  IPB51222S  \\\n",
       "2023-08                   96.0607                  99.5222   110.8632   \n",
       "2023-09                   97.0218                  99.6277   108.7412   \n",
       "2023-10                   96.7225                  98.8026   105.8353   \n",
       "2023-11                   96.1894                  99.0386   106.7216   \n",
       "2023-12                   96.7414                  99.0884   104.6715   \n",
       "\n",
       "         IP: Fuels  Capacity Utilization: Manufacturing  \\\n",
       "2023-08    90.0112                              77.7278   \n",
       "2023-09    90.5250                              77.7261   \n",
       "2023-10    90.3818                              76.9992   \n",
       "2023-11    89.4254                              77.1002   \n",
       "2023-12    91.0496                              77.0566   \n",
       "\n",
       "         Help-Wanted Index for United States  \\\n",
       "2023-08                                 9497   \n",
       "2023-09                                 9350   \n",
       "2023-10                                 8852   \n",
       "2023-11                                 8925   \n",
       "2023-12                                 9026   \n",
       "\n",
       "         Ratio of Help Wanted/No. Unemployed  Civilian Labor Force  \\\n",
       "2023-08                             1.497950                167840   \n",
       "2023-09                             1.473137                167897   \n",
       "2023-10                             1.373894                167723   \n",
       "2023-11                             1.425263                168127   \n",
       "2023-12                             1.440013                167451   \n",
       "\n",
       "         Civilian Employment  Civilian Unemployment Rate  \\\n",
       "2023-08               161500                         3.8   \n",
       "2023-09               161550                         3.8   \n",
       "2023-10               161280                         3.8   \n",
       "2023-11               161866                         3.7   \n",
       "2023-12               161183                         3.7   \n",
       "\n",
       "         Average Duration of Unemployment (Weeks)  \\\n",
       "2023-08                                      20.4   \n",
       "2023-09                                      21.4   \n",
       "2023-10                                      21.6   \n",
       "2023-11                                      19.5   \n",
       "2023-12                                      22.3   \n",
       "\n",
       "         Civilians Unemployed - Less Than 5 Weeks  \\\n",
       "2023-08                                      2224   \n",
       "2023-09                                      2053   \n",
       "2023-10                                      2269   \n",
       "2023-11                                      2069   \n",
       "2023-12                                      2191   \n",
       "\n",
       "         Civilians Unemployed for 5-14 Weeks  \\\n",
       "2023-08                                 1913   \n",
       "2023-09                                 2043   \n",
       "2023-10                                 1836   \n",
       "2023-11                                 2060   \n",
       "2023-12                                 1791   \n",
       "\n",
       "         Civilians Unemployed - 15 Weeks & Over  \\\n",
       "2023-08                                    2296   \n",
       "2023-09                                    2288   \n",
       "2023-10                                    2370   \n",
       "2023-11                                    2150   \n",
       "2023-12                                    2350   \n",
       "\n",
       "         Civilians Unemployed for 15-26 Weeks  \\\n",
       "2023-08                                   970   \n",
       "2023-09                                   985   \n",
       "2023-10                                  1079   \n",
       "2023-11                                   931   \n",
       "2023-12                                  1104   \n",
       "\n",
       "         Civilians Unemployed for 27 Weeks and Over  Initial Claims  \\\n",
       "2023-08                                        1326          237750   \n",
       "2023-09                                        1303          210800   \n",
       "2023-10                                        1291          210750   \n",
       "2023-11                                        1220          220250   \n",
       "2023-12                                        1245          210600   \n",
       "\n",
       "         All Employees: Total nonfarm  \\\n",
       "2023-08                        156476   \n",
       "2023-09                        156738   \n",
       "2023-10                        156843   \n",
       "2023-11                        157016   \n",
       "2023-12                        157232   \n",
       "\n",
       "         All Employees: Goods-Producing Industries  \\\n",
       "2023-08                                      21622   \n",
       "2023-09                                      21643   \n",
       "2023-10                                      21631   \n",
       "2023-11                                      21661   \n",
       "2023-12                                      21683   \n",
       "\n",
       "         All Employees: Mining and Logging: Mining  \\\n",
       "2023-08                                      597.6   \n",
       "2023-09                                      597.5   \n",
       "2023-10                                      596.4   \n",
       "2023-11                                      596.2   \n",
       "2023-12                                      595.8   \n",
       "\n",
       "         All Employees: Construction  All Employees: Manufacturing  \\\n",
       "2023-08                         7997                         12981   \n",
       "2023-09                         8006                         12992   \n",
       "2023-10                         8033                         12954   \n",
       "2023-11                         8039                         12980   \n",
       "2023-12                         8056                         12986   \n",
       "\n",
       "         All Employees: Durable goods  All Employees: Nondurable goods  \\\n",
       "2023-08                          8137                             4844   \n",
       "2023-09                          8147                             4845   \n",
       "2023-10                          8110                             4844   \n",
       "2023-11                          8144                             4836   \n",
       "2023-12                          8152                             4834   \n",
       "\n",
       "         All Employees: Service-Providing Industries  \\\n",
       "2023-08                                       134854   \n",
       "2023-09                                       135095   \n",
       "2023-10                                       135212   \n",
       "2023-11                                       135355   \n",
       "2023-12                                       135549   \n",
       "\n",
       "         All Employees: Trade, Transportation & Utilities  \\\n",
       "2023-08                                             28842   \n",
       "2023-09                                             28873   \n",
       "2023-10                                             28871   \n",
       "2023-11                                             28846   \n",
       "2023-12                                             28846   \n",
       "\n",
       "         All Employees: Wholesale Trade  All Employees: Retail Trade  \\\n",
       "2023-08                          6065.3                      15532.1   \n",
       "2023-09                          6080.9                      15533.8   \n",
       "2023-10                          6089.0                      15550.3   \n",
       "2023-11                          6093.5                      15525.9   \n",
       "2023-12                          6099.0                      15543.3   \n",
       "\n",
       "         All Employees: Financial Activities  All Employees: Government  \\\n",
       "2023-08                                 9153                      22790   \n",
       "2023-09                                 9153                      22853   \n",
       "2023-10                                 9145                      22914   \n",
       "2023-11                                 9146                      22951   \n",
       "2023-12                                 9148                      23003   \n",
       "\n",
       "         Avg Weekly Hours : Goods-Producing  \\\n",
       "2023-08                                40.6   \n",
       "2023-09                                40.5   \n",
       "2023-10                                40.6   \n",
       "2023-11                                40.6   \n",
       "2023-12                                40.4   \n",
       "\n",
       "         Avg Weekly Overtime Hours : Manufacturing  \\\n",
       "2023-08                                        3.6   \n",
       "2023-09                                        3.6   \n",
       "2023-10                                        3.5   \n",
       "2023-11                                        3.4   \n",
       "2023-12                                        3.5   \n",
       "\n",
       "         Avg Weekly Hours : Manufacturing  \\\n",
       "2023-08                              40.8   \n",
       "2023-09                              40.7   \n",
       "2023-10                              40.7   \n",
       "2023-11                              40.5   \n",
       "2023-12                              40.4   \n",
       "\n",
       "         Housing Starts: Total New Privately Owned  Housing Starts, Northeast  \\\n",
       "2023-08                                       1305                        128   \n",
       "2023-09                                       1356                         84   \n",
       "2023-10                                       1376                         79   \n",
       "2023-11                                       1525                        130   \n",
       "2023-12                                       1460                        108   \n",
       "\n",
       "         Housing Starts, Midwest  Housing Starts, South  Housing Starts, West  \\\n",
       "2023-08                      160                    732                   285   \n",
       "2023-09                      192                    784                   296   \n",
       "2023-10                      215                    727                   355   \n",
       "2023-11                      205                    829                   361   \n",
       "2023-12                      187                    787                   378   \n",
       "\n",
       "         New Private Housing Permits (SAAR)  \\\n",
       "2023-08                              1541.0   \n",
       "2023-09                              1471.0   \n",
       "2023-10                              1498.0   \n",
       "2023-11                              1467.0   \n",
       "2023-12                              1493.0   \n",
       "\n",
       "         New Private Housing Permits, Northeast (SAAR)  \\\n",
       "2023-08                                          120.0   \n",
       "2023-09                                          109.0   \n",
       "2023-10                                          128.0   \n",
       "2023-11                                           84.0   \n",
       "2023-12                                           98.0   \n",
       "\n",
       "         New Private Housing Permits, Midwest (SAAR)  \\\n",
       "2023-08                                        208.0   \n",
       "2023-09                                        189.0   \n",
       "2023-10                                        170.0   \n",
       "2023-11                                        190.0   \n",
       "2023-12                                        197.0   \n",
       "\n",
       "         New Private Housing Permits, South (SAAR)  \\\n",
       "2023-08                                      837.0   \n",
       "2023-09                                      818.0   \n",
       "2023-10                                      852.0   \n",
       "2023-11                                      793.0   \n",
       "2023-12                                      860.0   \n",
       "\n",
       "         New Private Housing Permits, West (SAAR)  \\\n",
       "2023-08                                     376.0   \n",
       "2023-09                                     355.0   \n",
       "2023-10                                     348.0   \n",
       "2023-11                                     400.0   \n",
       "2023-12                                     338.0   \n",
       "\n",
       "         New Orders for Consumer Goods  New Orders for Durable Goods  \\\n",
       "2023-08                       252413.0                      283917.0   \n",
       "2023-09                       252436.0                      295365.0   \n",
       "2023-10                       244652.0                      280217.0   \n",
       "2023-11                       245980.0                      295500.0   \n",
       "2023-12                            NaN                      295559.0   \n",
       "\n",
       "         New Orders for Nondefense Capital Goods  \\\n",
       "2023-08                                  85849.0   \n",
       "2023-09                                 101003.0   \n",
       "2023-10                                  86621.0   \n",
       "2023-11                                 102454.0   \n",
       "2023-12                                 103044.0   \n",
       "\n",
       "         Un lled Orders for Durable Goods  Total Business Inventories  \\\n",
       "2023-08                         1335197.0                   2548843.0   \n",
       "2023-09                         1352860.0                   2553086.0   \n",
       "2023-10                         1357730.0                   2550320.0   \n",
       "2023-11                         1375078.0                   2548858.0   \n",
       "2023-12                         1393373.0                         NaN   \n",
       "\n",
       "         Total Business: Inventories to Sales Ratio  M1 Money Stock  \\\n",
       "2023-08                                        1.37         18303.5   \n",
       "2023-09                                        1.36         18171.7   \n",
       "2023-10                                        1.37         18080.9   \n",
       "2023-11                                        1.37         18045.9   \n",
       "2023-12                                         NaN         18101.4   \n",
       "\n",
       "         M2 Money Stock  Real M2 Money Stock  BOGMBASE  \\\n",
       "2023-08         20825.6               6799.8   5559100   \n",
       "2023-09         20755.4               6750.1   5567300   \n",
       "2023-10         20725.7               6737.5   5601300   \n",
       "2023-11         20767.5               6744.5   5731500   \n",
       "2023-12         20865.2               6755.8   5827200   \n",
       "\n",
       "         Total Reserves of Depository Institutions  \\\n",
       "2023-08                                     3228.0   \n",
       "2023-09                                     3239.6   \n",
       "2023-10                                     3276.5   \n",
       "2023-11                                     3403.5   \n",
       "2023-12                                     3491.8   \n",
       "\n",
       "         Reserves Of Depository Institutions  Commercial and Industrial Loans  \\\n",
       "2023-08                              2971200                        2764.0963   \n",
       "2023-09                              3017400                        2765.4185   \n",
       "2023-10                              3104600                        2779.4523   \n",
       "2023-11                              3250100                        2769.5164   \n",
       "2023-12                              3358300                        2771.7764   \n",
       "\n",
       "         Real Estate Loans at All Commercial Banks  Total Nonrevolving Credit  \\\n",
       "2023-08                                  5483.8976                 3681.31604   \n",
       "2023-09                                  5498.2932                 3687.80456   \n",
       "2023-10                                  5499.5336                 3690.65220   \n",
       "2023-11                                  5507.6742                 3695.27091   \n",
       "2023-12                                  5521.1931                        NaN   \n",
       "\n",
       "         Nonrevolving consumer credit to Personal Income  \\\n",
       "2023-08                                         0.159560   \n",
       "2023-09                                         0.159311   \n",
       "2023-10                                         0.159015   \n",
       "2023-11                                         0.158603   \n",
       "2023-12                                              NaN   \n",
       "\n",
       "         S&P s Common Stock Price Index: Composite  \\\n",
       "2023-08                                    4457.36   \n",
       "2023-09                                    4409.10   \n",
       "2023-10                                    4269.40   \n",
       "2023-11                                    4460.06   \n",
       "2023-12                                    4685.05   \n",
       "\n",
       "         S&P s Common Stock Price Index: Industrials  \\\n",
       "2023-08                                      6383.62   \n",
       "2023-09                                      6323.90   \n",
       "2023-10                                      6149.76   \n",
       "2023-11                                      6416.34   \n",
       "2023-12                                      6708.35   \n",
       "\n",
       "         S&P s Composite Common Stock: Dividend Yield  \\\n",
       "2023-08                                           NaN   \n",
       "2023-09                                           NaN   \n",
       "2023-10                                           NaN   \n",
       "2023-11                                           NaN   \n",
       "2023-12                                           NaN   \n",
       "\n",
       "         S&P s Composite Common Stock: Price-Earnings Ratio  \\\n",
       "2023-08                                                 NaN   \n",
       "2023-09                                                 NaN   \n",
       "2023-10                                                 NaN   \n",
       "2023-11                                                 NaN   \n",
       "2023-12                                                 NaN   \n",
       "\n",
       "         E?ective Federal Funds Rate  \\\n",
       "2023-08                         5.33   \n",
       "2023-09                         5.33   \n",
       "2023-10                         5.33   \n",
       "2023-11                         5.33   \n",
       "2023-12                         5.33   \n",
       "\n",
       "         3-Month AA Financial Commercial Paper Rate  3-Month Treasury Bill:  \\\n",
       "2023-08                                        5.44                    5.30   \n",
       "2023-09                                        5.49                    5.32   \n",
       "2023-10                                        5.46                    5.34   \n",
       "2023-11                                        5.41                    5.27   \n",
       "2023-12                                        5.32                    5.24   \n",
       "\n",
       "         6-Month Treasury Bill:  1-Year Treasury Rate  5-Year Treasury Rate  \\\n",
       "2023-08                    5.28                  5.37                  4.31   \n",
       "2023-09                    5.31                  5.44                  4.49   \n",
       "2023-10                    5.33                  5.42                  4.77   \n",
       "2023-11                    5.24                  5.28                  4.49   \n",
       "2023-12                    5.12                  4.96                  4.00   \n",
       "\n",
       "         10-Year Treasury Rate  Moody s Seasoned Aaa Corporate Bond Yield  \\\n",
       "2023-08                   4.17                                       4.95   \n",
       "2023-09                   4.38                                       5.13   \n",
       "2023-10                   4.80                                       5.61   \n",
       "2023-11                   4.50                                       5.28   \n",
       "2023-12                   4.02                                       4.74   \n",
       "\n",
       "         Moody s Seasoned Baa Corporate Bond Yield  \\\n",
       "2023-08                                       6.02   \n",
       "2023-09                                       6.16   \n",
       "2023-10                                       6.63   \n",
       "2023-11                                       6.29   \n",
       "2023-12                                       5.64   \n",
       "\n",
       "         3-Month Commercial Paper Minus FEDFUNDS  \\\n",
       "2023-08                                     0.11   \n",
       "2023-09                                     0.16   \n",
       "2023-10                                     0.13   \n",
       "2023-11                                     0.08   \n",
       "2023-12                                    -0.01   \n",
       "\n",
       "         3-Month Treasury C Minus FEDFUNDS  6-Month Treasury C Minus FEDFUNDS  \\\n",
       "2023-08                              -0.03                              -0.05   \n",
       "2023-09                              -0.01                              -0.02   \n",
       "2023-10                               0.01                               0.00   \n",
       "2023-11                              -0.06                              -0.09   \n",
       "2023-12                              -0.09                              -0.21   \n",
       "\n",
       "         1-Year Treasury C Minus FEDFUNDS  5-Year Treasury C Minus FEDFUNDS  \\\n",
       "2023-08                              0.04                             -1.02   \n",
       "2023-09                              0.11                             -0.84   \n",
       "2023-10                              0.09                             -0.56   \n",
       "2023-11                             -0.05                             -0.84   \n",
       "2023-12                             -0.37                             -1.33   \n",
       "\n",
       "         10-Year Treasury C Minus FEDFUNDS  \\\n",
       "2023-08                              -1.16   \n",
       "2023-09                              -0.95   \n",
       "2023-10                              -0.53   \n",
       "2023-11                              -0.83   \n",
       "2023-12                              -1.31   \n",
       "\n",
       "         Moody s Aaa Corporate Bond Minus FEDFUNDS  \\\n",
       "2023-08                                      -0.38   \n",
       "2023-09                                      -0.20   \n",
       "2023-10                                       0.28   \n",
       "2023-11                                      -0.05   \n",
       "2023-12                                      -0.59   \n",
       "\n",
       "         Moody s Baa Corporate Bond Minus FEDFUNDS  TWEXAFEGSMTHx  \\\n",
       "2023-08                                       0.69       115.0546   \n",
       "2023-09                                       0.83       117.0418   \n",
       "2023-10                                       1.30       118.3751   \n",
       "2023-11                                       0.96       116.7327   \n",
       "2023-12                                       0.31       114.6051   \n",
       "\n",
       "         Switzerland / U.S. Foreign Exchange Rate  \\\n",
       "2023-08                                    0.8783   \n",
       "2023-09                                    0.8996   \n",
       "2023-10                                    0.9035   \n",
       "2023-11                                    0.8902   \n",
       "2023-12                                    0.8648   \n",
       "\n",
       "         Japan / U.S. Foreign Exchange Rate  \\\n",
       "2023-08                            144.7804   \n",
       "2023-09                            147.8450   \n",
       "2023-10                            149.5933   \n",
       "2023-11                            149.6790   \n",
       "2023-12                            143.9815   \n",
       "\n",
       "         U.S. / U.K. Foreign Exchange Rate  \\\n",
       "2023-08                             1.2706   \n",
       "2023-09                             1.2381   \n",
       "2023-10                             1.2175   \n",
       "2023-11                             1.2437   \n",
       "2023-12                             1.2657   \n",
       "\n",
       "         Canada / U.S. Foreign Exchange Rate  PPI: Finished Goods  \\\n",
       "2023-08                               1.3478              257.192   \n",
       "2023-09                               1.3531              259.616   \n",
       "2023-10                               1.3712              254.829   \n",
       "2023-11                               1.3707              254.116   \n",
       "2023-12                               1.3416              253.825   \n",
       "\n",
       "         PPI: Finished Consumer Goods  PPI: Intermediate Materials  \\\n",
       "2023-08                       275.952                      258.693   \n",
       "2023-09                       279.261                      260.179   \n",
       "2023-10                       272.340                      257.484   \n",
       "2023-11                       271.136                      256.356   \n",
       "2023-12                       270.728                      254.708   \n",
       "\n",
       "         PPI: Crude Materials  Crude Oil, spliced WTI and Cushing  \\\n",
       "2023-08               253.963                               81.39   \n",
       "2023-09               262.335                               89.43   \n",
       "2023-10               257.031                               85.64   \n",
       "2023-11               250.743                               77.69   \n",
       "2023-12               239.829                               71.90   \n",
       "\n",
       "         PPI: Metals and metal products:  CPI : All Items  CPI : Apparel  \\\n",
       "2023-08                          246.357          306.269        131.835   \n",
       "2023-09                          251.022          307.481        130.833   \n",
       "2023-10                          245.972          307.619        130.960   \n",
       "2023-11                          248.224          307.917        129.270   \n",
       "2023-12                          244.401          308.850        129.400   \n",
       "\n",
       "         CPI : Transportation  CPI : Medical Care  CPI : Commodities  \\\n",
       "2023-08               272.143             546.954            225.311   \n",
       "2023-09               273.006             548.079            225.542   \n",
       "2023-10               270.454             549.927            224.543   \n",
       "2023-11               268.729             553.095            223.072   \n",
       "2023-12               269.292             556.146            223.223   \n",
       "\n",
       "         CPI : Durables  CPI : Services  CPI : All Items Less Food  \\\n",
       "2023-08         126.743         385.778                    303.579   \n",
       "2023-09         126.290         387.981                    304.857   \n",
       "2023-10         125.660         389.314                    304.875   \n",
       "2023-11         125.192         391.403                    305.110   \n",
       "2023-12         124.578         393.211                    306.078   \n",
       "\n",
       "         CPI : All items less shelter  CPI : All items less medical care  \\\n",
       "2023-08                       279.774                            294.629   \n",
       "2023-09                       280.506                            295.843   \n",
       "2023-10                       280.201                            295.902   \n",
       "2023-11                       279.944                            296.069   \n",
       "2023-12                       280.561                            296.903   \n",
       "\n",
       "         Personal Cons. Expend.: Chain Index  \\\n",
       "2023-08                              120.803   \n",
       "2023-09                              121.267   \n",
       "2023-10                              121.299   \n",
       "2023-11                              121.218   \n",
       "2023-12                              121.421   \n",
       "\n",
       "         Personal Cons. Exp: Durable goods  \\\n",
       "2023-08                            107.261   \n",
       "2023-09                            107.154   \n",
       "2023-10                            106.855   \n",
       "2023-11                            106.391   \n",
       "2023-12                            105.964   \n",
       "\n",
       "         Personal Cons. Exp: Nondurable goods  Personal Cons. Exp: Services  \\\n",
       "2023-08                               120.012                       123.370   \n",
       "2023-09                               120.395                       123.976   \n",
       "2023-10                               120.011                       124.214   \n",
       "2023-11                               118.953                       124.539   \n",
       "2023-12                               118.879                       124.963   \n",
       "\n",
       "         Avg Hourly Earnings : Goods-Producing  \\\n",
       "2023-08                                  29.74   \n",
       "2023-09                                  29.90   \n",
       "2023-10                                  29.98   \n",
       "2023-11                                  30.23   \n",
       "2023-12                                  30.38   \n",
       "\n",
       "         Avg Hourly Earnings : Construction  \\\n",
       "2023-08                               34.45   \n",
       "2023-09                               34.54   \n",
       "2023-10                               34.64   \n",
       "2023-11                               34.94   \n",
       "2023-12                               34.92   \n",
       "\n",
       "         Avg Hourly Earnings : Manufacturing  Consumer Sentiment Index  \\\n",
       "2023-08                                26.50                      69.4   \n",
       "2023-09                                26.64                      67.9   \n",
       "2023-10                                26.70                      63.8   \n",
       "2023-11                                26.91                      61.3   \n",
       "2023-12                                27.16                       NaN   \n",
       "\n",
       "         Consumer Motor Vehicle Loans Outstanding  \\\n",
       "2023-08                                 504406.44   \n",
       "2023-09                                 508808.61   \n",
       "2023-10                                 512056.53   \n",
       "2023-11                                 515110.84   \n",
       "2023-12                                       NaN   \n",
       "\n",
       "         Total Consumer Loans and Leases Outstanding  \\\n",
       "2023-08                                    910966.86   \n",
       "2023-09                                    913938.95   \n",
       "2023-10                                    916941.34   \n",
       "2023-11                                    919957.82   \n",
       "2023-12                                          NaN   \n",
       "\n",
       "         Securities in Bank Credit at All Commercial Banks  VIXCLSx  \n",
       "2023-08                                          5111.5017  15.7822  \n",
       "2023-09                                          5074.6667  15.0424  \n",
       "2023-10                                          5015.7795  19.0462  \n",
       "2023-11                                          5003.5829  13.8563  \n",
       "2023-12                                          5085.0280  12.6960  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def map_column_names(df, defn_file):\n",
    "    \"\"\"\n",
    "    Maps FRED-MD column names to their descriptions.\n",
    "\n",
    "    Parameters:\n",
    "    - df: DataFrame with FRED-MD data whose column names need to be mapped.\n",
    "    - defn_file: Path to the CSV file containing column definitions.\n",
    "    \"\"\"\n",
    "    # Load the definitions file\n",
    "    defn = pd.read_csv(defn_file, encoding_errors=\"ignore\")\n",
    "\n",
    "    # Initialize an empty dictionary to store the mapping from variable names to descriptions\n",
    "    map_dict = {}\n",
    "\n",
    "    # Loop through each column in the DataFrame\n",
    "    for col in df.columns:\n",
    "        # If the column exists in the definitions DataFrame, add it to the mapping dictionary\n",
    "        if col in defn['fred'].values:\n",
    "            # Find the description corresponding to the column name\n",
    "            description = defn.loc[defn['fred'] == col, 'description'].values[0]\n",
    "            map_dict[col] = description\n",
    "\n",
    "    # Use the mapping dictionary to rename the columns of the DataFrame\n",
    "    df_renamed = df.rename(columns=map_dict)\n",
    "\n",
    "    # Return the DataFrame with renamed columns and the definitions DataFrame for reference\n",
    "    return df_renamed, defn\n",
    "\n",
    "\n",
    "column_defn_file = \"./data/FRED/FRED_Definitions_Mapping/fredmd_definitions.csv\"\n",
    "fred_orig, defn = map_column_names(fred, column_defn_file)\n",
    "fred_orig.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyse and Plot a few indicators to inspect the data using the function from utils/visualisations\n",
    "\n",
    "analyze_and_plot(fred_orig,'Total Business Inventories')\n",
    "analyze_and_plot(fred_orig,'CPI : All Items')\n",
    "analyze_and_plot(fred_orig,'All Employees: Total nonfarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Frequency Alignment:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Frequency Alignment for Comparative Analysis**\n",
    "\n",
    "</div>\n",
    "\n",
    "After the initial steps of loading, cleaning, and extracting relevant columns from both datasets, the datasets are indexed by different time frequencies. Specifically, the FRED database is organized monthly, while our PCE data from the BEA dataset follows a quarterly format. To bridge this gap, we implement a strategy to align their frequencies, facilitating a coherent and meaningful comparison.\n",
    "\n",
    "To achieve this alignment, we introduce a function `transform_to_quarterly` that converts monthly dates into the quarterly 'YYYYQX' format. This transformation involves selecting only the last month of each quarter (March, June, September, and December) from the FRED dataset, as these months effectively represent the quarter's economic activity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to transform monthly date to quarterly date in 'YYYYQX' format\n",
    "def transform_to_quarterly(date_str):\n",
    "    year, month = date_str.split(\"-\")\n",
    "    quarter = (int(month) - 1) // 3 + 1\n",
    "    return f\"{year}Q{quarter}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then apply this function to the FRED dataset, filtering for the end months of each quarter and converting the index to the quarterly format. This process not only standardizes the temporal resolution but also creates a new 'Quarter' column, which is then set as the new index.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert index to string to apply string methods\n",
    "fred_orig.index = fred_orig.index.astype(str)\n",
    "\n",
    "# Selecting only the last month of each quarter from the monthly dataset\n",
    "# The last month of each quarter are March (03), June (06), September (09), December (12)\n",
    "fred_orig_filtered = fred_orig[fred_orig.index.str.endswith((\"03\", \"06\", \"09\", \"12\"))]\n",
    "\n",
    "# Transform the index to the quarterly format and create a new 'Quarter' column\n",
    "fred_orig_filtered[\"Quarter\"] = fred_orig_filtered.index.map(transform_to_quarterly)\n",
    "\n",
    "# Set the new 'Quarter' column as the index\n",
    "fred_orig_filtered.set_index(\"Quarter\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compare the date ranges of the transformed FRED dataset and the PCE dataset to ensure they align properly for subsequent analyses.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('1959Q1', '2023Q4'), ('1947Q1', '2023Q4'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the date range of the monthly dataset\n",
    "monthly_date_range = fred_orig_filtered.index.min(), pce_df.index.max()\n",
    "\n",
    "# Checking the date range of the PCE dataset\n",
    "pce_date_range = pce_df.index.min(), pce_df.index.max()\n",
    "\n",
    "monthly_date_range, pce_date_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Quarterly Data Integration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Next We will merge quarterly BEA PCE rate of change data framework with the FRED transformed quarter-over-quarter rate of change into a unified framework using pandas, ensuring seamless integration and compatibility.\n",
    "\n",
    "- This step is vital for consolidating different economic indicators into a single, comprehensive analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>PCE</th>\n",
       "      <th>Real Personal Income</th>\n",
       "      <th>Real personal income ex transfer receipts</th>\n",
       "      <th>Real personal consumption expenditures</th>\n",
       "      <th>Real Manu. and Trade Industries Sales</th>\n",
       "      <th>Retail and Food Services Sales</th>\n",
       "      <th>IP Index</th>\n",
       "      <th>IP: Final Products and Nonindustrial Supplies</th>\n",
       "      <th>IP: Final Products (Market Group)</th>\n",
       "      <th>IP: Consumer Goods</th>\n",
       "      <th>IP: Durable Consumer Goods</th>\n",
       "      <th>IP: Nondurable Consumer Goods</th>\n",
       "      <th>IP: Business Equipment</th>\n",
       "      <th>IP: Materials</th>\n",
       "      <th>IP: Durable Materials</th>\n",
       "      <th>IP: Nondurable Materials</th>\n",
       "      <th>IP: Manufacturing (SIC)</th>\n",
       "      <th>IPB51222S</th>\n",
       "      <th>IP: Fuels</th>\n",
       "      <th>Capacity Utilization: Manufacturing</th>\n",
       "      <th>Help-Wanted Index for United States</th>\n",
       "      <th>Ratio of Help Wanted/No. Unemployed</th>\n",
       "      <th>Civilian Labor Force</th>\n",
       "      <th>Civilian Employment</th>\n",
       "      <th>Civilian Unemployment Rate</th>\n",
       "      <th>Average Duration of Unemployment (Weeks)</th>\n",
       "      <th>Civilians Unemployed - Less Than 5 Weeks</th>\n",
       "      <th>Civilians Unemployed for 5-14 Weeks</th>\n",
       "      <th>Civilians Unemployed - 15 Weeks &amp; Over</th>\n",
       "      <th>Civilians Unemployed for 15-26 Weeks</th>\n",
       "      <th>Civilians Unemployed for 27 Weeks and Over</th>\n",
       "      <th>Initial Claims</th>\n",
       "      <th>All Employees: Total nonfarm</th>\n",
       "      <th>All Employees: Goods-Producing Industries</th>\n",
       "      <th>All Employees: Mining and Logging: Mining</th>\n",
       "      <th>All Employees: Construction</th>\n",
       "      <th>All Employees: Manufacturing</th>\n",
       "      <th>All Employees: Durable goods</th>\n",
       "      <th>All Employees: Nondurable goods</th>\n",
       "      <th>All Employees: Service-Providing Industries</th>\n",
       "      <th>All Employees: Trade, Transportation &amp; Utilities</th>\n",
       "      <th>All Employees: Wholesale Trade</th>\n",
       "      <th>All Employees: Retail Trade</th>\n",
       "      <th>All Employees: Financial Activities</th>\n",
       "      <th>All Employees: Government</th>\n",
       "      <th>Avg Weekly Hours : Goods-Producing</th>\n",
       "      <th>Avg Weekly Overtime Hours : Manufacturing</th>\n",
       "      <th>Avg Weekly Hours : Manufacturing</th>\n",
       "      <th>Housing Starts: Total New Privately Owned</th>\n",
       "      <th>Housing Starts, Northeast</th>\n",
       "      <th>Housing Starts, Midwest</th>\n",
       "      <th>Housing Starts, South</th>\n",
       "      <th>Housing Starts, West</th>\n",
       "      <th>New Private Housing Permits (SAAR)</th>\n",
       "      <th>New Private Housing Permits, Northeast (SAAR)</th>\n",
       "      <th>New Private Housing Permits, Midwest (SAAR)</th>\n",
       "      <th>New Private Housing Permits, South (SAAR)</th>\n",
       "      <th>New Private Housing Permits, West (SAAR)</th>\n",
       "      <th>New Orders for Consumer Goods</th>\n",
       "      <th>New Orders for Durable Goods</th>\n",
       "      <th>New Orders for Nondefense Capital Goods</th>\n",
       "      <th>Un lled Orders for Durable Goods</th>\n",
       "      <th>Total Business Inventories</th>\n",
       "      <th>Total Business: Inventories to Sales Ratio</th>\n",
       "      <th>M1 Money Stock</th>\n",
       "      <th>M2 Money Stock</th>\n",
       "      <th>Real M2 Money Stock</th>\n",
       "      <th>BOGMBASE</th>\n",
       "      <th>Total Reserves of Depository Institutions</th>\n",
       "      <th>Reserves Of Depository Institutions</th>\n",
       "      <th>Commercial and Industrial Loans</th>\n",
       "      <th>Real Estate Loans at All Commercial Banks</th>\n",
       "      <th>Total Nonrevolving Credit</th>\n",
       "      <th>Nonrevolving consumer credit to Personal Income</th>\n",
       "      <th>S&amp;P s Common Stock Price Index: Composite</th>\n",
       "      <th>S&amp;P s Common Stock Price Index: Industrials</th>\n",
       "      <th>S&amp;P s Composite Common Stock: Dividend Yield</th>\n",
       "      <th>S&amp;P s Composite Common Stock: Price-Earnings Ratio</th>\n",
       "      <th>E?ective Federal Funds Rate</th>\n",
       "      <th>3-Month AA Financial Commercial Paper Rate</th>\n",
       "      <th>3-Month Treasury Bill:</th>\n",
       "      <th>6-Month Treasury Bill:</th>\n",
       "      <th>1-Year Treasury Rate</th>\n",
       "      <th>5-Year Treasury Rate</th>\n",
       "      <th>10-Year Treasury Rate</th>\n",
       "      <th>Moody s Seasoned Aaa Corporate Bond Yield</th>\n",
       "      <th>Moody s Seasoned Baa Corporate Bond Yield</th>\n",
       "      <th>3-Month Commercial Paper Minus FEDFUNDS</th>\n",
       "      <th>3-Month Treasury C Minus FEDFUNDS</th>\n",
       "      <th>6-Month Treasury C Minus FEDFUNDS</th>\n",
       "      <th>1-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>5-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>10-Year Treasury C Minus FEDFUNDS</th>\n",
       "      <th>Moody s Aaa Corporate Bond Minus FEDFUNDS</th>\n",
       "      <th>Moody s Baa Corporate Bond Minus FEDFUNDS</th>\n",
       "      <th>TWEXAFEGSMTHx</th>\n",
       "      <th>Switzerland / U.S. Foreign Exchange Rate</th>\n",
       "      <th>Japan / U.S. Foreign Exchange Rate</th>\n",
       "      <th>U.S. / U.K. Foreign Exchange Rate</th>\n",
       "      <th>Canada / U.S. Foreign Exchange Rate</th>\n",
       "      <th>PPI: Finished Goods</th>\n",
       "      <th>PPI: Finished Consumer Goods</th>\n",
       "      <th>PPI: Intermediate Materials</th>\n",
       "      <th>PPI: Crude Materials</th>\n",
       "      <th>Crude Oil, spliced WTI and Cushing</th>\n",
       "      <th>PPI: Metals and metal products:</th>\n",
       "      <th>CPI : All Items</th>\n",
       "      <th>CPI : Apparel</th>\n",
       "      <th>CPI : Transportation</th>\n",
       "      <th>CPI : Medical Care</th>\n",
       "      <th>CPI : Commodities</th>\n",
       "      <th>CPI : Durables</th>\n",
       "      <th>CPI : Services</th>\n",
       "      <th>CPI : All Items Less Food</th>\n",
       "      <th>CPI : All items less shelter</th>\n",
       "      <th>CPI : All items less medical care</th>\n",
       "      <th>Personal Cons. Expend.: Chain Index</th>\n",
       "      <th>Personal Cons. Exp: Durable goods</th>\n",
       "      <th>Personal Cons. Exp: Nondurable goods</th>\n",
       "      <th>Personal Cons. Exp: Services</th>\n",
       "      <th>Avg Hourly Earnings : Goods-Producing</th>\n",
       "      <th>Avg Hourly Earnings : Construction</th>\n",
       "      <th>Avg Hourly Earnings : Manufacturing</th>\n",
       "      <th>Consumer Sentiment Index</th>\n",
       "      <th>Consumer Motor Vehicle Loans Outstanding</th>\n",
       "      <th>Total Consumer Loans and Leases Outstanding</th>\n",
       "      <th>Securities in Bank Credit at All Commercial Banks</th>\n",
       "      <th>VIXCLSx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959Q1</th>\n",
       "      <td>309.4</td>\n",
       "      <td>2610.396</td>\n",
       "      <td>2452.7</td>\n",
       "      <td>15.491</td>\n",
       "      <td>277775.2539</td>\n",
       "      <td>18523.05762</td>\n",
       "      <td>22.7193</td>\n",
       "      <td>23.8483</td>\n",
       "      <td>22.5719</td>\n",
       "      <td>31.9337</td>\n",
       "      <td>19.4890</td>\n",
       "      <td>38.3324</td>\n",
       "      <td>8.2391</td>\n",
       "      <td>21.2103</td>\n",
       "      <td>13.0420</td>\n",
       "      <td>31.6645</td>\n",
       "      <td>21.5533</td>\n",
       "      <td>19.9986</td>\n",
       "      <td>35.0589</td>\n",
       "      <td>82.4769</td>\n",
       "      <td>1524</td>\n",
       "      <td>0.400947</td>\n",
       "      <td>68068</td>\n",
       "      <td>64267</td>\n",
       "      <td>5.6</td>\n",
       "      <td>15.3</td>\n",
       "      <td>1459</td>\n",
       "      <td>1093</td>\n",
       "      <td>1210</td>\n",
       "      <td>530</td>\n",
       "      <td>680</td>\n",
       "      <td>260346</td>\n",
       "      <td>53014</td>\n",
       "      <td>19069</td>\n",
       "      <td>704.1</td>\n",
       "      <td>3013</td>\n",
       "      <td>15259</td>\n",
       "      <td>8965</td>\n",
       "      <td>6294</td>\n",
       "      <td>33945</td>\n",
       "      <td>10873</td>\n",
       "      <td>2584.4</td>\n",
       "      <td>5431.6</td>\n",
       "      <td>2430</td>\n",
       "      <td>8132</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>40.4</td>\n",
       "      <td>1620</td>\n",
       "      <td>330</td>\n",
       "      <td>413</td>\n",
       "      <td>503</td>\n",
       "      <td>374</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>15745.42348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44781.64655</td>\n",
       "      <td>85620.18980</td>\n",
       "      <td>1.529007</td>\n",
       "      <td>139.7</td>\n",
       "      <td>289.2</td>\n",
       "      <td>998.3</td>\n",
       "      <td>49700</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17800</td>\n",
       "      <td>35.1304</td>\n",
       "      <td>25.4218</td>\n",
       "      <td>50.00773</td>\n",
       "      <td>0.126123</td>\n",
       "      <td>56.16</td>\n",
       "      <td>59.79</td>\n",
       "      <td>3.151709</td>\n",
       "      <td>18.999351</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.35</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.85</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3228</td>\n",
       "      <td>359.8417</td>\n",
       "      <td>2.8127</td>\n",
       "      <td>0.9698</td>\n",
       "      <td>33.2</td>\n",
       "      <td>33.3</td>\n",
       "      <td>30.7</td>\n",
       "      <td>31.5</td>\n",
       "      <td>2.97</td>\n",
       "      <td>32.9</td>\n",
       "      <td>28.97</td>\n",
       "      <td>44.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>21.3</td>\n",
       "      <td>33.2</td>\n",
       "      <td>38.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>30.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>15.189</td>\n",
       "      <td>63.634</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>6508.0</td>\n",
       "      <td>12349.0</td>\n",
       "      <td>81.6405</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959Q2</th>\n",
       "      <td>315.5</td>\n",
       "      <td>2650.841</td>\n",
       "      <td>2493.9</td>\n",
       "      <td>15.679</td>\n",
       "      <td>285279.5930</td>\n",
       "      <td>18849.75209</td>\n",
       "      <td>23.5797</td>\n",
       "      <td>24.5658</td>\n",
       "      <td>23.2885</td>\n",
       "      <td>32.4375</td>\n",
       "      <td>20.1628</td>\n",
       "      <td>38.6971</td>\n",
       "      <td>8.9338</td>\n",
       "      <td>22.2110</td>\n",
       "      <td>13.7981</td>\n",
       "      <td>32.8397</td>\n",
       "      <td>22.3449</td>\n",
       "      <td>20.4864</td>\n",
       "      <td>34.2326</td>\n",
       "      <td>84.7686</td>\n",
       "      <td>1717</td>\n",
       "      <td>0.500729</td>\n",
       "      <td>68278</td>\n",
       "      <td>64849</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1522</td>\n",
       "      <td>963</td>\n",
       "      <td>963</td>\n",
       "      <td>415</td>\n",
       "      <td>548</td>\n",
       "      <td>258258</td>\n",
       "      <td>53681</td>\n",
       "      <td>19462</td>\n",
       "      <td>715.0</td>\n",
       "      <td>3100</td>\n",
       "      <td>15554</td>\n",
       "      <td>9209</td>\n",
       "      <td>6345</td>\n",
       "      <td>34219</td>\n",
       "      <td>10968</td>\n",
       "      <td>2614.8</td>\n",
       "      <td>5467.8</td>\n",
       "      <td>2454</td>\n",
       "      <td>8143</td>\n",
       "      <td>40.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1503</td>\n",
       "      <td>254</td>\n",
       "      <td>343</td>\n",
       "      <td>518</td>\n",
       "      <td>388</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>15961.74553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45731.62674</td>\n",
       "      <td>88143.30155</td>\n",
       "      <td>1.529007</td>\n",
       "      <td>141.2</td>\n",
       "      <td>294.1</td>\n",
       "      <td>1010.3</td>\n",
       "      <td>50300</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17500</td>\n",
       "      <td>37.1404</td>\n",
       "      <td>26.2668</td>\n",
       "      <td>51.67544</td>\n",
       "      <td>0.127657</td>\n",
       "      <td>57.46</td>\n",
       "      <td>61.75</td>\n",
       "      <td>3.115211</td>\n",
       "      <td>19.102705</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.83</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.52</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.46</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.3115</td>\n",
       "      <td>359.8417</td>\n",
       "      <td>2.8125</td>\n",
       "      <td>0.9590</td>\n",
       "      <td>33.2</td>\n",
       "      <td>33.4</td>\n",
       "      <td>30.9</td>\n",
       "      <td>31.3</td>\n",
       "      <td>2.97</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.11</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>21.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>15.271</td>\n",
       "      <td>63.931</td>\n",
       "      <td>18.321</td>\n",
       "      <td>10.275</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6956.0</td>\n",
       "      <td>12926.0</td>\n",
       "      <td>78.6972</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PCE  Real Personal Income  \\\n",
       "1959Q1  309.4              2610.396   \n",
       "1959Q2  315.5              2650.841   \n",
       "\n",
       "        Real personal income ex transfer receipts  \\\n",
       "1959Q1                                     2452.7   \n",
       "1959Q2                                     2493.9   \n",
       "\n",
       "        Real personal consumption expenditures  \\\n",
       "1959Q1                                  15.491   \n",
       "1959Q2                                  15.679   \n",
       "\n",
       "        Real Manu. and Trade Industries Sales  Retail and Food Services Sales  \\\n",
       "1959Q1                            277775.2539                     18523.05762   \n",
       "1959Q2                            285279.5930                     18849.75209   \n",
       "\n",
       "        IP Index  IP: Final Products and Nonindustrial Supplies  \\\n",
       "1959Q1   22.7193                                        23.8483   \n",
       "1959Q2   23.5797                                        24.5658   \n",
       "\n",
       "        IP: Final Products (Market Group)  IP: Consumer Goods  \\\n",
       "1959Q1                            22.5719             31.9337   \n",
       "1959Q2                            23.2885             32.4375   \n",
       "\n",
       "        IP: Durable Consumer Goods  IP: Nondurable Consumer Goods  \\\n",
       "1959Q1                     19.4890                        38.3324   \n",
       "1959Q2                     20.1628                        38.6971   \n",
       "\n",
       "        IP: Business Equipment  IP: Materials  IP: Durable Materials  \\\n",
       "1959Q1                  8.2391        21.2103                13.0420   \n",
       "1959Q2                  8.9338        22.2110                13.7981   \n",
       "\n",
       "        IP: Nondurable Materials  IP: Manufacturing (SIC)  IPB51222S  \\\n",
       "1959Q1                   31.6645                  21.5533    19.9986   \n",
       "1959Q2                   32.8397                  22.3449    20.4864   \n",
       "\n",
       "        IP: Fuels  Capacity Utilization: Manufacturing  \\\n",
       "1959Q1    35.0589                              82.4769   \n",
       "1959Q2    34.2326                              84.7686   \n",
       "\n",
       "        Help-Wanted Index for United States  \\\n",
       "1959Q1                                 1524   \n",
       "1959Q2                                 1717   \n",
       "\n",
       "        Ratio of Help Wanted/No. Unemployed  Civilian Labor Force  \\\n",
       "1959Q1                             0.400947                 68068   \n",
       "1959Q2                             0.500729                 68278   \n",
       "\n",
       "        Civilian Employment  Civilian Unemployment Rate  \\\n",
       "1959Q1                64267                         5.6   \n",
       "1959Q2                64849                         5.0   \n",
       "\n",
       "        Average Duration of Unemployment (Weeks)  \\\n",
       "1959Q1                                      15.3   \n",
       "1959Q2                                      14.9   \n",
       "\n",
       "        Civilians Unemployed - Less Than 5 Weeks  \\\n",
       "1959Q1                                      1459   \n",
       "1959Q2                                      1522   \n",
       "\n",
       "        Civilians Unemployed for 5-14 Weeks  \\\n",
       "1959Q1                                 1093   \n",
       "1959Q2                                  963   \n",
       "\n",
       "        Civilians Unemployed - 15 Weeks & Over  \\\n",
       "1959Q1                                    1210   \n",
       "1959Q2                                     963   \n",
       "\n",
       "        Civilians Unemployed for 15-26 Weeks  \\\n",
       "1959Q1                                   530   \n",
       "1959Q2                                   415   \n",
       "\n",
       "        Civilians Unemployed for 27 Weeks and Over  Initial Claims  \\\n",
       "1959Q1                                         680          260346   \n",
       "1959Q2                                         548          258258   \n",
       "\n",
       "        All Employees: Total nonfarm  \\\n",
       "1959Q1                         53014   \n",
       "1959Q2                         53681   \n",
       "\n",
       "        All Employees: Goods-Producing Industries  \\\n",
       "1959Q1                                      19069   \n",
       "1959Q2                                      19462   \n",
       "\n",
       "        All Employees: Mining and Logging: Mining  \\\n",
       "1959Q1                                      704.1   \n",
       "1959Q2                                      715.0   \n",
       "\n",
       "        All Employees: Construction  All Employees: Manufacturing  \\\n",
       "1959Q1                         3013                         15259   \n",
       "1959Q2                         3100                         15554   \n",
       "\n",
       "        All Employees: Durable goods  All Employees: Nondurable goods  \\\n",
       "1959Q1                          8965                             6294   \n",
       "1959Q2                          9209                             6345   \n",
       "\n",
       "        All Employees: Service-Providing Industries  \\\n",
       "1959Q1                                        33945   \n",
       "1959Q2                                        34219   \n",
       "\n",
       "        All Employees: Trade, Transportation & Utilities  \\\n",
       "1959Q1                                             10873   \n",
       "1959Q2                                             10968   \n",
       "\n",
       "        All Employees: Wholesale Trade  All Employees: Retail Trade  \\\n",
       "1959Q1                          2584.4                       5431.6   \n",
       "1959Q2                          2614.8                       5467.8   \n",
       "\n",
       "        All Employees: Financial Activities  All Employees: Government  \\\n",
       "1959Q1                                 2430                       8132   \n",
       "1959Q2                                 2454                       8143   \n",
       "\n",
       "        Avg Weekly Hours : Goods-Producing  \\\n",
       "1959Q1                                40.0   \n",
       "1959Q2                                40.2   \n",
       "\n",
       "        Avg Weekly Overtime Hours : Manufacturing  \\\n",
       "1959Q1                                        2.8   \n",
       "1959Q2                                        2.9   \n",
       "\n",
       "        Avg Weekly Hours : Manufacturing  \\\n",
       "1959Q1                              40.4   \n",
       "1959Q2                              40.6   \n",
       "\n",
       "        Housing Starts: Total New Privately Owned  Housing Starts, Northeast  \\\n",
       "1959Q1                                       1620                        330   \n",
       "1959Q2                                       1503                        254   \n",
       "\n",
       "        Housing Starts, Midwest  Housing Starts, South  Housing Starts, West  \\\n",
       "1959Q1                      413                    503                   374   \n",
       "1959Q2                      343                    518                   388   \n",
       "\n",
       "        New Private Housing Permits (SAAR)  \\\n",
       "1959Q1                                 NaN   \n",
       "1959Q2                                 NaN   \n",
       "\n",
       "        New Private Housing Permits, Northeast (SAAR)  \\\n",
       "1959Q1                                            NaN   \n",
       "1959Q2                                            NaN   \n",
       "\n",
       "        New Private Housing Permits, Midwest (SAAR)  \\\n",
       "1959Q1                                          NaN   \n",
       "1959Q2                                          NaN   \n",
       "\n",
       "        New Private Housing Permits, South (SAAR)  \\\n",
       "1959Q1                                        NaN   \n",
       "1959Q2                                        NaN   \n",
       "\n",
       "        New Private Housing Permits, West (SAAR)  \\\n",
       "1959Q1                                       NaN   \n",
       "1959Q2                                       NaN   \n",
       "\n",
       "        New Orders for Consumer Goods  New Orders for Durable Goods  \\\n",
       "1959Q1                            NaN                   15745.42348   \n",
       "1959Q2                            NaN                   15961.74553   \n",
       "\n",
       "        New Orders for Nondefense Capital Goods  \\\n",
       "1959Q1                                      NaN   \n",
       "1959Q2                                      NaN   \n",
       "\n",
       "        Un lled Orders for Durable Goods  Total Business Inventories  \\\n",
       "1959Q1                       44781.64655                 85620.18980   \n",
       "1959Q2                       45731.62674                 88143.30155   \n",
       "\n",
       "        Total Business: Inventories to Sales Ratio  M1 Money Stock  \\\n",
       "1959Q1                                    1.529007           139.7   \n",
       "1959Q2                                    1.529007           141.2   \n",
       "\n",
       "        M2 Money Stock  Real M2 Money Stock  BOGMBASE  \\\n",
       "1959Q1           289.2                998.3     49700   \n",
       "1959Q2           294.1               1010.3     50300   \n",
       "\n",
       "        Total Reserves of Depository Institutions  \\\n",
       "1959Q1                                       18.4   \n",
       "1959Q2                                       18.5   \n",
       "\n",
       "        Reserves Of Depository Institutions  Commercial and Industrial Loans  \\\n",
       "1959Q1                                17800                          35.1304   \n",
       "1959Q2                                17500                          37.1404   \n",
       "\n",
       "        Real Estate Loans at All Commercial Banks  Total Nonrevolving Credit  \\\n",
       "1959Q1                                    25.4218                   50.00773   \n",
       "1959Q2                                    26.2668                   51.67544   \n",
       "\n",
       "        Nonrevolving consumer credit to Personal Income  \\\n",
       "1959Q1                                         0.126123   \n",
       "1959Q2                                         0.127657   \n",
       "\n",
       "        S&P s Common Stock Price Index: Composite  \\\n",
       "1959Q1                                      56.16   \n",
       "1959Q2                                      57.46   \n",
       "\n",
       "        S&P s Common Stock Price Index: Industrials  \\\n",
       "1959Q1                                        59.79   \n",
       "1959Q2                                        61.75   \n",
       "\n",
       "        S&P s Composite Common Stock: Dividend Yield  \\\n",
       "1959Q1                                      3.151709   \n",
       "1959Q2                                      3.115211   \n",
       "\n",
       "        S&P s Composite Common Stock: Price-Earnings Ratio  \\\n",
       "1959Q1                                           18.999351   \n",
       "1959Q2                                           19.102705   \n",
       "\n",
       "        E?ective Federal Funds Rate  \\\n",
       "1959Q1                         2.80   \n",
       "1959Q2                         3.39   \n",
       "\n",
       "        3-Month AA Financial Commercial Paper Rate  3-Month Treasury Bill:  \\\n",
       "1959Q1                                        3.35                    2.80   \n",
       "1959Q2                                        3.83                    3.21   \n",
       "\n",
       "        6-Month Treasury Bill:  1-Year Treasury Rate  5-Year Treasury Rate  \\\n",
       "1959Q1                    3.13                  3.61                  3.99   \n",
       "1959Q2                    3.52                  4.07                  4.50   \n",
       "\n",
       "        10-Year Treasury Rate  Moody s Seasoned Aaa Corporate Bond Yield  \\\n",
       "1959Q1                   3.99                                       4.13   \n",
       "1959Q2                   4.34                                       4.46   \n",
       "\n",
       "        Moody s Seasoned Baa Corporate Bond Yield  \\\n",
       "1959Q1                                       4.85   \n",
       "1959Q2                                       5.04   \n",
       "\n",
       "        3-Month Commercial Paper Minus FEDFUNDS  \\\n",
       "1959Q1                                     0.55   \n",
       "1959Q2                                     0.44   \n",
       "\n",
       "        3-Month Treasury C Minus FEDFUNDS  6-Month Treasury C Minus FEDFUNDS  \\\n",
       "1959Q1                               0.00                               0.33   \n",
       "1959Q2                              -0.18                               0.13   \n",
       "\n",
       "        1-Year Treasury C Minus FEDFUNDS  5-Year Treasury C Minus FEDFUNDS  \\\n",
       "1959Q1                              0.81                              1.19   \n",
       "1959Q2                              0.68                              1.11   \n",
       "\n",
       "        10-Year Treasury C Minus FEDFUNDS  \\\n",
       "1959Q1                               1.19   \n",
       "1959Q2                               0.95   \n",
       "\n",
       "        Moody s Aaa Corporate Bond Minus FEDFUNDS  \\\n",
       "1959Q1                                       1.33   \n",
       "1959Q2                                       1.07   \n",
       "\n",
       "        Moody s Baa Corporate Bond Minus FEDFUNDS  TWEXAFEGSMTHx  \\\n",
       "1959Q1                                       2.05            NaN   \n",
       "1959Q2                                       1.65            NaN   \n",
       "\n",
       "        Switzerland / U.S. Foreign Exchange Rate  \\\n",
       "1959Q1                                    4.3228   \n",
       "1959Q2                                    4.3115   \n",
       "\n",
       "        Japan / U.S. Foreign Exchange Rate  U.S. / U.K. Foreign Exchange Rate  \\\n",
       "1959Q1                            359.8417                             2.8127   \n",
       "1959Q2                            359.8417                             2.8125   \n",
       "\n",
       "        Canada / U.S. Foreign Exchange Rate  PPI: Finished Goods  \\\n",
       "1959Q1                               0.9698                 33.2   \n",
       "1959Q2                               0.9590                 33.2   \n",
       "\n",
       "        PPI: Finished Consumer Goods  PPI: Intermediate Materials  \\\n",
       "1959Q1                          33.3                         30.7   \n",
       "1959Q2                          33.4                         30.9   \n",
       "\n",
       "        PPI: Crude Materials  Crude Oil, spliced WTI and Cushing  \\\n",
       "1959Q1                  31.5                                2.97   \n",
       "1959Q2                  31.3                                2.97   \n",
       "\n",
       "        PPI: Metals and metal products:  CPI : All Items  CPI : Apparel  \\\n",
       "1959Q1                             32.9            28.97           44.7   \n",
       "1959Q2                             33.0            29.11           45.0   \n",
       "\n",
       "        CPI : Transportation  CPI : Medical Care  CPI : Commodities  \\\n",
       "1959Q1                  29.6                21.3               33.2   \n",
       "1959Q2                  29.8                21.5               33.3   \n",
       "\n",
       "        CPI : Durables  CPI : Services  CPI : All Items Less Food  \\\n",
       "1959Q1            38.3            23.0                       28.9   \n",
       "1959Q2            38.4            23.2                       29.2   \n",
       "\n",
       "        CPI : All items less shelter  CPI : All items less medical care  \\\n",
       "1959Q1                          30.7                               29.6   \n",
       "1959Q2                          30.8                               29.7   \n",
       "\n",
       "        Personal Cons. Expend.: Chain Index  \\\n",
       "1959Q1                               15.189   \n",
       "1959Q2                               15.271   \n",
       "\n",
       "        Personal Cons. Exp: Durable goods  \\\n",
       "1959Q1                             63.634   \n",
       "1959Q2                             63.931   \n",
       "\n",
       "        Personal Cons. Exp: Nondurable goods  Personal Cons. Exp: Services  \\\n",
       "1959Q1                                18.289                        10.185   \n",
       "1959Q2                                18.321                        10.275   \n",
       "\n",
       "        Avg Hourly Earnings : Goods-Producing  \\\n",
       "1959Q1                                   2.15   \n",
       "1959Q2                                   2.17   \n",
       "\n",
       "        Avg Hourly Earnings : Construction  \\\n",
       "1959Q1                                2.45   \n",
       "1959Q2                                2.50   \n",
       "\n",
       "        Avg Hourly Earnings : Manufacturing  Consumer Sentiment Index  \\\n",
       "1959Q1                                 2.07                       NaN   \n",
       "1959Q2                                 2.09                       NaN   \n",
       "\n",
       "        Consumer Motor Vehicle Loans Outstanding  \\\n",
       "1959Q1                                    6508.0   \n",
       "1959Q2                                    6956.0   \n",
       "\n",
       "        Total Consumer Loans and Leases Outstanding  \\\n",
       "1959Q1                                      12349.0   \n",
       "1959Q2                                      12926.0   \n",
       "\n",
       "        Securities in Bank Credit at All Commercial Banks  VIXCLSx  \n",
       "1959Q1                                            81.6405      NaN  \n",
       "1959Q2                                            78.6972      NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows in pce_df that are not in fred_orig_filtered\n",
    "pce_df = pce_df[pce_df.index.isin(fred_orig_filtered.index)]\n",
    "\n",
    "# Merging the datasets on the 'Quarter' column\n",
    "joined_dataset = pd.merge(\n",
    "    pce_df, fred_orig_filtered, left_index=True, right_index=True, how=\"left\"\n",
    ")\n",
    "\n",
    "# Display the first few rows of the merged dataset\n",
    "joined_dataset.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sanity check for later\n",
    "index_pretransform = joined_dataset.index[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Data Integrity Check\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Verify Data Consistency with Column Labels**\n",
    "\n",
    "</div>\n",
    "\n",
    "- Ensure each column label accurately represents the data it holds.\n",
    "- Correct any typos or inconsistencies in column names.\n",
    "- `Caveat`: they will be a logical key on which we will join transformation codes and economic group mappings from the FREDmd_definitions dataframe so if you changes names here, ensure to change it there as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PCE', 'Real Personal Income',\n",
      "       'Real personal income ex transfer receipts',\n",
      "       'Real personal consumption expenditures',\n",
      "       'Real Manu. and Trade Industries Sales',\n",
      "       'Retail and Food Services Sales', 'IP Index',\n",
      "       'IP: Final Products and Nonindustrial Supplies',\n",
      "       'IP: Final Products (Market Group)', 'IP: Consumer Goods',\n",
      "       ...\n",
      "       'Personal Cons. Exp: Nondurable goods', 'Personal Cons. Exp: Services',\n",
      "       'Avg Hourly Earnings : Goods-Producing',\n",
      "       'Avg Hourly Earnings : Construction',\n",
      "       'Avg Hourly Earnings : Manufacturing', 'Consumer Sentiment Index',\n",
      "       'Consumer Motor Vehicle Loans Outstanding',\n",
      "       'Total Consumer Loans and Leases Outstanding',\n",
      "       'Securities in Bank Credit at All Commercial Banks', 'VIXCLSx'],\n",
      "      dtype='object', length=128)\n"
     ]
    }
   ],
   "source": [
    "# Print column labels\n",
    "print(joined_dataset.columns)\n",
    "\n",
    "# To rename columns if necessary\n",
    "# joined_dataset.rename(columns={'old_name': 'new_name'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Ensure Adherence to Field-specific Rules**\n",
    "\n",
    "</div>\n",
    "\n",
    "- Check for adherence to field-specific constraints (e.g., non-negative values for certain economic indicators).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Reserves Of Depository Institutions has negative values\n",
      "Column 3-Month Commercial Paper Minus FEDFUNDS has negative values\n",
      "Column 3-Month Treasury C Minus FEDFUNDS has negative values\n",
      "Column 6-Month Treasury C Minus FEDFUNDS has negative values\n",
      "Column 1-Year Treasury C Minus FEDFUNDS has negative values\n",
      "Column 5-Year Treasury C Minus FEDFUNDS has negative values\n",
      "Column 10-Year Treasury C Minus FEDFUNDS has negative values\n",
      "Column Moody s Aaa Corporate Bond Minus FEDFUNDS has negative values\n",
      "Column Moody s Baa Corporate Bond Minus FEDFUNDS has negative values\n"
     ]
    }
   ],
   "source": [
    "# iterate ofver joined_dataset and check each column,check for negative values in a column\n",
    "for column in joined_dataset.columns:\n",
    "    if joined_dataset[column].min() < 0:\n",
    "        print(f\"Column {column} has negative values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These Economic indicators can exhibit negative values, particularly those representing differences or spreads between two measures. This phenomenon is most common in indicators that calculate the spread between interest rates or yields, such as:\n",
    "\n",
    "- **Reserves Of Depository Institutions**: Negative values are unusual but may occur due to specific policy conditions or accounting adjustments.\n",
    "- **Spread-Related Indicators**: Indicators like \"3-Month Commercial Paper Minus FEDFUNDS\" or \"Treasury C Minus FEDFUNDS\" show the difference between two rates. Negative values indicate that the first rate is lower than the second, which can happen in periods of economic stress or unusual monetary policy environments.\n",
    "\n",
    "**Significance**: Negative spreads can signal investor sentiment, expectations about future interest rates, or the health of financial markets. For instance, a negative spread between Treasury securities and the Federal Funds rate might indicate investor expectations of future rate cuts by the Federal Reserve.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Real Manu. and Trade Industries Sales has infinite values\n",
      "Column New Private Housing Permits (SAAR) has infinite values\n",
      "Column New Private Housing Permits, Northeast (SAAR) has infinite values\n",
      "Column New Private Housing Permits, Midwest (SAAR) has infinite values\n",
      "Column New Private Housing Permits, South (SAAR) has infinite values\n",
      "Column New Private Housing Permits, West (SAAR) has infinite values\n",
      "Column New Orders for Consumer Goods has infinite values\n",
      "Column New Orders for Nondefense Capital Goods has infinite values\n",
      "Column Total Business Inventories has infinite values\n",
      "Column Total Business: Inventories to Sales Ratio has infinite values\n",
      "Column Total Nonrevolving Credit has infinite values\n",
      "Column Nonrevolving consumer credit to Personal Income has infinite values\n",
      "Column S&P s Composite Common Stock: Dividend Yield has infinite values\n",
      "Column S&P s Composite Common Stock: Price-Earnings Ratio has infinite values\n",
      "Column TWEXAFEGSMTHx has infinite values\n",
      "Column Consumer Sentiment Index has infinite values\n",
      "Column Consumer Motor Vehicle Loans Outstanding has infinite values\n",
      "Column Total Consumer Loans and Leases Outstanding has infinite values\n",
      "Column VIXCLSx has infinite values\n"
     ]
    }
   ],
   "source": [
    "#iterate over joined_dataset and check each column, check for infinite values in a column and count them\n",
    "for column in joined_dataset.columns:\n",
    "    if not np.isfinite(joined_dataset[column]).all():\n",
    "        print(f\"Column {column} has infinite values\")\n",
    "        #replace infinite values with NaN\n",
    "        joined_dataset[column].replace([np.inf, -np.inf], np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Duplicate Entries**\n",
    "\n",
    "</div>\n",
    "\n",
    "- Evaluate the dataset for duplicate rows and drop duplicates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicate rows\n",
    "print(f\"Duplicate rows: {joined_dataset.duplicated().sum()}\")\n",
    "\n",
    "# To drop duplicates if necessary\n",
    "# joined_dataset.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Ensure Proper Data Types for Each Column**\n",
    "\n",
    "</div>\n",
    "\n",
    "- Check and correct data types for numerical, categorical, and datetime columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check current data types\n",
    "# print(joined_dataset.dtypes)\n",
    "\n",
    "# iterate ofver joined_dataset and check each column, Change the data type of a column to float if not already\n",
    "for column in joined_dataset.columns:\n",
    "    if joined_dataset[column].dtype != \"float64\":\n",
    "        joined_dataset[column] = joined_dataset[column].astype(float)\n",
    "\n",
    "#sanity check \n",
    "for column in joined_dataset.columns:\n",
    "    if joined_dataset[column].dtype != \"float64\":\n",
    "        print(column)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Ensure Each Column in joined_dataset is in Defintions dataframe**\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column PCE not in defn.description\n",
      "Column IPB51222S not in defn.description\n",
      "Column BOGMBASE not in defn.description\n",
      "Column TWEXAFEGSMTHx not in defn.description\n",
      "Column VIXCLSx not in defn.description\n",
      "##############################################\n",
      "Description IP: Residential Utilities not in joined_dataset columns\n",
      "Description ISM Manufacturing: Production Index not in joined_dataset columns\n",
      "Description ISM Manufacturing: Employment Index not in joined_dataset columns\n",
      "Description ISM : PMI Composite Index not in joined_dataset columns\n",
      "Description ISM : New Orders Index not in joined_dataset columns\n",
      "Description ISM : Supplier Deliveries Index not in joined_dataset columns\n",
      "Description ISM : Inventories Index not in joined_dataset columns\n",
      "Description St. Louis Adjusted Monetary Base not in joined_dataset columns\n",
      "Description Trade Weighted U.S. Dollar Index: Major Currencies not in joined_dataset columns\n",
      "Description ISM Manufacturing: Prices Index not in joined_dataset columns\n",
      "Description MZM Money Stock not in joined_dataset columns\n",
      "Description VXO not in joined_dataset columns\n"
     ]
    }
   ],
   "source": [
    "# Convert defn.description to a list once for efficiency\n",
    "defn_descriptions = defn.description.to_list()\n",
    "\n",
    "# Check that each column name is in defn.description\n",
    "for column in joined_dataset.columns:\n",
    "    if column not in defn_descriptions:\n",
    "        print(f\"Column {column} not in defn.description\")\n",
    "\n",
    "print('##############################################')\n",
    "\n",
    "# Check which descriptions are not in the column names\n",
    "for description in defn.description:\n",
    "    if description not in joined_dataset.columns:\n",
    "        print(f\"Description {description} not in joined_dataset columns\")\n",
    "\n",
    "# Identify and drop columns that are not in defn.description except \"PCE\"\n",
    "columns_to_drop = [column for column in joined_dataset.columns if column not in defn_descriptions and column != \"PCE\"]\n",
    "joined_dataset.drop(columns=columns_to_drop, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Filter Date Range\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Converting Index to Datetime**\n",
    "\n",
    "</div>\n",
    "\n",
    "We use the `convert_index_to_datetime` function. This function checks if the index is already in the pd.Timestamp format, and if not, it converts the format into a datetime object. This conversion process breaks down the index into year and quarter components, calculates the corresponding month, and constructs a proper datetime object.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2022Q4', '2023Q1', '2023Q2', '2023Q3', '2023Q4']\n",
      "[Timestamp('2022-12-31 00:00:00'), Timestamp('2023-03-31 00:00:00'), Timestamp('2023-06-30 00:00:00'), Timestamp('2023-09-30 00:00:00'), Timestamp('2023-12-31 00:00:00')]\n"
     ]
    }
   ],
   "source": [
    "def convert_index_to_datetime(index):\n",
    "    \"\"\"Convert 'YYYYQX' format to the end of the quarter datetime.\"\"\"\n",
    "    \n",
    "    if isinstance(index, pd.Timestamp):\n",
    "        return index  # Return the input directly if it's already a Timestamp\n",
    "\n",
    "    year = int(index[:4])\n",
    "    quarter = int(index[-1])\n",
    "    # Determine the last month of the quarter\n",
    "    month = quarter * 3\n",
    "    # Find the last day of the month\n",
    "    last_day_of_month = pd.Timestamp(year=year, month=month, day=1).days_in_month\n",
    "\n",
    "    return pd.Timestamp(year=year, month=month, day=last_day_of_month)\n",
    "\n",
    "\n",
    "# Ensure the index is in datetime format representing the end of each quarter\n",
    "joined_dataset.index = joined_dataset.index.map(convert_index_to_datetime)\n",
    "\n",
    "#sanity check ensure its the last day of each quater\n",
    "\n",
    "print(index_pretransform.to_list())\n",
    "print(joined_dataset.index[-5:].to_list())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Filtering Data for Analysis**\n",
    "\n",
    "</div>\n",
    "\n",
    "Once the index is in datetime format, we filter our dataset to include only the observations from the year 1960 onwards. This step narrows down our analysis to a specific period, allowing us to focus on more contemporary trends and patterns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Now, filter the data to include only observations from 1960 onwards\n",
    "joined_dataset = joined_dataset[joined_dataset.index.year >= 1980]\n",
    "\n",
    "# drop columns reported to not be as reliable according to McCracken, M.W., Ng, S., 2015; FRED-MD: A Monthly Database for Macroeconomic Research, Federal Reserve Bank of St. Louis Working Paper 2015-012. URL https://doi.org/10.20955/wp.2015.012\n",
    "# drop Help-Wanted Index for United States', 'Ratio of Help Wanted/No. Unemployed'\n",
    "joined_dataset.drop(\n",
    "    [\n",
    "        \"Help-Wanted Index for United States\",\n",
    "        \"Ratio of Help Wanted/No. Unemployed\",\n",
    "        \"S&P s Composite Common Stock: Price-Earnings Ratio\", #is released with roughly a 6-month lag\n",
    "        'Consumer Sentiment Index', #is available only quarterly prior to 1977:11, and recent data are available in FRED with a 1-year lag.\n",
    "    ],\n",
    "    axis=1,\n",
    "    inplace=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Inspecting and Handling Missing Values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section of our notebook, we tackle the critical step of inspecting and handling missing values within our dataset. Dealing with NaNs (Not a Number values) in economic data requires careful consideration due to the potential implications on analysis and interpretation.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Inspecting Missing Values**\n",
    "\n",
    "</div>\n",
    "\n",
    "- To start, we utilize the `report_missing_values` function to systematically identify and report columns within our dataset that contain missing values.\n",
    "\n",
    "- This function calculates the total count of NaNs in each column and prints a summary of those columns where NaNs are present, along with the count of missing values.\n",
    "\n",
    "- This initial inspection is crucial for understanding the extent and distribution of missing data across our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import missingno as msno\n",
    "# %matplotlib inline\n",
    "# msno.matrix(joined_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columns with NaN values and their count:\n",
      "Real Manu. and Trade Industries Sales               1\n",
      "New Orders for Consumer Goods                      49\n",
      "Total Business Inventories                          1\n",
      "Total Business: Inventories to Sales Ratio          1\n",
      "Total Nonrevolving Credit                           1\n",
      "Nonrevolving consumer credit to Personal Income     1\n",
      "S&P s Composite Common Stock: Dividend Yield        2\n",
      "Consumer Motor Vehicle Loans Outstanding            1\n",
      "Total Consumer Loans and Leases Outstanding         1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def report_missing_values(df):\n",
    "    # Check for NaN values in the dataset\n",
    "    nan_summary = df.isna().sum()\n",
    "\n",
    "    # Display columns with NaNs and their count, if any\n",
    "    columns_with_nans = nan_summary[nan_summary > 0]\n",
    "    if not columns_with_nans.empty:\n",
    "        print(\"\\nColumns with NaN values and their count:\")\n",
    "        print(columns_with_nans)\n",
    "    else:\n",
    "        print(\"\\nNo NaN values found.\")\n",
    "    return columns_with_nans\n",
    "\n",
    "\n",
    "missing = report_missing_values(joined_dataset)\n",
    "\n",
    "# #plot with missingno\n",
    "# msno.matrix(joined_dataset[missing.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Handling Missing Values**\n",
    "\n",
    "</div>\n",
    "\n",
    "Given the sensitive nature of economic data, where each variable can hold significant analytical value, it's essential to approach the handling of NaNs judiciously.\n",
    "\n",
    "- Our strategy involves dropping columns that exceed a certain threshold of missing values, which we've set at 30.\n",
    "- This threshold is chosen based on a balance between retaining as much data as possible and ensuring the reliability of our analysis.\n",
    "\n",
    "- We can see that New Orders for Consumer Goods, New Orders for Nondefense Capital Goods and Consumer Sentiment Index have passed that threshold and we have decided to drop these.\n",
    "\n",
    "The `drop_columns_with_many_nans` function implements this strategy.\n",
    "\n",
    "- It first calculates the total number of NaNs per column, identifies those columns where the count exceeds our threshold, and subsequently drops these columns from the dataset.\n",
    "\n",
    "- This process helps in maintaining the integrity of our dataset by focusing on more complete datasets, while also acknowledging the reality that some level of missing data is often unavoidable in large economic datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 1 columns with more than 30 NaN values:\n",
      "['New Orders for Consumer Goods']\n"
     ]
    }
   ],
   "source": [
    "def drop_columns_with_many_nans(df, threshold=30):\n",
    "    \"\"\"\n",
    "    Drops columns from the dataframe that have more than a specified number of NaN values.\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculate the count of NaN values per column\n",
    "    nan_counts = df.isna().sum()\n",
    "\n",
    "    # Identify columns that exceed the threshold for NaN values\n",
    "    columns_to_drop = nan_counts[nan_counts > threshold].index\n",
    "    columns_to_fill = nan_counts[nan_counts <= 5].index\n",
    "\n",
    "    # Drop these columns from the dataframe\n",
    "    df_dropped = df.drop(columns=columns_to_drop)\n",
    "\n",
    "    ## interpolate the missing values\n",
    "    df_dropped = df[columns_to_fill].interpolate(method=\"linear\")\n",
    "\n",
    "    #backwards fill any missing data\n",
    "    df_dropped = df[columns_to_fill].fillna(method=\"bfill\")\n",
    "\n",
    "    #forward fill any missing data\n",
    "    df_dropped = df[columns_to_fill].fillna(method=\"ffill\")\n",
    "\n",
    "    # print the names of dropped columns for review\n",
    "    if len(columns_to_drop) > 0:\n",
    "        print(\n",
    "            f\"Dropped {len(columns_to_drop)} columns with more than {threshold} NaN values:\"\n",
    "        )\n",
    "        print(columns_to_drop.tolist())\n",
    "    else:\n",
    "        print(\"No columns were dropped based on the threshold criteria.\")\n",
    "\n",
    "    return df_dropped\n",
    "\n",
    "\n",
    "joined_dataset = drop_columns_with_many_nans(joined_dataset, threshold=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Inspect and Handle Outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we identify and handle outliers within our dataset.\n",
    "\n",
    "Outliers, which represent data points significantly different from the rest of the data, can skew our analysis and lead to misleading conclusions. Therefore, it is imperative to address them appropriately.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Outlier Detection and Handling Strategy**\n",
    "\n",
    "</div>\n",
    "\n",
    "Our strategy for identifying and handling outliers involves a statistical approach based on the Z-score method.\n",
    "\n",
    "This method assesses how far away a data point is from the mean in terms of standard deviations. A common threshold to identify outliers is a Z-score greater than 3, indicating that the data point is significantly distant from the bulk of the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_outliers(df, column):\n",
    "\n",
    "    outlier_values = pd.DataFrame()\n",
    "    # Z-score method for dollar values and indexes\n",
    "    z = np.abs((df[column] - df[column].mean()) / df[column].std())\n",
    "    threshold = 3  # Typically, a threshold of 3 is used\n",
    "    mask = z > threshold\n",
    "    outlier_values = df.loc[mask, column].reset_index()\n",
    "    \n",
    "    df.loc[mask, column] = np.nan\n",
    "    \n",
    "    # Interpolate the dropped values\n",
    "    df[column] = df[column].interpolate(method=\"linear\")\n",
    "\n",
    "    return df, outlier_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Applying the Outlier Handling Function**\n",
    "\n",
    "</div>\n",
    "\n",
    "We systematically apply the `handle_outliers` function across our dataset, focusing on each column individually.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Review and Analysis of Outliers**\n",
    "\n",
    "</div>\n",
    "\n",
    "After handling outliers across all columns, we compile our findings into a structured format, presenting a summary of columns affected by outliers along with the count of outliers removed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               outliers\n",
      "M1 Money Stock                                       12\n",
      "Civilians Unemployed for 27 Weeks and Over            5\n",
      "3-Month Treasury Bill:                                4\n",
      "3-Month AA Financial Commercial Paper Rate            4\n",
      "3-Month Treasury C Minus FEDFUNDS                     4\n",
      "U.S. / U.K. Foreign Exchange Rate                     3\n",
      "All Employees: Mining and Logging: Mining             3\n",
      "Moody s Baa Corporate Bond Minus FEDFUNDS             3\n",
      "Civilians Unemployed for 15-26 Weeks                  3\n",
      "10-Year Treasury C Minus FEDFUNDS                     3\n",
      "5-Year Treasury C Minus FEDFUNDS                      3\n",
      "E?ective Federal Funds Rate                           3\n",
      "Civilians Unemployed - 15 Weeks & Over                3\n",
      "Moody s Aaa Corporate Bond Minus FEDFUNDS             3\n",
      "Switzerland / U.S. Foreign Exchange Rate              2\n",
      "3-Month Commercial Paper Minus FEDFUNDS               2\n",
      "1-Year Treasury C Minus FEDFUNDS                      2\n",
      "6-Month Treasury C Minus FEDFUNDS                     2\n",
      "Capacity Utilization: Manufacturing                   2\n",
      "1-Year Treasury Rate                                  2\n",
      "6-Month Treasury Bill:                                2\n",
      "S&P s Composite Common Stock: Dividend Yield          2\n",
      "S&P s Common Stock Price Index: Industrials           2\n",
      "S&P s Common Stock Price Index: Composite             2\n",
      "Initial Claims                                        2\n",
      "CPI : Durables                                        2\n",
      "Civilians Unemployed for 5-14 Weeks                   1\n",
      "New Private Housing Permits, West (SAAR)              1\n",
      "New Private Housing Permits, Northeast (SAAR)         1\n",
      "Housing Starts, Northeast                             1\n",
      "PPI: Crude Materials                                  1\n",
      "Crude Oil, spliced WTI and Cushing                    1\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store outlier data for each column\n",
    "columns_with_outliers = {}\n",
    "\n",
    "# Loop through each column in joined_dataset and apply the outlier handling function\n",
    "for column in joined_dataset.columns:\n",
    "    # Get the measure type for the column\n",
    "    joined_dataset, outliers = handle_outliers(joined_dataset, column)\n",
    "\n",
    "    # Store the outliers if any\n",
    "    if not outliers.empty:\n",
    "        # add to dictionary columns_with_outliers\n",
    "        columns_with_outliers[column] = outliers.count()[1]\n",
    "\n",
    "# convert dictionary to dataframe and sort by number of outliers\n",
    "columns_with_outliers = pd.DataFrame.from_dict(\n",
    "    columns_with_outliers, orient=\"index\", columns=[\"outliers\"]\n",
    ").sort_values(by=\"outliers\", ascending=False)\n",
    "print(columns_with_outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Excluding the COVID-19 Pandemic Period (2020-2021) for Nowcasting**\n",
    "\n",
    "</div>\n",
    "\n",
    "Deciding on Removing the COVID-19 pandemic period from our economic data analysis was considered for the following reasons:\n",
    "\n",
    "- **Historical Consistency**: Helps in maintaining consistency within our historical data, enabling the identification of long-term trends and relationships unaffected by the pandemic's extraordinary circumstances.\n",
    "\n",
    "- **Model Accuracy**: Enhances the accuracy of models by focusing on the typical dynamics of economic indicators, excluding the extreme anomalies presented by the pandemic.\n",
    "\n",
    "- **Understanding Anomalies**: Separately analyzing the impact of COVID-19 on economic indicators provides valuable insights into the dynamics under extreme conditions, useful for scenario analysis and stress testing.\n",
    "\n",
    "This approach would allow us to concentrate on stable and predictable underlying trends and relationships in economic data.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Including the COVID-19 Pandemic Period (2020-2021) for Nowcasting**\n",
    "\n",
    "</div>\n",
    "\n",
    "We opted to include the COVID-19 pandemic period in our economic data analysis because:\n",
    "\n",
    "- **Comprehensive Understanding**: Incorporating the pandemic period offers a more comprehensive view of how extraordinary events can impact economic indicators. \n",
    "\n",
    "- **Resilience and Adaptability**: Analyzing data from the pandemic period allows us to assess the resilience and adaptability of economies in the face of unprecedented challenges. Understanding these aspects can improve forecasting models by integrating the capacity of economies to respond to extreme external shocks.\n",
    "\n",
    "- **Model Robustness**: By accounting for a wider range of data variability, our models can become more adept at handling future anomalies and extreme events, potentially offering more accurate predictions in scenarios of uncertainty.\n",
    "\n",
    "- **Policy Analysis**: The pandemic period provides unique insights into the effectiveness of various policy responses under extreme conditions. \n",
    "\n",
    "- **Future Preparedness**: Incorporating this period into our analysis helps in identifying indicators that are most sensitive to such shocks, guiding better preparedness and response strategies for future events.\n",
    "\n",
    "By including the COVID-19 pandemic period in our analysis, we aim to enhance the realism, adaptability, and forward-looking relevance of our economic models, ensuring they are equipped to navigate both typical and extraordinary economic landscapes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter out the period from 2020 to 2021\n",
    "\n",
    "#joined_dataset = joined_dataset[(joined_dataset.index.year < 2020) | (joined_dataset.index.year > 2021)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Unit conversion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Understanding Measure Types**\n",
    "\n",
    "</div>\n",
    "\n",
    "- Before we move further we need to establish a clear understanding of the different types of measures present in our dataset.\n",
    "\n",
    "- This understanding is crucial for further analysis as the analysis method may vary depending on the nature of the data (e.g., dollar values, indexes).\n",
    "\n",
    "- To facilitate this, we create a mapping of FRED-MD variable names to their corresponding measure types, enhancing our dataset with a comprehensive categorization of each variable. This mapping includes an additional entry for \"PCE\" (Personal Consumption Expenditures), classified under 'billions of dollars'.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Ensure consistent data scales** by standardizing measurement units across economic indicators.\n",
    "\n",
    "</div>\n",
    "\n",
    "1. **Import Metadata**\n",
    "\n",
    "   - Load variable metadata from `fredmd_information.csv` and convert to a dictionary mapping variable descriptions to measurement units.\n",
    "\n",
    "2. **Update Measurement Info**\n",
    "   - Manually add measurement info for **PCE** as 'billions of dollars'.\n",
    "   - Create a DataFrame to display measurement units for each variable which facilitates understanding of data scale and units.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['avg dollars per hour', 'avg hours', 'avg no of weeks',\n",
       "       'billions of 1982-84 dollars), deflated by cpi',\n",
       "       'billions of 2012 dollars), deflated by core pce',\n",
       "       'billions of chained 2012 dollars', 'billions of dollars',\n",
       "       'billions of dollars, adjusted for inflation and excluding government transfer payments.',\n",
       "       'exchange rate', 'index = 100',\n",
       "       'millions of 2012 dollars, deflated by core pce',\n",
       "       'millions of chained 2012 dollars', 'millions of dollars',\n",
       "       'thousands of persons', 'percent', 'ratio', 'thousands of units',\n",
       "       'thousands, seasonally adjusted annual rate'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary mapping FRED-MD variable names to their descriptions\n",
    "measuremnet_info = pd.read_csv(\"./data/fredmd_information.csv\")\n",
    "measure_type_dict = measuremnet_info.set_index(\"description\")[\"measure\"].to_dict()\n",
    "\n",
    "# Add \"PCE\": 'billions of dollars' to the dictionary\n",
    "measure_type_dict[\"PCE\"] = \"billions of dollars\"\n",
    "\n",
    "# display the measure types in a dataframe\n",
    "unique_measures = pd.DataFrame.from_dict(\n",
    "    measure_type_dict, orient=\"index\", columns=[\"measure\"]\n",
    ")\n",
    "unique_measures.measure.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Measurement Scale Harmonisation**\n",
    "\n",
    "</div>\n",
    "\n",
    "- We first define conversion factors to standardize data (e.g., converting thousands to millions, millions to billions). Then apply to relevant variables to ensure consistent analysis base.\n",
    "\n",
    "- Hereafter we map the dataset columns to their measurement units and apply conversion factors to adjust data scales where necessary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion_factors = {\n",
    "    # Convert millions to billions\n",
    "    \"millions of dollars\": 1e-3,\n",
    "    \"millions of 2012 dollars, deflated by core pce\": 1e-3,\n",
    "    \"millions of chained 2012 dollars\": 1e-3,\n",
    "    # For non-monetary values, Convert thousands to millions (absolute numbers)\n",
    "    \"thousands of persons\": 1e-3,\n",
    "    \"thousands of units\": 1e-3,\n",
    "    \"thousands, seasonally adjusted annual rate\": 1e-3,\n",
    "}\n",
    "\n",
    "# Mapping column names in joined_dataset to their respective measures from defn_dataset\n",
    "column_measures = {}\n",
    "for col in joined_dataset.columns:\n",
    "    if any(defn[\"fred\"] == col):\n",
    "        matching_rows = defn[defn[\"description\"] == col]\n",
    "        if not matching_rows.empty:\n",
    "            measure_value = matching_rows[\"measure\"].iloc[0]\n",
    "            column_measures[col] = measure_value\n",
    "\n",
    "# Apply conversion factors\n",
    "for column, measure in column_measures.items():\n",
    "    if measure in conversion_factors:\n",
    "        joined_dataset[column] *= conversion_factors[measure]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Explore Comparability between PCE and economic indicators\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "Next Steps:\n",
    "</div>\n",
    "\n",
    "To gain a better understanding of these relationships, we will proceed by plotting line graphs of selected indicators against PCE. This visual analysis will help us observe the trends over time and may provide insights into any anomalies or patterns that warrant a deeper investigation.\n",
    "\n",
    "We employ the `top_indicators_against_pce_line_graph` function from our `utils.visualisation` module to illustrate the relationship between Personal Consumption Expenditures (PCE) and its top correlated economic indicators. This visualization helps us understand how these indicators interact with PCE over time.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "Key Steps:\n",
    "\n",
    "</div>\n",
    "\n",
    "1. **Identify Strong Correlations:** Calculates correlations between PCE and dataset variables to pinpoint the most influential indicators.\n",
    "\n",
    "2. **Visual Comparison:** Generates line graphs for each top indicator against PCE, offering a clear visual of their movements over a selected period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from visualisations.top_indicators_against_pce_line_graph import top_indicators_against_pce_line_graph\n",
    "\n",
    "# Calculate the Spearman's rank correlation with the private consumption expenditure,\n",
    "correlation_matrix = joined_dataset.corr(method=\"spearman\")\n",
    "\n",
    "# target_correlations will have the Spearman's rank correlation coefficients\n",
    "target_correlations = correlation_matrix[\"PCE\"].sort_values(ascending=False)\n",
    "\n",
    "top_indicators_against_pce_line_graph(joined_dataset, target_correlations,top_n=2) # top_n is the number of top indicators to plot for demonstartion purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "Observations:\n",
    "</div>\n",
    "\n",
    "Analyzing top indicators alongside Personal Consumption Expenditures (PCE) reveals scale discrepancies that hinder direct comparison. These differences highlight the necessity for preprocessing to normalize scales, enhancing analysis quality for several reasons:\n",
    "\n",
    "- **Improved Comparability:** Scale normalization or transformation enhances the comparability between indicators, enabling more precise evaluations of their relationships with PCE.\n",
    "\n",
    "- **Sharper Insights:** Scale adjustments aid in clearer visual analysis, facilitating the detection of trends, patterns, and anomalies requiring further exploration.\n",
    "\n",
    "- **Better Decision-Making:** Unified scaling ensures that subsequent analysis, such as correlation studies and predictive modeling, rests on a balanced dataset, promoting dependable insights for forecasting or policy-making.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Log and differencing Transformation on joined dataset for comparability\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log transformation is used to stabilize the variance in data that exhibits exponential growth or large fluctuations. This is especially crucial for datasets like FRED's, where certain indicators can show significant variability over time. Given the information from the FRED database and their suggested transformation types, it seems reasonable to align with their expertise and apply these transformations to the dataset. This approach will save time and ensure that the data is treated consistently with established economic analysis practices.\n",
    "\n",
    "citation: McCracken, M.W., Ng, S., 2015; FRED-MD: A Monthly Database for Macroeconomic Research, Federal Reserve Bank of St. Louis Working Paper 2015-012. URL https://doi.org/10.20955/wp.2015.012\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Transformation Types (as per FRED):**\n",
    "</div>\n",
    "\n",
    "1. **No Transformation (1)**: The data is used as is, without any modification.\n",
    "2. **First Difference (∆x_t) (2)**: The change from one period to the next, useful for highlighting trends.\n",
    "3. **Second Difference (∆^2x_t) (3)**: The change in the first difference, often used to capture acceleration or deceleration in a series.\n",
    "4. **Natural Log (log(x_t)) (4)**: Useful for stabilizing variance and making exponential growth trends linear.\n",
    "5. **First Difference of Log (∆ log(x_t)) (5)**: Commonly used to convert data into a stationary series, representing percentage change.\n",
    "6. **Second Difference of Log (∆^2 log(x_t)) (6)**: The change in the first difference of the log, similar to the second difference but for logged data.\n",
    "7. **Percentage Change from Prior Period (∆(x*t/x_t*−_1 − 1.0)) (7)**: This calculates the percentage change from the previous period, emphasizing relative changes.\n",
    "\n",
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Approach:**\n",
    "\n",
    "</div>\n",
    "\n",
    "- **Apply Transformations:** Apply FRED Transformations and use the transformation codes provided in the `fred_indicator_mappings` dataset to transform the corresponding series in `joined_dataset`.\n",
    "- This approach should streamline our analysis process and align with the methodology with FRED's established practices.\n",
    "- Additionally, it ensures that the data is treated in a manner that is suitable for economic analysis.\n",
    "- **FRED Logarithmic Key Mapping:** We will map the transformation codes in the FREDmd_defn dataset to our dataset's indicators and then perform the necessary transformations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying log first difference to column 'PCE'\n",
      "Applying log first difference to column 'Real Personal Income'\n",
      "Applying log first difference to column 'Real personal income ex transfer receipts'\n",
      "Applying log first difference to column 'Real personal consumption expenditures'\n",
      "Applying log first difference to column 'Real Manu. and Trade Industries Sales'\n",
      "Applying log first difference to column 'Retail and Food Services Sales'\n",
      "Applying log first difference to column 'IP Index'\n",
      "Applying log first difference to column 'IP: Final Products and Nonindustrial Supplies'\n",
      "Applying log first difference to column 'IP: Final Products (Market Group)'\n",
      "Applying log first difference to column 'IP: Consumer Goods'\n",
      "Applying log first difference to column 'IP: Durable Consumer Goods'\n",
      "Applying log first difference to column 'IP: Nondurable Consumer Goods'\n",
      "Applying log first difference to column 'IP: Business Equipment'\n",
      "Applying log first difference to column 'IP: Materials'\n",
      "Applying log first difference to column 'IP: Durable Materials'\n",
      "Applying log first difference to column 'IP: Nondurable Materials'\n",
      "Applying log first difference to column 'IP: Manufacturing (SIC)'\n",
      "Applying log first difference to column 'IP: Fuels'\n",
      "Applying first difference to column 'Capacity Utilization: Manufacturing'\n",
      "Applying log first difference to column 'Civilian Labor Force'\n",
      "Applying log first difference to column 'Civilian Employment'\n",
      "Applying first difference to column 'Civilian Unemployment Rate'\n",
      "Applying first difference to column 'Average Duration of Unemployment (Weeks)'\n",
      "Applying log first difference to column 'Civilians Unemployed - Less Than 5 Weeks'\n",
      "Applying log first difference to column 'Civilians Unemployed for 5-14 Weeks'\n",
      "Applying log first difference to column 'Civilians Unemployed - 15 Weeks & Over'\n",
      "Applying log first difference to column 'Civilians Unemployed for 15-26 Weeks'\n",
      "Applying log first difference to column 'Civilians Unemployed for 27 Weeks and Over'\n",
      "Applying log first difference to column 'Initial Claims'\n",
      "Applying log first difference to column 'All Employees: Total nonfarm'\n",
      "Applying log first difference to column 'All Employees: Goods-Producing Industries'\n",
      "Applying log first difference to column 'All Employees: Mining and Logging: Mining'\n",
      "Applying log first difference to column 'All Employees: Construction'\n",
      "Applying log first difference to column 'All Employees: Manufacturing'\n",
      "Applying log first difference to column 'All Employees: Durable goods'\n",
      "Applying log first difference to column 'All Employees: Nondurable goods'\n",
      "Applying log first difference to column 'All Employees: Service-Providing Industries'\n",
      "Applying log first difference to column 'All Employees: Trade, Transportation & Utilities'\n",
      "Applying log first difference to column 'All Employees: Wholesale Trade'\n",
      "Applying log first difference to column 'All Employees: Retail Trade'\n",
      "Applying log first difference to column 'All Employees: Financial Activities'\n",
      "Applying log first difference to column 'All Employees: Government'\n",
      "No transformation applied to column 'Avg Weekly Hours : Goods-Producing'\n",
      "Applying first difference to column 'Avg Weekly Overtime Hours : Manufacturing'\n",
      "No transformation applied to column 'Avg Weekly Hours : Manufacturing'\n",
      "Applying log transformation to column 'Housing Starts: Total New Privately Owned'\n",
      "Applying log transformation to column 'Housing Starts, Northeast'\n",
      "Applying log transformation to column 'Housing Starts, Midwest'\n",
      "Applying log transformation to column 'Housing Starts, South'\n",
      "Applying log transformation to column 'Housing Starts, West'\n",
      "Applying log transformation to column 'New Private Housing Permits (SAAR)'\n",
      "Applying log transformation to column 'New Private Housing Permits, Northeast (SAAR)'\n",
      "Applying log transformation to column 'New Private Housing Permits, Midwest (SAAR)'\n",
      "Applying log transformation to column 'New Private Housing Permits, South (SAAR)'\n",
      "Applying log transformation to column 'New Private Housing Permits, West (SAAR)'\n",
      "Applying log first difference to column 'New Orders for Durable Goods'\n",
      "Applying log first difference to column 'New Orders for Nondefense Capital Goods'\n",
      "Applying log first difference to column 'Un lled Orders for Durable Goods'\n",
      "Applying log first difference to column 'Total Business Inventories'\n",
      "Applying first difference to column 'Total Business: Inventories to Sales Ratio'\n",
      "Applying log second difference to column 'M1 Money Stock'\n",
      "Applying log second difference to column 'M2 Money Stock'\n",
      "Applying log first difference to column 'Real M2 Money Stock'\n",
      "Applying log second difference to column 'Total Reserves of Depository Institutions'\n",
      "Applying exact percent change to column 'Reserves Of Depository Institutions'\n",
      "Applying log second difference to column 'Commercial and Industrial Loans'\n",
      "Applying log second difference to column 'Real Estate Loans at All Commercial Banks'\n",
      "Applying log second difference to column 'Total Nonrevolving Credit'\n",
      "Applying first difference to column 'Nonrevolving consumer credit to Personal Income'\n",
      "Applying log first difference to column 'S&P s Common Stock Price Index: Composite'\n",
      "Applying log first difference to column 'S&P s Common Stock Price Index: Industrials'\n",
      "Applying first difference to column 'S&P s Composite Common Stock: Dividend Yield'\n",
      "Applying first difference to column 'E?ective Federal Funds Rate'\n",
      "Applying first difference to column '3-Month AA Financial Commercial Paper Rate'\n",
      "Applying first difference to column '3-Month Treasury Bill:'\n",
      "Applying first difference to column '6-Month Treasury Bill:'\n",
      "Applying first difference to column '1-Year Treasury Rate'\n",
      "Applying first difference to column '5-Year Treasury Rate'\n",
      "Applying first difference to column '10-Year Treasury Rate'\n",
      "Applying first difference to column 'Moody s Seasoned Aaa Corporate Bond Yield'\n",
      "Applying first difference to column 'Moody s Seasoned Baa Corporate Bond Yield'\n",
      "No transformation applied to column '3-Month Commercial Paper Minus FEDFUNDS'\n",
      "No transformation applied to column '3-Month Treasury C Minus FEDFUNDS'\n",
      "No transformation applied to column '6-Month Treasury C Minus FEDFUNDS'\n",
      "No transformation applied to column '1-Year Treasury C Minus FEDFUNDS'\n",
      "No transformation applied to column '5-Year Treasury C Minus FEDFUNDS'\n",
      "No transformation applied to column '10-Year Treasury C Minus FEDFUNDS'\n",
      "No transformation applied to column 'Moody s Aaa Corporate Bond Minus FEDFUNDS'\n",
      "No transformation applied to column 'Moody s Baa Corporate Bond Minus FEDFUNDS'\n",
      "Applying log first difference to column 'Switzerland / U.S. Foreign Exchange Rate'\n",
      "Applying log first difference to column 'Japan / U.S. Foreign Exchange Rate'\n",
      "Applying log first difference to column 'U.S. / U.K. Foreign Exchange Rate'\n",
      "Applying log first difference to column 'Canada / U.S. Foreign Exchange Rate'\n",
      "Applying log second difference to column 'PPI: Finished Goods'\n",
      "Applying log second difference to column 'PPI: Finished Consumer Goods'\n",
      "Applying log second difference to column 'PPI: Intermediate Materials'\n",
      "Applying log second difference to column 'PPI: Crude Materials'\n",
      "Applying log second difference to column 'Crude Oil, spliced WTI and Cushing'\n",
      "Applying log second difference to column 'PPI: Metals and metal products:'\n",
      "Applying log second difference to column 'CPI : All Items'\n",
      "Applying log second difference to column 'CPI : Apparel'\n",
      "Applying log second difference to column 'CPI : Transportation'\n",
      "Applying log second difference to column 'CPI : Medical Care'\n",
      "Applying log second difference to column 'CPI : Commodities'\n",
      "Applying log second difference to column 'CPI : Durables'\n",
      "Applying log second difference to column 'CPI : Services'\n",
      "Applying log second difference to column 'CPI : All Items Less Food'\n",
      "Applying log second difference to column 'CPI : All items less shelter'\n",
      "Applying log second difference to column 'CPI : All items less medical care'\n",
      "Applying log second difference to column 'Personal Cons. Expend.: Chain Index'\n",
      "Applying log second difference to column 'Personal Cons. Exp: Durable goods'\n",
      "Applying log second difference to column 'Personal Cons. Exp: Nondurable goods'\n",
      "Applying log second difference to column 'Personal Cons. Exp: Services'\n",
      "Applying log second difference to column 'Avg Hourly Earnings : Goods-Producing'\n",
      "Applying log second difference to column 'Avg Hourly Earnings : Construction'\n",
      "Applying log second difference to column 'Avg Hourly Earnings : Manufacturing'\n",
      "Applying log second difference to column 'Consumer Motor Vehicle Loans Outstanding'\n",
      "Applying log second difference to column 'Total Consumer Loans and Leases Outstanding'\n",
      "Applying log second difference to column 'Securities in Bank Credit at All Commercial Banks'\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#  transformation function to handle the time column and a special case for PCE\n",
    "def modified_log_transform(\n",
    "    column, time_column, transformation_code=4, column_name=None\n",
    "):\n",
    "    \"\"\"\n",
    "    Applies the specified transformation to a Pandas Series, considering the time column and special cases.\n",
    "    \"\"\"\n",
    "    time_column = time_column.astype(str)\n",
    "    # Special instruction for the PCE column\n",
    "    if column_name in (\"PCE\"):\n",
    "        transformation_code = 5\n",
    "\n",
    "    # Check if the data is quarterly based on the time column\n",
    "    mult = 4 if any(time_column.str.endswith((\"Q1\", \"Q2\", \"Q3\", \"Q4\"))) else 1\n",
    "\n",
    "    if transformation_code == 1:\n",
    "        # No transformation -> Mathematical Equation: x(t)\n",
    "        # It leaves the data in its original form, without any alteration.\n",
    "        print(f\"No transformation applied to column '{column_name}'\")\n",
    "        return column\n",
    "\n",
    "    elif transformation_code == 2:\n",
    "        # First Difference -> Mathematical Equation: x(t) - x(t-1)\n",
    "        # It measures the absolute change from one period to the next, helping to detrend the data.\n",
    "        print(f\"Applying first difference to column '{column_name}'\")\n",
    "        return column.diff()\n",
    "\n",
    "    elif transformation_code == 3:\n",
    "        # Second Difference -> Mathematical Equation: (x(t) - x(t-1)) - (x(t-1) - x(t-2))\n",
    "        # It measures the change in the first difference, capturing the acceleration or deceleration in the data's movement.\n",
    "        print(f\"Applying second difference to column '{column_name}'\")\n",
    "        return column.diff().diff()\n",
    "\n",
    "    elif transformation_code == 4:\n",
    "        # Log Transformation -> Mathematical Equation: ln(x(t))\n",
    "        # It stabilizes the variance across the data series and can help make a skewed distribution more normal.\n",
    "        print(f\"Applying log transformation to column '{column_name}'\")\n",
    "        return np.log(column)\n",
    "\n",
    "    elif transformation_code == 5:\n",
    "        # Log First Difference -> Mathematical Equation: 100 * (ln(x(t)) - ln(x(t-1)))\n",
    "        # It measures the growth rate from one period to the next and multiplies by 100 for percentage change.\n",
    "        # The 'mult' variable allows for scaling the growth rate if necessary.\n",
    "        print(f\"Applying log first difference to column '{column_name}'\")\n",
    "        return np.log(column).diff() * 100 * mult\n",
    "\n",
    "    elif transformation_code == 6:\n",
    "        # Log Second Difference -> Mathematical Equation: 100 * ((ln(x(t)) - ln(x(t-1))) - (ln(x(t-1)) - ln(x(t-2))))\n",
    "        # It measures the change in the growth rate (change in log first difference), capturing the momentum of change.\n",
    "        # The 'mult' variable allows for scaling the change in growth rate if necessary.\n",
    "        print(f\"Applying log second difference to column '{column_name}'\")\n",
    "        return np.log(column).diff().diff() * 100 * mult\n",
    "\n",
    "    elif transformation_code == 7:\n",
    "        # Exact Percent Change -> Mathematical Equation: 100 * ((x(t)/x(t-1))^mult - 1)\n",
    "        # It measures the percentage change from one period to the next, with an option to compound the change using 'mult'.\n",
    "        print(f\"Applying exact percent change to column '{column_name}'\")\n",
    "        return ((column / column.shift(1)) ** mult - 1.0) * 100\n",
    "\n",
    "    else:\n",
    "        print(f\"Invalid transformation code for column '{column_name}'\")\n",
    "        return column\n",
    "\n",
    "\n",
    "# Create a mapping of columns to transformation codes\n",
    "transformation_mapping = defn.set_index(\"description\")[\"tcode\"].to_dict()\n",
    "\n",
    "# Extracting the time column\n",
    "time_column = joined_dataset.index\n",
    "\n",
    "# Applying the transformations to the dataframe\n",
    "transformed_dataset = joined_dataset.copy()\n",
    "\n",
    "for column in transformed_dataset.columns:\n",
    "    # Check if the column is in the mapping, else apply special instruction for PCE\n",
    "    tcode = transformation_mapping.get(column, None)\n",
    "    transformed_dataset[column] = modified_log_transform(\n",
    "        transformed_dataset[column], time_column, tcode, column\n",
    "    )\n",
    "\n",
    "# Drop the first 5 rows containing NaN values resulting from the transformation\n",
    "joined_dataset = transformed_dataset.iloc[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No NaN values found.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values after transformation\n",
    "missing = report_missing_values(joined_dataset)\n",
    "missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#FF7F50\">\n",
    "\n",
    "**Informed Decision-Making:** \n",
    "</div>\n",
    "\n",
    "With indicators on a unified scale, subsequent analyses, including correlation assessments and predictive modeling, will be based on a more balanced and representative dataset, leading to more reliable conclusions and informed economic forecasting or policy development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_indicators_against_pce_line_graph(joined_dataset, target_correlations,top_n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"color:#00BFFF\">\n",
    "\n",
    "---\n",
    "\n",
    "##### Save the final cleaned and preprocessed dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save the data\n",
    "joined_dataset.to_csv(\n",
    "    \"./results/merged_data/joined_dataset_transformed.csv\", index=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "m1_venv",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}