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!! Warning  !! Don't waste your time in the event that you stumble across this repo. This is a personal learning project for me... a noob... I don't know if it is an original idea, a novel approach, or will even accomplish the intended purpose. This is the first project that I have attempted to use Poetry, which was a total PITA. Additionally, I had to manually load the files into the repository because I have some issues with Git that I am still working out. Worst of all, I really suck at coding at the moment. I'm making this public for two reason. First, I wanted to see if there were any differences in uploading Private and Public repos (I did upload my first project ever the other day set to 'Private'). Secondly, I felt like it was kind of a jerk move to only upload private stuff to an open source platform. Then again, uploading totally shit noob projects could be a faux pas. I simply haven't learned that yet.  All of that said, one must start somewhere... And I am starting here.

README.md

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+# FRED Data Visualization App
+
+This Streamlit app creates visualizations using data from the Federal Reserve Bank of St. Louis (FRED, ALFRED, and Maps APIs).
+
+## Setup
+
+1. Perquisites
+
+a. Ensure you have a FRED API which can be obtained by creating an account at https://fredaccount.stlouisfed.org/login/secure/ and requesting an API from the menu on the left hand side of the page.
+
+b. Python 3.7+ installed on your system.
+
+2. Install Poetry if you haven't already:
+   ```
+   pip install poetry
+   ```
+
+3. Clone this repository:
+   ```
+   git clone <repository-url>
+   cd fred-visualization-app
+   ```
+
+4. Install the project dependencies using Poetry:
+   ```
+   poetry install
+   ```
+
+5. Activate the virtual environment created by Poetry:
+   ```
+   poetry shell
+   ```
+
+6. Create an `.env` file in the project root and add your FRED API key:
+   ```
+   FRED_API_KEY=your_api_key_here
+   ```
+
+## Running the App
+
+To run the Streamlit app, use the following command within the Poetry shell:
+
+```
+streamlit run app.py
+```
+
+The app should open in your default web browser. If it doesn't, you can access it at `http://localhost:8501`.
+
+## Using the App
+
+1. Enter a FRED Series ID in the sidebar (e.g., 'GDP' for Gross Domestic Product).
+2. Select start and end dates for the data you want to visualize.
+3. Click 'Fetch Data' to retrieve and visualize the data.
+4. Use the ALFRED section to view historical releases of a specific series.
+5. Use the Maps section to view geographical data (note: this is a simplified representation and doesn't include actual map visualizations).
+
+## Available Data Sources
+
+- FRED (Federal Reserve Economic Data)
+- ALFRED (Archival Federal Reserve Economic Data)
+- FRED Maps (Geographical Economic Data)
+
+## Troubleshooting
+
+If you encounter any issues:
+
+1. Ensure you have the latest version of Poetry installed:
+   ```
+   pip install --upgrade poetry
+   ```
+
+2. Try removing the existing virtual environment and reinstalling dependencies:
+   ```
+   poetry env remove python
+   poetry install
+   ```
+
+3. If you're still having issues with specific packages, you can try updating them individually:
+   ```
+   poetry update package_name
+   ```
+
+4. Verify that your API key is correct in the `.env` file.
+
+5. Check your internet connection, as the app requires access to the FRED API.
+
+6. If you're experiencing issues related to Streamlit or its dependencies, you can try using a specific Python version that's known to work well with the current Streamlit version:
+   ```
+   poetry env use python3.9
+   poetry install
+   ```
+
+For more information on available FRED series IDs and API usage, visit: https://fred.stlouisfed.org/docs/api/fred/
+
+## License
+
+This project is open source and available under the [MIT License](LICENSE).

fred-visualization-app/app.py

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+"""
+FRED Data Visualization App
+
+This module provides a Streamlit-based web application for visualizing
+Federal Reserve Economic Data (FRED).
+It allows users to select and compare different economic indicators,
+apply various aggregations,
+and generate insights from the data.
+"""
+import os
+import pandas as pd
+import plotly.graph_objects as go
+import streamlit as st
+from dotenv import load_dotenv
+from fredapi import Fred
+
+from .fred_categories import category_component, fetch_fred_categories
+
+# Load environment variables
+load_dotenv()
+
+# Initialize FRED API
+fred = Fred(api_key=os.getenv('FRED_API_KEY'))
+
+# Streamlit configuration
+st.set_page_config(layout="wide")
+st.title('FRED Data Visualization App')
+
+# --- Data Selection Sidebar ---
+st.sidebar.header('Data Selection')
+
+# 1. Category Selection (using memoization for efficiency)
+@st.cache_data
+def get_fred_categories():
+    return fetch_fred_categories()
+
+
+categories = get_fred_categories()
+category_names = [cat['name'] for cat in categories]
+selected_category_name = st.sidebar.selectbox("Select Category", options=category_names)
+selected_category_id = next(
+    cat['id'] for cat in categories if cat['name'] == selected_category_name
+)
+
+# 2. Series Selection (using memoization for efficiency)
+@st.cache_data
+def get_series_list(category_id):
+    return fred.get_series_in_category(category_id)
+
+
+series_list = get_series_list(selected_category_id)
+series_id = st.sidebar.selectbox('Select FRED Series:', options=series_list)
+
+# 3. Comparison Series (Conditional)
+enable_comparison = st.sidebar.checkbox('Compare with another dataset')
+series_id2 = None
+if enable_comparison:
+    series_id2 = st.sidebar.selectbox(
+        'Select second FRED Series:',
+        options=series_list,
+        key='series2'  # Important for widget state
+    )
+
+# 4. Date Range Selection
+start_date = st.sidebar.date_input('Start Date')
+end_date = st.sidebar.date_input('End Date')
+
+# Aggregation option
+aggregation = st.sidebar.selectbox(
+    'Aggregation',
+    options=['None', 'Weekly', 'Monthly', 'Quarterly', 'Yearly']
+)
+
+# Visualization options
+viz_type = st.sidebar.selectbox(
+    'Visualization Type',
+    options=['Line', 'Bar', 'Scatter', '3D Scatter', 'Area']
+)
+
+@st.cache_data
+def get_fred_data(series_id, start_date, end_date):
+    """
+    Fetch data from FRED API for a given series and date range.
+
+    Args:
+        series_id (str): The FRED series identifier
+        start_date (str): Start date for the data range
+        end_date (str): End date for the data range
+
+    Returns:
+        pd.DataFrame: DataFrame containing the fetched data
+    """
+    try:
+        data = fred.get_series(series_id, start_date, end_date)
+        df = pd.DataFrame(data).reset_index()
+        df.columns = ['Date', 'Value']
+        return df
+    except fredapi.fred.HTTPError as e:
+        st.error(f"Error fetching data from FRED: {str(e)}")
+        return pd.DataFrame(columns=['Date', 'Value'])
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {str(e)}")
+        return pd.DataFrame(columns=['Date', 'Value'])
+
+
+def aggregate_data(df, agg_type):
+    """
+    Aggregate data based on the specified aggregation type.
+
+    Args:
+        df (pd.DataFrame): Input DataFrame
+        agg_type (str): Aggregation type (
+            'None', 'Weekly', 'Monthly', 'Quarterly', 'Yearly'
+        )
+
+    Returns:
+        pd.DataFrame: Aggregated DataFrame
+    """
+    df['Date'] = pd.to_datetime(df['Date'])
+
+    resampling_options = {
+        'Weekly': 'W',
+        'Monthly': 'M',
+        'Quarterly': 'Q',
+        'Yearly': 'Y'
+    }
+
+    if agg_type in resampling_options:
+        return df.resample(
+            """ How to use this app:
+            1. Select a category and series from the sidebar
+            2. Choose to compare with another dataset (optional)
+            3. Select start and end dates
+            4. Choose aggregation and visualization type
+            5. Click 'Fetch Data' to visualize
+            6. Explore insights below the visualization
+            7. Use the FRED Categories section for more information"""
+        )

fred-visualization-app/fred_categories.py

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+import requests
+from dash import html, dcc
+import dash_bootstrap_components as dbc
+
+FRED_API_KEY = 'your_fred_api_key_here' 
+# Replace with your actual FRED API key
+
+def fetch_fred_categories():
+    url = f"https://api.stlouisfed.org/fred/category/children?category_id=0&api_key={FRED_API_KEY}&file_type=json"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json()['categories']
+    else:
+        return []
+
+
+def create_category_list():
+    categories = fetch_fred_categories()
+    return html.Div([
+        html.H3("FRED Categories"),
+        dbc.ListGroup([
+            dbc.ListGroupItem([
+                html.Div(f"{category['name']} (ID: {category['id']})"),
+                html.A(
+                    "View on FRED",
+                    href=f"https://fred.stlouisfed.org/categories/{category['id']}",
+                    target="_blank"
+                )
+            ]) for category in categories
+        ]),
+        html.Div([
+            html.A("View all categories on FRED", href="https://fred.stlouisfed.org/categories", target="_blank"),
+            html.Br(),
+            html.A("Search for FRED series", href="https://fred.stlouisfed.org/tags/series", target="_blank")
+        ], className="mt-3")
+    ])
+
+
+def category_component():
+    return html.Div([
+        html.H2("FRED Categories and Series"),
+        dbc.Input(
+            id="category-search",
+            type="text",
+            placeholder="Search categories..."
+        ),
+        html.Div(id="category-list", children=[create_category_list()])
+    ])