Data Visualization Examples

This section provides examples of how to visualize economic data fetched using the fedfred library. These examples demonstrate how to use popular Python libraries like Matplotlib, Seaborn, and GeoPandas for creating insightful visualizations.

Line Plot of Time Series Data

This example demonstrates how to create a simple line plot of time series data using Matplotlib.

import matplotlib.pyplot as plt
import fedfred as fd

# Fetch data for a specific economic indicator
fred = fd.FredAPI(api_key="your_api_key_here")
series_id = "GDP"  # Example series ID
data = fred.get_series_observations(series_id)

# Create a visualization
plt.figure(figsize=(10, 6))
plt.plot(data.index, data.values, 'b-', linewidth=2)

# Add labels and title
plt.title(f"{series_id} Time Series Data", fontsize=14)
plt.xlabel("Date", fontsize=12)
plt.ylabel("Value", fontsize=12)
plt.grid(True, alpha=0.3)

# Format the plot
plt.tight_layout()
plt.xticks(rotation=45)

# Show the plot
plt.show()

Bar Plot of Category Data

This example demonstrates how to create a bar plot to visualize data for multiple categories.

import matplotlib.pyplot as plt
import fedfred as fd

# Fetch data for a specific category
fred = fd.FredAPI(api_key="your_api_key_here")
category_id = 125  # Example category ID
series = fred.get_category_series(category_id)

# Extract series names and values
series_names = [s.title for s in series]
series_values = [s.popularity for s in series]

# Create a bar plot
plt.figure(figsize=(12, 6))
plt.bar(series_names, series_values, color='skyblue')

# Add labels and title
plt.title("Category Series Popularity", fontsize=14)
plt.xlabel("Series", fontsize=12)
plt.ylabel("Popularity", fontsize=12)
plt.xticks(rotation=45, ha='right')

# Format the plot
plt.tight_layout()

# Show the plot
plt.show()

Heatmap of Correlations

This example demonstrates how to create a heatmap of correlations between multiple time series using Seaborn.

import seaborn as sns
import pandas as pd
import fedfred as fd

# Fetch data for multiple series
fred = fd.FredAPI(api_key="your_api_key_here")
series_ids = ["GDP", "UNRATE", "CPIAUCSL"]  # Example series IDs
data_frames = [fred.get_series_observations(series_id) for series_id in series_ids]

# Combine data into a single DataFrame
combined_data = pd.concat(data_frames, axis=1)
combined_data.columns = series_ids

# Compute correlations
correlations = combined_data.corr()

# Create a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(correlations, annot=True, cmap="coolwarm", fmt=".2f")

# Add title
plt.title("Correlation Heatmap of Economic Indicators", fontsize=14)

# Show the plot
plt.show()

Geographic Visualization with GeoPandas

This example demonstrates how to visualize geographic data using GeoPandas.

import geopandas as gpd
import fedfred as fd

# Fetch geographic data for unemployment rates by state
fred_maps = fd.MapsAPI(api_key="your_api_key_here")
unemployment_by_state = fred_maps.get_regional_data(
    series_group="unemployment",
    region_type="state",
    date="2023-01-01",
    season="nsa",  # Not seasonally adjusted
    units="percent"
)

# Plot the data
unemployment_by_state.plot(
    column="value",
    cmap="OrRd",
    legend=True,
    figsize=(12, 8),
    edgecolor="black"
)

# Add title
plt.title("Unemployment Rates by State (January 2023)", fontsize=14)

# Show the plot
plt.show()