Learn Challenge: Economic Indicator Correlation Heatmap | Advanced Economic Analysis and Visualization

In economic analysis, understanding the relationships between different indicators is crucial for drawing meaningful insights about countries' economic health and trends. Correlation analysis helps you measure the strength and direction of linear relationships between variables such as GDP, inflation, unemployment, and interest rates. A correlation matrix conveniently summarizes these relationships, and visualizing it as a heatmap provides an intuitive overview of the connections among multiple indicators.

To tackle this challenge, you will use a hardcoded pandas DataFrame containing economic data for several countries. Your goal is to write a function that computes the correlation matrix, visualizes it as a heatmap using seaborn, and identifies the strongest positive and negative correlations among the indicators.


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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Hardcoded economic data for multiple countries
data = {
    "Country": ["USA", "Germany", "Japan", "Canada", "UK"],
    "GDP": [21.4, 4.2, 5.1, 1.8, 2.7],  # Trillions USD
    "Inflation": [1.8, 1.4, 0.5, 2.0, 1.7],  # Percent
    "Unemployment": [3.7, 3.1, 2.4, 5.7, 4.0],  # Percent
    "Interest Rate": [1.75, 0.0, -0.1, 1.75, 0.75]  # Percent
}

df = pd.DataFrame(data)
df.set_index("Country", inplace=True)

def correlation_heatmap_and_extremes(df):
    # Compute the correlation matrix
    corr_matrix = df.corr()
    
    # Visualize the correlation matrix as a heatmap
    plt.figure(figsize=(8, 6))
    sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", center=0)
    plt.title("Correlation Heatmap of Economic Indicators")
    plt.show()
    
    # Find the strongest positive and negative correlations
    corr_pairs = corr_matrix.unstack()
    # Remove self-correlations
    corr_pairs = corr_pairs[corr_pairs.index.get_level_values(0) != corr_pairs.index.get_level_values(1)]
    # Drop duplicate pairs
    corr_pairs = corr_pairs.groupby([frozenset(pair) for pair in corr_pairs.index]).first()
    
    strongest_positive = corr_pairs.idxmax()
    strongest_negative = corr_pairs.idxmin()
    
    print(f"Strongest positive correlation: {tuple(strongest_positive)} = {corr_pairs.max():.2f}")
    print(f"Strongest negative correlation: {tuple(strongest_negative)} = {corr_pairs.min():.2f}")

# Run the function
correlation_heatmap_and_extremes(df)

This approach lets you quickly spot which economic indicators move together and which tend to move in opposite directions. Positive correlations indicate that two variables tend to increase or decrease together, while negative correlations suggest that as one increases, the other decreases. Identifying the strongest relationships can guide further economic investigation or policy analysis.

Task

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Write a function named correlation_heatmap_and_extremes that:

Accepts a pandas DataFrame containing columns for GDP, Inflation, Unemployment, and Interest Rate.
Computes the correlation matrix for these columns.
Uses seaborn to display a heatmap of the correlation matrix with annotations.
Prints out the pair of indicators with the strongest positive correlation and the pair with the strongest negative correlation, including their correlation values.

Test your function on the DataFrame below:

Country	GDP	Inflation	Unemployment	Interest Rate
USA	21.4	1.8	3.7	1.75
Germany	4.2	1.4	3.1	0.0
Japan	5.1	0.5	2.4	-0.1
Canada	1.8	2.0	5.7	1.75
UK	2.7	1.7	4.0	0.75

The heatmap should be displayed using seaborn.
The printed output should clearly state the indicator pairs and their correlation values.

Solution

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Hardcoded economic data for multiple countries
data = {
    "Country": ["USA", "Germany", "Japan", "Canada", "UK"],
    "GDP": [21.4, 4.2, 5.1, 1.8, 2.7],  # Trillions USD
    "Inflation": [1.8, 1.4, 0.5, 2.0, 1.7],  # Percent
    "Unemployment": [3.7, 3.1, 2.4, 5.7, 4.0],  # Percent
    "Interest Rate": [1.75, 0.0, -0.1, 1.75, 0.75]  # Percent
}

df = pd.DataFrame(data)
df.set_index("Country", inplace=True)

def correlation_heatmap_and_extremes(df):
    # Compute the correlation matrix
    corr_matrix = df.corr()
    
    # Visualize the correlation matrix as a heatmap
    plt.figure(figsize=(8, 6))
    sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", center=0)
    plt.title("Correlation Heatmap of Economic Indicators")
    plt.show()
    
    # Find the strongest positive and negative correlations
    corr_pairs = corr_matrix.unstack()
    # Remove self-correlations
    corr_pairs = corr_pairs[corr_pairs.index.get_level_values(0) != corr_pairs.index.get_level_values(1)]
    # Drop duplicate pairs
    corr_pairs = corr_pairs.groupby([frozenset(pair) for pair in corr_pairs.index]).first()
    
    strongest_positive = corr_pairs.idxmax()
    strongest_negative = corr_pairs.idxmin()
    
    print(f"Strongest positive correlation: {tuple(strongest_positive)} = {corr_pairs.max():.2f}")
    print(f"Strongest negative correlation: {tuple(strongest_negative)} = {corr_pairs.min():.2f}")

# Run the function
correlation_heatmap_and_extremes(df)