Challenge: Economic Indicator Correlation Heatmap
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.
1234567891011121314151617181920212223242526272829303132333435363738394041import 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.
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Write a function named correlation_heatmap_and_extremes that:
- Accepts a pandas DataFrame containing columns for
GDP,Inflation,Unemployment, andInterest 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.
Solución
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Can you explain how the correlation values are interpreted in this context?
What do the strongest positive and negative correlations mean for these countries?
Can you help me modify the function to include more economic indicators?
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Completion tasa mejorada a 4.76Challenge: Economic Indicator Correlation Heatmap
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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.
1234567891011121314151617181920212223242526272829303132333435363738394041import 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.
Swipe to start coding
Write a function named correlation_heatmap_and_extremes that:
- Accepts a pandas DataFrame containing columns for
GDP,Inflation,Unemployment, andInterest 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.
Solución
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