List Highest Correlation Pairs from a Large Correlation Matrix in Pandas?

List Highest Correlation Pairs from a Large Correlation Matrix in Pandas?

To find the highest correlation pairs from a large correlation matrix in Pandas, you can use the following steps:

  1. Compute the correlation matrix using the .corr() method on your DataFrame.

  2. Create a mask to filter out the upper triangle of the correlation matrix since it's symmetric and contains redundant information.

  3. Find the pairs of features with the highest correlation values.

Here's a step-by-step implementation:

import pandas as pd

# Sample DataFrame (replace this with your actual data)
data = {
    'Feature1': [1.0, 2.0, 3.0, 4.0],
    'Feature2': [2.0, 3.0, 4.0, 5.0],
    'Feature3': [3.0, 4.0, 5.0, 6.0],
    'Feature4': [4.0, 5.0, 6.0, 7.0],
}

df = pd.DataFrame(data)

# Step 1: Compute the correlation matrix
corr_matrix = df.corr()

# Step 2: Create a mask to filter out the upper triangle
mask = (corr_matrix
        .where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
        .stack())

# Step 3: Find the pairs with the highest correlation values
highest_corr_pairs = mask[mask.abs() >= 0.7]  # Adjust the threshold as needed

# Display the highest correlation pairs
print(highest_corr_pairs)

In this example, we compute the correlation matrix using .corr(), create a mask to filter out the upper triangle using np.triu(), and then stack the mask to get the correlation values. Finally, we filter the pairs with the highest correlation values based on a threshold (0.7 in this case) and print them.

Replace the data dictionary with your actual data, and adjust the threshold as needed to find the correlation pairs that match your requirements.

Examples

  1. "Pandas find highest correlation pairs from large matrix" Description: This query seeks methods to identify the pairs of variables with the highest correlation from a large correlation matrix using Pandas.

    import pandas as pd

# Assuming 'correlation_matrix' is your correlation matrix DataFrame
correlation_matrix = pd.DataFrame(...)  # Your correlation matrix here

# Find highest correlated pairs
pairs = correlation_matrix.unstack().sort_values(ascending=False).drop_duplicates()
print("Top correlation pairs:")
print(pairs.head(10))

  2. "Pandas highest correlation pairs from correlation matrix" Description: This query targets techniques for extracting the highest correlated pairs from a correlation matrix using Pandas.

    import pandas as pd

# Assuming 'correlation_matrix' is your correlation matrix DataFrame
correlation_matrix = pd.DataFrame(...)  # Your correlation matrix here

# Extract highest correlated pairs
pairs = correlation_matrix.unstack().sort_values(ascending=False).head(10)
print("Top correlation pairs:")
print(pairs)

  3. "Pandas find top correlated pairs from correlation matrix" Description: This query looks for methods to find the top correlated pairs from a given correlation matrix in Pandas.

    import pandas as pd

# Assuming 'correlation_matrix' is your correlation matrix DataFrame
correlation_matrix = pd.DataFrame(...)  # Your correlation matrix here

# Get top correlated pairs
pairs = correlation_matrix.unstack().sort_values(ascending=False)[:10]
print("Top correlation pairs:")
print(pairs)

  4. "Pandas extract highest correlation pairs from matrix" Description: This query aims to find ways to extract the pairs with the highest correlation from a given matrix using Pandas.

    import pandas as pd

# Assuming 'correlation_matrix' is your correlation matrix DataFrame
correlation_matrix = pd.DataFrame(...)  # Your correlation matrix here

# Get highest correlation pairs
pairs = correlation_matrix.unstack().nlargest(10)
print("Top correlation pairs:")
print(pairs)

  5. "Pandas identify highest correlation pairs from large matrix" Description: This query focuses on methods to identify the pairs with the highest correlation from a large correlation matrix using Pandas.

    import pandas as pd

# Assuming 'correlation_matrix' is your correlation matrix DataFrame
correlation_matrix = pd.DataFrame(...)  # Your correlation matrix here

# Find highest correlation pairs
pairs = correlation_matrix.unstack().nlargest(10)
print("Top correlation pairs:")
print(pairs)

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