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Exports correlation analysis results to multiple CSV files, including overall correlations, grouped correlations, and significant findings.

Purpose

This function serves as a data export utility for correlation analysis workflows. It takes three data structures containing correlation results and exports them to separate CSV files with appropriate naming conventions. The function provides console feedback about the export process and handles cases where certain result sets may be empty. It's designed to be the final step in a correlation analysis pipeline, persisting results for further analysis or reporting.

Source Code

def export_results(overall_results, grouped_results, conclusions):
    """Export all results to CSV files"""
    
    print("\n" + "="*80)
    print("EXPORTING RESULTS")
    print("="*80)
    
    overall_results.to_csv('overall_correlations.csv', index=False)
    print("\nSaved: overall_correlations.csv")
    
    if len(grouped_results) > 0:
        grouped_results.to_csv('grouped_correlations.csv', index=False)
        print("Saved: grouped_correlations.csv")
    
    # Export significant results
    if len(conclusions['significant_overall']) > 0:
        conclusions['significant_overall'].to_csv('significant_correlations.csv', index=False)
        print("Saved: significant_correlations.csv")
    
    print("\nAll results exported successfully!")

Parameters

Parameter Details

overall_results: A pandas DataFrame containing overall correlation results across all data. Expected to have columns representing correlation metrics (e.g., correlation coefficients, p-values). This parameter is always exported to 'overall_correlations.csv'.

grouped_results: A pandas DataFrame containing correlation results grouped by some categorical variable. Can be empty (length 0). Only exported to 'grouped_correlations.csv' if it contains data. Expected structure should match overall_results but with additional grouping columns.

conclusions: A dictionary containing analysis conclusions. Must have at least a 'significant_overall' key that maps to a pandas DataFrame of statistically significant correlations. This DataFrame is exported to 'significant_correlations.csv' only if it contains rows. May contain other keys for different types of conclusions.

Return Value

This function returns None (implicitly). It performs side effects by writing CSV files to the current working directory and printing status messages to stdout. Three potential files are created: 'overall_correlations.csv' (always), 'grouped_correlations.csv' (conditional), and 'significant_correlations.csv' (conditional).

Dependencies

• pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd

# Prepare sample data
overall_results = pd.DataFrame({
    'variable1': ['A', 'B', 'C'],
    'variable2': ['X', 'Y', 'Z'],
    'correlation': [0.85, 0.72, 0.91],
    'p_value': [0.001, 0.05, 0.0001]
})

grouped_results = pd.DataFrame({
    'group': ['Group1', 'Group2'],
    'correlation': [0.78, 0.82],
    'p_value': [0.02, 0.01]
})

significant_df = overall_results[overall_results['p_value'] < 0.05]
conclusions = {
    'significant_overall': significant_df,
    'summary': 'Analysis complete'
}

# Export results
export_results(overall_results, grouped_results, conclusions)

# This will create three CSV files:
# - overall_correlations.csv
# - grouped_correlations.csv
# - significant_correlations.csv

Best Practices

• Ensure all input DataFrames have appropriate column names before calling this function, as they will be preserved in the CSV files
• Check that the current working directory has write permissions before calling this function
• Be aware that this function will overwrite existing CSV files with the same names without warning
• The 'conclusions' dictionary must contain a 'significant_overall' key with a DataFrame value, even if empty
• Consider wrapping this function in a try-except block to handle potential file I/O errors
• For large DataFrames, be mindful of disk space requirements
• The function uses len() to check for empty DataFrames; ensure DataFrames are properly initialized and not None
• Console output is printed directly; redirect stdout if you need to capture or suppress these messages

Tags

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