correlation_report
correlation_report
Functions
| Name | Description |
|---|---|
| correlation_report | Compute pairwise correlations between numeric columns of a DataFrame. |
correlation_report
correlation_report.correlation_report(df, method='pearson')Compute pairwise correlations between numeric columns of a DataFrame.
This function is intended for exploratory data analysis diagnostics without plotting. It computes pairwise correlations between numeric features and returns a long-format report table, where each row corresponds to a unique feature pair.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| df | Input pandas DataFrame containing the data to analyze. | required | |
| method | str | Correlation method to use. Supported values are: - “pearson”: linear correlation. - “spearman”: rank-based correlation. - “kendall”: rank-based correlation. | 'pearson' |
Returns
| Name | Type | Description |
|---|---|---|
| pandas.DataFrame | A long-format correlation report with the following columns: - feature_1: name of the first feature - feature_2: name of the second feature - correlation: correlation value - abs_correlation: absolute value of the correlation Each row represents a unique pair of numeric features. Self-correlations and duplicate symmetric pairs are excluded. |
Raises
| Name | Type | Description |
|---|---|---|
| TypeError | If df is not a pandas DataFrame. | |
| ValueError | If method is not one of the supported correlation methods. If fewer than two numeric columns are available for correlation. |
Notes
- Only numeric columns are considered.
- Missing values are handled according to pandas’ correlation behavior.
- This function does not generate plots or files.
- The output is intended to be machine-readable and suitable for use in automated analysis or reporting pipelines.
Examples
>>> import pandas as pd
>>> df = pd.DataFrame({
... "age": [20, 30, 40],
... "income": [40000, 60000, 80000],
... "score": [3, 2, 1],
... })
>>> correlation_report(df, method="pearson")
feature_1 feature_2 correlation abs_correlation
0 age income 1.0 1.0
1 age score -1.0 1.0
2 income score -1.0 1.0