edaflow.visualize_categorical_values

edaflow.visualize_categorical_values(df: DataFrame, max_unique_values: int | None = 20, show_counts: bool = True, show_percentages: bool = True) → None[source]

Visualize unique values in categorical (object-type) columns with counts and percentages.

This function provides a comprehensive overview of categorical columns by displaying: - Unique values in each categorical column - Value counts (frequency of each unique value) - Percentages (relative frequency) - Summary statistics for each column

Parameters:

  • df (pd.DataFrame) – The input DataFrame to analyze

  • max_unique_values (Optional[int], optional) – Maximum number of unique values to display per column. If a column has more unique values, only the top N most frequent will be shown. Defaults to 20.

  • show_counts (bool, optional) – Whether to show the count of each unique value. Defaults to True.

  • show_percentages (bool, optional) – Whether to show the percentage of each unique value. Defaults to True.

Returns:

Prints visualization results directly to console with formatting

Return type:

None

Example

>>> import pandas as pd
>>> import edaflow
>>> df = pd.DataFrame({
...     'category': ['A', 'B', 'A', 'C', 'B', 'A'],
...     'status': ['active', 'inactive', 'active', 'pending', 'active', 'active'],
...     'region': ['North', 'South', 'North', 'East', 'West', 'North'],
...     'score': [85, 92, 78, 88, 95, 82]
... })
>>>
>>> # Basic visualization
>>> edaflow.visualize_categorical_values(df)
>>>
>>> # Show only top 10 values per column, without percentages
>>> edaflow.visualize_categorical_values(df, max_unique_values=10, show_percentages=False)
>>>
>>> # Alternative import style:
>>> from edaflow.analysis import visualize_categorical_values
>>> visualize_categorical_values(df, max_unique_values=15)

Notes

  • Only analyzes columns with object dtype (categorical/string columns)

  • Columns with many unique values are truncated to show most frequent ones

  • Provides summary statistics including total unique values and most common value

  • Uses color coding to highlight column names and important information