simplify.categorical_plot

simplify.categorical_plot(
    df,
    target_column,
    categorical_target,
    max_categories=10,
    categorical_features=None,
)

Perform EDA on categorical columns in a dataset.

This function creates Altair plots for the specified columns, assuming them to contain categorical data. It creates sorted horizontal bar charts to show the frequency and the proportion of each categories. Also create box plots for features vs target if the target is numerical, or stacked bar charts if the target is categorical.

Parameters

Name	Type	Description	Default
df	pandas.DataFrame	A pandas DataFrame containing the dataset	required
target_column	str	The name of the target column.	required
categorical_target	bool	A boolean value indicating if the target column is categorical or not.	required
max_categories	int	The maximum categories to plot for high cardinality features	10
categorical_features	list	A list of strings containing column names of the categorical features. If this is not passed, keep all	None

Returns

Name	Type	Description
	list	A list of Altair plot objects of all the plots created

Raises

Name	Type	Description
	TypeError	If df is not a dataframe, target_column is not a string, or categorical_features is not a list
	ValueError	If df is empty, target_column is not in the DataFrame, or categorical_features is empty or contains columns not in the DataFrame

Examples

>>> import pandas as pd
>>> df = pd.DataFrame({
...     "artist": ["A", "B", "C", "D"],
...     "popularity": [80, 75, 90, 85],
...     "danceability": [0.8, 0.6, 0.9, 0.7],
...     "energy": [0.7, 0.8, 0.6, 0.9]
... })
>>> plots = categorical_plot(df, 'popularity', False, categorical_features=["artist"])