API Reference

Complete function reference for InsightfulPy v0.2.0.

Import pattern for all examples:

import pandas as pd
import insightfulpy as ipy

df = pd.read_csv('data.csv')

Helper Functions
Basic Analysis
Statistical Functions
Data Quality
Visualization
Individual Analysis
Dataset Comparison

Helper Functions

help()

Display help information with function categories.

ipy.help()

list_all()

List all available functions organized by category.

ipy.list_all()

quick_start()

Show quick start examples.

ipy.quick_start()

examples()

Show practical usage examples.

ipy.examples()

Basic Analysis

columns_info()

Display dataset structure with column details.

ipy.columns_info('Sales Data', df)

analyze_data()

General analysis for numerical and categorical columns.

ipy.analyze_data(df)

num_summary()

Statistical summary for numerical columns. Returns DataFrame with count, mean, std, min, quartiles, max, mode, range, IQR, variance, skewness, kurtosis, and Shapiro-Wilk test.

summary = ipy.num_summary(df)

cat_summary()

Statistical summary for categorical columns. Returns DataFrame with count, unique values, top category, frequency, and percentage.

summary = ipy.cat_summary(df)

grouped_summary()

Summary statistics grouped by categorical variable. Returns TableOne object.

summary = ipy.grouped_summary(df, groupby='category')

Statistical Functions

calc_stats()

Calculate statistical measures for a series. Returns dictionary with count, mean, trimmed mean, MAD, std, min, quartiles, max, mode, range, IQR, variance, skewness, and kurtosis.

stats = ipy.calc_stats(df['price'])

calculate_skewness_kurtosis()

Calculate skewness and kurtosis for numerical columns.

dist_shape = ipy.calculate_skewness_kurtosis(df)

iqr_trimmed_mean()

Calculate trimmed mean using IQR method (excludes outliers beyond 1.5*IQR).

trimmed = ipy.iqr_trimmed_mean(df['price'])

mad()

Calculate Median Absolute Deviation.

deviation = ipy.mad(df['price'])

Data Quality

missing_inf_values()

Detect missing and infinite values. If both missing and inf are False, checks both.

ipy.missing_inf_values(df)  # Both
ipy.missing_inf_values(df, missing=True)  # Only missing
ipy.missing_inf_values(df, df_table=True)  # Return DataFrame

detect_outliers()

Detect outliers using IQR method. Returns DataFrame with Q1, Q3, IQR, bounds, outlier count and values.

outliers = ipy.detect_outliers(df)
outliers = ipy.detect_outliers(df, max_display=20)

detect_mixed_data_types()

Detect columns with mixed data types.

ipy.detect_mixed_data_types(df)

cat_high_cardinality()

Identify categorical columns with high cardinality (default threshold: 20 unique values).

high_card = ipy.cat_high_cardinality(df, threshold=100)

Visualization

Batch Functions: Many visualization functions support batch processing. Call without batch_num to see available batches, then specify batch_num to plot. See User Guide - Working with Batches for workflow details.

show_missing()

Visualize missing data patterns using matrix and bar charts.

ipy.show_missing(df)

plot_boxplots()

Create box plots for all numerical columns.

ipy.plot_boxplots(df)

kde_batches()

Display KDE plots in batches.

batches = ipy.kde_batches(df)
ipy.kde_batches(df, batch_num=1)

box_plot_batches()

Display box plots in batches.

ipy.box_plot_batches(df, batch_num=1)

qq_plot_batches()

Display QQ plots in batches.

ipy.qq_plot_batches(df, batch_num=1)

cat_bar_batches()

Display bar charts for categorical columns in batches.

ipy.cat_bar_batches(df, batch_num=1)
ipy.cat_bar_batches(df, batch_num=1, show_percentage=True, high_cardinality_limit=20)

cat_pie_chart_batches()

Display pie charts for categorical columns in batches.

ipy.cat_pie_chart_batches(df, batch_num=1)
ipy.cat_pie_chart_batches(df, batch_num=1, high_cardinality_limit=10)

num_vs_num_scatterplot_pair_batch()

Create scatter plots for numerical column pairs in batches.

pairs = ipy.num_vs_num_scatterplot_pair_batch(df)
ipy.num_vs_num_scatterplot_pair_batch(df, pair_num=0, batch_num=1)
ipy.num_vs_num_scatterplot_pair_batch(df, pair_num=0, batch_num=1, hue_column='category')

cat_vs_cat_pair_batch()

Create heatmaps for categorical column pairs in batches.

pairs = ipy.cat_vs_cat_pair_batch(df)
ipy.cat_vs_cat_pair_batch(df, pair_num=0, batch_num=1)
ipy.cat_vs_cat_pair_batch(df, pair_num=0, batch_num=1, high_cardinality_limit=15)

num_vs_cat_box_violin_pair_batch()

Create box and violin plots for numerical vs categorical pairs in batches.

pairs = ipy.num_vs_cat_box_violin_pair_batch(df)
ipy.num_vs_cat_box_violin_pair_batch(df, pair_num=0, batch_num=1)

Individual Analysis

num_analysis_and_plot()

Analyze and visualize individual numerical column with histogram, KDE, and box plot.

ipy.num_analysis_and_plot(df, 'price')
ipy.num_analysis_and_plot(df, 'price', target='category')
ipy.num_analysis_and_plot(df, 'price', visualize=True, return_df=True)

cat_analyze_and_plot()

Analyze and visualize individual categorical column with bar charts.

ipy.cat_analyze_and_plot(df, 'category')
ipy.cat_analyze_and_plot(df, 'category', target='status')

Dataset Comparison

compare_df_columns()

Compare columns across multiple DataFrames. Returns base profile and linked profiles.

dfs = {'sales': df1, 'inventory': df2, 'orders': df3}
base, linked = ipy.compare_df_columns('sales', dfs)

linked_key()

Identify common columns across multiple DataFrames.

dfs = {'sales': df1, 'inventory': df2}
ipy.linked_key(dfs)

display_key_columns()

Display linked columns from base DataFrame.

ipy.display_key_columns('sales', dfs)

interconnected_outliers()

Identify rows with outliers in multiple columns.

outliers = ipy.interconnected_outliers(df, ['price', 'quantity', 'discount'])

comp_cat_analysis()

Analysis for categorical columns with optional missing value separation.

summary = ipy.comp_cat_analysis(df)
missing, non_missing = ipy.comp_cat_analysis(df, missing_df=True)

comp_num_analysis()

Analysis for numerical columns with optional missing/outlier separation. Includes normality tests (Shapiro-Wilk for n<=5000, Kolmogorov-Smirnov for n>5000).

summary = ipy.comp_num_analysis(df)
outlier, non_outlier = ipy.comp_num_analysis(df, outlier_df=True)

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