pandas-cat 0.1.5

Profile pandas DataFrames and generate self-contained HTML reports with categorical and continuous column analysis.

pandas-cat

pandas-cat (PANDAS-CATegorical profiling) is a library for profiling categorical datasets and preparing them for analysis. It generates HTML reports with category distributions, correlations, and missing-value summaries, and automatically reorders numeric-like categories into their natural order.

Datasets have typically mixed variables (both categorical and continuous) and package can show both of them. Types of variables are detected automatically. Because pandas-cat focuses on categorical profiling, the default preparation engine converts numeric columns with few distinct values (<= cat_limit, default 20) to ordered categoricals so a 0/1 flag or a 1–5 rating scale gets a frequency bar chart rather than histogram. Numeric columns with more distinct values are left as continuous. If you really need it, this behaviour can be overridden by passing auto_prepare=False or by casting the column to float before calling profile().

Pass any DataFrame and get a self-contained HTML report in one call:

import pandas_cat
pandas_cat.profile(df, dataset_name="Road accidents")

The report gives you:

• Bar charts — frequency counts and percentages for every categorical column.
• Histograms — distribution for every numeric column.
• Correlations — between all variables and between categorical values.
• Missing-value summary — sentinel detection and gap counts per column.
• Memory breakdown — usage by column.

Two preparation helpers keep the data clean before profiling (you can use them also separately):

• prepare(df) detects numeric-like categories and converts them to ordered CategoricalDtype so charts and correlations respect natural order.

  Without prepare(), pandas sorts categories alphabetically — a common trap:

  # Alphabetical (wrong) — pandas default
  16–20, 21–25, 26–35, 36–45, 46–55, 56–65, 66–75, 6–10, 76+, Under 6
  

  After prepare(), the natural numeric order is restored:

  # Natural order (correct) — after prepare()
  Under 6, 6–10, 16–20, 21–25, 26–35, 36–45, 46–55, 56–65, 66–75, 76+
  

• handle_missing_values(df) replaces 75+ sentinel strings ("Unknown", "N/A", "–", "Missing", …) with pd.NA so they are counted as missing rather than treated as valid categories.

Installation

pip install pandas-cat

Quick start

import pandas as pd
import pandas_cat

df = pd.read_csv('data.csv')
pandas_cat.profile(df=df, dataset_name="My dataset")
# generates report/report.html

Continuous variables

Numeric columns are profiled out of the box as continuous. The built-in preparation engine (auto_prepare=True) preserves numeric columns with many unique values (above cat_limit) as continuous — they are profiled with histograms rather than excluded. Low-cardinality numeric columns are converted to ordered categoricals instead.

df = pd.read_csv('https://petrmasa.com/pandas-cat/data/accidents.zip',
                 encoding='cp1250', sep='\t')

pandas_cat.profile(df=df, dataset_name="Accidents", out_html="accidents_full.html")
# Columns like Driver_Age, Hour, Engine_Capacity are profiled with histograms.
# String-encoded categories like Driver_Age_Band are ordered correctly.

Categorical data with numeric-looking values

Many real datasets store ordered categories as strings: "0-10", "Over 75", "60+". Alphabetical sorting produces "Over 75" before "Under 5". pandas-cat fixes this automatically:

df = pd.read_csv('https://petrmasa.com/pandas-cat/data/accidents.zip',
                 encoding='cp1250', sep='\t')
df = df[['Driver_Age_Band', 'Driver_IMD', 'Sex', 'Journey']]

pandas_cat.profile(df=df, dataset_name="Accidents")

auto_prepare=True (default) converts Driver_Age_Band to an ordered pandas.Categorical sorted by the extracted numeric values before profiling.

Report templates

# Default — static HTML with SVG charts
pandas_cat.profile(df=df, dataset_name="Accidents")

# Modern — same content, refreshed visual style
pandas_cat.profile(df=df, dataset_name="Accidents", template="modern")

# Interactive — three correlation metrics (Cramér's V, Spearman, Theil's U),
# per-category crosstabs, raw data driven
pandas_cat.profile(df=df, dataset_name="Accidents", template="interactive")

All options

pandas_cat.profile(
    df=df,
    dataset_name="Accidents",
    out_html="report.html",   # written to report/<out_html>
    opts={
        "auto_prepare":    True,         # convert numeric-string categories
        "cat_limit":       20,           # max categories before column is excluded
        "na_values":       ["MyNA"],     # extra missing-value sentinels
        "na_ignore":       ["NA"],       # built-in sentinels to keep as-is
        "keep_default_na": True,         # False = use only na_values
    }
)

Data preparation only

# Built-in engine (default) — preserves high-cardinality numeric columns as continuous
df = pandas_cat.prepare(df)

# CleverMiner engine — opt in explicitly
df = pandas_cat.prepare(df, auto_data_prep="CLM")

Missing-value handling only

df, detected, counts = pandas_cat.handle_missing_values(
    df,
    na_values=["TBD"],
    na_ignore=["-"],
)

75+ built-in sentinel strings are detected by default (NA, N/A, NULL, None, Unknown, Missing, …).

Sample reports

• Short report — default template
• Full report — all columns, continuous variables
• Modern template
• Interactive report

Credits

Petr Masa — base package, data preparation, maintaining the package and development

Jan Nejedly — interactive report (first version), missing-value handling (first version)