EDA VizKit

Reusable visualizations for inspecting and exploring data.

EDA VizKit provides high-level Python functions for common exploratory data analysis visualizations. The package works with ordinary pandas DataFrames and returns Matplotlib Axes objects.

EDA VizKit does not clean data, classify variables, select relationships, make analytical decisions, or interpret results.

Design

• Accept ordinary pandas DataFrames and explicit column names.
• Provide reusable visualizations for common EDA tasks.
• Return Matplotlib Axes objects.
• Never call plt.show().
• Keep analytical choices visible to the caller.
• Avoid dependencies on analytical workflow frameworks.
• Keep the implementation readable and replaceable.

Install

uv add eda-vizkit

Example

from eda_vizkit import show_numeric_distribution

ax = show_numeric_distribution(
    df,
    column="body_mass_g",
)

ax.set_title("Penguin Body Mass")

The caller controls display and composition. In a script, for example:

import matplotlib.pyplot as plt

from eda_vizkit import show_numeric_distribution

ax = show_numeric_distribution(
    df,
    column="body_mass_g",
)

plt.show()

Initial API

Distributions:

• show_numeric_distribution()
• show_categorical_distribution()

Relationships:

• show_numeric_relationship()
• show_numeric_by_category()

Data quality:

• show_missing_values()

Output:

• save_chart()

Public Contract

Every public visualization helper:

• accepts already-available data,
• keeps the analytical choice explicit,
• returns a Matplotlib Axes object,
• never calls plt.show().

This allows the visualization helpers to work in scripts, applications, notebooks, documentation, and other presentation environments.

Example: Numeric Distribution

from eda_vizkit import show_numeric_distribution

ax = show_numeric_distribution(
    df,
    column="flipper_length_mm",
)

Example: Categorical Distribution

from eda_vizkit import show_categorical_distribution

ax = show_categorical_distribution(
    df,
    column="species",
)

Example: Numeric Relationship

from eda_vizkit import show_numeric_relationship

ax = show_numeric_relationship(
    df,
    x="flipper_length_mm",
    y="body_mass_g",
)

Example: Numeric Variable by Category

from eda_vizkit import show_numeric_by_category

ax = show_numeric_by_category(
    df,
    numeric="body_mass_g",
    category="species",
)

Example: Missing Values

from eda_vizkit import show_missing_values

ax = show_missing_values(df)

Example: Save Chart

from eda_vizkit import save_chart, show_numeric_relationship

ax = show_numeric_relationship(
    df,
    x="flipper_length_mm",
    y="body_mass_g",
)

save_chart(
    ax,
    "docs/images/feature-target-scatter.png",
)

Example: Show Chart

import matplotlib.pyplot as plt

plt.show()

Developer Command Reference

git clone https://github.com/analytics-toolworks/eda-vizkit

cd eda-vizkit
code .

uv self update
uv python pin 3.14
uv python install
uv lock --upgrade
uv sync

uv run pre-commit install
uv run pre-commit autoupdate

git add -A
uv run pre-commit run --all-files
# repeat if changes were made
uv run pre-commit run --all-files

# types, tests, docs
uv run ty check
uv run python -m pytest
uv run python -m zensical build

# save progress
git add -A
git commit -m "update"
git push -u origin main

Documentation

• Documentation

Annotations

.annotations/annotations.md

Citation

CITATION.cff

License

MIT