simple-eda-deyuzhu

Tiny EDA helper for pandas. It gives you a handful of boring, useful functions for a first look at a DataFrame — plain dicts, lists, and a pandas Series — plus a few matplotlib charts built on top of them.

Install

pip install simple-eda-deyuzhu

Or, from a local checkout (editable / development install):

# run from the folder that contains pyproject.toml
python -m pip install -e .

Use

import pandas as pd
import simple_eda as eda

df = pd.read_csv("penguins.csv")

# --- plain-data helpers ---
eda.summarize(df)            # {'rows': 344, 'columns': 8, 'names': [...], 'dtypes': {...}}
eda.missing(df)              # null counts per column, sorted descending
eda.numeric_columns(df)      # ['bill_length_mm', 'bill_depth_mm', ...]
eda.categorical_columns(df)  # ['species', 'island', 'sex']

# --- charts (matplotlib) ---
eda.plot_missing(df)
eda.plot_category_counts(df, "species")
eda.plot_scatter(df, "bill_length_mm", "bill_depth_mm", hue="species")
eda.plot_scatter(df, "flipper_length_mm", "body_mass_g", hue="species", legend=True)
eda.plot_group_means(df, "body_mass_g", "species", hue="sex")

See demo/demo.ipynb for a full walkthrough on the Palmer Penguins dataset.

API

The charts follow the Evergreen & Emery Data Visualization Checklist: descriptive titles, direct labels, intentional ordering, one action colour with muted supporting data, and a colourblind-safe palette.

Notes

• Input: pandas DataFrame only (not Polars, Spark, Dask, or Arrow yet).
• Data helpers return plain objects and never modify the DataFrame in place.
• Plotting needs matplotlib; it is imported lazily, so import simple_eda works even where matplotlib is absent — you only need it when you draw.

License

MIT — see LICENSE.