Metadata-Version: 2.4
Name: simple-eda-deyuzhu
Version: 0.2.0
Summary: Tiny EDA helper for pandas
Author: Deyu Zhu
License: MIT License
        
        Copyright (c) 2026 dzhu9
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/helloadder/ds-class-note
Keywords: eda,pandas,data-analysis,visualization
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5
Requires-Dist: matplotlib>=3.5
Dynamic: license-file

# 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

```bash
pip install simple-eda-deyuzhu
```

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

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

## Use

```python
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`](demo/demo.ipynb) for a full walkthrough on the
Palmer Penguins dataset.

## API

| Function | Returns | What it does |
| --- | --- | --- |
| `summarize(df)` | `dict` | Rows, columns, column names, and dtypes. |
| `missing(df)` | `pandas.Series` | Null counts per column, sorted descending. |
| `numeric_columns(df)` | `list[str]` | Names of numeric columns. |
| `categorical_columns(df)` | `list[str]` | Names of object/category columns. |
| `plot_missing(df)` | `Axes` | Horizontal bar of missing values per column. |
| `plot_category_counts(df, column)` | `Axes` | Bar chart of a category's counts, most to least. |
| `plot_scatter(df, x, y, hue=None, legend=False)` | `Axes` | Scatter of two numeric columns, coloured by a category. |
| `plot_group_means(df, value, group, hue=None)` | `Axes` | Dot plot of a value's mean per group. |

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](LICENSE).
