Metadata-Version: 2.4
Name: simple-eda-dzhu9
Version: 0.1.0
Summary: Tiny EDA helper for pandas
Author: dzhu9
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
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        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
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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Project-URL: Homepage, https://github.com/helloadder/ds-class-note
Keywords: eda,pandas,data-analysis
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5
Dynamic: license-file

# simple-eda-dzhu9

Tiny EDA helper for pandas. It gives you a handful of boring, useful
functions for a first look at a `DataFrame` — no dashboards, no custom
classes, just plain dicts, lists, and a pandas `Series`.

## Install

```bash
pip install simple-eda-dzhu9
```

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.DataFrame({
    "name": ["Ana", "Bo", None],
    "age": [25, None, 31],
})

print(eda.summarize(df))
# {'rows': 3, 'columns': 2, 'names': ['name', 'age'],
#  'dtypes': {'name': 'object', 'age': 'float64'}}

print(eda.missing(df))
# age     1
# name    1
# dtype: int64

print(eda.numeric_columns(df))      # ['age']
print(eda.categorical_columns(df))  # ['name']
```

## 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. |

## Notes

- Input: pandas `DataFrame` only (not Polars, Spark, Dask, or Arrow yet).
- Output: plain objects (dicts, lists, ints, strings, pandas `Series`).
- Functions never modify the DataFrame in place.

This is a `0.1.0` release: the first working version — installable and
understandable, not complete.

## License

MIT — see [LICENSE](LICENSE).
