Metadata-Version: 2.4
Name: pandas-quickviz
Version: 0.1.0
Summary: Quick pandas DataFrame visualizations with matplotlib and seaborn.
Author: a6itya
License-Expression: MIT
Project-URL: Homepage, https://github.com/a6itya/viz-lib
Project-URL: Repository, https://github.com/a6itya/viz-lib
Keywords: pandas,visualization,matplotlib,seaborn,eda,plotting
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5
Requires-Dist: matplotlib>=3.5
Requires-Dist: seaborn>=0.12
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Dynamic: license-file

# pandas-quickviz

Quick, one-line visualizations for pandas DataFrames, built on
[matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/).

A seaborn theme is applied automatically on import. Every plotting function
takes a `DataFrame` and returns the matplotlib `Axes` (single plots) or `Figure`
(multi-plot grids) — nothing is shown for you, so you stay in control of
displaying or saving the result.

## Installation

```bash
pip install pandas-quickviz
```

## Usage

```python
import pandas as pd
import matplotlib.pyplot as plt
import pandas_quickviz as v

df = pd.DataFrame(
    {
        "age": [23, 45, 31, 35, None, 52, 40],
        "score": [88, 72, 95, 60, 77, 84, 90],
        "grade": ["A", "C", "A", "D", "B", "B", "A"],
    }
)

# One-liner charts (return an Axes)
v.histogram(df, "age")
v.missing_plot(df)
plt.show()
```

## API

**One-liner charts** (return an `Axes`):

- `histogram(df, col, bins=30)` — distribution of a numeric column
- `bar(df, col)` — value counts of a categorical column
- `scatter(df, x, y, hue=None)` — relationship between two columns
- `box(df, col, by=None)` — spread of a column, optionally grouped

**Auto-EDA** (return a `Figure` with a subplot grid):

- `plot_numeric(df)` — histograms for every numeric column
- `plot_categorical(df)` — bar charts for every categorical column
- `missing_plot(df)` — null counts per column

**Styling**:

- `set_theme(style="whitegrid", palette="deep")` — thin wrapper over
  `seaborn.set_theme`, called automatically on import

## License

MIT
