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skimpy

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skimpy is a light weight tool that provides summary statistics about variables in data frames within the console.

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# Skimpy

A light weight tool for creating summary statistics from dataframes.
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**skimpy** is a light weight tool that provides summary statistics about variables in **pandas** or **Polars** data frames within the console or your interactive Python window.

Think of it as a super-charged version of **pandas**' `df.describe()`.
[You can find the documentation here](https://aeturrell.github.io/skimpy/).

## Quickstart

`skim` a **pandas** or **polars** dataframe and produce summary statistics within the console
using:

```python
from skimpy import skim

skim(df)
```

where `df` is a **pandas** or **polars** dataframe.

If you need to a dataset to try *skimpy* out on, you can use the built-in test **Pandas** data frame:

```python
from skimpy import generate_test_data, skim

df = generate_test_data()
skim(df)
```

<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮
│ Data Summary Data Types Categories │
│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓ ┏━━━━━━━━━━━━━━━━━━━━━━━┓ │
│ ┃ Dataframe ┃ Values ┃ ┃ Column Type ┃ Count ┃ ┃ Categorical Variables ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩ ┡━━━━━━━━━━━━━━━━━━━━━━━┩ │
│ │ Number of rows │ 1000 │ │ float64 │ 3 │ │ class │ │
│ │ Number of columns │ 13 │ │ category │ 2 │ │ location │ │
│ └───────────────────┴────────┘ │ datetime64 │ 2 │ └───────────────────────┘ │
│ │ object │ 2 │ │
│ │ int64 │ 1 │ │
│ │ bool │ 1 │ │
│ │ string │ 1 │ │
│ │ timedelta64 │ 1 │ │
│ └─────────────┴───────┘ │
│ number │
│ ┏━━━━━━━━━┳━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ mean ┃ sd ┃ p0 ┃ p25 ┃ p50 ┃ p75 ┃ p100 ┃ hist ┃ │
│ ┡━━━━━━━━━╇━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━┩ │
│ │ length │ 0 │ 0 │ 0.5016 │ 0.3597 │ 1.573e-06 │ 0.134 │ 0.4976 │ 0.8602 │ 1 │ █▃▃▃▄█ │ │
│ │ width │ 0 │ 0 │ 2.037 │ 1.929 │ 0.002057 │ 0.603 │ 1.468 │ 2.953 │ 13.91 │ █▃▁ │ │
│ │ depth │ 0 │ 0 │ 10.02 │ 3.208 │ 2 │ 8 │ 10 │ 12 │ 20 │ ▁▄█▆▃▁ │ │
│ │ rnd │ 118 │ 11.8 │ -0.01977 │ 1.002 │ -2.809 │ -0.7355 │ -0.0007736 │ 0.6639 │ 3.717 │ ▁▄█▅▁ │ │
│ └─────────┴──────┴───────┴───────────┴─────────┴────────────┴─────────┴────────────┴────────┴───────┴────────┘ │
│ category │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ ordered ┃ unique ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ │
│ │ class │ 0 │ 0 │ False │ 2 │ │
│ │ location │ 1 │ 0.1 │ False │ 5 │ │
│ └─────────────────────────────┴────────────┴─────────────────┴─────────────────────────┴─────────────────────┘ │
│ bool │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ true ┃ true rate ┃ hist ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ │
│ │ booly_col │ 516 │ 0.52 │ █ █ │ │
│ └─────────────────────────────────┴──────────────────┴────────────────────────────────┴──────────────────────┘ │
│ datetime │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ first ┃ last ┃ frequency ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │
│ │ datetime │ 0 │ 0 │ 2018-01-31 │ 2101-04-30 │ ME │ │
│ │ datetime_no_freq │ 3 │ 0.3 │ 1992-01-05 │ 2023-03-04 │ None │ │
│ └──────────────────────────────┴───────┴──────────┴────────────────────┴───────────────────┴─────────────────┘ │
│ &lt;class 'datetime.date'&gt; │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ first ┃ last ┃ frequency ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ │
│ │ datetime.date │ 0 │ 0 │ 2018-01-31 │ 2101-04-30 │ ME │ │
│ │ datetime.date_no_freq │ 0 │ 0 │ 1992-01-05 │ 2023-03-04 │ None │ │
│ └───────────────────────────────────┴───────┴─────────┴──────────────────┴──────────────────┴────────────────┘ │
│ timedelta64 │
│ ┏━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ mean ┃ median ┃ max ┃ │
│ ┡━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩ │
│ │ time diff │ 5 │ 0.5 │ 8 days 00:05:47 │ 0 days 00:00:00 │ 26 days 00:00:00 │ │
│ └────────────────┴──────┴─────────┴────────────────────────┴────────────────────────┴────────────────────────┘ │
│ string │
│ ┏━━━━━━━━┳━━━━┳━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓ │
│ ┃ ┃ ┃ ┃ ┃ ┃ ┃ ┃ chars per ┃ words per ┃ total ┃ │
│ ┃ column ┃ NA ┃ NA % ┃ shortest ┃ longest ┃ min ┃ max ┃ row ┃ row ┃ words ┃ │
│ ┡━━━━━━━━╇━━━━╇━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩ │
│ │ text │ 6 │ 0.6 │ How are │ Indeed, │ How are │ What │ 31.1 │ 5.8 │ 5761 │ │
│ │ │ │ │ you? │ it was │ you? │ weather! │ │ │ │ │
│ │ │ │ │ │ the most │ │ │ │ │ │ │
│ │ │ │ │ │ outrageou │ │ │ │ │ │ │
│ │ │ │ │ │ sly │ │ │ │ │ │ │
│ │ │ │ │ │ pompous │ │ │ │ │ │ │
│ │ │ │ │ │ cat I │ │ │ │ │ │ │
│ │ │ │ │ │ have ever │ │ │ │ │ │ │
│ │ │ │ │ │ seen. │ │ │ │ │ │ │
│ └────────┴────┴──────┴────────────┴───────────┴────────────┴───────────┴────────────┴───────────┴────────────┘ │
│ object │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ │
│ ┃ column ┃ NA ┃ NA % ┃ │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ │
│ │ datetime.date │ 0 │ 0 │ │
│ │ datetime.date_no_freq │ 0 │ 0 │ │
│ └──────────────────────────────────────────────────────────────────────────┴─────────────┴───────────────────┘ │
╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯
</pre>

It is recommended that you set your datatypes before using **skimpy** (for example converting any text columns to pandas string datatype), as this will produce richer statistical summaries. However, the `skim()` function will try and guess what the datatypes of your columns are.

## Requirements

You can find a full list of requirements in the [pyproject.toml](https://github.com/aeturrell/skimpy/blob/main/pyproject.toml) file.

You can try this package out right now in your browser using this
[Google Colab notebook](https://colab.research.google.com/gist/aeturrell/7bf183c559dc1d15ab7e7aaac39ea0ed/skimpy_demo.ipynb)
(requires a Google account). Note that the Google Colab notebook uses the latest package released on PyPI (rather than the development release).

## Installation

You can install the latest release of *skimpy* via
[pip](https://pip.pypa.io/) from [PyPI](https://pypi.org/):

```bash
$ pip install skimpy
```

To install the development version from git, use:

```bash
$ pip install git+https://github.com/aeturrell/skimpy.git
```

For development, see [contributing](docs/contributing/contributing.qmd).

## License

Distributed under the terms of the [MIT license](https://opensource.org/licenses/MIT), *skimpy* is free and open source software.

## Issues

If you encounter any problems, please [file an issue](https://github.com/aeturrell/skimpy/issues) along with a detailed description.

## Credits

This project was generated from [\@cjolowicz](https://github.com/cjolowicz)\'s [Hypermodern Python Cookiecutter](https://github.com/cjolowicz/cookiecutter-hypermodern-python) template.

**skimpy** was inspired by the R package [**skimr**](https://docs.ropensci.org/skimr/articles/skimr.html) and by exploratory Python packages including [**ydata_profiling**](https://docs.profiling.ydata.ai) and [**dataprep**](https://dataprep.ai/), from which the `clean_columns` function comes.

This package would not have been possible without the [**Rich**](https://github.com/Textualize/rich) package.

The package is built with [uv](https://docs.astral.sh/uv/), while the documentation is built with [Quarto](https://quarto.org/) and [great-docs](https://posit-dev.github.io/great-docs) (a Python package). Tests are run with [nox](https://nox.thea.codes/en/stable/).

Using **skimpy** in your paper? Let us know by raising an issue beginning with "citation" and we'll add it to this page.

skimpy

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