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skimpy

(★ 517)

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

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# Installation Guide

1. Get the code

git clone https://github.com/aeturrell/skimpy

Downloads the entire project code from GitHub to your computer.

cd skimpy

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

$ pip install skimpy

Installs the Python libraries listed in requirements.txt (or similar).

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

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

3. Make

Medium

Prerequisites

Git Needed to download the project code from GitHub.
Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).

make

Compiles the code based on the generated build configuration to produce an executable.

✅ If it finishes without errors, it worked. Try running the generated executable directly.

// repository documentation

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# Skimpy

A light weight tool for creating summary statistics from dataframes.
![png](https://raw.githubusercontent.com/aeturrell/skimpy/main/docs/logo.png)

![](https://raw.githubusercontent.com/aeturrell/skimpy/main/logo.png)

[![PyPI](https://img.shields.io/pypi/v/skimpy.svg)](https://pypi.org/project/skimpy/)
[![Status](https://img.shields.io/pypi/status/skimpy.svg)](https://pypi.org/project/skimpy/)
[![Python Version](https://img.shields.io/pypi/pyversions/skimpy)](https://pypi.org/project/skimpy)
[![License](https://img.shields.io/pypi/l/skimpy)](https://opensource.org/licenses/MIT)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Tests](https://github.com/aeturrell/skimpy/workflows/Tests/badge.svg)](https://github.com/aeturrell/skimpy/actions?workflow=Tests)
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[![Read the documentation at https://aeturrell.github.io/skimpy/](https://img.shields.io/badge/Go%20to%20the%20docs-purple?style=flat)](https://aeturrell.github.io/skimpy/)
[![Downloads](https://static.pepy.tech/badge/skimpy)](https://pepy.tech/projects/skimpy)

![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black)
![macOS](https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=macos&logoColor=F0F0F0)
![Windows](https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white)
[![Source](https://img.shields.io/badge/source%20code-github-lightgrey?style=for-the-badge)](https://github.com/aeturrell/skimpy)

**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 ─────────────────────────────────────────────────╮
│ <span style="font-style: italic">         Data Summary         </span> <span style="font-style: italic">      Data Types       </span> <span style="font-style: italic">       Categories        </span>                                │
│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓ ┏━━━━━━━━━━━━━━━━━━━━━━━┓                                │
│ ┃<span style="color: #008080; text-decoration-color: #008080; font-weight: bold"> Dataframe         </span>┃<span style="color: #008080; text-decoration-color: #008080; font-weight: bold"> Values </span>┃ ┃<span style="color: #008080; text-decoration-color: #008080; font-weight: bold"> Column Type </span>┃<span style="color: #008080; text-decoration-color: #008080; font-weight: bold"> Count </span>┃ ┃<span style="color: #008080; text-decoration-color: #008080; font-weight: bold"> Categorical Variables </span>┃                                │
│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩ ┡━━━━━━━━━━━━━━━━━━━━━━━┩                                │
│ │ 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     │                                                          │
│                                └─────────────┴───────┘                                                          │
│ <span style="font-style: italic">                                                    number                                                    </span>  │
│ ┏━━━━━━━━━┳━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column  </span>┃<span style="font-weight: bold"> NA   </span>┃<span style="font-weight: bold"> NA %  </span>┃<span style="font-weight: bold"> mean      </span>┃<span style="font-weight: bold"> sd      </span>┃<span style="font-weight: bold"> p0         </span>┃<span style="font-weight: bold"> p25     </span>┃<span style="font-weight: bold"> p50        </span>┃<span style="font-weight: bold"> p75    </span>┃<span style="font-weight: bold"> p100  </span>┃<span style="font-weight: bold"> hist   </span>┃  │
│ ┡━━━━━━━━━╇━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">length </span> │ <span style="color: #008080; text-decoration-color: #008080">   0</span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">   0.5016</span> │ <span style="color: #008080; text-decoration-color: #008080"> 0.3597</span> │ <span style="color: #008080; text-decoration-color: #008080"> 1.573e-06</span> │ <span style="color: #008080; text-decoration-color: #008080">  0.134</span> │ <span style="color: #008080; text-decoration-color: #008080">    0.4976</span> │ <span style="color: #008080; text-decoration-color: #008080">0.8602</span> │ <span style="color: #008080; text-decoration-color: #008080">    1</span> │ <span style="color: #008000; text-decoration-color: #008000">█▃▃▃▄█</span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">width  </span> │ <span style="color: #008080; text-decoration-color: #008080">   0</span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">    2.037</span> │ <span style="color: #008080; text-decoration-color: #008080">  1.929</span> │ <span style="color: #008080; text-decoration-color: #008080">  0.002057</span> │ <span style="color: #008080; text-decoration-color: #008080">  0.603</span> │ <span style="color: #008080; text-decoration-color: #008080">     1.468</span> │ <span style="color: #008080; text-decoration-color: #008080"> 2.953</span> │ <span style="color: #008080; text-decoration-color: #008080">13.91</span> │ <span style="color: #008000; text-decoration-color: #008000"> █▃▁  </span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">depth  </span> │ <span style="color: #008080; text-decoration-color: #008080">   0</span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">    10.02</span> │ <span style="color: #008080; text-decoration-color: #008080">  3.208</span> │ <span style="color: #008080; text-decoration-color: #008080">         2</span> │ <span style="color: #008080; text-decoration-color: #008080">      8</span> │ <span style="color: #008080; text-decoration-color: #008080">        10</span> │ <span style="color: #008080; text-decoration-color: #008080">    12</span> │ <span style="color: #008080; text-decoration-color: #008080">   20</span> │ <span style="color: #008000; text-decoration-color: #008000">▁▄█▆▃▁</span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">rnd    </span> │ <span style="color: #008080; text-decoration-color: #008080"> 118</span> │ <span style="color: #008080; text-decoration-color: #008080"> 11.8</span> │ <span style="color: #008080; text-decoration-color: #008080"> -0.01977</span> │ <span style="color: #008080; text-decoration-color: #008080">  1.002</span> │ <span style="color: #008080; text-decoration-color: #008080">    -2.809</span> │ <span style="color: #008080; text-decoration-color: #008080">-0.7355</span> │ <span style="color: #008080; text-decoration-color: #008080">-0.0007736</span> │ <span style="color: #008080; text-decoration-color: #008080">0.6639</span> │ <span style="color: #008080; text-decoration-color: #008080">3.717</span> │ <span style="color: #008000; text-decoration-color: #008000">▁▄█▅▁ </span> │  │
│ └─────────┴──────┴───────┴───────────┴─────────┴────────────┴─────────┴────────────┴────────┴───────┴────────┘  │
│ <span style="font-style: italic">                                                   category                                                   </span>  │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column                      </span>┃<span style="font-weight: bold"> NA         </span>┃<span style="font-weight: bold"> NA %            </span>┃<span style="font-weight: bold"> ordered                 </span>┃<span style="font-weight: bold"> unique              </span>┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">class                      </span> │ <span style="color: #008080; text-decoration-color: #008080">         0</span> │ <span style="color: #008080; text-decoration-color: #008080">              0</span> │ <span style="color: #00d7ff; text-decoration-color: #00d7ff">False                  </span> │ <span style="color: #008080; text-decoration-color: #008080">                  2</span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">location                   </span> │ <span style="color: #008080; text-decoration-color: #008080">         1</span> │ <span style="color: #008080; text-decoration-color: #008080">            0.1</span> │ <span style="color: #00d7ff; text-decoration-color: #00d7ff">False                  </span> │ <span style="color: #008080; text-decoration-color: #008080">                  5</span> │  │
│ └─────────────────────────────┴────────────┴─────────────────┴─────────────────────────┴─────────────────────┘  │
│ <span style="font-style: italic">                                                     bool                                                     </span>  │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column                          </span>┃<span style="font-weight: bold"> true             </span>┃<span style="font-weight: bold"> true rate                      </span>┃<span style="font-weight: bold"> hist                 </span>┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">booly_col                      </span> │ <span style="color: #008080; text-decoration-color: #008080">             516</span> │ <span style="color: #008080; text-decoration-color: #008080">                          0.52</span> │ <span style="color: #008000; text-decoration-color: #008000">       █    █       </span> │  │
│ └─────────────────────────────────┴──────────────────┴────────────────────────────────┴──────────────────────┘  │
│ <span style="font-style: italic">                                                   datetime                                                   </span>  │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column                       </span>┃<span style="font-weight: bold"> NA    </span>┃<span style="font-weight: bold"> NA %     </span>┃<span style="font-weight: bold"> first              </span>┃<span style="font-weight: bold"> last              </span>┃<span style="font-weight: bold"> frequency       </span>┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime                    </span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">       0</span> │ <span style="color: #800000; text-decoration-color: #800000">    2018-01-31    </span> │ <span style="color: #800000; text-decoration-color: #800000">   2101-04-30    </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">ME             </span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime_no_freq            </span> │ <span style="color: #008080; text-decoration-color: #008080">    3</span> │ <span style="color: #008080; text-decoration-color: #008080">     0.3</span> │ <span style="color: #800000; text-decoration-color: #800000">    1992-01-05    </span> │ <span style="color: #800000; text-decoration-color: #800000">   2023-03-04    </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">None           </span> │  │
│ └──────────────────────────────┴───────┴──────────┴────────────────────┴───────────────────┴─────────────────┘  │
│ <span style="font-style: italic">                                           &lt;class 'datetime.date'&gt;                                            </span>  │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column                            </span>┃<span style="font-weight: bold"> NA    </span>┃<span style="font-weight: bold"> NA %    </span>┃<span style="font-weight: bold"> first            </span>┃<span style="font-weight: bold"> last             </span>┃<span style="font-weight: bold"> frequency      </span>┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime.date                    </span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">      0</span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">2018-01-31      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">2101-04-30      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">ME            </span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime.date_no_freq            </span> │ <span style="color: #008080; text-decoration-color: #008080">    0</span> │ <span style="color: #008080; text-decoration-color: #008080">      0</span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">1992-01-05      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">2023-03-04      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">None          </span> │  │
│ └───────────────────────────────────┴───────┴─────────┴──────────────────┴──────────────────┴────────────────┘  │
│ <span style="font-style: italic">                                                 timedelta64                                                  </span>  │
│ ┏━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column         </span>┃<span style="font-weight: bold"> NA   </span>┃<span style="font-weight: bold"> NA %    </span>┃<span style="font-weight: bold"> mean                   </span>┃<span style="font-weight: bold"> median                 </span>┃<span style="font-weight: bold"> max                    </span>┃  │
│ ┡━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">time diff     </span> │ <span style="color: #008080; text-decoration-color: #008080">   5</span> │ <span style="color: #008080; text-decoration-color: #008080">    0.5</span> │ <span style="color: #008080; text-decoration-color: #008080">       8 days 00:05:47</span> │ <span style="color: #008080; text-decoration-color: #008080">       0 days 00:00:00</span> │ <span style="color: #008080; text-decoration-color: #008080">      26 days 00:00:00</span> │  │
│ └────────────────┴──────┴─────────┴────────────────────────┴────────────────────────┴────────────────────────┘  │
│ <span style="font-style: italic">                                                    string                                                    </span>  │
│ ┏━━━━━━━━┳━━━━┳━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold">        </span>┃<span style="font-weight: bold">    </span>┃<span style="font-weight: bold">      </span>┃<span style="font-weight: bold">            </span>┃<span style="font-weight: bold">           </span>┃<span style="font-weight: bold">            </span>┃<span style="font-weight: bold">           </span>┃<span style="font-weight: bold"> chars per  </span>┃<span style="font-weight: bold"> words per </span>┃<span style="font-weight: bold"> total      </span>┃  │
│ ┃<span style="font-weight: bold"> column </span>┃<span style="font-weight: bold"> NA </span>┃<span style="font-weight: bold"> NA % </span>┃<span style="font-weight: bold"> shortest   </span>┃<span style="font-weight: bold"> longest   </span>┃<span style="font-weight: bold"> min        </span>┃<span style="font-weight: bold"> max       </span>┃<span style="font-weight: bold"> row        </span>┃<span style="font-weight: bold"> row       </span>┃<span style="font-weight: bold"> words      </span>┃  │
│ ┡━━━━━━━━╇━━━━╇━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">text  </span> │ <span style="color: #008080; text-decoration-color: #008080"> 6</span> │ <span style="color: #008080; text-decoration-color: #008080"> 0.6</span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">How are   </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">Indeed,  </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">How are   </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">What     </span> │ <span style="color: #008080; text-decoration-color: #008080">      31.1</span> │ <span style="color: #008080; text-decoration-color: #008080">      5.8</span> │ <span style="color: #008080; text-decoration-color: #008080">      5761</span> │  │
│ │        │    │      │ <span style="color: #af87ff; text-decoration-color: #af87ff">you?      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">it was   </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">you?      </span> │ <span style="color: #af87ff; text-decoration-color: #af87ff">weather! </span> │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">the most </span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">outrageou</span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">sly      </span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">pompous  </span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">cat I    </span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">have ever</span> │            │           │            │           │            │  │
│ │        │    │      │            │ <span style="color: #af87ff; text-decoration-color: #af87ff">seen.    </span> │            │           │            │           │            │  │
│ └────────┴────┴──────┴────────────┴───────────┴────────────┴───────────┴────────────┴───────────┴────────────┘  │
│ <span style="font-style: italic">                                                    object                                                    </span>  │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓  │
│ ┃<span style="font-weight: bold"> column                                                                   </span>┃<span style="font-weight: bold"> NA          </span>┃<span style="font-weight: bold"> NA %              </span>┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime.date                                                           </span> │ <span style="color: #008080; text-decoration-color: #008080">          0</span> │ <span style="color: #008080; text-decoration-color: #008080">                0</span> │  │
│ │ <span style="color: #af87ff; text-decoration-color: #af87ff">datetime.date_no_freq                                                   </span> │ <span style="color: #008080; text-decoration-color: #008080">          0</span> │ <span style="color: #008080; text-decoration-color: #008080">                0</span> │  │
│ └──────────────────────────────────────────────────────────────────────────┴─────────────┴───────────────────┘  │
╰────────────────────────────────────────────────────── 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.