skimpy 0.0.2

skimpy

Welcome

Welcome to skimpy! skimpy is a light weight tool that provides summary statistics about variables in data frames within the console. Think of it as a super version of df.summary().

Quickstart

skim a dataframe and produce summary statistics within the console using:

from skimpy import skim

skim(df)

If you need to a dataset to try skimpy out on, use the in-built one:

from skimpy import skim, generate_test_data

df = generate_test_data()
skim(df)

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.

Features

• Support for boolean, numeric, datetime, string, and category datatypes

• Command line interface in addition to interactive console functionality

• Light weight, with results printed to terminal using the rich package.

Requirements

You can find a full list of requirements in the pyproject.toml file. The main requirements are:

• python = “>=3.7.1,<4.0.0”

• click = “^8.0.1”

• rich = “^10.9.0”

• pandas = “^1.3.2”

Installation

You can install the latest release of skimpy via pip from PyPI:

$ pip install skimpy

To install the development version from git, use:

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

For development, see the Contributor Guide.

Usage

This package is mostly designed to be used within an interactive console session or Jupyter notebook

from skimpy import skim

skim(df)

However, you can also use it on the command line:

$ skimpy file.csv

skimpy will do its best to infer column datatypes.

Contributing

Contributions are very welcome. To learn more, see the Contributor Guide.

License

Distributed under the terms of the MIT license, skimpy is free and open source software.

Issues

If you encounter any problems, please file an issue along with a detailed description.

Credits

This project was generated from @cjolowicz’s Hypermodern Python Cookiecutter template.

skimpy was inspired by the R package skimr and by exploratory Python packages including pandas_profiling and dataprep.