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An extension to pandas dataframes describe function.
Jupyter Notebook Python
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A tool that goes deeper than pandas dataframe's describe function.

The module contains DataFrameSummary and a DataFrameOverview objects.

The DataFrameSummary is an extended version of pandas' describe() method with:

  • methods
    • summary(columns=None): extends the describe() function with detailed info:
      • count, mean, std, min, 25%, 50%, 75%, max, counts, uniques, mising, missing %, type
    • dfs.type_summary(type=None): summary for a specific type, it has a more specialized info:
      • numeric: iqr, kurtosis, skewness, sum, mad, cv, zeros_num, zeros %, deviating of mean, deviating of mean %
      • constant: top row
      • categorical: values, top row
      • bool: left value, left value %, right value, right value %
      • unique: is not that unique :/
  • properties
    • dfs.columns_types: a count of the types of columns
    • dfs.types: internal list of supported types
    • dfs[column]: more in depth summary of the column

The DataFrameOverview displays an overview of the data, including:

  • methods
    • overview(dfs, first_level=None): Data overview showing:
      • Summary
      • Columns
      • Head
      • Tail
      • Correlations
      • Histogram for numeric columns
      • Range for unique and date columns
      • Values for constant columns

Installation (Not published yet)

The module can be easily installed with pip:

> pip install pandas-overview

This module depends on numpy and pandas. Optionally you can get also some nice visualisations if you have matplotlib installed.


To run the tests, execute the command python test.


For detailed information check this this and this out.


This is a forked version of the original pandas-summary plus great additions from pandas-summary-master

Contribution and License Agreement

If you contribute code to this project, you are implicitly allowing your code to be distributed under the MIT license. You are also implicitly verifying that all code is your original work.


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