# pandas-validator

**Repository Path**: mirrors_c-bata/pandas-validator

## Basic Information

- **Project Name**: pandas-validator
- **Description**: Validation Library for pandas' DataFrame and Series.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-23
- **Last Updated**: 2026-01-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

================
pandas-validator
================

.. image:: https://travis-ci.org/c-bata/pandas-validator.svg?branch=master
    :target: https://travis-ci.org/c-bata/pandas-validator

.. image:: https://badge.fury.io/py/pandas_validator.svg
    :target: http://badge.fury.io/py/pandas_validator

.. image:: https://coveralls.io/repos/github/c-bata/pandas-validator/badge.svg?branch=master
    :target: https://coveralls.io/github/c-bata/pandas-validator?branch=master
    :alt: Coveralls Status



Validates the pandas object such as DataFrame and Series.
And this can define validator like django form class.


Why bugs occur in Data Wrangling with pandas
--------------------------------------------

When we wrangle our data with pandas, We use `DataFrame` frequently.
`DataFrame` is very powerfull and easy to handle.
But `DataFrame` has no it's schema, so It allows irregular values without being aware of it.
We are confused by these values and affect the results of data wrangling.

`pandas-validator` offers the functions for validating `DataFrame` or `Series` objects.


Overview
--------

.. code-block:: python

    import pandas as pd
    import pandas_validator as pv

    class SampleDataFrameValidator(pv.DataFrameValidator):
        row_num = 5
        column_num = 2
        label1 = pv.IntegerColumnValidator('label1', min_value=0, max_value=10)
        label2 = pv.FloatColumnValidator('label2', min_value=0, max_value=10)

    validator = SampleDataFrameValidator()

    df = pd.DataFrame({'label1': [0, 1, 2, 3, 4], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})
    validator.is_valid(df)  # True.

    df = pd.DataFrame({'label1': [11, 12, 13, 14, 15], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})
    validator.is_valid(df)  # False.

    df = pd.DataFrame({'label1': [0, 1, 2], 'label2': [5.0, 6.0, 7.0]})
    validator.is_valid(df)  # False


Getting Started
===============

Requirements
------------

* Support python version: 2.7, 3.4, 3.5, 3.6
* Support pandas version: 0.18, 0.19

Installation
------------

.. code-block:: console

    $ pip install pandas_validator

Usage
-----

Please see the following demo written by ipython notebook.

* `Demo in Japanese <https://github.com/c-bata/pandas-validator/blob/master/example/pandas_validator_example_ja.ipynb>`_
* `Demo in English <https://github.com/c-bata/pandas-validator/blob/master/example/pandas_validator_example_en.ipynb>`_


License
=======

This software is licensed under the MIT License.


Resources
=========

* `Github <https://github.com/c-bata/pandas-validator>`_
* `PyPI <https://pypi.python.org/pypi/pandas_validator>`_