Welcome to pandas-validator’s documentation!¶
pandas-validator¶
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-schema offeres the functions for validating DataFrame or Series objects and generating factory data.
Example¶
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¶
$ pip install pandas_validator
Please see the following demo written by ipython notebook.
Documentation¶
The latest documentation is hosted at ReadTheDocs.
Requirements¶
- Support python version: 2.7, 3.3, 3.4, 3.5
- Support pandas version: 0.14, 0.15, 0.16, 0.17
License¶
This software is licensed under the MIT License.