pandas-schema 0.1.0

A validation library for Pandas data frames using user-friendly schemas

For the full documentation, refer to the Github Pages Website.

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PandasSchema is a module for validating tabulated data, such as CSVs (Comma Separated Value files), and TSVs (Tab Separated Value files). It uses the incredibly powerful data analysis tool pandas to do so quickly and efficiently.

For example, say your code expects a CSV that looks a bit like this:

Given Name,Family Name,Age,Sex,Customer ID
Gerald,Hampton,82,Male,2582GABK
Yuuwa,Miyake,27,Male,7951WVLW
Edyta,Majewska,50,Female,7758NSID

Now you want to be able to ensure that the data in your CSV is in the correct format:

import pandas as pd
from io import StringIO
from pandas_schema import Column, Schema
from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, CanConvertValidation, MatchesRegexValidation, InRangeValidation, InListValidation

schema = Schema([
    Column('Given Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
    Column('Family Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
    Column('Age', [InRangeValidation(0, 120)]),
    Column('Sex', [InListValidation(['Male', 'Female', 'Other'])]),
    Column('Customer ID', [MatchesRegexValidation(r'\d{4}[A-Z]{4}')])
])

test_data = pd.read_csv(StringIO('''Given Name,Family Name,Age,Sex,Customer ID
Gerald ,Hampton,82,Male,2582GABK
Yuuwa,Miyake,270,male,7951WVLW
Edyta,Majewska ,50,Female,775ANSID
'''))

errors = schema.validate(test_data)

for error in errors:
    print(error)

PandasSchema would then output

{row: 0, column: "Given Name"}: "Gerald " contains trailing whitespace
{row: 1, column: "Age"}: "270" was not in the range [0, 120)
{row: 1, column: "Sex"}: "male" is not in the list of legal options (Male, Female, Other)
{row: 2, column: "Family Name"}: "Majewska " contains trailing whitespace
{row: 2, column: "Customer ID"}: "775ANSID" does not match the regex "\d{4}[A-Z]{4}"