behave-pandas

Utility package for the Behave BDD testing framework, to make converting gherkin tables to and from pandas data frames a breeze.

Build Status

Installation

pip install behave-pandas

Features

• Easily convert a Gherkin table into a pandas data frame with explicit dtype information
• Easily convert a pandas data frame into a behave table that can be parsed by behave-pandas
• Support converting data frames with multiple index levels either on columns or rows
• Handle missing data for dtypes that support it.

Changelog

See the changelog here.

API

The behave-pandas api is extremely simple, and consists in two functions:

from behavepandas import tabletodataframe, dataframeto_table

Example

Feature: Table printer
as a tester   I want to be able to create gherkin tables from existing data frames
Scenario: simple index     Given a gherkin table as input       | str       | float     | str                 |       | indexcol | floatcol | str_col             |       | egg       | 3.0       | silly walks         |       | spam      | 4.1       | spanish inquisition |       | bacon     | 5.2       | dead parrot         |     When converted to a data frame using 1 row as column names and 1 column as index     And printed using dataframeto_table     Then it prints a valid string copy pasteable into gherkin files     """     | object    | float64   | object              |     | indexcol | floatcol | str_col             |     | egg       | 3.0       | silly walks         |     | spam      | 4.1       | spanish inquisition |     | bacon     | 5.2       | dead parrot         |     """

Associated steps:

from behave import *
from behavepandas import tabletodataframe, dataframeto_table
usestepmatcher("parse")
@given("a gherkin table as input") def step_impl(context,):     context.input = context.table
@when('converted to a data frame using {columnlevels:d} row as column names and {indexlevels:d} column as index') def stepimpl(context, columnlevels, index_levels):     context.parsed = tabletodataframe(context.input, columnlevels=columnlevels, indexlevels=indexlevels)
@then("it prints a valid string copy pasteable into gherkin files") def step_impl(context):     assert context.result == context.text
@step("printed using dataframeto_table") def step_impl(context):     context.result = dataframetotable(context.parsed)

Parsed dataframe:

>>> context.parsed
           floatcol              strcol
index_col
egg              3.0          silly walks
spam             4.1  spanish inquisition
bacon            5.2          dead parrot
>>> context.parsed.info() <class 'pandas.core.frame.DataFrame'> Index: 3 entries, egg to bacon Data columns (total 2 columns): float_col    3 non-null float64 str_col      3 non-null object dtypes: float64(1), object(1) memory usage: 72.0+ bytes