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-pandasFeatures
- 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
API
The behave-pandas api is extremely simple, and consists in two functions:
from behave_pandas import table_to_dataframe, dataframe_to_tableExample
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 |
| index_col | float_col | 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 data_frame_to_table
Then it prints a valid string copy pasteable into gherkin files
"""
| object | float64 | object |
| index_col | float_col | str_col |
| egg | 3.0 | silly walks |
| spam | 4.1 | spanish inquisition |
| bacon | 5.2 | dead parrot |
"""Associated steps:
from behave import *
from behave_pandas import table_to_dataframe, dataframe_to_table
use_step_matcher("parse")
@given("a gherkin table as input")
def step_impl(context,):
context.input = context.table
@when('converted to a data frame using {column_levels:d} row as column names and {index_levels:d} column as index')
def step_impl(context, column_levels, index_levels):
context.parsed = table_to_dataframe(context.input, column_levels=column_levels, index_levels=index_levels)
@then("it prints a valid string copy pasteable into gherkin files")
def step_impl(context):
assert context.result == context.text
@step("printed using data_frame_to_table")
def step_impl(context):
context.result = dataframe_to_table(context.parsed)Parsed dataframe:
>>> context.parsed
float_col str_col
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
