pandas-contract
Provide decorators to check functions arguments and return values using pandas DataFrame.
The decorators utilize the pandera.io library to validate data types and constraints of the input arguments and output values of functions.
Documentation
Documentation on pandas-contract.readthedocs.io
Installation
pip install pandas-contract
Setup
See Setup for first-time setup information.
Usage
ℹ️ Info: Generally, the standard abbreviations for the package imports are
import pandas as pd import pandas_contract as pc import pandera.pandas as pa
Check Dataframe structure
The following defines a function that takes a DataFrame with a column 'x' of type
integer as input and returns a DataFrame with the column 'x' of type string as output.
The Pandera.io documentation provides a full overview of the DataFrame/DataSeries checks.
@pc.argument("df", pa.DataFrameSchema({"x": pa.Int}))
@pc.result(pa.DataFrameSchema({"x": pa.String}))
def col_x_to_string(df: pd.DataFrame) -> pd.DataFrame:
"""Convert column x to string"""
return df.assign(x=df["x"].astype(str))
Dynamic Arguments and return values
Required columns and arguments can also be specified dynamically using a function that returns a schema.
@pc.argument("df", pa.DataFrameSchema({pc.from_arg("col"): pa.Column()}))
@pc.result(pa.DataFrameSchema({pc.from_arg("col"): pa.String}))
def col_to_string(df: pd.DataFrame, col: str) -> pd.DataFrame:
return df.assign(**{col: df[col].astype(str)})
Multiple columns in function argument
The decorator also supports multiple columns from the function argument.
@pc.argument("df", pa.DataFrameSchema({pc.from_arg("cols"): pa.Column()}))
@pc.result(pa.DataFrameSchema({pc.from_arg("cols"): pa.String}))
def cols_to_string(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:
return df.assign(**{col: df[col].astype(str) for col in cols})
Retrieve dataframes from a more complex argument
Sometimes the dataframe is not a direct argument of the function, but is part of a more complex argument.
In this case, the decorator argument key can be used to specify the key of the dataframe in the argument.
If key is a callable, it will be called with the argument and the result will be used as the dataframe.
Otherwise, it will be used as a key to retrieve the dataframe from the argument, i.e. arg[key].
Dataframe result is wrapped within another object
@pc.result(key="data")
def into_dict():
"""Dataframe wrapped in a dict"""
return dict(data=pd.DataFrame())
See Key Type for more information and examples.
1 maintainer
Metadata
1 maintainer
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