pandas-contract 0.1.1

Define input and output columns for functions working on pandas dataframes.

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.

Installation

pip install pandas-contract

Usage

The library provides decorators to check the input arguments and return values of functions.

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.

import pandas as pd
import pandas_contract as pc
import pandera as pa

@pc.argument("df", schema=pa.DataFrameSchema({"x": pa.Int}))
@pc.result(schema=pa.DataFrameSchema({"x": pa.String}))
def col_x_to_string(df: pd.DataFrame) -> pd.DataFrame:
    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.

import pandas as pd
import pandas_contract as pc
import pandera as pa

@pc.argument("df", schema=pa.DataFrameSchema({pc.from_arg("val_col"): pa.Int}))
@pc.result(schema=pa.DataFrameSchema({pc.from_arg("val_col"): pa.String}))
def col_to_string(df: pd.DataFrame, val_col: str) -> pd.DataFrame:
    return df.assign(**{val_col: df[val_col].astype(str)})

Cross-argument and output constraints

Additionally, it provides checks to ensure cross-argument and output constraints like

Dataframes should have the same index.

import pandas as pd
import pandas_contract as pc

@pc.result(same_index_as=["df1", "df2"])
def my_func(df1: pd.DataFrame, df2: pd.DataFrame):
  # Output has the same index as input
  return pd.DataFrame(index=df1.index)

@pc.argument("df1", same_index_as="df2")
def my_func(df1, df2):
  # Input dataframes have the same index
  df1["x"] = df2["x"]

Size of dataframes should be equal.

import pandas as pd
import pandas_contract as pc

@pc.result(same_size_as="df1")
def my_func(df1: pd.DataFrame):
  # Output has the same size as input
  return pd.DataFrame(index=df1.index  + 1)

Additionally, it allows to extract a dataframe from a more complex argument or return value by specifying a key.

import pandas as pd
import pandas_contract as pc
import pandera as pa

@pc.argument(arg="df", schema=pa.DataFrameSchema({"x": pa.Int}))
@pc.result(key="data", schema=pa.DataFrameSchema({"x": pa.Int}), same_index_as="df")
def into_dict(df: pd.DataFrame) -> dict[str, pd.DataFrame]:
    return dict(data=df)