pandas_transform_checker-0.1.1.tar.gz

# Pandas transform checker ## what is it ? This library is focused on data quality checking on pandas transformations. Transformations are functions that takes a pandas DataFrame as input ( plus other params ) and output a DataFrame. This library allow the user to specify a contract that the function must respect. In this contract the user can specify: - the added columns - the deleted columns - the modified columns - if the function add/drop records - if the function modify the index ( ex: resampling ) Once the contract if specified, the function will raise a RuntimeError if one of it's specifications is violated. ## how to use it ? The package contains the decorator that performs the check it can be imported the following way: ``` from pandas_transform_checker.decorator_contract_checker import input_df_contract ``` ### Args df_param: name of the param of the function that is the input df contract_params: dict defining the contract of the function in the following format: ``` contract_dict = { "col_additions": { "col_a": "int", "col_b": "float" }, "col_deletions": { "col_c", "col_d" }, "col_editions": { "col_e", "col_f" }, "allow_index_edition": False, "allow_drop_record": True } ``` which means that the function must create "col_a", "col_b", delete "col_c", "col_d", must not modify any column data except "col_e", "col_f", and must not edit the index here is the list of keys allowed in this dict: - col_additions: dict where keys are column names and values are dtypes (string) - col_deletions: set of str representing the deleted columns - col_editions: set of str representing the modified columns - allow_index_edition: bool indicating if the function modify the index - allow_add_drop_record (bool): indicate if the function can drop some records (ex. when dropna is used) ### Usage when you have a function that takes a df as input: ``` def super_func(df_input): ... ``` just add the annotation to automatically check properties ``` @input_df_contract(df_param="df_input", contract_dict={"col_editions": {"col_e","col_f"}}) def super_func(df_input): ... ```