From 2fca007b24fc532a4de96981354862b44aef37a9 Mon Sep 17 00:00:00 2001 From: Kunal Bhattacharya Date: Tue, 16 Jul 2024 13:57:09 +0530 Subject: [PATCH] README changes for _df change --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index c9cebabc..c9513c3a 100644 --- a/README.md +++ b/README.md @@ -44,17 +44,17 @@ quinn.validate_presence_of_columns(source_df, ["name", "age", "fun"]) Raises an exception unless `source_df` contains all the `StructFields` defined in the `required_schema`. By default, `ignore_nullable` is set to False, so exception will be raised even if column names and data types are matching but nullability conditions are mismatching. ```python -quinn.validate_schema(required_schema, _df=source_df) +quinn.validate_schema(required_schema, df_to_be_validated=source_df) ``` You can also set `ignore_nullable` to True, so the validation will happen only on column names and data types, not on nullability. ```python -quinn.validate_schema(required_schema, ignore_nullable=True, _df=source_df) +quinn.validate_schema(required_schema, ignore_nullable=True, df_to_be_validated=source_df) ``` > [!TIP] -> This function can also be used as a decorator to other functions that return a dataframe. This can help validate the schema of the returned df. When used as a decorator, you don't need to pass the `_df` argument as this validation is performed on the df returned by the base function on which the decorator is applied. +> This function can also be used as a decorator to other functions that return a dataframe. This can help validate the schema of the returned df. When used as a decorator, you don't need to pass the `df_to_be_validated` argument as this validation is performed on the df returned by the base function on which the decorator is applied. > > ```python > @quinn.validate_schema(required_schema, ignore_nullable=True)