34cd4f7599e47bfaaac20b9ebde1e35fc1cd44ee,catalyst/utils/pandas.py,,read_multiple_dataframes,#Any#Any#Any#Any#Any#Any#,173

Before Change

            whole dataframe, train part, valid part and infer part
    
    df_train = merge_multiple_fold_csv(fold_name="train", paths=in_csv_train)
    df_valid = merge_multiple_fold_csv(fold_name="valid", paths=in_csv_valid)
    df_infer = merge_multiple_fold_csv(fold_name="infer", paths=in_csv_infer)

    if args_are_not_none(tag2class, tag_column, class_column):
        df_train = map_dataframe(df_train, tag_column, class_column, tag2class)
        df_valid = map_dataframe(df_valid, tag_column, class_column, tag2class)
        df_infer = map_dataframe(df_infer, tag_column, class_column, tag2class)

    result_dataframe = df_train. \
        append(df_valid, ignore_index=True). \
        append(df_infer, ignore_index=True)

    return result_dataframe, df_train, df_valid, df_infer


def read_csv_data(
    in_csv: str = None,

After Change

        [x is not None for x in (in_csv_train, in_csv_valid, in_csv_infer)]
    )

    result_df = None
    fold_dfs = {}
    for fold_df, fold_name in zip(
        (in_csv_train, in_csv_valid, in_csv_infer),
        ("train", "valid", "infer")
    ):
        if fold_df is not None:
            fold_df = merge_multiple_fold_csv(
                fold_name=fold_name, paths=fold_df
            )
            if args_are_not_none(tag2class, tag_column, class_column):
                fold_df = map_dataframe(
                    fold_df, tag_column, class_column, tag2class
                )
            fold_dfs[fold_name] = fold_df

            result_df = fold_df \
                if result_df is None \
                else result_df.append(fold_df, ignore_index=True)

    output = (
        result_df,
        fold_dfs.get("train", None),
        fold_dfs.get("valid", None),
        fold_dfs.get("infer", None),
    )

    return output


def read_csv_data(
    in_csv: str = None,

Instances