def filter_col_name_by_dtypes(df, data_type):
"""
Return column names filtered by the column data type
:param df: Dataframe which columns are going to be filtered
:param data_type: Datatype used to filter the column.
:type data_type: str or list
:return:
"""
data_type = parse_spark_dtypes(data_type)
# isinstace require a tuple
data_type = tuple(val_to_list(data_type))
# Filter columns by data type
return [c for c in df.columns if isinstance(df.schema[c].dataType, data_type)]
def is_column_a(df, column, dtypes):
"""
Check if column match a list of data types
:param df:
:param column:
:param dtypes:
:return:
"""
data_type = tuple(val_to_list(parse_spark_dtypes(dtypes)))
column = one_list_to_val(column)
# Filter columns by data type
return isinstance(df.schema[column].dataType, data_type)
def is_column_a(df, column, dtypes):
"""
Check if column match a list of data types
:param df: dataframe
:param column: column to be compared with
:param dtypes: types to be checked
:return:
"""
column = val_to_list(column)
if len(column) > 1:
RaiseIt.length_error(column, 1)
data_type = tuple(val_to_list(parse_spark_dtypes(dtypes)))
column = one_list_to_val(column)
# Filter columns by data type
return isinstance(df.schema[column].dataType, data_type)