def drop_by_dtypes(input_cols, data_type=None):
"""
Drop rows by cell data type
:param input_cols: Column in which the filter is going to be apllied
:param data_type: filter by string, integer, float or boolean
:return: Spark DataFrame
"""
validate_columns_names(self, input_cols)
return self.rows.drop(fbdt(input_cols, data_type))
def drop_by_dtypes(input_cols, data_type=None):
"""
Drop rows by cell data type
:param input_cols: Column in which the filter is going to be apllied
:param data_type: filter by string, integer, float or boolean
:return: Spark DataFrame
"""
df = self
validate_columns_names(df, input_cols)
df = df.rows.drop(fbdt(input_cols, data_type))
df = df.preserve_meta(self, Actions.DROP_ROW.value, df.cols.names())
return df