pandas.DataFrame.to_records

DataFrame.to_records(self, index=True, convert_datetime64=None, column_dtypes=None, index_dtypes=None) [source]

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters:

index : bool, default True Include index in resulting record array, stored in ‘index’ field or using the index label, if set. convert_datetime64 : bool, default None Deprecated since version 0.23.0. Whether to convert the index to datetime.datetime if it is a DatetimeIndex. column_dtypes : str, type, dict, default None New in version 0.24.0. If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types. index_dtypes : str, type, dict, default None New in version 0.24.0. If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types. This mapping is applied only if index=True.

Returns:

numpy.recarray NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

See also

DataFrame.from_records

Convert structured or record ndarray to DataFrame.

numpy.recarray

An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples

>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to ‘index’. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

>>> index_dtypes = "<S{}".format(df.index.str.len().max())
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])