# pandas.DataFrame.to_records

DataFrame.to_records(index=True, column_dtypes=None, index_dtypes=None)

  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.

    column_dtypes str, type, dict, default None

      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

      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 = f"<S{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')])