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# 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:
    indexbool, default True

      Include index in resulting record array, stored in ‘index’ field or using the index label, if set.

    column_dtypesstr, 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_dtypesstr, 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.rec.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.rec.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')])