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# numpy.rec.array

rec.array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, copy=True)

  Construct a record array from a wide-variety of objects.

  A general-purpose record array constructor that dispatches to the appropriate `recarray` creation function based on the inputs (see Notes).

  Parameters:
    obj any

      Input object. See Notes for details on how various input types are treated.

    dtype data-type, optional

      Valid dtype for array.

    shape int or tuple of ints, optional

      Shape of each array.

    offset int, optional

      Position in the file or buffer to start reading from.

    strides tuple of ints, optional

      Buffer (buf) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory).

    formats, names, titles, aligned, byteorder

      If `dtype` is `None`, these arguments are passed to numpy.format_parser to construct a dtype. See that function for detailed documentation.

    copy bool, optional

      Whether to copy the input object (True), or to use a reference instead. This option only applies when the input is an ndarray or recarray. Defaults to True.

  Returns:
    np.recarray

      Record array created from the specified object.

  Notes

  If obj is `None`, then call the `recarray` constructor. If obj is a string, then call the `fromstring` constructor. If obj is a list or a tuple, then if the first object is an `ndarray`, call `fromarrays`, otherwise call `fromrecords`. If obj is a `recarray`, then make a copy of the data in the recarray (if `copy=True`) and use the new formats, names, and titles. If obj is a file, then call `fromfile`. Finally, if obj is an `ndarray`, then return `obj.view(recarray)`, making a copy of the data if `copy=True`.

  Examples

      >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      >>> a
      array([[1, 2, 3],
             [4, 5, 6],
             [7, 8, 9]])

      >>> np.rec.array(a)
      rec.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]],
                dtype=int64)

      >>> b = [(1, 1), (2, 4), (3, 9)]
      >>> c = np.rec.array(b, formats = ['i2', 'f2'], names = ('x', 'y'))
      >>> c
      rec.array([(1, 1.), (2, 4.), (3, 9.)],
                dtype=[('x', '<i2'), ('y', '<f2')])

      >>> c.x
      array([1, 2, 3], dtype=int16)

      >>> c.y
      array([1.,  4.,  9.], dtype=float16)

      >>> r = np.rec.array(['abc','def'], names=['col1','col2'])
      >>> print(r.col1)
      abc

      >>> r.col1
      array('abc', dtype='<U3')

      >>> r.col2
      array('def', dtype='<U3')