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numpy.rec.array — NumPy v2.3 Manual

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

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:

objany

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

dtypedata-type, optional

Valid dtype for array.

shapeint or tuple of ints, optional

Shape of each array.

offsetint, optional

Position in the file or buffer to start reading from.

stridestuple 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.

copybool, 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')

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