numpy.core.rec.recarray

Parameters

shape : tuple

Shape of output array.

dtype : data-type, optional

The desired data-type. By default, the data-type is determined from formats, names, titles, aligned and byteorder.

formats : list of data-types, optional

A list containing the data-types for the different columns, e.g. ['i4', 'f8', 'i4']. formats does not support the new convention of using types directly, i.e. (int, float, int). Note that formats must be a list, not a tuple. Given that formats is somewhat limited, we recommend specifying dtype instead.

names : tuple of str, optional

The name of each column, e.g. ('x', 'y', 'z').

buf : buffer, optional

By default, a new array is created of the given shape and data-type. If buf is specified and is an object exposing the buffer interface, the array will use the memory from the existing buffer. In this case, the offset and strides keywords are available.

Other Parameters

titles : tuple of str, optional

Aliases for column names. For example, if names were ('x', 'y', 'z') and titles is ('x_coordinate', 'y_coordinate', 'z_coordinate'), then arr['x'] is equivalent to both arr.x and arr.x_coordinate.

byteorder : {'<', '>', '='}, optional

Byte-order for all fields.

aligned : bool, optional

Align the fields in memory as the C-compiler would.

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).

offset : int, optional

Start reading buffer (buf) from this offset onwards.

order : {'C', 'F'}, optional

Row-major (C-style) or column-major (Fortran-style) order.

Returns

rec : recarray

Empty array of the given shape and type.

See Also

core.records.fromrecords : Construct a record array from data. record : fundamental data-type for recarray. format_parser : determine a data-type from formats, names, titles.

Notes

This constructor can be compared to empty: it creates a new record array but does not fill it with data. To create a record array from data, use one of the following methods:

1. Create a standard ndarray and convert it to a record array, using arr.view(np.recarray)
2. Use the buf keyword.
3. Use np.rec.fromrecords.

Examples

Create an array with two fields, x and y:

>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])
>>> x
array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])

>>> x['x']
array([1., 3.])

View the array as a record array:

>>> x = x.view(np.recarray)

>>> x.x
array([1., 3.])

>>> x.y
array([2, 4])

Create a new, empty record array:

>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
       (3471280, 1.2134086255804012e-316, 0)],
      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])