Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous to columns in a spread sheet. An example is [(x, int), (y, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['x'] and arr['y']. Record arrays allow the fields to be accessed as members of the array, using arr.x and arr.y.
Shape of output array.
The desired data-type. By default, the data-type is determined from formats, names, titles, aligned and byteorder.
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.
The name of each column, e.g. ('x', 'y', 'z').
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.
Empty array of the given shape and type.
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.
Byte-order for all fields.
Align the fields in memory as the C-compiler would.
Buffer (buf) is interpreted according to these strides (strides define how many bytes each array element, row, column, etc. occupy in memory).
Start reading buffer (buf) from this offset onwards.
Row-major (C-style) or column-major (Fortran-style) order.
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:
Create a standard ndarray and convert it to a record array, using arr.view(np.recarray)
Use the buf keyword.
Use np.rec.fromrecords.
Examples
Create an array with two fields, x and y:
>>> import numpy as np
>>> 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)
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)])
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
T
View of the transposed array.
base
Base object if memory is from some other object.
ctypes
An object to simplify the interaction of the array with the ctypes module.
data
Python buffer object pointing to the start of the array’s data.
dtype
Data-type of the array’s elements.
flags
Information about the memory layout of the array.
flat
A 1-D iterator over the array.
imag
The imaginary part of the array.
itemsize
Length of one array element in bytes.
mT
View of the matrix transposed array.
nbytes
Total bytes consumed by the elements of the array.
ndim
Number of array dimensions.
real
The real part of the array.
shape
Tuple of array dimensions.
size
Number of elements in the array.
strides
Tuple of bytes to step in each dimension when traversing an array.
Methods
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