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bundles / numpy 2.4.4 / numpy / asarray

built-in

numpy:asarray

Signature

built-in asarray ( a ,  dtype = None ,  order = None ,  * ,  device = None ,  copy = None ,  like = None )

Summary

Convert the input to an array.

Parameters

a : array_like

Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.

dtype : data-type, optional

By default, the data-type is inferred from the input data.

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

The memory layout of the output. 'C' gives a row-major layout (C-style), 'F' gives a column-major layout (Fortran-style). 'C' and 'F' will copy if needed to ensure the output format. 'A' (any) is equivalent to 'F' if input a is non-contiguous or Fortran-contiguous, otherwise, it is equivalent to 'C'. Unlike 'C' or 'F', 'A' does not ensure that the result is contiguous. 'K' (keep) is the default and preserves the input order for the output.

device : str, optional

The device on which to place the created array. Default: None. For Array-API interoperability only, so must be "cpu" if passed.

copy : bool, optional

If True, then the object is copied. If None then the object is copied only if needed, i.e. if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.). For False it raises a ValueError if a copy cannot be avoided. Default: None.

like : array_like, optional

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

Returns

out : ndarray

Array interpretation of a. No copy is performed if the input is already an ndarray with matching dtype and order. If a is a subclass of ndarray, a base class ndarray is returned.

Examples

Convert a list into an array: 
a = [1, 2]
import numpy as np
np.asarray(a)
✓
Existing arrays are not copied: 
a = np.array([1, 2])
np.asarray(a) is a
✓
If `dtype` is set, array is copied only if dtype does not match: 
a = np.array([1, 2], dtype=np.float32)
np.shares_memory(np.asarray(a, dtype=np.float32), a)
np.shares_memory(np.asarray(a, dtype=np.float64), a)
✓
Contrary to `asanyarray`, ndarray subclasses are not passed through: 
issubclass(np.recarray, np.ndarray)
a = np.array([(1., 2), (3., 4)], dtype='f4,i4').view(np.recarray)
np.asarray(a) is a
np.asanyarray(a) is a
✓

See also

asanyarray

Similar function which passes through subclasses.

asarray_chkfinite

Similar function which checks input for NaNs and Infs.

ascontiguousarray

Convert input to a contiguous array.

asfortranarray

Convert input to an ndarray with column-major memory order.

fromfunction

Construct an array by executing a function on grid positions.

fromiter

Create an array from an iterator.

Aliases

  • numpy.asarray

Referenced by

This package

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