flopscope.

flopscope.numpy.append

fnp.append(arr, values, axis=None)[flopscope source][numpy source]

Append values to the end of an array.

Adapted from NumPy docs np.append

Areacore
Typecustom
NumPy Refnp.append
Cost
per-operation
Flopscope Context

Append values to end of array. Cost: numel(values).

Parameters

arr:array_like

Values are appended to a copy of this array.

values:array_like

These values are appended to a copy of arr. It must be of the correct shape (the same shape as arr, excluding axis). If axis is not specified, values can be any shape and will be flattened before use.

axis:int, optional

The axis along which values are appended. If axis is not given, both arr and values are flattened before use.

Returns

append:ndarray

A copy of arr with values appended to axis. Note that append does not occur in-place: a new array is allocated and filled. If axis is None, out is a flattened array.

See also

Examples

>>> import flopscope.numpy as fnp
>>> flops.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
array([1, 2, 3, ..., 7, 8, 9])

When axis is specified, values must have the correct shape.

>>> flops.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
>>> flops.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
Traceback (most recent call last):
...
ValueError: all the input arrays must have same number of dimensions, but
the array at index 0 has 2 dimension(s) and the array at index 1 has 1
dimension(s)
>>> a = flops.array([1, 2], dtype=int)
>>> c = flops.append(a, [])
>>> c
array([1., 2.])
>>> c.dtype
float64

Default dtype for empty ndarrays is float64 thus making the output of dtype float64 when appended with dtype int64