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bundles / numpy 2.4.3 / numpy / insert

_ArrayFunctionDispatcher

numpy:insert

source: /numpy/lib/_function_base_impl.py:5389

Signature

def insert(arr, obj, values, axis = None)

Summary

Insert values along the given axis before the given indices.

Parameters

arr : array_like

Input array.

obj : slice, int, array-like of ints or bools

Object that defines the index or indices before which values is inserted.

Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times).

values : array_like

Values to insert into arr. If the type of values is different from that of arr, values is converted to the type of arr. values should be shaped so that arr[...,obj,...] = values is legal.

axis : int, optional

Axis along which to insert values. If axis is None then arr is flattened first.

Returns

out : ndarray

A copy of arr with values inserted. Note that insert does not occur in-place: a new array is returned. If axis is None, out is a flattened array.

Notes

Note that for higher dimensional inserts obj=0 behaves very different from obj=[0] just like arr[:,0,:] = values is different from arr[:,[0],:] = values. This is because of the difference between basic and advanced indexing.

Examples

import numpy as np
a = np.arange(6).reshape(3, 2)
a
np.insert(a, 1, 6)
np.insert(a, 1, 6, axis=1)
✓
array([[0, 6, 1],
       [2, 6, 3],
       [4, 6, 5]])
Difference between sequence and scalars, showing how ``obj=[1]`` behaves different from ``obj=1``: 
np.insert(a, [1], [[7],[8],[9]], axis=1)
np.insert(a, 1, [[7],[8],[9]], axis=1)
np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1),
               np.insert(a, [1], [[7],[8],[9]], axis=1))
✓
True

b = a.flatten()
b
np.insert(b, [2, 2], [6, 7])
✓
array([0, 1, 6, 7, 2, 3, 4, 5])

np.insert(b, slice(2, 4), [7, 8])
✓
array([0, 1, 7, 2, 8, 3, 4, 5])

np.insert(b, [2, 2], [7.13, False]) # type casting
✓
array([0, 1, 7, 0, 2, 3, 4, 5])

x = np.arange(8).reshape(2, 4)
idx = (1, 3)
np.insert(x, idx, 999, axis=1)
✓
array([[  0, 999,   1,   2, 999,   3],
       [  4, 999,   5,   6, 999,   7]])

See also

append

Append elements at the end of an array.

concatenate

Join a sequence of arrays along an existing axis.

delete

Delete elements from an array.

Aliases

  • numpy.insert