dask.array.insert
dask.array.insert¶
- dask.array.insert(arr, obj, values, axis)[source]¶
Insert values along the given axis before the given indices.
This docstring was copied from numpy.insert.
Some inconsistencies with the Dask version may exist.
- Parameters
- arrarray_like
Input array.
- objint, slice or sequence of ints
Object that defines the index or indices before which values is inserted.
New in version 1.8.0.
Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times).
- valuesarray_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.- axisint, optional
Axis along which to insert values. If axis is None then arr is flattened first.
- Returns
- outndarray
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.
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.
Notes
Note that for higher dimensional inserts
obj=0
behaves very different fromobj=[0]
just likearr[:,0,:] = values
is different fromarr[:,[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 array([[0, 1], [2, 3], [4, 5]]) >>> np.insert(a, 1, 6) array([0, 6, 1, 2, 3, 4, 5]) >>> 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 fromobj=1
:>>> np.insert(a, [1], [[7],[8],[9]], axis=1) array([[0, 7, 1], [2, 8, 3], [4, 9, 5]]) >>> np.insert(a, 1, [[7],[8],[9]], axis=1) array([[0, 7, 8, 9, 1], [2, 7, 8, 9, 3], [4, 7, 8, 9, 5]]) >>> 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 array([0, 1, 2, 3, 4, 5]) >>> 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]])