numpy.insert — NumPy v1.15 Manual (original) (raw)
numpy. insert(arr, obj, values, axis=None)[source]¶
Insert values along the given axis before the given indices.
| Parameters: | arr : array_like Input array. obj : int, 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). 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,...] = valuesis legal. axis : int, optional Axis along which to insert values. If axis is None then _arr_is flattened first. |
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| Returns: | out : ndarray A copy of arr with values inserted. Note that insertdoes not occur in-place: a new array is returned. If_axis_ is None, out is a flattened array. |
See also
Append elements at the end of an array.
Join a sequence of arrays along an existing axis.
Delete elements from an 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_.
Examples
a = np.array([[1, 1], [2, 2], [3, 3]]) a array([[1, 1], [2, 2], [3, 3]]) np.insert(a, 1, 5) array([1, 5, 1, 2, 2, 3, 3]) np.insert(a, 1, 5, axis=1) array([[1, 5, 1], [2, 5, 2], [3, 5, 3]])
Difference between sequence and scalars:
np.insert(a, [1], [[1],[2],[3]], axis=1) array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]) np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1), ... np.insert(a, [1], [[1],[2],[3]], axis=1)) True
b = a.flatten() b array([1, 1, 2, 2, 3, 3]) np.insert(b, [2, 2], [5, 6]) array([1, 1, 5, 6, 2, 2, 3, 3])
np.insert(b, slice(2, 4), [5, 6]) array([1, 1, 5, 2, 6, 2, 3, 3])
np.insert(b, [2, 2], [7.13, False]) # type casting array([1, 1, 7, 0, 2, 2, 3, 3])
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]])