How do i add elements to a numpy array in python?

Append values to the end of an array.

Values are appended to a copy of this array.

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.

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

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

Insert elements into an array.

Delete elements from an array.

Examples

>>> np.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.

>>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
>>> np.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)

Insert values along the given axis before the given indices.

Input array.

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

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 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, 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, ..., 2, 3, 3])

>>> np.insert(b, slice(2, 4), [5, 6])
array([1, 1, 5, ..., 2, 3, 3])

>>> np.insert(b, [2, 2], [7.13, False]) # type casting
array([1, 1, 7, ..., 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]])