Numpy-equivalent Of List.pop?

Is there a numpy method which is equivalent to the builtin pop for python lists? Popping obviously doesn't work on numpy arrays, and I want to avoid a list conversion.

Solution 1:

There is no pop method for NumPy arrays, but you could just use basic slicing (which would be efficient since it returns a view, not a copy):

In [104]: y = np.arange(5); y
Out[105]: array([0, 1, 2, 3, 4])

In [106]: last, y = y[-1], y[:-1]

In [107]: last, y
Out[107]: (4, array([0, 1, 2, 3]))

If there were a pop method it would return the last value in y and modify y.

Above,

last, y = y[-1], y[:-1]

assigns the last value to the variable last and modifies y.

Solution 2:

Here is one example using numpy.delete():

import numpy as np
arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
print(arr)
#  array([[ 1,  2,  3,  4],
#         [ 5,  6,  7,  8],
#         [ 9, 10, 11, 12]])
arr = np.delete(arr, 1, 0)
print(arr)
# array([[ 1,  2,  3,  4],
#        [ 9, 10, 11, 12]])

Solution 3:

Pop doesn't exist for NumPy arrays, but you can use NumPy indexing in combination with array restructuring, for example hstack/vstack or numpy.delete(), to emulate popping.

Here are some example functions I can think of (which apparently don't work when the index is -1, but you can fix this with a simple conditional):

defpoprow(my_array,pr):
    """ row popping in numpy arrays
    Input: my_array - NumPy array, pr: row index to pop out
    Output: [new_array,popped_row] """
    i = pr
    pop = my_array[i]
    new_array = np.vstack((my_array[:i],my_array[i+1:]))
    return [new_array,pop]

defpopcol(my_array,pc):
    """ column popping in numpy arrays
    Input: my_array: NumPy array, pc: column index to pop out
    Output: [new_array,popped_col] """
    i = pc
    pop = my_array[:,i]
    new_array = np.hstack((my_array[:,:i],my_array[:,i+1:]))
    return [new_array,pop]

This returns the array without the popped row/column, as well as the popped row/column separately:

>>>A = np.array([[1,2,3],[4,5,6]])>>>[A,poparow] = poprow(A,0)>>>poparow
array([1, 2, 3])

>>>A = np.array([[1,2,3],[4,5,6]])>>>[A,popacol] = popcol(A,2)>>>popacol
array([3, 6])

Solution 4:

There isn't any pop() method for numpy arrays unlike List, Here're some alternatives you can try out-

• Using Basic Slicing

>>>x = np.array([1,2,3,4,5])>>>x = x[:-1]; x>>>[1,2,3,4]

• Or, By Using delete()

Syntax - np.delete(arr, obj, axis=None)

arr: Input array obj: Row or column number to delete axis: Axis to delete

>>>x = np.array([1,2,3,4,5])>>>x = x = np.delete(x, len(x)-1, 0)>>>[1,2,3,4]

Solution 5:

The most 'elegant' solution for retrieving and removing a random item in Numpy is this:

import numpy as np
import random

arr = np.array([1, 3, 5, 2, 8, 7])
element = random.choice(arr)
elementIndex = np.where(arr == element)[0][0]
arr = np.delete(arr, elementIndex)

For curious coders:

The np.where() method returns two lists. The first returns the row indexes of the matching elements and the second the column indexes. This is useful when searching for elements in a 2d array. In our case, the first element of the first returned list is interesting.