Remove rows in a 2d-numpy array if they contain a specific element

Question

I have a matrix as 2d-np.array and I would like to remove all rows that contain an element x in a specific column. My goal is to return a matrix without these rows, so it should be smaller. My function looks like this:

def delete_rows(matrix, x, col):
    for i in range(matrix.shape[0]-1):
        if(matrix[i,col] == x):
            np.delete(matrix, i, axis = 0)
    return matrix

Sadly in my test the shape of the matrix stayed the same after removing rows. I think the deleted rows were substituted by rows with 0s. Any advice on how I can achieve my goal?

Answer 1

This can be simply done in one line:

import numpy as np

def delete_rows(matrix, x, col):
    return matrix[matrix[:,col]!=x,:]

For example, if we want to remove all the rows that contain 5 in the second column from matrix A :

>>> A = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> print(delete_rows(A, 5, 1))
[[1 2 3]
 [7 8 9]]

Answer 2

Assuming you have an array like this:

array([[12,  5,  0,  3, 11,  3,  7,  9,  3,  5],
       [ 2,  4,  7,  6,  8,  8, 12, 10,  1,  6],
       [ 7,  7, 14,  8,  1,  5,  9, 13,  8,  9],
       [ 4,  3,  0,  3,  5, 14,  0,  2,  3,  8],
       [ 1,  3, 13,  3,  3, 14,  7,  0,  1,  9],
       [ 9,  0, 10,  4,  7,  3, 14, 11,  2,  7],
       [12,  2,  0,  0,  4,  5,  5,  6,  8,  4],
       [ 1,  4,  9, 10, 10,  8,  1,  1,  7,  9],
       [ 9,  3,  6,  7, 11, 14,  2, 11,  0, 14],
       [ 3,  5, 12,  9, 10,  4, 11,  4,  6,  4]])

You can remove all rows containing a 3 like this:

row_mask = np.apply_along_axis(np.any, 1, arr == 3)
arr = arr[~row_mask]

Your new array looks like this

array([[ 2,  4,  7,  6,  8,  8, 12, 10,  1,  6],
       [ 7,  7, 14,  8,  1,  5,  9, 13,  8,  9],
       [12,  2,  0,  0,  4,  5,  5,  6,  8,  4],
       [ 1,  4,  9, 10, 10,  8,  1,  1,  7,  9]])

Answer 3

EDIT: added condition for the specific column to check

You don't need to use any apply methods for this. It can be solved with basic boolean indexing as follows -

arr[~(arr[:,col] == val),:]

1. arr[:,col] selects the specific column from array
2. arr[:,col] == val checks for the value and returns True where it exists, else False
3. ~(arr[:,col] == val) inverses the True and False
4. arr[~(arr[:,col] == val),:] keeps only the rows which have the boolean index as True and discards all False

Example solution

arr = np.array([[12, 10, 12,  0,  9,  4, 12, 11],
                [ 3, 10, 14,  5,  4,  3,  6,  6],
                [12, 10,  1,  0,  5,  7,  5, 10],
                [12,  8, 14, 14, 12,  3, 14, 10],
                [ 9, 14,  3,  8,  1, 10,  9,  6],
                [10,  3, 11,  3, 12, 13, 11, 10],
                [ 0,  6,  8,  8,  5,  5,  1, 10], #<- this to remove
                [13,  6,  1, 10,  7, 10, 10, 13],
                [ 3,  3,  8, 10, 13,  0,  0, 10], #<- this to remove
                [ 6,  2, 13,  5,  8,  2,  8, 10]])
#                         ^
#                 this column to check


#boolean indexing approach
val, col = 8,2 #value to check is 8 and column to check is 2

out = arr[~(arr[:,col] == val),:] #<-----
out

array([[12, 10, 12,  0,  9,  4, 12, 11],
       [ 3, 10, 14,  5,  4,  3,  6,  6],
       [12, 10,  1,  0,  5,  7,  5, 10],
       [12,  8, 14, 14, 12,  3, 14, 10],
       [ 9, 14,  3,  8,  1, 10,  9,  6],
       [10,  3, 11,  3, 12, 13, 11, 10],
       [13,  6,  1, 10,  7, 10, 10, 13],
       [ 6,  2, 13,  5,  8,  2,  8, 10]])

---

If you want to check for the value in all columns then try this -

arr[~(arr == val).any(1),:]

And if you want to keep ONLY rows with the value instead, just remove ~ from the condition.

arr[(arr[:,col] == val),:]

If you want to remove the column as well, using np.delete -

np.delete(arr[~(arr[:,col] == val),], col, axis=1)

Note: You cannot remove both rows and columns at once using np.delete so if you plan to use it, you will need to do np.delete two times once for axis = 0 (rows) and once for axis = 1 (columns)