Numpy array delete row by mutiple condition

Numpy

In this post, we are going to learn how to delete rows by multiple conditions in a numpy array. We are going to use np. delete() and np.where() function or np.any() or np.all() function with examples.

NumPy delete function

The numpy. delete() function returns a new array after deleting rows and columns along with the given axis. It takes three arguments as given below

Parameters

  • array: The array in which rows and columns to be deleted
  • obj: index position, list of indexes position or slice index to be delete from numpy array
  • axis: row or column along which to delete values from numpy arraydefulat value is 0.
    • axis =0 : it deletes the rows
    • axis=1 : it deletes the colums
  • numpy. where() : It will operates on numpy array based on given condition as per given axis.

1. Numpy array delete row by mutiple condition

The numpy. delete() method delete the rows and columns as per the given axis, We have passed axis=0 to delete rows and condition by using np.where() function.To delete the columns by condition we can pass axis =1.

  • It delete row that contain any element which are greater and equal to 5 and element <=20
  • The rows 1st,2nd,3rd contain the element that satisfy the passed condition.
  • So the row 0 is returns after deleteing row from numpy array.

import numpy as np
 
myarr = np.arange(25).reshape((5, 5))

print(myarr)

myarr = np.delete(myarr, np.where((myarr >= 5) & (myarr <= 20))[0], axis=0)

 
print('The result array:\n',myarr)

Output

Original Array:
 [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]

The result array:
 [[0 1 2 3 4]]

2. Numpy array delete row by mutiple condition

In this python program example we are deleting rows by multiple conditions which first element is greater than 3 and less than =12 by passing condition by using np.where() method and result return by np where is used by np.delete() method to delete rows

import numpy as np

myarr = np.arange(25).reshape((5, 5))

myarr = np.delete(myarr, np.where(
    (myarr[:, 0] >= 3) & (myarr[:, 0] <=12 ))[0], axis=0)
 
print('The result array:\n',myarr)

Output

Original Array:
 [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]

The result array:
 [[ 0  1  2  3  4]
 [15 16 17 18 19]
 [20 21 22 23 24]]

3. NumPy.any() to delete any rows by multiple condition

In this example we are using the np.any() methd to delete the numpy array any rows has any element is 5 or 12.So the row 1st,3rd and 4th rows is deleted. 

import numpy as np

myarr = np.arange(25).reshape((5, 5))

print(myarr)
print('\nAfter deleting row on mutiple condition\n',myarr[np.any((myarr == 5) | (myarr == 12), axis=0)])

Output

Original Array:
 [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]

After deleting row on mutiple condition
 [[ 0  1  2  3  4]
 [10 11 12 13 14]]

4. NumPy.all() to delete rows on mutiple condition

The numpy.all() function will return true if all element of numpy array passes the given condition and else return False.

  • We have created a numpy array using of size(5,5).
  • The np.all() function return an numpy array of elements that satisfy the condition.
  • Finally printing the result array using the print() function
import numpy as np

myarr = np.arange(25).reshape((5, 5))

print(myarr)


print('result array:',myarr[1, ~np.all(myarr<=5, axis=0)])

Output

Original Array:
 [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]
 [20 21 22 23 24]]

Result array:
[5 6 7 8 9]

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