## Deleting elements from a NumPy Array by value or conditions in Python.

**Numpy remove element from array: **In this article we will discuss about how to delete elements from a NumPy Array by based on matching values or multiple conditions.

### Remove all occurrences of an element with given value from numpy array :

**Remove element from array numpy: **Suppose we have a NumPy array and we want to delete all the occurrences of that particular element from that Numpy array.

#Program : import numpy as np # numpy array created from a list arr = np.array([40,50,60,70,80,90,40,10,20,40]) print('Numpy Array before deleting all occurrences of 40 :') print(arr) # Removing all occurrences of elements with value 40 from numpy array arr = arr[arr != 40] print('Modified Numpy Array after deleting all occurrences of 40 :') print(arr)

Output : Numpy Array before deleting all occurrences of 40 : [40 50 60 70 80 90 40 10 20 40] Modified Numpy Array after deleting all occurrences of 40 : [50 60 70 80 90 10 20]

- np.delete(): Remove items/rows/columns from Numpy Array | How to Delete Rows/Columns in a Numpy Array?
- Count occurrences of a value in NumPy array in Python | numpy.count() in Python
- Convert 2D NumPy array to list of lists in python

The condition `arr != 40`

returns a bool array of same size as arr with value True at places where value is not equal to 40 and returns False at other places. Like this

[ False True True True True True False True True False]

When we will pass it to `[]`

of numpy array `arr`

then it will select the elemnts whose value is True. Means it will return the elements from `arr`

which are not equal to 40.

You can also delete column using numpy delete column tutorial.

### Delete elements in Numpy Array based on multiple conditions :

**Numpy array remove element: **Like above example, it will create a bool array using multiple conditions on numpy array and when it will be passed to `[]`

operator of numpy array to select the elements then it will return a copy of the numpy array satisfying the condition suppose `(arr > 40) & (arr < 80)`

means elements greater than 40 and less than 80 will be returned.

#Program : import numpy as np # numpy array created from a list arr = np.array([40,50,60,70,80,90,40,10,20,40]) print('Numpy Array before deleting any element :') print(arr) # Remove all occurrences of elements below 40 & greater than 80 # Means keeping elements greater than 40 and less than 80 arr = arr[ (arr > 40) & (arr < 80) ] print('Modified Numpy Array after deleting elements :') print(arr)

Output : Numpy Array before deleting any element : [40 50 60 70 80 90 40 10 20 40] Modified Numpy Array after deleting elements : [50 60 70]

### Delete elements by value or condition using np.argwhere() & np.delete() :

**np.delete python: **By using `np.argwhere()`

& `np.delete()`

we can also delete any elements.

#Program : import numpy as np # numpy array created from a list arr = np.array([40,50,60,70,80,90,40,10,20,40]) print('Numpy Array before deleting any element :') print(arr) # deleting all occurrences of element with value 40 arr = np.delete(arr, np.argwhere(arr == 40)) print('Modified Numpy Array after deleting all occurrences of 40') print(arr)

Output : Numpy Array before deleting all occurrences of 40 : [40 50 60 70 80 90 40 10 20 40] Modified Numpy Array after deleting all occurrences of 40 : [50 60 70 80 90 10 20]

In the above example `np.delete()`

function will delete the element and `np.argwhere()`

function will detect the index.

Means the condition `arr == 40`

will return a bool array like

[True False False False False False True False False True]

Where the condition `arr == 40`

matches it returns True and if condition fails it returns False. And when the bool array will be passed inside the function `np.argwhere()`

then it will return the index positions where the values are True.

i.e

[ [0] [6] [9]]

These are the indices which represents the value 40.

When this index positions will be passed inside `np.delete()`

function then the element present at those index positions will be deleted.

### Delete elements by multiple conditions using np.argwhere() & np.delete() :

**Numpy delete: **The concept is same like above only the difference is the elements will be deleted based on multiple conditions.

So, let’s see the implementation of it.

#Program : import numpy as np # numpy array created from a list arr = np.array([40,50,60,70,80,90,40,10,20,40]) print('Numpy Array before deleting any element :') print(arr) #It will delete all the elements which are greater than 40 and less than 80 arr = np.delete(arr, np.argwhere( (arr >= 40) & (arr <= 80) )) print('Modified Numpy Array after deleting :') print(arr)

Output : Numpy Array before deleting any element : [40 50 60 70 80 90 40 10 20 40] Modified Numpy Array after deleting elements : [90 10 20]