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NumPy Array Filter

Learn all about NumPy Array Filter in this comprehensive tutorial.

5 min read intermediate

NumPy Array Sort

Random Intro

•Getting some elements out of an existing array and creating a new array out of them is called filtering.
•In the example above we hard-coded the True and False values, but the common use is to create a filter array based on conditions.
•The above example is quite a common task in NumPy and NumPy provides a nice way to tackle it.

Filtering Arrays

Getting some elements out of an existing array and creating a new array out of them is called filtering.

In NumPy, you filter an array using a boolean index list.

Note: A boolean index list is a list of booleans corresponding to indexes in the array.

If the value at an index is True that element is contained in the filtered array, if the value at that index is False that element is excluded from the filtered array.

python

import numpy as nparr = np.array([41, 42, 43, 44])val_1 = [True, 
  False, True, False]newarr = arr[val_1]print(newarr)

The example above will return [41, 43], why?

Because the new array contains only the values where the filter array had the value True, in this case, index 0 and 2.

Creating the Filter Array

In the example above we hard-coded the True and False values, but the common use is to create a filter array based on conditions.

python

import numpy as nparr = np.array([41, 42, 43, 44])# 
  Create an empty listfilter_arr = []# go through each element in 
  arrfor element in arr:  # if the element is higher than 42, set 
  the value to True, otherwise False:  if element > 42:    
  filter_arr.append(True)  else:    filter_arr.append(False)
newarr = arr[filter_arr]print(filter_arr)print(newarr)

python

import numpy as nparr = np.array([1, 2, 3, 4, 5, 6, 7])# 
  Create an empty listfilter_arr = []# go through each element in 
  arrfor element in arr:  # if the element is completely divisble 
  by 2, set the value to True, otherwise False  if element % 2 == 0:    
  filter_arr.append(True)  else:    filter_arr.append(False)
newarr = arr[filter_arr]print(filter_arr)print(newarr)

Creating Filter Directly From Array

The above example is quite a common task in NumPy and NumPy provides a nice way to tackle it.

We can directly substitute the array instead of the iterable variable in our condition and it will work just as we expect it to.

python

import numpy as nparr = np.array([41, 42, 43, 44])
  filter_arr = arr 
  > 42newarr = arr[filter_arr]print(filter_arr)print(newarr)

python

import numpy as nparr = np.array([1, 2, 3, 4, 5, 6, 7])filter_arr = arr 
  % 2 == 0newarr = arr[filter_arr]print(filter_arr)print(newarr)

Module quiz

2 questions

Which of the following is true about NumPy Array Filter?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with NumPy Array Filter?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.

NumPy Array Sort

Random Intro

NumPy Array Filter

AI Summary

Filtering Arrays

Creating the Filter Array

Creating Filter Directly From Array

Module quiz