{"id":905,"date":"2024-02-23T13:24:38","date_gmt":"2024-02-23T13:24:38","guid":{"rendered":"https:\/\/learnpython.elegantwallp.com\/?p=905"},"modified":"2024-02-23T13:24:40","modified_gmt":"2024-02-23T13:24:40","slug":"numpy-filter-array","status":"publish","type":"post","link":"https:\/\/learnpython.elegantwallp.com\/2024\/02\/23\/numpy-filter-array\/","title":{"rendered":"NumPy\u00a0Filter Array"},"content":{"rendered":"\n

Filtering Arrays<\/h2>\n\n\n\n

Getting some elements out of an existing array and creating a new array out of them is called filtering<\/em>.<\/p>\n\n\n\n

In NumPy, you filter an array using a boolean index list<\/em>.<\/p>\n\n\n\n

A boolean index list<\/em> is a list of booleans corresponding to indexes in the array.<\/p>\n\n\n\n

If the value at an index is True<\/code> that element is contained in the filtered array, if the value at that index is False<\/code> that element is excluded from the filtered array.<\/p>\n\n\n\n

ExampleGet your own Python Server<\/h3>\n\n\n\n

Create an array from the elements on index 0 and 2:<\/p>\n\n\n\n

import numpy as np

arr = np.array([41, 42, 43, 44])

x = [True, False, True, False]

newarr = arr[x]

print(newarr)<\/code><\/pre>\n\n\n\n
Try it Yourself \u00bb<\/a><\/p>\n\n\n\n
The example above will return [41, 43]<\/code>, why?<\/p>\n\n\n\n
Because the new array contains only the values where the filter array had the value True<\/code>, in this case, index 0 and 2.<\/p>\n\n\n\n
\n\n\n\n
Creating the Filter Array<\/h2>\n\n\n\n
In the example above we hard-coded the True<\/code> and False<\/code> values, but the common use is to create a filter array based on conditions.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
Create a filter array that will return only values higher than 42:<\/p>\n\n\n\n
import numpy as np

arr = np.array([41, 42, 43, 44])

# Create an empty list
filter_arr = []

# go through each element in arr
for 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)<\/code><\/pre>\n\n\n\n
Try it Yourself \u00bb<\/a><\/p>\n\n\n\n
\n\n\n\n
\n\n\n\n
Example<\/h3>\n\n\n\n
Create a filter array that will return only even elements from the original array:<\/p>\n\n\n\n
import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7])

# Create an empty list
filter_arr = []

# go through each element in arr
for 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)<\/code><\/pre>\n\n\n\n
Try it Yourself \u00bb<\/a><\/p>\n\n\n\n
\n\n\n\n
Creating Filter Directly From Array<\/h2>\n\n\n\n
The above example is quite a common task in NumPy and NumPy provides a nice way to tackle it.<\/p>\n\n\n\n
We can directly substitute the array instead of the iterable variable in our condition and it will work just as we expect it to.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
Create a filter array that will return only values higher than 42:<\/p>\n\n\n\n
import numpy as np

arr = np.array([41, 42, 43, 44])

filter_arr = arr > 42

newarr = arr[filter_arr]

print(filter_arr)
print(newarr)<\/code><\/pre>\n\n\n\n
Try it Yourself \u00bb<\/a><\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
Create a filter array that will return only even elements from the original array:<\/p>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np\n\narr = np.array([1,\u00a02,\u00a03,\u00a04,\u00a05,\u00a06,\u00a07])\n\nfilter_arr = arr\u00a0%\u00a02\u00a0==\u00a00\n\nnewarr = arr[filter_arr]\n\nprint(filter_arr)\nprint(newarr)\n\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
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. 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-905","post","type-post","status-publish","format-standard","hentry","category-numpy"],"_links":{"self":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/905","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/comments?post=905"}],"version-history":[{"count":1,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/905\/revisions"}],"predecessor-version":[{"id":906,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/905\/revisions\/906"}],"wp:attachment":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/media?parent=905"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/categories?post=905"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/tags?post=905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}