{"id":901,"date":"2024-02-23T13:22:26","date_gmt":"2024-02-23T13:22:26","guid":{"rendered":"https:\/\/learnpython.elegantwallp.com\/?p=901"},"modified":"2024-02-23T13:22:29","modified_gmt":"2024-02-23T13:22:29","slug":"numpy-joining-array","status":"publish","type":"post","link":"https:\/\/learnpython.elegantwallp.com\/2024\/02\/23\/numpy-joining-array\/","title":{"rendered":"NumPy\u00a0Joining Array"},"content":{"rendered":"\n

Joining NumPy Arrays<\/h2>\n\n\n\n

Joining means putting contents of two or more arrays in a single array.<\/p>\n\n\n\n

In SQL we join tables based on a key, whereas in NumPy we join arrays by axes.<\/p>\n\n\n\n

We pass a sequence of arrays that we want to join to the concatenate()<\/code> function, along with the axis. If axis is not explicitly passed, it is taken as 0.<\/p>\n\n\n\n

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

Join two arrays<\/p>\n\n\n\n

import\u00a0numpy\u00a0as\u00a0np\n\narr1 = np.array([1,\u00a02,\u00a03])\n\narr2 = np.array([4,\u00a05,\u00a06])\n\narr = np.concatenate((arr1, arr2))\n\nprint(arr)\n\n<\/code><\/pre>\n\n\n\n
Example<\/h3>\n\n\n\n
Join two 2-D arrays along rows (axis=1):<\/p>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np

arr1 = np.array([[1,\u00a02], [3,\u00a04]])

arr2 =\u00a0np.array([[5,\u00a06], [7,\u00a08]])

arr = np.concatenate((arr1, arr2), axis=1)

print(arr)<\/code><\/pre>\n\n\n\n
\n\n\n\n
Joining Arrays Using Stack Functions<\/h2>\n\n\n\n
Stacking is same as concatenation, the only difference is that stacking is done along a new axis.<\/p>\n\n\n\n
We can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking.<\/p>\n\n\n\n
We pass a sequence of arrays that we want to join to the stack()<\/code> method along with the axis. If axis is not explicitly passed it is taken as 0.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np\n\narr1 = np.array([1,\u00a02,\u00a03])\n\narr2 =\u00a0np.array([4,\u00a05,\u00a06])\n\narr = np.stack((arr1, arr2), axis=1)\n\nprint(arr)\n\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
\n\n\n\n
Stacking Along Rows<\/h2>\n\n\n\n
NumPy provides a helper function: hstack()<\/code> to stack along rows.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
import numpy as np

arr1 = np.array([1, 2, 3])

arr2 = np.array([4, 5, 6])

arr = np.hstack((arr1, arr2))

print(arr)<\/code><\/pre>\n\n\n\n
Try it Yourself \u00bb<\/a><\/p>\n\n\n\n
\n\n\n\n
Stacking Along Columns<\/h2>\n\n\n\n
NumPy provides a helper function: vstack() <\/code> to stack along columns.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np\n\narr1 = np.array([1,\u00a02,\u00a03])\n\narr2 = np.array([4,\u00a05,\u00a06])\n\narr = np.vstack((arr1, arr2))\n\nprint(arr)\n\n<\/code><\/pre>\n\n\n\n
\n\n\n\n
Stacking Along Height (depth)<\/h2>\n\n\n\n
NumPy provides a helper function: dstack()<\/code> to stack along height, which is the same as depth.<\/p>\n\n\n\n
Example<\/h3>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np

arr1 = np.array([1,\u00a02,\u00a03])

arr2 = np.array([4,\u00a05,\u00a06])

arr = np.dstack((arr1, arr2))

print(arr)<\/code><\/pre>\n\n\n\n
\n\n\n\n
Test Yourself With Exercises<\/h2>\n\n\n\n
Exercise:<\/h2>\n\n\n\n
Use a correct NumPy method to join two arrays into a single array.arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) arr = np.((arr1, arr2))<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
Joining NumPy Arrays Joining means putting contents of two or more arrays in a single array. In SQL we join tables based on a key, whereas in NumPy we join arrays by axes. We pass a sequence of arrays that we want to join to the concatenate() function, along with the axis. If axis is not explicitly […]<\/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-901","post","type-post","status-publish","format-standard","hentry","category-numpy"],"_links":{"self":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/901","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=901"}],"version-history":[{"count":1,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/901\/revisions"}],"predecessor-version":[{"id":902,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/posts\/901\/revisions\/902"}],"wp:attachment":[{"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/media?parent=901"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/categories?post=901"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/learnpython.elegantwallp.com\/wp-json\/wp\/v2\/tags?post=901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}