{"id":144651,"date":"2025-03-28T12:00:24","date_gmt":"2025-03-28T06:30:24","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=144651"},"modified":"2025-03-28T12:00:24","modified_gmt":"2025-03-28T06:30:24","slug":"arithmetic-operations-in-numpy","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/","title":{"rendered":"Arithmetic Operations in NumPy"},"content":{"rendered":"<h3>Program 1<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># Difference between copy and view\r\n# digonal function\r\n#Arithmetic operations beetween  two array\r\n\r\nimport numpy as np\r\n\r\n# Rules of Arithmetic operations in numpy array\r\n# 1. Shape of Arrays must be same\r\n  #     or \r\n# 2. Second Array must have atleast one dimension and \r\n# the number of element should be same as first array\r\n        # or \r\n# 3. Second array having one element \r\n# ar1=np.array([[1,2,3],[4,5,6]])\r\n# ar2=np.array([[1,2,3],[4,5,6]])\r\n# print(ar1 )\r\n# print()\r\n# print(ar2)\r\n# print(\"Addition: \")\r\n# ar3=np.add(ar1,ar2)\r\n# print(ar3)\r\n# print(\"------------------------------\")\r\n# print(\"subtraction: \")\r\n# ar3=np.subtract(ar1,ar2)\r\n# print(ar3)\r\n# print(\"------------------------------\")\r\n# print(\"Multiply: \")\r\n# ar3=np.multiply(ar1,ar2)\r\n# print(ar3)\r\n\r\n# print(\"Divide: \")\r\n# ar3=np.divide(ar1,ar2)\r\n# print(ar3)\r\n\r\n# ar1=np.array([[100,200,300],[45,55,600]])\r\n# print(ar1)\r\n# print()\r\n# ar2=np.mod(ar1,10)\r\n# print(ar2)\r\n\r\n# ar1=np.array([[1,2,3],[4,5,6]])\r\n# ar2=np.power(ar1,2)\r\n# print(ar1)\r\n# print()\r\n# print(ar2)\r\n\r\n# ar1=np.array([[1,2,3],[4,5,6]])\r\n# ar2=np.array(100)\r\n# print(ar1.shape)\r\n# print(ar2.shape)\r\n# ar3=np.add(ar1,ar2)\r\n# print(ar1)\r\n# print(\"-------------------------------'\")\r\n# print(ar2)\r\n# print(\"-------------------------------'\")\r\n# print(ar3)\r\n# print(ar1.shape)\r\n# print(ar2.shape)\r\n\r\n\r\n\r\n# ar=np.array([[1,2,3,10],[4,5,6,10],[7,8,9,10],[10,11,12,13]])\r\n# print(ar)\r\n# print(ar.shape)\r\n# print(ar.diagonal())\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# ar1=np.array([10,20,30,40,50,60])\r\n# ar2=ar1.view()\r\n\r\n# ar1[0]=500\r\n# print(ar1)\r\n# print(\"---------------------------------------\")\r\n# print(ar2)\r\n\r\n# ar2[1]=700\r\n# print(\"******************************\")\r\n# print(ar1)\r\n# print(\"---------------------------------------\")\r\n# print(ar2)\r\n\r\n# print(\"Base of Array1:  \",ar1.base)\r\n# print(\"Base of Array2:  \",ar2.base)\r\n\r\n# ar1=np.array([10,20,30,40,50,60])\r\n# ar2=ar1.copy()\r\n# print(ar1.base)\r\n# print(\"---------------------------------------\")\r\n# print(ar2.base)<\/pre>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 # Difference between copy and view # digonal function #Arithmetic operations beetween two array import numpy as np # Rules of Arithmetic operations in numpy array # 1. Shape of Arrays must&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22401],"tags":[31809,33920,9170,22775,31785,31784,33921,9175],"class_list":["post-144651","post","type-post","status-publish","format-standard","hentry","category-numpy","tag-arithmetic-operations","tag-arithmetic-operations-in-numpy","tag-numpy","tag-numpy-arithmetic-operations","tag-numpy-practical","tag-numpy-program","tag-numpy-program-on-arithmetic-operations","tag-numpy-tutorial"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Arithmetic Operations in NumPy - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Arithmetic Operations in NumPy - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 # Difference between copy and view # digonal function #Arithmetic operations beetween two array import numpy as np # Rules of Arithmetic operations in numpy array # 1. 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Shape of Arrays must&#46;&#46;&#46;","og_url":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2025-03-28T06:30:24+00:00","author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/c187795dc82ab948373cca526df7c445"},"headline":"Arithmetic Operations in NumPy","datePublished":"2025-03-28T06:30:24+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/"},"wordCount":6,"commentCount":0,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"keywords":["arithmetic operations","arithmetic operations in numpy","Numpy","NumPy Arithmetic Operations","numpy practical","numpy program","numpy program on arithmetic operations","NumPy Tutorial"],"articleSection":["NumPy Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/","url":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/","name":"Arithmetic Operations in NumPy - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"datePublished":"2025-03-28T06:30:24+00:00","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/arithmetic-operations-in-numpy\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"NumPy Tutorials","item":"https:\/\/data-flair.training\/blogs\/category\/numpy\/"},{"@type":"ListItem","position":3,"name":"Arithmetic Operations in NumPy"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. 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