{"id":129394,"date":"2023-12-14T13:55:41","date_gmt":"2023-12-14T08:25:41","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=129394"},"modified":"2023-12-14T13:55:41","modified_gmt":"2023-12-14T08:25:41","slug":"practical-implementation-of-aggregate-functions-in-pandas","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/","title":{"rendered":"Practical Implementation of Aggregate Functions in Pandas"},"content":{"rendered":"

Program 1<\/h3>\n
import pandas as pd\r\nproduct=[('Limca',25,'BK'),('Limca',40,'NM'),('Frooti',20,'AK'),\r\n          ('BMilk',20,'Amul'),('Milk',20,'Amul'),('BMilk',18,'Sanchi'),\r\n          ('Limca',15,'AK'),('Frooti',17,'BK'),('BMilk',23,'Sanchi'),\r\n          ('Milk',27,'NK'),('Frooti',23,'BK'),('BMilk',23,'Sanchi'),\r\n         ]\r\ndf=pd.DataFrame(product,columns=['Name','Price','Distb'])\r\n#print(df)\r\ndf1=df.groupby('Name')\r\n# for name,rows in df1:\r\n#     print(name)\r\n#     print(rows)\r\nprint(df1['Price'].agg(['max','min','mean','count']))<\/pre>\n
 <\/p>\n","protected":false},"excerpt":{"rendered":"
Program 1 import pandas as pd product=[(‘Limca’,25,’BK’),(‘Limca’,40,’NM’),(‘Frooti’,20,’AK’), (‘BMilk’,20,’Amul’),(‘Milk’,20,’Amul’),(‘BMilk’,18,’Sanchi’), (‘Limca’,15,’AK’),(‘Frooti’,17,’BK’),(‘BMilk’,23,’Sanchi’), (‘Milk’,27,’NK’),(‘Frooti’,23,’BK’),(‘BMilk’,23,’Sanchi’), ] df=pd.DataFrame(product,columns=[‘Name’,’Price’,’Distb’]) #print(df) df1=df.groupby(‘Name’) # for name,rows in df1: # print(name) # print(rows) print(df1[‘Price’].agg([‘max’,’min’,’mean’,’count’]))  <\/p>\n","protected":false},"author":86671,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19475],"tags":[30085,30086,30038,30084,10755],"class_list":["post-129394","post","type-post","status-publish","format-standard","hentry","category-pandas","tag-aggregate-functions-in-pandas","tag-pandas-aggregate-functions","tag-pandas-practical","tag-practical-implementation-of-aggregate-functions-in-pandas","tag-python-pandas"],"yoast_head":"\nPractical Implementation of Aggregate Functions in Pandas - 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\/practical-implementation-of-aggregate-functions-in-pandas\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Practical Implementation of Aggregate Functions in Pandas - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 import pandas as pd product=[('Limca',25,'BK'),('Limca',40,'NM'),('Frooti',20,'AK'), ('BMilk',20,'Amul'),('Milk',20,'Amul'),('BMilk',18,'Sanchi'), ('Limca',15,'AK'),('Frooti',17,'BK'),('BMilk',23,'Sanchi'), ('Milk',27,'NK'),('Frooti',23,'BK'),('BMilk',23,'Sanchi'), ] df=pd.DataFrame(product,columns=['Name','Price','Distb']) #print(df) df1=df.groupby('Name') # for name,rows in df1: # print(name) # print(rows) print(df1['Price'].agg(['max','min','mean','count']))  \" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-14T08:25:41+00:00\" \/>\n<meta name=\"author\" content=\"TechVidvan Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"TechVidvan Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Practical Implementation of Aggregate Functions in Pandas - DataFlair","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/","og_locale":"en_US","og_type":"article","og_title":"Practical Implementation of Aggregate Functions in Pandas - DataFlair","og_description":"Program 1 import pandas as pd product=[('Limca',25,'BK'),('Limca',40,'NM'),('Frooti',20,'AK'), ('BMilk',20,'Amul'),('Milk',20,'Amul'),('BMilk',18,'Sanchi'), ('Limca',15,'AK'),('Frooti',17,'BK'),('BMilk',23,'Sanchi'), ('Milk',27,'NK'),('Frooti',23,'BK'),('BMilk',23,'Sanchi'), ] df=pd.DataFrame(product,columns=['Name','Price','Distb']) #print(df) df1=df.groupby('Name') # for name,rows in df1: # print(name) # print(rows) print(df1['Price'].agg(['max','min','mean','count']))  ","og_url":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2023-12-14T08:25:41+00:00","author":"TechVidvan Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"TechVidvan Team","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/"},"author":{"name":"TechVidvan Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/0e594f928e31fc96628ac40f6ae74f49"},"headline":"Practical Implementation of Aggregate Functions in Pandas","datePublished":"2023-12-14T08:25:41+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/"},"wordCount":9,"commentCount":0,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"keywords":["aggregate functions in pandas","pandas aggregate functions","pandas practical","Practical Implementation of Aggregate Functions in Pandas","Python Pandas"],"articleSection":["Pandas Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/","url":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/","name":"Practical Implementation of Aggregate Functions in Pandas - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"datePublished":"2023-12-14T08:25:41+00:00","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/practical-implementation-of-aggregate-functions-in-pandas\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"Pandas Tutorials","item":"https:\/\/data-flair.training\/blogs\/category\/pandas\/"},{"@type":"ListItem","position":3,"name":"Practical Implementation of Aggregate Functions in Pandas"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. Lead Tomorrow.","publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/data-flair.training\/blogs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/data-flair.training\/blogs\/#organization","name":"DataFlair","url":"https:\/\/data-flair.training\/blogs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","width":106,"height":48,"caption":"DataFlair"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataFlairWS\/","https:\/\/x.com\/DataFlairWS","https:\/\/www.linkedin.com\/company\/dataflair-web-services-pvt-ltd\/","https:\/\/www.youtube.com\/user\/DataFlairWS"]},{"@type":"Person","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/0e594f928e31fc96628ac40f6ae74f49","name":"TechVidvan Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/c89190da3d4010c71ba476b618ab10fdc2335c82cdfa0ad5002d98d0f2473444?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/c89190da3d4010c71ba476b618ab10fdc2335c82cdfa0ad5002d98d0f2473444?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/c89190da3d4010c71ba476b618ab10fdc2335c82cdfa0ad5002d98d0f2473444?s=96&d=mm&r=g","caption":"TechVidvan Team"},"description":"TechVidvan Team provides high-quality content & courses on AI, ML, Data Science, Data Engineering, Data Analytics, programming, Python, DSA, Android, Flutter, full stack web dev, MERN, and many latest technology.","url":"https:\/\/data-flair.training\/blogs\/author\/test001\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/129394","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/users\/86671"}],"replies":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/comments?post=129394"}],"version-history":[{"count":3,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/129394\/revisions"}],"predecessor-version":[{"id":132144,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/129394\/revisions\/132144"}],"wp:attachment":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media?parent=129394"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/categories?post=129394"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/tags?post=129394"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}