Python has Pandas its tool to handle matrices, also known as dataframes in Pyhon speak. It is possible to join dataframes, much like joining tables. I found a nice example on the internet with several possible choices.<\/p>\n\n\n\n
As a first step, two dataframes were declared:<\/p>\n\n\n\n
import pandas as pd<\/pre>\n\n\n\nThe first dataframe:<\/p>\n\n\n\n
customer=pd.DataFrame({
'id':[1,2,3,4,5,6,7,8,9],
'name':['Olivia','Aditya','Cory','Isabell','Dominic','Tyler','Samuel','Daniel','Jeremy'],
'age':[20,25,15,10,30,65,35,18,23],
'Product_ID':[101,0,106,0,103,104,0,0,107],
'Purchased_Product':['Watch','NA','Oil','NA','Shoes','Smartphone','NA','NA','Laptop'],
'City':['Mumbai','Delhi','Bangalore','Chennai','Chennai','Delhi','Kolkata','Delhi','Mumbai']
})<\/pre>\n\n\n\nThe second dataframe:<\/p>\n\n\n\n
product=pd.DataFrame({
'Product_ID':[101,102,103,104,105,106,107],
'Product_name':['Watch','Bag','Shoes','Smartphone','Books','Oil','Laptop'],
'Category':['Fashion','Fashion','Fashion','Electronics','Study','Grocery','Electronics'],
'Price':[299.0,1350.50,2999.0,14999.0,145.0,110.0,79999.0],
'Seller_City':['Delhi','Mumbai','Chennai','Kolkata','Delhi','Chennai','Bengalore']
})<\/pre>\n\n\n\nThe inner join:<\/p>\n\n\n\n
ff_inner = pd.merge(product,customer,on= 'Product_ID')<\/pre>\n\n\n\nAn outer join<\/p>\n\n\n\n
ff_outer = pd.merge(product,customer,left_on='Product_ID',right_on='Product_ID',how='outer',indicator=True)<\/pre>\n\n\n\nThe left join is as follows:<\/p>\n\n\n\n
ff_left = pd.merge(product,customer,left_on=['Product_ID','Seller_City'],right_on=['Product_ID','City'],how='left')<\/pre>\n","protected":false},"excerpt":{"rendered":"Python has Pandas its tool to handle matrices, also known as dataframes in Pyhon speak. It is possible to join dataframes, much like joining tables. I found a nice example on the internet with several possible choices. As a first step, two dataframes were declared: import pandas as pd The first dataframe: customer=pd.DataFrame({‘id’:[1,2,3,4,5,6,7,8,9],’name’:[‘Olivia’,’Aditya’,’Cory’,’Isabell’,’Dominic’,’Tyler’,’Samuel’,’Daniel’,’Jeremy’],’age’:[20,25,15,10,30,65,35,18,23],’Product_ID’:[101,0,106,0,103,104,0,0,107],’Purchased_Product’:[‘Watch’,’NA’,’Oil’,’NA’,’Shoes’,’Smartphone’,’NA’,’NA’,’Laptop’],’City’:[‘Mumbai’,’Delhi’,’Bangalore’,’Chennai’,’Chennai’,’Delhi’,’Kolkata’,’Delhi’,’Mumbai’]}) The second […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3369","post","type-post","status-publish","format-standard","hentry","category-allgemein"],"_links":{"self":[{"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=\/wp\/v2\/posts\/3369","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3369"}],"version-history":[{"count":0,"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=\/wp\/v2\/posts\/3369\/revisions"}],"wp:attachment":[{"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3369"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3369"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/archief.van-maanen.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3369"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}