Pandas: Join dataframes on selected columns

Question

I have two data frames as following

 Data Set A ID type msg 1 High Lets do 2 Low whats it 3 Medium thats it Data Set B ID Accounttype 2 Facebook 3 Linkedin

How can I get an updated table with help of join in pandas, it should look like an

Updated DatasetA ID Account type msg 1 High Lets do 2 Facebook Low whats it 3 Linkedin Medium thats it

I can easily do it in SQL with Update and inner join, how to perform it in pandas, I tried to do it, but most of the operations for append/ merge. Any help will be appreciated

Can you post what you tried as it maybe a simple fix, I think that A.merge(B, how='left') should work — EdChum
– EdChum, Commented Jul 4, 2016 at 15:45
So, in reality, I have 100 columns in the right table, and I want just 1 column after the merge, just Account, that's again 1 more complexity — Manu Sharma
– Manu Sharma, Commented Jul 4, 2016 at 16:59

Nerxis · Accepted Answer · 2021-08-06 16:03:11Z

Try this:

df4: # ID type msg # 0 1 High Letsdo # 1 2 Low whatsit # 2 3 Medium thatsit

df3: # ID Accounttype xxx # 0 2 Facebook 24 # 1 3 Linkedin 44

df4.merge(df3[['ID', 'Accounttype']], how='left').fillna("") # ID type msg Accounttype # 0 1 High Letsdo # 1 2 Low whatsit Facebook # 2 3 Medium thatsit Linkedin

Manu Sharma · Accepted Answer · 2016-07-04 17:48:54Z

Seems there is no direct way to do it, so following is suggested

 a=b.merge(account,how='left',on='ID')

create a list of columns you want in final data set

 list=['ID','Account','type','msg'] final=a[[col for col in list if col in b.columns]]

It will give you only desired columns after the left join

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