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+--- Thread: SQL select join operation in python(Select.. join) (/thread-16123.html)
SQL select join operation in python(Select.. join) - pradeepkumarbe - Feb-14-2019
I have data set called Account_universe
ACC_ID ZIP_CODE
001AAD 638400
002ADF 638402
003AAS 638902
004AGF 123480
005AQA 987650
Next data set Alt_Account_univ
ACC_ID ALT_ID SOURCE
001AAD 10000 confd
002ADF 10001 Non confd
003AAS 10002 Non confd
004AGF 10003 Non confd
005AQA 10004 confd
In sql I will do like
Select a.ACCT_ID,b.ALT_ID,a.ZIP_CODE
from Account_universe a
left join Alt_Account_univ b on b.acct_id = a.acct_id
In pandas I will do like
df_a = Account_universe df_b= Alt_Account_univ df1 =df_a.merge(df_b[['ACCT_ID','ALT_ID']],on='ACCT_ID',how='left')What I have tried in python without(pandas)
set(df_a) & set(df_b)Here how to join on specific columns and I have to write only a.ACCT_ID,b.ALT_ID,a.ZIP_CODE these columns in another list. I am aware this set will act like A intersect B any other possible ways to do this operation?.
Thanks in Advance!!
RE: SQL select join operation in python(Select.. join) - woooee - Feb-14-2019
If I understand you it would be something along the lines of
## using you pseudo-code
Select ACCT_ID, ZIP_CODE from a
recs_a=cur_a.fetchall()
select ALT_ID from b
recs_b=cur_b.fetchall()
combined_list=[]
for ctr, row in enumerate(recs_a): ## assumes a and b are the same length
combined_list.append([row[0], recs_b[ctr], row[1]])