I'm quite new to pandas dataframes, and I'm experiencing some troubles joining two tables.
The first df has just 3 columns:
DF1:
item_id position document_id
336 1 10
337 2 10
338 3 10
1001 1 11
1002 2 11
1003 3 11
38 10 146
And the second has exactly same two columns (and plenty of others):
DF2:
item_id document_id col1 col2 col3 ...
337 10 ... ... ...
1002 11 ... ... ...
1003 11 ... ... ...
What I need is to perform an operation which, in SQL, would look as follows:
DF1 join DF2 on
DF1.document_id = DF2.document_id
and
DF1.item_id = DF2.item_id
And, as a result, I want to see DF2, complemented with column 'position':
item_id document_id position col1 col2 col3 ...
What is a good way to do this using pandas?
I think you need merge with default inner join, but is necessary no duplicated combinations of values in both columns:
print (df2)
item_id document_id col1 col2 col3
0 337 10 s 4 7
1 1002 11 d 5 8
2 1003 11 f 7 0
df = pd.merge(df1, df2, on=['document_id','item_id'])
print (df)
item_id position document_id col1 col2 col3
0 337 2 10 s 4 7
1 1002 2 11 d 5 8
2 1003 3 11 f 7 0
But if necessary position column in position 3:
df = pd.merge(df2, df1, on=['document_id','item_id'])
cols = df.columns.tolist()
df = df[cols[:2] + cols[-1:] + cols[2:-1]]
print (df)
item_id document_id position col1 col2 col3
0 337 10 2 s 4 7
1 1002 11 2 d 5 8
2 1003 11 3 f 7 0