Reputation: 8604
In Pandas
, I am trying to add a new column / update an existing column to a data frame (DF2) with a value from another data frame (DF1). I can think of how to do this in SQL
UPDATE DF2
SET DF2['Column'] = DF1['Column']
FROM DF2
JOIN DF1 ON DF1['NonIndexColumn'] = DF2['NonIndexColumn']
Data Example:
d =[{'CustomerID': 1, 'SignUpDate': '2014-01-01'}, {'CustomerID': 2, 'SignUpDate': '2014-02-01'}, {'CustomerID': 3, 'SignUpDate': '2014-03-01'}, {'CustomerID': 4, 'SignUpDate': '2014-04-01'}]
DF1 = pd.DataFrame(data=d)
d2 = [{'OrderID': 1, 'CustomerID': 1, 'OrderDate': '2014-01-15'}, {'OrderID': 2, 'CustomerID': 1, 'OrderDate': '2014-01-15'}, {'OrderID': 3, 'CustomerID': 2, 'OrderDate': '2014-03-28'}, {'OrderID': 4, 'CustomerID': 1, 'OrderDate': '2014-03-29'}, {'OrderID': 5, 'CustomerID': 3, 'OrderDate': '2014-04-28'}, {'OrderID': 6, 'CustomerID': 2, 'OrderDate': '2014-06-01'}, {'OrderID': 7, 'CustomerID': 1, 'OrderDate': '2014-11-06'}, {'OrderID': 8, 'CustomerID': 3, 'OrderDate': '2015-01-28'}, {'OrderID': 9, 'CustomerID': 1, 'OrderDate': '2015-02-15'} ]
DF2 = pd.DataFrame(data=d2)
I am trying to add DF1['SignUpDate']
on to DF2
, so that DF2
would look like this:
CustomerID OrderDate OrderID SignUpDate
0 1 2014-01-15 1 2014-01-01
1 1 2014-01-15 2 2014-01-01
2 2 2014-03-28 3 2014-02-01
3 1 2014-03-29 4 2014-01-01
4 3 2014-04-28 5 2014-03-01
5 2 2014-06-01 6 2014-02-01
6 1 2014-11-06 7 2014-01-01
7 3 2015-01-28 8 2014-03-01
8 1 2015-02-15 9 2014-01-01
I know the merge
would allow me to add the column, but I would have to either overwrite the existing DF or create a new one, like this:
DF1 = pd.merge(DF1, DF2) #overwrite
DF3 = pd.merge(DF1, DF2) #new dataframe
Is there not a way to join on one field (maybe an indexed column, maybe not an indexed column) and update / add the field?
Upvotes: 0
Views: 1791
Reputation: 394459
Perform a left merge
:
In [4]:
DF2.merge(DF1, on='CustomerID', how='left')
Out[4]:
CustomerID OrderDate OrderID SignUpDate
0 1 2014-01-15 1 2014-01-01
1 1 2014-01-15 2 2014-01-01
2 2 2014-03-28 3 2014-02-01
3 1 2014-03-29 4 2014-01-01
4 3 2014-04-28 5 2014-03-01
5 2 2014-06-01 6 2014-02-01
6 1 2014-11-06 7 2014-01-01
7 3 2015-01-28 8 2014-03-01
8 1 2015-02-15 9 2014-01-01
Upvotes: 1