Pandas groupby and aggregation output should include all the original columns (including the ones not aggregated on)

Question

I have the following data frame and want to:

• Group records by month
• Sum QTY_SOLDand NET_AMT of each unique

Answer 1

agg with a dict of functions

Create a dict of functions and pass it to agg. You'll also need as_index=False to prevent the group columns from becoming the index in your output.

f = {'NET_AMT': 'sum', 'QTY_SOLD': 'sum', 'UPC_DSC': 'first'}
df.groupby(['month', 'UPC_ID'], as_index=False).agg(f)

     month  UPC_ID UPC_DSC  NET_AMT  QTY_SOLD
0  2017.02     111   desc1       10         2
1  2017.02     222   desc2       15         3
2  2017.02     333   desc3        4         1
3  2017.03     111   desc1       25         5

---

Blanket sum

Just call sum without any column names. This handles the numeric columns. For UPC_DSC, you'll need to handle it separately.

g = df.groupby(['month', 'UPC_ID'])
i = g.sum()
j = g[['UPC_DSC']].first()

pd.concat([i, j], 1).reset_index()

     month  UPC_ID  QTY_SOLD  NET_AMT UPC_DSC
0  2017.02     111         2       10   desc1
1  2017.02     222         3       15   desc2
2  2017.02     333         1        4   desc3
3  2017.03     111         5       25   desc1

Answer 2

I am thinking about this long time, thanks for your question push me to make it .By using agg and if...else

df.groupby(['month', 'UPC_ID'],as_index=False).agg(lambda x : x.sum() if x.dtype=='int64' else x.head(1))
Out[1221]: 
   month  UPC_ID UPC_DSC     D_DATE  QTY_SOLD  NET_AMT
0      2     111   desc1 2017-02-26         2       10
1      2     222   desc2 2017-02-26         3       15
2      2     333   desc3 2017-02-26         1        4
3      3     111   desc1 2017-03-01         5       25