how to add to rows with duplicate key in pandas

Question

I have following pandas dataframe

   ID      Quantity     Key     Product   Code   decant
   0       10           12      HS        MT     123
   1       13           13      HS        LT     124
   2       15           13      HS        LT     124
   3       10           14      MS        PQ     145
   4       50           15      MS        PQ     146

My desired dataframe would be

  ID      Quantity     Key     Product   Code    decant
   0       10           12      HS        MT     123 
   1       28           13      HS        LT     124
   2       10           14      MS        PQ     145
   3       50           15      MS        PQ     146

I want add Quantity where Key is duplicated. I know we can do a groupby and add. But is there any simpler way to do it with numpy? Because I have other columns as well,so groupby will not be a suitable solution

U13-Forward · Accepted Answer · 2018-09-23 11:14:35Z

2

Try groupby:

print(df.groupby('Key',as_index=False).sum())

if want to fix ID:

df2=df.groupby('Key',as_index=False).sum()
df2['ID']=df2.index
print(df2)

Update try:

print(df.groupby(['Key','Product','Code'],as_index=False).sum())

If want to fix ID:

df2=df.groupby(['Key','Product','Code'],as_index=False).sum()
df2['ID']=df2.index
print(df2)

edited Sep 23, 2018 at 11:14

answered Sep 23, 2018 at 11:07

U13-Forward

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4 Comments

Neil Over a year ago

I have edited the question. I have other columns as well which are of type object.

U13-Forward Over a year ago

@Neil Edited mine

Neil Over a year ago

I have 9 more numeric columns,in that case what would be the solution? Edited the question just for a reference

Andy Over a year ago

you can use groupby().agg() to aggregate different columns: pandas.pydata.org/pandas-docs/stable/generated/…

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