drop rows in Groupby python

Question

I want to get result

df3=df.groupby(['Region']).apply(lambda x: x[x['Region'].isin(["North", "East"])]['Sales'].sum()).reset_index(name='sum')

      Region    sum
0   East    455.0
1   North   665.0
2   South   0.0
3   West    0.0

I want to do drop rows with value = 0 or another conditions

   Region   sum
0   East    455.0
1   North   665.0

i'm pretty sure you can find online how to drop rows where a column value equals to something — sophocles, Dec 11 '22 at 12:23
Minial and reproducible example are needed to help you. https://stackoverflow.com/help/minimal-reproducible-example — Panda Kim, Dec 11 '22 at 12:28

Hans · Answer 1 · 2022-12-12T16:10:25.250

0

You can use df.loc

df[1]!=0 -> True/False filter

df.loc[df[1]!=0] # Apply the filter
df=pd.DataFrame([['East',  455.0],
['North',  665.0],
['South',  0.0],
['West',  0.0]])
df
Out[11]: 
       0      1
0   East  455.0
1  North  665.0
2  South    0.0
3   West    0.0
df.loc[df[1]!=0]
Out[12]: 
       0      1
0   East  455.0
1  North  665.0


Answer to the comment: 
df.rename(columns={0:'region', 1:'sum'}).assign(**{'sum':lambda p:[q if q !=0 else pd.NA for q in p['sum']] }).dropna() (I am not sure if I understood it. Do you mean that?)

edited Dec 12 '22 at 16:10

answered Dec 11 '22 at 13:27

Hans

148
2
7

Thanks, what I want to do is to do it in one command. Without making new command. – 월봉리 Dec 12 '22 at 13:15

score 0 · Answer 2 · answered Dec 12 '22 at 16:14

0

Using df.loc is the easiest method it comes to mind

filtered_df = df3.loc[df3["sum"] != 0]

answered Dec 12 '22 at 16:14

alec_djinn

10,104
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drop rows in Groupby python

2 Answers2