Dropping rows of panadas dataframe based on conditional

Question

Im sure this has been asked before but i cannot phrase it in a way such that I find a similar question. What I would like to do is drop rows of my dataframe where rows with the same item name that all have value of 0 are dropped.

For example. One of my columns is the 'itemname' and the other is the 'value'. the 'itemname' may be repeated many times. I want to check for each 'itemname', if all other items with the same name have value 0, then drop these rows

I know this should be simple, however I cannot get my head around it.

Just to make it clearer, here

    itemname value
0      a       0
1      b       100
2      c       0
3      a       0
4      b       75
5      c       90

As all a items have a value of 0, They should be dropped.

    itemname value
1      b       100
2      c       0
3      b       75
4      c       90

Hope that makes sense. I check if someone else has asked something similar and couldnt find something in this case.

score 1 · Accepted Answer · answered Oct 01 '18 at 14:13

1

Use:

df = df[df.groupby('itemname')['value'].transform('any')]
print (df)
  itemname  value
1        b    100
2        c      0
4        b     75
5        c     90

answered Oct 01 '18 at 14:13

jezrael

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@ jezrael, which version of pandas this tested on, i'm on `0.23.3` and it did not `transform`. – Karn Kumar Oct 01 '18 at 14:42

score 1 · Answer 2 · answered Oct 01 '18 at 14:17

1

Using isin

df[df.itemname.isin(df.loc[df.value!=0,'itemname'])]
Out[543]: 
  itemname  value
1        b    100
2        c      0
4        b     75
5        c     90

answered Oct 01 '18 at 14:17

BENY

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Dropping rows of panadas dataframe based on conditional

2 Answers2