3

I want to reverse a column values in my dataframe, but only on a individual "groupby" level. Below you can find a minimal demonstration example, where I want to "flip" values that belong the same letter A,B or C: 

df = pd.DataFrame({"group":["A","A","A","B","B","B","B","C","C"],
                   "value": [1,3,2,4,4,2,3,2,5]})

  group  value
0     A      1
1     A      3
2     A      2
3     B      4
4     B      4
5     B      2
6     B      3
7     C      2
8     C      5

My desired output looks like this: (column is added instead of replaced only for the brevity purposes)

  group  value  value_desired
0     A      1              2
1     A      3              3
2     A      2              1
3     B      4              3
4     B      4              2
5     B      2              4
6     B      3              4
7     C      2              5
8     C      5              2

As always, when I don't see a proper vector-style approach, I end messing with loops just for the sake of final output, but my current code hurts me very much: 

for i in list(set(df["group"].values.tolist())):
    reversed_group = df.loc[df["group"]==i,"value"].values.tolist()[::-1]
    df.loc[df["group"]==i,"value_desired"] = reversed_group

Pandas gurus, please show me the way :)

Marjan Moderc
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1 Answers1

5

You can use transform

In [900]: df.groupby('group')['value'].transform(lambda x: x[::-1])
Out[900]:
0    2
1    3
2    1
3    3
4    2
5    4
6    4
7    5
8    2
Name: value, dtype: int64

Details

In [901]: df['value_desired'] = df.groupby('group')['value'].transform(lambda x: x[::-1])

In [902]: df
Out[902]:
  group  value  value_desired
0     A      1              2
1     A      3              3
2     A      2              1
3     B      4              3
4     B      4              2
5     B      2              4
6     B      3              4
7     C      2              5
8     C      5              2
Zero
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  • Thanks! Transform for the rescue! I you happen to know - could you be so kind and show me also apply-style approach, since I am having some troubles distinguishing between the two... – Marjan Moderc Jul 14 '17 at 12:37
  • `df.groupby('group')['value'].apply(lambda x: x[::-1])`? – Zero Jul 14 '17 at 13:11
  • This only worked for me if I added `values` and the end of this line. Otherwise I got thrown error `TypeError: incompatible index of inserted column with frame index`. Looks like transform returns series with original index, apply returns multindex... Thanks anyways! – Marjan Moderc Jul 14 '17 at 13:30