I have a DF such as the following:
df =
vid pos value sente
1 a A 21
2 b B 21
3 b A 21
3 a A 21
1 d B 22
1 a C 22
1 a D 22
2 b A 22
3 a A 22
Now I want to combine all rows with the same value for sente and vid into one row with the values for value joined by an " "
df2 =
vid pos value sente
1 a A 21
2 b B 21
3 b a A A 21
1 d a a B C D 22
2 b A 22
3 a A 22
I suppose a modification of this should do the trick:
df2 = df.groupby["sente"].agg(lambda x: " ".join(x))
But I can't seem to figure out how to add the second column to the statement.
Groupers can be passed as lists. Furthermore, you can simplify your solution a bit by ridding your code of the lambda—it isn't needed.
df.groupby(['vid', 'sente'], as_index=False, sort=False).agg(' '.join)
vid sente pos value
0 1 21 a A
1 2 21 b B
2 3 21 b a A A
3 1 22 d a a B C D
4 2 22 b A
5 3 22 a A
Some other notes: specifying as_index=False means your groupers will be present as columns in the result (and not as the index, as is the default). Furthermore, sort=False will preserve the original order of the columns.
As of this edit, @cᴏʟᴅsᴘᴇᴇᴅ's answer is way better.
Fun Way! Only works because single char values
df.set_index(['sente', 'vid']).sum(level=[0, 1]).applymap(' '.join).reset_index()
sente vid pos value
0 21 1 a A
1 21 2 b B
2 21 3 b a A A
3 22 1 d a a B C D
4 22 2 b A
5 22 3 a A
somewhat ok answer
df.set_index(['sente', 'vid']).groupby(level=[0, 1]).apply(
lambda d: pd.Series(d.to_dict('l')).str.join(' ')
).reset_index()
sente vid pos value
0 21 1 a A
1 21 2 b B
2 21 3 b a A A
3 22 1 d a a B C D
4 22 2 b A
5 22 3 a A
not recommended
df.set_index(['sente', 'vid']).add(' ') \
.sum(level=[0, 1]).applymap(str.strip).reset_index()
sente vid pos value
0 21 1 a A
1 21 2 b B
2 21 3 b a A A
3 22 1 d a a B C D
4 22 2 b A
5 22 3 a A
来源：https://stackoverflow.com/questions/50357837/aggregating-string-columns-using-pandas-groupby