I have a dataframe in Pandas with duplicate values in Col1:
| Col1 |
|---|
| a |
| a |
| b |
| a |
| a |
| b |
What I want to do is to split this df into different df-s with unique Col1 values in each.
DF1:
| Col1 |
|---|
| a |
| b |
DF2:
| Col1 |
|---|
| a |
| b |
DF3:
| Col1 |
|---|
| a |
DF4:
| Col1 |
|---|
| a |
Any suggestions ?
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1 Answer
I don't think you can achieve this in a vectorial way.
One possibility is to use a custom function to iterate the items and keep track of the unique ones. Then use this to split with groupby:
def cum_uniq(s):
i = 0
seen = set()
out = []
for x in s:
if x in seen:
i+=1
seen = set()
out.append(i)
seen.add(x)
return pd.Series(out, index=s.index)
out = [g for _,g in df.groupby(cum_uniq(df['Col1']))]
output:
[ Col1
0 a,
Col1
1 a
2 b,
Col1
3 a,
Col1
4 a
5 b]
intermediate:
cum_uniq(df['Col1'])
0 0
1 1
2 1
3 2
4 3
5 3
dtype: int64
if order doesn't matter
Let's ad a Col2 to the example:
Col1 Col2
0 a 0
1 a 1
2 b 2
3 a 3
4 a 4
5 b 5
the previous code gives:
[ Col1 Col2
0 a 0,
Col1 Col2
1 a 1
2 b 2,
Col1 Col2
3 a 3,
Col1 Col2
4 a 4
5 b 5]
If order does not matter, you can vectorize it:
out = [g for _,g in df.groupby(df.groupby('Col1').cumcount())]
output:
[ Col1 Col2
0 a 0
2 b 2,
Col1 Col2
1 a 1
5 b 5,
Col1 Col2
3 a 3,
Col1 Col2
4 a 4]
