I have the data in dataframes like below. I want to split the item into same number of rows
>>> df
idx a
0 3
1 5
2 4
from above dataframe, I want the below as
>>> df
idx a
0 1
1 2
2 3
3 1
4 2
5 3
6 4
7 5
8 1
9 2
10 3
11 4
I tried several ways, but no success.
Here is a way with series.repeat +Groupby. cumcount assuming idx is the index- if not df.set_index('idx')['a']..rest of the code..
(df['a'].repeat(df['a']).groupby(level=0).cumcount().add(1)
.reset_index(drop=True).rename_axis('idx'))
idx
0 1
1 2
2 3
3 1
4 2
5 3
6 4
7 5
8 1
9 2
10 3
11 4
dtype: int64
A Fun way
df.a.map(range).explode()+1 # may add reset_index(), however, I think keep the original index is good, and help us convert back.
Out[158]:
idx
0 1
0 2
0 3
1 1
1 2
1 3
1 4
1 5
2 1
2 2
2 3
2 4
Name: a, dtype: object
Here's a numpy based one:
a = (np.arange(df.a.max())+1)
m = a <= df.a.values[:,None]
df = pd.DataFrame(m.cumsum(1)[m], columns=['a'])
print(df)
a
0 1
1 2
2 3
3 1
4 2
5 3
6 4
7 5
8 1
9 2
10 3
11 4
pd.DataFrame({'a': [x + 1 for y in df['a'] for x in range(y)]})
a
0 1
1 2
2 3
3 1
4 2
5 3
6 4
7 5
8 1
9 2
10 3
11 4