ENH: (explode) Splitting a column content over multiple rows while duplicating other columns content to these rows

[BLMeltdown]

Hello
I know it is not a problem per se but I think there should be some pandas built in solution for this problem, as no simple function exists for this.
It is largely discussed here with various ideas (https://stackoverflow.com/questions/12680754/split-pandas-dataframe-string-entry-to-separate-rows) but they are quite tricky actually, not using built in things but hacking normal behavior for the most part.

I believe that could use some strategy drawn from the reindexing/filling functions, but as I am not a Pandas specialist, I am not sure this would use the most efficient function to perform this task.

Let's say we have

df

var1  var2

0 a,b,c x
1 d,e,f y

df.desired_function("var1")

var1 var2
0 a x
1 b x
2 c x
3 d y
4 e y
5 f y

Best regards

[jreback]

can you post a copy-pastable example. having these in-line in the top description (as well as a link) is useful.

[BLMeltdown]

Done;)

[BLMeltdown]

d=pd.DataFrame(list(zip(*s2.str.split(","))))
Out[93]: 
     0    1    2
0   a2   b2   c2
1   a3   b3   c3

d.stack()
>

0  0     a2
   1     b2
   2     c2
1  0     a3
   1     b3
   2     c3

Is a good starting point, but i guess that reindexing the other columns is not that easy. And anyway, it would be better to use a dedicated algorithm than to make a general call to various functions.

[jreback]

you are asking for explode: see some other references here: #8517 (comment)

sure I think we could have this as a string method. It is generally useful.

[BLMeltdown]

Thanks a lot! yes, this is it. actually the most satisfactory method I found is from SO InoDB (https://stackoverflow.com/users/1894184/inodb)

a=pd.DataFrame({"var1":"a,b,c d,e,f".split(),"var2":[1,2]})
s = a.var1.str.split(",").apply(pd.Series, 1).stack()
s.index = s.index.droplevel(-1)
del a['var1']
a.join(s)
var2 var1
0 1 a
0 1 b
0 1 c
1 2 d
1 2 e
1 2 f

(I came up with the stack and before, I was not good with reindexing and joining)

But anyway a general purpose function with adjustable setting will be nice. Thanks!

[Dr-Irv]

Caught this on pandas-dev-request, and there is a similar feature that I need with respect to indexing.
Here's an example. In this example, I have a Series that has a MultiIndex with names 'ab' and 'ott'. Now I want to repeat that Series for every element in the list xy. So the code I use to do this is as follows:

In [2]: idx = pd.MultiIndex.from_product([['a','b'], [1,2,3]], names=['ab
   ...: ','ott'])
   ...: s = pd.Series(range(6), index=idx)
   ...: s
Out[2]:
ab  ott
a   1      0
    2      1
    3      2
b   1      3
    2      4
    3      5
dtype: int64

In [3]: xy = ['x', 'y']
   ...: (s
   ...:  .to_frame('s')
   ...:  .assign(one=1)
   ...:  .reset_index()
   ...:  .merge(pd.Series(1, index=pd.Index(xy,name='xy'))
   ...:         .to_frame('one').reset_index(), on='one')
   ...:  .set_index(s.index.names+['xy'])
   ...:  .drop(columns=['one'])
   ...: )
Out[3]:
           s
ab ott xy
a  1   x   0
       y   0
   2   x   1
       y   1
   3   x   2
       y   2
b  1   x   3
       y   3
   2   x   4
       y   4
   3   x   5
       y   5

I'd like to "explode" the Series (or a DataFrame ) from the existing MultiIndex by adding a level to the MultiIndex using a list (or any sequence) that I provide. Alternatively, just adding 'xy' as column would work as well.