Converting Column With String Separated Values Into Rows

I am trying to implement the below: I know how to use 2 columns but I need to extend it to more than 2 columns. In other words, var2 and var3 need to be replicated and extended dow

Solution 1:

In one big oneliner:

In [107]: df
Out[107]: 
                var1   var2        var3
0        47429,47404  10700  1403298300
1  23030,23831,23147  99999  1403297100

In [108]: pd.concat((pd.Series((v, row['var2'], row['var3']), df.columns) for _, row in df.iterrows() for v in row['var1'].split(',')), axis=1).T
Out[108]: 
    var1   var2        var3
0  47429  10700  1403298300
1  47404  10700  1403298300
2  23030  99999  1403297100
3  23831  99999  1403297100
4  23147  99999  1403297100

The inner nested generators are the ones doing the trick. They basically doing the same work as these for-loops:

In [112]: for _, row in df.iterrows():
    for v in row['var1'].split(","):
        print (v, row['var2'], row['var3'])
   .....:         
('47429', 10700, 1403298300)
('47404', 10700, 1403298300)
('23030', 99999, 1403297100)
('23831', 99999, 1403297100)
('23147', 99999, 1403297100)

I also added the column-headers of the original data-frame to the produced Series.

Finally, since I'm no pandas expert, I resolved concatenating the series along axis 1 and then transposing the data-frame to get it in the correct structure.