Convert cells in dataframe with multiple values to multiple rows

Question

My data is like this:

Name    test1     test2      Count
Emp1    X,Y        A           1
Emp2    X          A,B,C       2
Emp3    Z          C           3
<         


        

Answer 1

I don't believe it is that straightforward to adapt this answer highlighted by @wen to this question, so I'll propose a solution.

You might create a function that takes a df, a column to be expanded and a separator for that column, and chain calls as many times as needed.

def expand(df, col, sep=','):
    r = df[col].str.split(sep)
    d = {c: df[c].values.repeat(r.str.len(), axis=0) for c in df.columns}
    d[col] = [i for sub in r for i in sub]
    return pd.DataFrame(d)

expand(expand(df, 'test1'), 'test2')

    Name    test1   test2   Count
0   Emp1    X       A       1
1   Emp1    Y       A       1
2   Emp2    X       A       2
3   Emp2    X       B       2
4   Emp2    X       C       2
5   Emp3    Z       C       3

Suppose you have a

df['test3'] = ['X1|X2|X3', 'X4', 'X5']

such that

>>> print(df)

    Name    test1   test2   Count   test3
0   Emp1    X,Y     A       1       X1|X2|X3
1   Emp2    X       A,B,C   2       X4
2   Emp3    Z       C       3       X5

Then,

>>> expand(df,'test3', '|')

    Name    test1   test2   Count   test3
0   Emp1    X,Y     A       1       X1
1   Emp1    X,Y     A       1       X2
2   Emp1    X,Y     A       1       X3
3   Emp2    X       A,B,C   2       X4
4   Emp3    Z       C       3       X5

If you think columns size may increase substantially, you can define a function expand_all to avoid having something like expand(expand(expand(expand(........)))))). For example:

def expand_all(df, cols, seps):
    ret = df
    for c,s in zip(cols,seps): ret = expand(ret,c,s)
    return ret

>>> expand_all(df, ['test1', 'test2', 'test3'], [',', ',', '|'])

    Name    test1   test2   Count   test3
0   Emp1    X       A       1       X1
1   Emp1    X       A       1       X2
2   Emp1    X       A       1       X3
3   Emp1    Y       A       1       X1
4   Emp1    Y       A       1       X2
5   Emp1    Y       A       1       X3
6   Emp2    X       A       2       X4
7   Emp2    X       B       2       X4
8   Emp2    X       C       2       X4
9   Emp3    Z       C       3       X5

Or however suitable ;)

---

Detail:

>>> expand(df, 'test1')

    Name    test1   test2   Count
0   Emp1    X       A       1
1   Emp1    Y       A       1
2   Emp2    X       A,B,C   2
3   Emp3    Z       C       3

>>> expand(df, 'test2')

    Name    test1   test2   Count
0   Emp1    X,Y     A       1
1   Emp2    X       A       2
2   Emp2    X       B       2
3   Emp2    X       C       2
4   Emp3    Z       C       3

>>> expand(expand(df, 'test2'), 'test1') 

    Name    test1   test2   Count
0   Emp1    X       A       1
1   Emp1    Y       A       1
2   Emp2    X       A       2
3   Emp2    X       B       2
4   Emp2    X       C       2
5   Emp3    Z       C       3


>>> expand(expand(df, 'test2'), 'test1').eq(expand(expand(df, 'test1'), 'test2')).all()

Name     True
test1    True
test2    True
Count    True
dtype: bool