Convert columns into rows and insert missing values

lets say i have this table

df = pd.DataFrame({
               'name':["a","b","c"],
               'from':[1,1,2],
               'to':[3,4,3]
    })

what i am trying is to have this

df_result = pd.DataFrame({
               'name':["a","a","a","b","b","b","b","c","c"],
               'key':[1,2,3,1,2,3,4,2,3]
    })

any ideas? i could simply use the melt function but then how do i fill the missing values?

>Solution :

You can create helper column key by range and then DataFrame.explode:

df = (df.assign(key = df.apply(lambda x: range(x['from'], x['to'] + 1), 1))
        .explode('key')[['name','key']]
        .reset_index(drop=True))
print (df)
  name key
0    a   1
1    a   2
2    a   3
3    b   1
4    b   2
5    b   3
6    b   4
7    c   2
8    c   3

Or if need better performance use Index.repeat with DataFrame.loc and then use GroupBy.cumcount with add column from for column key:

df = df.assign(key = df['to'].add(1).sub(df['from']))
df = df.loc[df.index.repeat(df['key'])]
df['key'] = df.groupby(level=0).cumcount().add(df['from'])
df = df[['name','key']].reset_index(drop=True)
print (df)
  name  key
0    a    1
1    a    2
2    a    3
3    b    1
4    b    2
5    b    3
6    b    4
7    c    2
8    c    3