Move values in rows in a new column in pandas

Question

I have a DataFrame with an Ids column an several columns with data, like the column "value" in this example.

For this DataFrame I want to move all the values that correspond to the same id to a new column in the row as shown below:

I guess there is an opposite function to "melt" that allow this, but I'm not getting how to pivot this DF.

The dicts for the input and out DFs are:

d = {"id":[1,1,1,2,2,3,3,4,5],"value":[12,13,1,22,21,23,53,64,9]}
d2 = {"id":[1,2,3,4,5],"value1":[12,22,23,64,9],"value2":[1,21,53,"","",],"value3":[1,"","","",""]}

Answer 1

Create MultiIndex by cumcount, reshape by unstack and add change columns names by add_prefix:

df = (df.set_index(['id',df.groupby('id').cumcount()])['value']
        .unstack()
        .add_prefix('value')
        .reset_index())
print (df)

   id  value0  value1  value2
0   1    12.0    13.0     1.0
1   2    22.0    21.0     NaN
2   3    23.0    53.0     NaN
3   4    64.0     NaN     NaN
4   5     9.0     NaN     NaN

Missing values is possible replace by fillna, but get mixed numeric with strings data, so some function should failed:

df = (df.set_index(['id',df.groupby('id').cumcount()])['value']
        .unstack()
        .add_prefix('value')
        .reset_index()
        .fillna(''))
print (df)

   id  value0 value1 value2
0   1    12.0     13      1
1   2    22.0     21       
2   3    23.0     53       
3   4    64.0              
4   5     9.0