Q:

Convert the data frame from long to wide format and dynamically name columns

I am converting the data frame from long to wide format, however the problem I am facing is generating the right number of translated columns and dynamically renaming the new data frame columns. 
So lets say I have a sample data frame as follows:
data = {'name':['Tom', 'nick', 'Tom', 'nick','Tom'], 'id':[20, 21, 20, 21,22], 'plan' : [100,101,102,101,100], 'drug' : ['a','b','b','c','a']}

df = pd. DataFrame(data)

drug id  name   plan
a    20  Tom    100
b    21  nick   101
b    20  Tom    102
c    21  nick   101
a    22  Tom    100

So for every given name and id I want to create multiple columns for plan and drugs.  For example there are 3 distinct plans and 3 distinct drugs , so ideally I should get 6 new columns which indicate whether a particular plan/drug has been taken or not. 
I tried converting from long to wide but I am not getting the desired result. 
Convert long to wide:
df1 = df.groupby(['name','id'])['plan', 'drug'].apply(lambda x: pd. DataFrame(x.values)).unstack().reset_index()

Actual output:
name    id  0   1    0   1
Tom     20  100 102  a  b
nick    21  101 101  b  c
Tom     22  100 None a  None

Expected output:
   name    age  100 101 102   a   b  c 
   Tom     20   1   0    1    1   1  0
   Tom     22   1   0    0    1   0  0
   nick    21   0   1    0    0   1  1

A:

Use get_dummies with max:
df1 = pd.get_dummies(df.set_index(['name','id']).astype(str)).max(level=[0,1]).reset_index()
print(df1)
   name  id  plan_100  plan_101  plan_102  drug_a  drug_b  drug_c
0   Tom  20         1         0         1       1       1       0
1  nick  21         0         1         0       0       1       1
2   Tom  22         1         0         0       1       0       0

df2 = (pd.get_dummies(df.set_index(['name','id'])
        .astype(str), prefix='', prefix_sep='')
        .max(level=[0,1])
        .reset_index())
print(df2)
   name  id  100  101  102  a  b  c
0   Tom  20    1    0    1  1  1  0
1  nick  21    0    1    0  0  1  1
2   Tom  22    1    0    0  1  0  0

EDIT: Solution with DataFrame.pivot_table, concat and DataFrame.clip:
df1 = df.pivot_table(index=['name','id'], 
                     columns=['plan'], 
                      aggfunc='size',
                      fill_value=0)

df2 = df.pivot_table(index=['name','id'], 
                     columns=['drug'], 
                      aggfunc='size',
                      fill_value=0)

df = pd.concat([df1, df2], axis=1).clip(upper=1).reset_index()
print(df)
   name  id  100  101  102  a  b  c
0   Tom  20    1    0    1  1  1  0
1   Tom  22    1    0    0  1  0  0
2  nick  21    0    1    0  0  1  1

