Pandas column reformatting

This is a little bit complicate

(df.set_index('Code')
   .Items.str.split(';',expand=True)
   .stack()
   .str.split('-',expand=True)
   .set_index(0,append=True)[1]
   .unstack()
   .fillna('')
   .sum(level=0))

0       ca  co  eq  go   tp
Code                       
123             hk         
147         ml      ml     
258     us                 
321                 ch     
369   usmy                 
456     eu              lbe
654     au          au     
741     us                 
789     us                 
852     eu                 
963     ml  ml      ml     
987                 jp     


# using str split to get unnest the column, 
#then we do stack, and str split again , then set the first column to index 
# after unstack we yield the result  

List comprehensions work better (read: much faster) for string problems like this which require multiple levels of splitting.

df2 = pd.DataFrame([
         dict(y.split('-') for y in x.split('; ')) 
           for x in df.Items]).fillna('')
df2.insert(0, 'Code', df.Code)

print(df2)
    Code  ca  co  eq  go   tp
0    123          hk         
1    456  eu              lbe
2    789  us                 
3    321              ch     
4    654  au          au     
5    987              jp     
6    147      ml      ml     
7    258  us                    # Should be "us,my"... see below.
8    369  my                 
9    741  us                 
10   852  eu                 
11   963  ml  ml      ml   

This does not handle the situation where multiple items with the same key can be present in a row. For that, a slightly more involved solution is needed.

from itertools import chain

v = [x.split('; ') for x in df.Items] 
X = pd.Series(df.Code.values.repeat([len(x) for x in v]))
Y = pd.DataFrame([x.split('-') for x in chain.from_iterable(v)])

df2 = pd.concat([X, Y], axis=1, ignore_index=True)

(df2.set_index([0, 1, 3])[2]
    .unstack(1)
    .fillna('')
    .groupby(level=0)
    .agg(lambda x: ','.join(x).strip(','))

1       ca  co  eq  go   tp
0                          
123             hk         
147         ml      ml     
258     us                 
321                 ch     
369  us,my                 
456     eu              lbe
654     au          au     
741     us                 
789     us                 
852     eu                 
963     ml  ml      ml     
987                 jp    

import pandas as pd
df = pd.DataFrame([
    ('123', 'eq-hk'),
    ('456', 'ca-eu; tp-lbe'),
    ('789', 'ca-us'),
    ('321', 'go-ch'),
    ('654', 'ca-au; go-au'),
    ('987', 'go-jp'),
    ('147', 'co-ml; go-ml'),
    ('258', 'ca-us'),
    ('369', 'ca-us; ca-my'),
    ('741', 'ca-us'),
    ('852', 'ca-eu'),
    ('963', 'ca-ml; co-ml; go-ml')],
    columns=['Code', 'Items'])


# Get item type list from each row, sum (concatenate) the lists and convert
# to a set to remove duplicates 
item_types = set(df['Items'].str.findall('(\w+)-').sum())
print(item_types)
# {'ca', 'co', 'eq', 'go', 'tp'}

# Generate a column for each item type
df1 = pd.DataFrame(df['Code'])
for t in item_types:
    df1[t] = df['Items'].str.findall('%s-(\w+)' % t).apply(lambda x: ''.join(x))
print(df1)

#   Code    ca   tp  eq  co  go
#0   123             hk        
#1   456    eu  lbe            
#2   789    us                 
#3   321                     ch
#4   654    au               au
#5   987                     jp
#6   147                 ml  ml
#7   258    us                 
#8   369  usmy                 
#9   741    us                 
#10  852    eu                 
#11  963    ml           ml  ml