Reorder multiple column levels at once in a pandas DataFrame

I am trying to create a report using pandas pivot table and currently I have below code with this output

    df = pd.pivot_table(raw, values=['Shipped','Sold'], index=['Category', 'Model No'], columns=['Customer', 'Week Start Date'], aggfunc=np.sum, fill_value=0)

output

But the output i am desiring is below

how can i make it like the second report?

thank you!

Use MultiIndex.reorder_levels and then sort the column axis using sort_index with axis=1:

df.columns = df.columns.reorder_levels((1, 2, 0))
df = df.sort_index(axis=1)

Example:

np.random.seed(42)
columns = pd.MultiIndex.from_product(
   [['Shipped', 'Sold'], ['A', 'B'], ['d1', 'd2']])
data = np.random.randint(0, 100, size=(5, 8))
df = pd.DataFrame(data, columns=columns)

df   
  Shipped             Sold                # level 0    
        A       B        A       B        # level 1
       d1  d2  d1  d2   d1  d2  d1  d2    # level 2
0      51  92  14  71   60  20  82  86
1      74  74  87  99   23   2  21  52
2       1  87  29  37    1  63  59  20
3      32  75  57  21   88  48  90  58
4      41  91  59  79   14  61  61  46

<!-- -->

# 1st level now 0th, 2nd level now 1st, 0th level now last
df.columns = df.columns.reorder_levels((1, 2, 0))
df = df.sort_index(axis=1)
df
        A                         B                  
       d1           d2           d1           d2     
  Shipped Sold Shipped Sold Shipped Sold Shipped Sold
0      51   60      92   20      14   82      71   86
1      74   23      74    2      87   21      99   52
2       1    1      87   63      29   59      37   20
3      32   88      75   48      57   90      21   58
4      41   14      91   61      59   61      79   46

For posterity I'll also include the option by Quang Hoang in the comments using stack plus unstack:

df.stack(0).unstack(-1)

        A                         B                  
       d1           d2           d1           d2     
  Shipped Sold Shipped Sold Shipped Sold Shipped Sold
0      51   60      92   20      14   82      71   86
1      74   23      74    2      87   21      99   52
2       1    1      87   63      29   59      37   20
3      32   88      75   48      57   90      21   58
4      41   14      91   61      59   61      79   46

Although note that this is generally not a very performant option since it has to actually reshape your DataFrame.