Pandas change in df operation in major version?

I have a piece of code that worked fine in Pandas v1.4.2, but throws an error in 2.0.3.

So I have a dataframe order_lines with order lines, each line identified by unique EAN code (GTIN) and having number of bottles with that GTIN, and how many bottles fit in a box.

 lineitemid |     gtin      | items_per_box | box_qty | qty
------------+---------------+---------------+---------+------
          1 | 8002793000504 |             6 |     100 |  600
          2 | 8002793000597 |             6 |     200 | 1200

And a simple list of stamps stamp_lines: GTIN - unique stamp[,...] like this:

    gtin      |             datamatrix_stamp             
--------------+------------------------------------
8002793000504 | 010406770001455921fx9p;Fq[GS]N1
8002793000504 | 010406770001455921Cpp;_e![GS]JX
8002793000504 | 010406770001455921_Y"<anI[GS]9W
...

And I had a loop that generated for each stamp, which box it was in, what was the box range, and which stamp was which in the box, like this:

     gtin      | box_number | box_range | stamp_nr_in_box | datamatrix_stamp
---------------+------------+-----------+-----------------+-------------
 8002793000504 |          1 | 1-6       |               1 | 010406770001455921fx9p;Fq[GS]N1
 8002793000504 |          1 | 1-6       |               2 | 010406770001455921Cpp;_e![GS]JX
 8002793000504 |          1 | 1-6       |               3 | 010406770001455921_Y"<anI[GS]9W
 8002793000504 |          1 | 1-6       |               4 | ...
 8002793000504 |          1 | 1-6       |               5 | ...
 8002793000504 |          1 | 1-6       |               6 | ...
 8002793000504 |          2 | 7-15      |               7 | ...
 8002793000504 |          2 | 7-15      |               8 | ...
 8002793000504 |          2 | 7-15      |               9 | ...
 8002793000504 |          2 | 7-15      |              10 | ...

The code to do it was like this:

	stamp_lines['gtin_num']=stamp_lines.groupby(['gtin']).cumcount() # list and number all stamps grouped by the GTIN 
	for i,row in self.order_lines.iterrows(): # go through order lines
		step=int(row['items_per_box'])  # for each order line see how many bottles in box
		bottles=int(row['qty']) # total number of boxes for that order line 
		box=1 # start numbering with 1 for human readouts
		if bottles <=0 or step <=0: 
			raise ValueError(f"Missing quantity of bottles in line {i-1}.")
		for n in range(0,bottles, step):   # do this for each box of bottles
			if n+step > bottles: # if last box is not full
				step=bottles-n
			# fill in box_number,stamp_nr_in_box and box_range fields for 
			# appropriate stamp_lines column with respective numbers and ranges:
			stamp_lines.loc[( stamp_lines['gtin']==row['gtin'] ) 
						& (stamp_lines['gtin_num'].isin(range(n,n+step))),
							['box_number', 'stamp_nr_in_box','box_range']] = box, range(n+1, n+step+1), '{}-{}'.format(n+1,n+step)
			box+=1

This worked fine in 1.4.2.

As I upgraded to Pandas 2, however, the line stamp_lines.loc[( stamp_lines['gtin']==row['gtin'] )

now throws out an error:

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (3,) + inhomogeneous part.

I looked into this, but can't seem to understand, what seems to be the problem? Can I not now use range() and scalars together to fill out .loc[] ?

Turns out, I needed to make the thing into dataframe first.

So, instead of this:

stamp_lines.loc[( stamp_lines['gtin']==row['gtin'] ) 
                        & (stamp_lines['gtin_num'].isin(range(n,n+step))),
                            ['box_number', 'stamp_nr_in_box','box_range']] = 
                               box, range(n+1, n+step+1), '{}-{}'.format(n+1,n+step)

I had to do:

fill_df=pd.DataFrame(pd.Series(list(range(n+1, n+step+1))),  pd.Series(['{}-{}'.format(n+1,n+step) ] * step))

stamp_lines.loc[( stamp_lines['gtin']==row['gtin'] ) 
                        & (stamp_lines['gtin_num'].isin(range(n,n+step))),
                            ['box_number', 'stamp_nr_in_box','box_range']] = fill_df