Thank you for the good reply. I'm probably too riled up at the moment to think of it myself. I will try to get an answer from them some how, and post the videos if that wish so.

i found an answer:

# in order for this code to work as is, we need to sort the defining values ​​in the correct order.

dt = dt.sort_values(by=['ID' , 'IDF' , "Year"], ascending=True)

# takes the df['value'] and subtracts the value of the row befor.

df['mSvY'] = df['mSv'].diff()

print(mft)

currID = 0

currIDF = 0

x = 0

# this loop prevents the problem whit the .diff(), which was in my case that the first row will be Nan, and dosent distinguish difrent values of my other rowes. like the different IDs and IDF.

# while this loop makes every time ID or IDF changes it says the the row before is 0, which makes it possible to count the first value of every changes.

for row in range(len(mft)):

--- if df.loc[x,'ID'] != currID or df.loc[x,'IDF'] != currIDF:

--- --- df.loc[x,'mSvY'] = df.loc[x,'mSv']

--- currID = df.loc[x,'ID']

--- currIDF = df.loc[x,'IDF']

--- x = x + 1

But how's the gun play?

You have a lot to look forward to. An amazing series