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Let's say I have main df:

 ID title grade1 grade2 "1111" "something" 1 2 "2222" "something1" 2 2 "3333" "something2" 1 1 "something3" "8865" 1 1 "4444" "something3" 1 2 "5555" "something4" 1 1 "6666" "something5" 2 2 "somethingX" "8888" 1 1

And as u can see it's thera are mixed up values in columns 1 and 2

So what I do:

  1. put all wrong names to new df: temp = df[df['ID'].str.len() > 4]
  2. create list from new df: listToMove = temp['ID].unique()
  3. delete all records from df by for: df = df[df['ID'] != listToMove[i])
  4. change column plce in temp df
  5. append temp df to df.

But i think it's not the best practice. it's work fine... but that for... i think it's slow, and I can do it faster.

Any suggestion?

Result shoud be:

 ID title grade1 grade2 "1111" "something" 1 2 "2222" "something1" 2 2 "3333" "something2" 1 1 "8865" "something3" 1 1 "4444" "something3" 1 2 "5555" "something4" 1 1 "6666" "something5" 2 2 "8888" "somethingX" 1 1
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1 Answer 1

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You can swap values by condition in DataFrame.loc:

m1 = df['ID'].str.len() != 4 df.loc[m1, ['ID', 'title']] = df.loc[m1, ['title', 'ID']].to_numpy() print (df) ID title grade1 grade2 0 1111 something 1 2 1 2222 something1 2 2 2 3333 something2 1 1 3 8865 something3 1 1 4 4444 something3 1 2 5 5555 something4 1 1 6 6666 something5 2 2 7 8888 somethingX 1 1
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