How to switch column elements for only one specific row in pandas?

Question

So, I am working w/data from my research lab and am trying to sort it and move it around etc. And most of the stuff isn't important to my issue and I don't want to go into detail because confidentiality stuff, but I have a big table w/columns and rows and I want to specifically switch the elements of two columns ONLY in one row.

The extremely bad attempt at code I have for it is this (I rewrote the variables to be more vague though so they make sense):

for x in df.columna.values:

*some if statements*

df.loc[df.index([df.loc[df['columna'] == x]]), ['columnb', 'columna']] = df[df.index([df.loc[df['columna'] == x]]), ['columna', 'columnb']].numpy()

I am aware that the code I have is trash (and also the method - w/the for loops and if statements. I know I can abstract it a TON but I just want to actually figure out a way to make it work and them I will clean it up and make it prettier and more efficient. I learned pandas existed on tuesday so I am not an expert), but I think my issue lies in the way I'm getting the row.

One error I was recently getting for a while is the method I was using to get the row was giving me 1 row x 22 columns and I think I needed the name/index of the row instead. Which is why the index function is now there. However, I am now getting the error:

TypeError: 'RangeIndex' object is not callable

And I am just so confused all around. Sorry I've written a ton of text, basically: is there any simpler way to just switch the elements of two columns for one specific row (in terms of x, an element in that row)?

I think my biggest issue is trying to like- get the rows "name" in the format it wants. Although I may have a ton of other problems because honestly I am just really lost.

Answer 1

I think it was probably a syntax problem. I am assuming you are using tensorflow with the numpy() function? Try this it switches the columns based on the code you provided:

for x in df.columna.values:
    # *some if statements*
    df.loc[ 
           (df["columna"] == x), 
           ['columna', 'columnb']
          ] = df.loc[(df["columna"] == x), ['columnb', 'columna']].values.numpy()

I am also a beginner and would recommend you aim to make it pretty from the get go. It will save you a lot of extra time in the long run. Trial and error!

Answer 2

You're sooooo close! The error you're getting stems from trying to slice df.index([df.loc[df['columna'] == x]]). The parentheses are unneeded here and this should read as: df.index[df.loc[df['columna'] == x]].

However, here's an example on how to swap values between columns when provided a value (or multiple values) to swap at.

Sample Data

df = pd.DataFrame({
    "A": list("abcdefg"),
    "B": [1,2,3,4,5,6,7]
})

print(df)
   A  B
0  a  1
1  b  2
2  c  3
3  d  4
4  e  5
5  f  6
6  g  7

Let's say we're going to swap the values where A is either "c" or "f". To do this we need to first create a mask that just selects those rows. To accomplish this, we can use .isin. Then to perform our swap, we actually take the same exact approach you had! Including the .to_numpy() is very important, because without it Pandas will actually realign your columns for you and cause the values to not be swapped. Putting it all together:

swap_at = ["c", "f"]
swap_at_mask = df["A"].isin(swap_at) # mask where columns "A" is either equal to "c" or "f"

# Without the `.to_numpy()` at the end, pandas will realign the Dataframe
#  and no values will be swapped
df.loc[swap_at_mask, ["A", "B"]] = df.loc[swap_at_mask, ["B", "A"]].to_numpy()

print(df)
   A  B
0  a  1
1  b  2
2  3  c
3  d  4
4  e  5
5  6  f
6  g  7