Remove duplicates from rows and columns (cell) in a dataframe, python

Question

I have two columns with a lot of duplicated items per cell in a dataframe. Something similar to this:

Index x y 
 1 1 ec, us, us, gbr, lst
 2 5 ec, us, us, us, us, ec, ec, ec, ec
 3 8 ec, us, us, gbr, lst, lst, lst, lst, gbr
 4 5 ec, ec, ec, us, us, ir, us, ec, ir, ec, ec
 5 7 chn, chn, chn, ec, ec, us, us, gbr, lst

I need to eliminate all the duplicate items an get a resulting dataframe like this:

Index x y 
 1 1 ec, us, gbr, lst
 2 5 ec, us
 3 8 ec, us, gbr,lst
 4 5 ec, us, ir
 5 7 chn, ec, us, gbr, lst

Thanks!!

• 1313e: So, what did you already try out in order to get the result you want?
• BENY: stackoverflow.com/questions/7794208/… mutiple function there, what you need is just apply those to your dataframe

Answer 1

Split and apply set and join i.e

df['y'].str.split(', ').apply(set).str.join(', ')
0 us, ec, gbr, lst
1 us, ec
2 us, ec, gbr, lst
3 us, ec, ir
4 us, lst, ec, gbr, chn
Name: y, dtype: object

Update based on comment :

df['y'].str.replace('nan|[{}\s]','', regex=True).str.split(',').apply(set).str.join(',').str.strip(',').str.replace(",{2,}",",", regex=True)
# Replace all the braces and nan with `''`, then split and apply set and join

• PAstudilloE: it works perfect @Dark ... but I forgot to include that all the [y] column is like this: {ec, us, us, gbr, lst, nan, nan}. I need to erase erase the {} and the nan. Do you know how to do it?
• Bharath M Shetty: @PAstudilloE are you saying the y column is like {ec,us.. before running this code or after running this code?
• PAstudilloE: before running the code. the original columns are {ec, us, ..., nan} @Dark
• PAstudilloE: it works well. The only problem that I have now is that the results I'm getting are like this: , , us, ec... (the nan's are erased but the commas are still there). Do you have any guidance on how to solve that?
• kağan hazal koçdemir: For FutureWarning error add regex=True in replace

Answer 2

Try this:

d['y'] = d['y'].apply(lambda x: ', '.join(sorted(set(x.split(', ')))))

• PAstudilloE: it works perfect!... but I forgot to include that all the [y] column is like this: {ec, us, us, gbr, lst, nan, nan}. I need to erase erase the {} and the nan. Do you know how to do it?

Answer 3

If you don't care about item order, and assuming the data type of everything in column y is a string, you can use the following snippet:

df['y'] = df['y'].apply(lambda s: ', '.join(set(s.split(', '))))

The set() conversion is what removes duplicates. I think in later versions of python it might preserve order (3.4+ maybe?), but that is an implementation detail rather than a language specification.

• Turn: That call to list isn't needed.
• PAstudilloE: I forgot to include that all the [y] column is like this: {ec, us, us, gbr, lst, nan, nan}. I need to erase erase the {} and the nan. Do you know how to do it?
• Peter O.: Even in Python 3.10, sets are documented as unordered collections, so they should not be used if the order in which items are inserted or enumerated is important to a program.

Answer 4

use the apply method on the dataframe.

# change this function according to your needs
def dedup(row):
 return list(set(row.y))
df['deduped'] = df.apply(dedup, axis=1)