Remove element from every list in a column in pandas dataframe based on another column

Question

I'd like to remove values in list from column B based on column A, wondering how.

Given:

df = pd.DataFrame({
    'A': ['a1', 'a2', 'a3', 'a4'],
    'B': [['a1', 'a2'], ['a1', 'a2', 'a3'], ['a1', 'a3'], []]
})

I want:

result = pd.DataFrame({
    'A': ['a1', 'a2', 'a3', 'a4'],
    'B': [['a1', 'a2'], ['a1', 'a2', 'a3'], ['a1', 'a3'], []],
    'Output': [['a2'], ['a1', 'a3'], ['a1'], []]
})

How so? What is the logic of "based on column A" and what have you tried so far? — Joe
– Joe, Commented Jan 28, 2020 at 18:39
At the index 0 the output column in df2 shows that, a1 is removed from column B based on a1 in column A. Similarly for all rows, the corresponding value in column A is removed from column B. If column B is empty then it would be empty. — user12229564
– user12229564, Commented Jan 28, 2020 at 18:47

gseva · Accepted Answer · 2020-01-28 18:43:37Z

6

One way of doing that is applying a filtering function to each row via DataFrame.apply:

df['Output'] = df.apply(lambda x: [i for i in x.B if i != x.A], axis=1)

answered Jan 28, 2020 at 18:43

gseva

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1 Comment

piRSquared Over a year ago

With a comprehension instead of apply: df.assign(Output=[[z for z in x if z != y] for x, y in zip(df.B, df.A)])

Celius Stingher · Accepted Answer · 2020-01-28 18:51:28Z

2

Another solution using iterrows():

for i,value in df.iterrows():
    try:
        value['B'].remove(value['A'])
    except ValueError:
        pass
print(df)

Output:

    A         B
0  a1      [a2]
1  a2  [a1, a3]
2  a3      [a1]
3  a4        []

answered Jan 28, 2020 at 18:51

Celius Stingher

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