Filter a Pandas dataframe with a single condition on multiple columns, programmatically

Question

I'm trying to filter a dataframe with the same condition on multiple columns.

This is possible by doing as follows:

>>> df = pd.DataFrame({
    "A1": [1, 2, 5, 1, 2],
    "B1": [0, 2, 0, 4, 1],
    "A2": [2, 3, 5, 1, 5],
    "B2": [4, 1, 6, 2, 1]
})

>>> df_filtered = df[(df['A1'] > 3) | (df['A2'] > 3)]

>>> df_filtered
   A1  B1  A2  B2
2   5   0   5   6
4   2   1   5   1

However, I'd like to be able to fix the columns programmatically from a predefined variable, instead of manually, like in this example:

cols = ['A1', 'A2']

df_filtered = df[df[cols].any(> 3)]

Except the code just above obviously doesn't work.

Jivan · Accepted Answer · 2019-04-15 21:54:06Z

3

Looks like it is possible by doing:

>>> df_filtered = df[(df[cols] > 3).any(axis=1)]

>>> df_filtered
   A1  B1  A2  B2
2   5   0   5   6
4   2   1   5   1

(This answer was redacted while Wen-Ben was posting his own, but I decided to post it anyway because it offers a slightly different syntax)

answered Apr 15, 2019 at 21:54

Jivan

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BENY · Accepted Answer · 2019-04-15 21:52:16Z

2

Then we make it work :-)

cols = ['A1', 'A2']
df[df[cols].gt(3).any(axis = 1)]
Out[556]: 
   A1  B1  A2  B2
2   5   0   5   6
4   2   1   5   1

answered Apr 15, 2019 at 21:52

BENY

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