Using two conditions inside pandas dataframe filter

Question

I'm looking to filter out any rows where two columns values are equal to 0 and 1. The following was my attempt at this but hasn't yielded any results.

x = x[x.value_1 != 0 and x.value_2 !=1]

Running this returns the following error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I know it is possible to filter using either one of these two conditions alone. However, it doesn't seem to like when I use them together. Any advice on a quick fix that doesn't include merging? Thanks!

see explanation [here](http://stackoverflow.com/questions/21415661/logic-operator-for-boolean-indexing-in-pandas?rq=1) — bunji, Nov 21 '16 at 17:58

score 1 · Answer 1 · answered Nov 21 '16 at 17:58

1

Just try it with parenthesis and & rather than and :

x = x[(x.value_1 != 0) & (x.value_2 !=1)]

I had the same issue

answered Nov 21 '16 at 17:58

Mohamed AL ANI

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Note also the & rather than "and" – simon Nov 21 '16 at 17:58
That is removing all instances of value_1 not equaling 0 and all instances where value_2 is not 1. I would like to remove only the instances where both value_1 is 0 and value_2 is 1. – user3451026 Nov 21 '16 at 18:01
I'm not sure but I believe you can't do that using this syntax. try using "pd.where" instead – Mohamed AL ANI Nov 21 '16 at 18:09

score 0 · Accepted Answer · answered Nov 21 '16 at 18:38

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Try this if you want anything except when value_1 is 0 and value_2 is 1:

x = x[~((x.value_1 == 0) & (x.value_2 == 1))]

answered Nov 21 '16 at 18:38

burhan

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Using two conditions inside pandas dataframe filter

2 Answers2