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

bunji
– bunji

2016-11-21 17:58:59 +00:00
Commented Nov 21, 2016 at 17:58 — bunji
– bunji, Commented Nov 21, 2016 at 17:58

Mohamed AL ANI · Accepted Answer · 2016-11-21 17:58:03Z

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, 2016 at 17:58

Mohamed AL ANI

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3 Comments

simon Over a year ago

Note also the & rather than "and"

user3451026 Over a year ago

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.

Mohamed AL ANI Over a year ago

I'm not sure but I believe you can't do that using this syntax. try using "pd.where" instead

burhan · Accepted Answer · 2016-11-21 18:38:00Z

0

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, 2016 at 18:38

burhan

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