How to select rows in pandas based on list of values

Question

I'm trying to find out a way how I can select rows in pandas dataframe based that some values will be in my list. For example

df = pd.DataFrame(np.arange(6).reshape(3,2), columns=['A','B'])
   A  B
0  0  1
1  2  3
2  4  5

I know that I can select certain row, e.g.

df[df.A==0]

will select me row with A=0. What I want is to select multiple rows whose values will be in my list, e.g. A in [0,2]. I tried

df[df.A in [0,2]]
df[list(df.A)==[0,2]]

but nothing works. In R language I can provide %in% operator. In python syntax we can use A in [0,2], etc. How I can select subset of rows in pandas in this case? Thanks, Valentin.

Brian Huey · Accepted Answer · 2016-02-06 17:48:22Z

30

pd.isin() will select multiple values:

>>> df[df.A.isin([0,2])]
   A  B
0  0  1
1  2  3

answered Feb 6, 2016 at 17:48

Brian Huey

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8 Comments

Valentin Over a year ago

Brian, thanks that works. How about negation operation, i.e, not in?

Brian Huey Over a year ago

You can use numpy's logical_not: df[np.logical_not(df.A.isin([0,2]))]

Valentin Over a year ago

Great, that's answer completely my question.

Rutger Hofste Over a year ago

Is there a way to use pandas df.loc , iloc or ix to do this?

Jeff Ellen Over a year ago

I don't think so, unless you are 'cheating' by knowing the which rows you are looking for. (In this example, df.iloc[0:2] (1st and 2nd rows) and df.loc[0:1] (rows with index value in the range of 0-1 (the index being unlabeled column on the left) both give you the equivalent output, but you had to know in advance. If you want a different syntax, there is a df.query() method.

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Jeff Ellen · Accepted Answer · 2018-03-18 08:58:09Z

3

if you don't like that syntax, you can use also use query (introduced in pandas 0.13 which is from 2014):

>>> df.query('A in [0,2]')
   A  B
0  0  1
1  2  3

answered Mar 18, 2018 at 8:58

Jeff Ellen

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