# To select rows from a Pandas dataframe, use a list of values.

## Problem

Let’s say I have the Pandas dataframe below:

``````df = DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3, 5]})
df

A   B
0    5   1
1    6   2
2    3   3
3    4   5
``````

I have the ability to subset depending on a specified value:

``````x = df[df['A'] == 3]
x

A   B
2    3   3
``````

How, on the other hand, can I subset based on a list of values? – anything along these lines:

``````list_of_values = [3,6]

y = df[df['A'] in list_of_values]
``````

To get:

``````     A    B
1    6    2
2    3    3
``````

## Solution #1

The isin technique can be used:

``````In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})

In [2]: df
Out[2]:
A  B
0  5  1
1  6  2
2  3  3
3  4  5

In [3]: df[df['A'].isin([3, 6])]
Out[3]:
A  B
1  6  2
2  3  3
``````

And here’s how to use it the other way around:

``````In [4]: df[~df['A'].isin([3, 6])]
Out[4]:
A  B
0  5  1
3  4  5
``````

## Solution #2

You can use the query method:

``````df.query('A in [6, 3]')
# df.query('A == [6, 3]')
``````

or

``````lst = [6, 3]
df.query('A in @lst')
# df.query('A == @lst')
``````

## Solution #3

Another method;

``````df.loc[df.apply(lambda x: x.A in [3,6], axis=1)]
``````

This method, unlike the isin method, is especially useful for determining whether the list contains a function of column A. As an example, consider the function f(A) = 2*A – 5;

``````df.loc[df.apply(lambda x: 2*x.A-5 in [3,6], axis=1)]
``````

This method is slower than the isin method, so keep that in mind.