## 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
```

Asked by zach

## 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
```

Answered by Wouter Overmeire

## 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')
```

Answered by Mykola Zotko

## 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.

Answered by Achintha Ihalage

**Post is based on https://stackoverflow.com/questions/12096252/use-a-list-of-values-to-select-rows-from-a-pandas-dataframe**