## How to implement ‘in’ and ‘not in’ for a pandas DataFrame?

Pandas offers two methods: `Series.isin`

and `DataFrame.isin`

for Series and DataFrames, respectively.

## Filter DataFrame Based on ONE Column (also applies to Series)

The most common scenario is applying an `isin`

condition on a specific column to filter rows in a DataFrame.

```
df = pd.DataFrame({'countries': ['US', 'UK', 'Germany', np.nan, 'China']})
df
countries
0 US
1 UK
2 Germany
3 China
c1 = ['UK', 'China'] # list
c2 = {'Germany'} # set
c3 = pd.Series(['China', 'US']) # Series
c4 = np.array(['US', 'UK']) # array
```

`Series.isin`

accepts various types as inputs. The following are all valid ways of getting what you want:

```
df['countries'].isin(c1)
0 False
1 True
2 False
3 False
4 True
Name: countries, dtype: bool
# `in` operation
df[df['countries'].isin(c1)]
countries
1 UK
4 China
# `not in` operation
df[~df['countries'].isin(c1)]
countries
0 US
2 Germany
3 NaN
```

```
# Filter with `set` (tuples work too)
df[df['countries'].isin(c2)]
countries
2 Germany
```

```
# Filter with another Series
df[df['countries'].isin(c3)]
countries
0 US
4 China
```

```
# Filter with array
df[df['countries'].isin(c4)]
countries
0 US
1 UK
```

## Filter on MANY Columns

Sometimes, you will want to apply an ‘in’ membership check with some search terms over multiple columns,

```
df2 = pd.DataFrame({
'A': ['x', 'y', 'z', 'q'], 'B': ['w', 'a', np.nan, 'x'], 'C': np.arange(4)})
df2
A B C
0 x w 0
1 y a 1
2 z NaN 2
3 q x 3
c1 = ['x', 'w', 'p']
```

To apply the `isin`

condition to both columns “A” and “B”, use `DataFrame.isin`

:

```
df2[['A', 'B']].isin(c1)
A B
0 True True
1 False False
2 False False
3 False True
```

From this, **to retain rows where at least one column is **`True`

, we can use `any`

along the first axis:

```
df2[['A', 'B']].isin(c1).any(axis=1)
0 True
1 False
2 False
3 True
dtype: bool
df2[df2[['A', 'B']].isin(c1).any(axis=1)]
A B C
0 x w 0
3 q x 3
```

Note that if you want to search every column, you’d just omit the column selection step and do

```
df2.isin(c1).any(axis=1)
```

Similarly, **to retain rows where ALL columns are **`True`

, use `all`

in the same manner as before.

```
df2[df2[['A', 'B']].isin(c1).all(axis=1)]
A B C
0 x w 0
```

## Notable Mentions: `numpy.isin`

, `query`

, list comprehensions (string data)

In addition to the methods described above, you can also use the numpy equivalent: `numpy.isin`

.

```
# `in` operation
df[np.isin(df['countries'], c1)]
countries
1 UK
4 China
# `not in` operation
df[np.isin(df['countries'], c1, invert=True)]
countries
0 US
2 Germany
3 NaN
```

Why is it worth considering? NumPy functions are usually a bit faster than their pandas equivalents because of lower overhead. Since this is an elementwise operation that does not depend on index alignment, there are very few situations where this method is not an appropriate replacement for pandas’ `isin`

.

Pandas routines are usually iterative when working with strings, because string operations are hard to vectorise. There is a lot of evidence to suggest that list comprehensions will be faster here..

We resort to an `in`

check now.

```
c1_set = set(c1) # Using `in` with `sets` is a constant time operation...
# This doesn't matter for pandas because the implementation differs.
# `in` operation
df[[x in c1_set for x in df['countries']]]
countries
1 UK
4 China
# `not in` operation
df[[x not in c1_set for x in df['countries']]]
countries
0 US
2 Germany
3 NaN
```

It is a lot more unwieldy to specify, however, so don’t use it unless you know what you’re doing.

Lastly, there’s also `DataFrame.query`

which has been covered in this answer. numexpr FTW!