[ Python 常見問題 ] How to implement 'in' and 'not in' for Pandas dataframe

Source From Here 
Question 
How can I achieve the equivalents of SQL's IN and NOT IN? I have a list with the required values. Here's the scenario: 

>>> import pandas as pd 
>>> df = pd.DataFrame({'countries':['US', 'UK', 'Germany', 'China'], 'population':[100, 200, 300, 400]}) 
>>> countries = ['UK', 'China']

My current way of doing this is as follows: 

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1. rows = []  
2. for ri, row in df.iterrows():  
3.     if row.countries in countries:  
4.         rows.append(ri)  
5.   
6. print(df.loc[rows])  

How-To 
You can use pd.Series.isin: 

* For "IN": somewhere.isin(list_of_place) 
* For "NOT IN": ~somewhere.isin(list_of_place)

As an example: 

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1. >>> df.head()  
2.   countries  population  
3. 0        US         100  
4. 1        UK         200  
5. 2   Germany         300  
6. 3     China         400  
7. >>> countries  
8. ['UK', 'China']  
9. >>> df[df.countries.isin(countries)]  
10.   countries  population  
11. 1        UK         200  
12. 3     China         400  
13. >>> df[~df.countries.isin(countries)]  
14.   countries  population  
15. 0        US         100  
16. 2   Germany         300