[Python] How to filter Pandas dataframe using 'in' and 'not in' like in SQL

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:

df = pd.DataFrame({'country': ['US', 'UK', 'Germany', 'China']})
countries_to_keep = ['UK', 'China']

# pseudo-code:
df[df['country'] not in countries_to_keep]


My current way of doing this is as follows:

df = pd.DataFrame({'country': ['US', 'UK', 'Germany', 'China']})
df2 = pd.DataFrame({'country': ['UK', 'China'], 'matched': True})

# IN
df.merge(df2, how='inner', on='country')

# NOT IN
not_in = df.merge(df2, how='left', on='country')
not_in = not_in[pd.isnull(not_in['matched'])]


But this seems like a horrible kludge. Can anyone improve on it?

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