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

实现:

df[df.countries.isin(countries)]

实施不像在其他国家:

df[df.countries.isin([x for x in np.unique(df.countries) if x not in countries])]

我的2c价值: 我需要一个数据框架的in和ifelse语句的组合，这对我来说很有用。

sale_method = pd.DataFrame(model_data["Sale Method"].str.upper())
sale_method["sale_classification"] = np.where(
    sale_method["Sale Method"].isin(["PRIVATE"]),
    "private",
    np.where(
        sale_method["Sale Method"].str.contains("AUCTION"), "auction", "other"
    ),
)

你可以使用pd.Series.isin。

对于“IN”的用法:something.isin(某处)

或者对于"NOT IN": ~something.isin(某处)

举个例子:

>>> df
    country
0        US
1        UK
2   Germany
3     China
>>> countries_to_keep
['UK', 'China']
>>> df.country.isin(countries_to_keep)
0    False
1     True
2    False
3     True
Name: country, dtype: bool
>>> df[df.country.isin(countries_to_keep)]
    country
1        UK
3     China
>>> df[~df.country.isin(countries_to_keep)]
    country
0        US
2   Germany

你也可以在.query()中使用.isin():

df.query('country.isin(@countries_to_keep).values')

# Or alternatively:
df.query('country.isin(["UK", "China"]).values')

要否定你的查询，使用~:

df.query('~country.isin(@countries_to_keep).values')

更新:

另一种方法是使用比较操作符:

df.query('country == @countries_to_keep')

# Or alternatively:
df.query('country == ["UK", "China"]')

要对查询求反，使用!=:

df.query('country != @countries_to_keep')

如果你想保持列表的顺序，有一个小技巧:

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


ind=[df.index[df['country']==i].tolist() for i in countries_to_keep]
flat_ind=[item for sublist in ind for item in sublist]

df.reindex(flat_ind)

   country
2  Germany
0       US

为什么没有人谈论各种过滤方法的性能?事实上，这个主题经常出现在这里(参见示例)。我为一个大型数据集做了自己的性能测试。这是非常有趣和有教育意义的。

df = pd.DataFrame({'animals': np.random.choice(['cat', 'dog', 'mouse', 'birds'], size=10**7), 
                   'number': np.random.randint(0,100, size=(10**7,))})

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000000 entries, 0 to 9999999
Data columns (total 2 columns):
 #   Column   Dtype 
---  ------   ----- 
 0   animals  object
 1   number   int64 
dtypes: int64(1), object(1)
memory usage: 152.6+ MB

%%timeit
# .isin() by one column
conditions = ['cat', 'dog']
df[df.animals.isin(conditions)]

367 ms ± 2.34 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
# .query() by one column
conditions = ['cat', 'dog']
df.query('animals in @conditions')

395 ms ± 3.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
# .loc[]
df.loc[(df.animals=='cat')|(df.animals=='dog')]

987 ms ± 5.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
df[df.apply(lambda x: x['animals'] in ['cat', 'dog'], axis=1)]

41.9 s ± 490 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
new_df = df.set_index('animals')
new_df.loc[['cat', 'dog'], :]

3.64 s ± 62.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
new_df = df.set_index('animals')
new_df[new_df.index.isin(['cat', 'dog'])]

469 ms ± 8.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%%timeit
s = pd.Series(['cat', 'dog'], name='animals')
df.merge(s, on='animals', how='inner')

796 ms ± 30.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

因此，isin方法是最快的，而带有apply()的方法是最慢的，这并不奇怪。