本文翻译自：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 ? 如何实现SQL的IN和NOT IN的等效项？

I have a list with the required values. 我有一个包含所需值的列表。 Here's the scenario: 这是场景：

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

# pseudo-code:
df[df['countries'] not in countries]

My current way of doing this is as follows: 我目前的做法如下：

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

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

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

But this seems like a horrible kludge. 但这似乎是一个可怕的冲突。 Can anyone improve on it? 有人可以改进吗？

#1楼

参考：https://stackoom.com/question/1LkWj/如何像在SQL中一样使用-in-和-not-in-过滤Pandas数据帧

#2楼

You can use pd.Series.isin . 您可以使用pd.Series.isin 。

For "IN" use: something.isin(somewhere) 对于“ IN”，请使用： something.isin(somewhere)

Or for "NOT IN": ~something.isin(somewhere) 或对于“ NOT IN”： ~something.isin(somewhere)

As a worked example: 作为一个工作示例：

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

#3楼

I've been usually doing generic filtering over rows like this: 我通常对这样的行进行通用过滤：

扫描二维码关注公众号，回复： 11499360 查看本文章

criterion = lambda row: row['countries'] not in countries
not_in = df[df.apply(criterion, axis=1)]

#4楼

我想过滤出dfbc行，该行的BUSINESS_ID也在dfProfilesBusIds的BUSINESS_ID中

dfbc = dfbc[~dfbc['BUSINESS_ID'].isin(dfProfilesBusIds['BUSINESS_ID'])]

#5楼

Alternative solution that uses .query() method: 使用.query（）方法的替代解决方案：

In [5]: df.query("countries in @countries")
Out[5]:
  countries
1        UK
3     China

In [6]: df.query("countries not in @countries")
Out[6]:
  countries
0        US
2   Germany

#6楼

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

implement in : 实施于 ：

df[df.countries.isin(countries)]

implement not in as in of rest countries: 不在其他国家/地区实施 ：

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