经常在处理数据中从一个总数据中清洗出数据, 但是有时候需要把没有处理的数据也统计出来.
这时候就需要使用:
pandas.DataFrame.isin
DataFrame中的每个元素是否都包含在值中
例子:
如何实现SQL的等价物IN和NOT IN?
我有一个包含所需值的列表。下面是一个场景：
df = pd.DataFrame({'countries':['US','UK','Germany','China']})
countries = ['UK','China']
# pseudo-code:
df[df['countries'] not in countries]之前的做法是这样:
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'])]但上面这样做觉得很不好, 也翻了文档才找到比较好解决方式.
# IN something.isin(somewhere) # NOT IN ~something.isin(somewhere)
例子:
>>> 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
pandas中经常会需要对某列做一些筛选，比如筛选某列里的不包含某些值的行，类似sql里的in和not in功能，那么怎么实现呢。
import pandas as pd
columns = ['name','country']
index = [1,2,3,4]
row1 = ['a','China']
row2 = ['b','UK']
row3 = ['c','USA']
row4 = ['d','HK']
df = pd.DataFrame([row1,row2,row3,row4],
index=index,
columns=columns)
df
chinese = ['China','HK']那么想查看数据中是chines的，
df[df.country.isin(chinese)]
查看数据中不是chines的，
