在 pandas 中dropna()的对面 [英] Opposite of dropna() in pandas

问题描述

我有一个pandas DataFrame，我想将其分为无缺失值的观察值和有缺失值的观察值.我可以使用dropna()来获取没有缺失值的行.有没有类似的方法来获取缺少值的行?

I have a pandas DataFrame that I want to separate into observations for which there are no missing values and observations with missing values. I can use dropna() to get rows without missing values. Is there any analog to get rows with missing values?

#Example DataFrame
import pandas as pd
df = pd.DataFrame({'col1': [1,np.nan,3,4,5],'col2': [6,7,np.nan,9,10],})

#Get observations without missing values
df.dropna()

推荐答案

按行检查null并使用布尔索引进行过滤:

Check null by row and filter with boolean indexing:

df[df.isnull().any(1)]

#  col1 col2
#1  NaN  7.0
#2  3.0  NaN

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