[英]Opposite of dropna() in pandas
I have a pandas
DataFrame
that I want to separate into observations for which there are no missing values and observations with missing values. 我有一个pandas
DataFrame
,我希望将其分成观察,其中没有缺失值和缺少值的观察。 I can use dropna()
to get rows without missing values. 我可以使用dropna()
来获取没有缺少值的行。 Is there any analog to get rows with missing values? 是否有任何模拟来获取缺少值的行?
#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()
Check null
by row and filter with boolean indexing: 检查null
的行,并与布尔索引筛选:
df[df.isnull().any(1)]
# col1 col2
#1 NaN 7.0
#2 3.0 NaN
~
= Opposite :-) ~
= 对面 :-)
df.loc[~df.index.isin(df.dropna().index)]
Out[234]:
col1 col2
1 NaN 7.0
2 3.0 NaN
Or 要么
df.loc[df.index.difference(df.dropna().index)]
Out[235]:
col1 col2
1 NaN 7.0
2 3.0 NaN
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