pandas 保留 NaN 值并删除非缺失值
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Keeping NaN values and dropping nonmissing values
提问by ctan
I have a DataFrame where I would like to keep the rows when a particular variable has a NaN value and drop the nonmissing values.
我有一个 DataFrame，当特定变量具有 NaN 值并删除非缺失值时，我想在其中保留行。
Example:
例子：
ticker opinion x1 x2
aapl GC 100 70
msft NaN 50 40
goog GC 40 60
wmt GC 45 15
abm NaN 80 90
In the above DataFrame, I would like to drop all observations where opinion is not missing (so, I would like to drop the rows where ticker is aapl, goog, and wmt).
在上面的 DataFrame 中，我想删除所有没有缺少意见的观察（因此，我想删除股票代码为 aapl、goog 和 wmt 的行）。
Is there anything in pandas that is the opposite to .dropna()?
Pandas中有什么与.dropna()?
回答by Roger Fan
Use pandas.isnullon the column to find the missing values and index with the result.
pandas.isnull在列上使用以查找缺失值和结果索引。
import pandas as pd
data = pd.DataFrame({'ticker': ['aapl', 'msft', 'goog'],
'opinion': ['GC', nan, 'GC'],
'x1': [100, 50, 40]})
data = data[pd.isnull(data['opinion'])]
