pandas 如何在 python 中为熊猫创建“非”过滤器
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How do I create a "not" filter in python for pandas
提问by staten12
I have this large dataframe I've imported into pandas and I want to chop it down via a filter. Here is my basic sample code:
我有这个大数据框，我已经导入到 Pandas 中，我想通过过滤器将其切碎。这是我的基本示例代码：
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
df = DataFrame({'A':[12345,0,3005,0,0,16455,16454,10694,3005],'B':[0,0,0,1,2,4,3,5,6]})
df2= df[df["A"].map(lambda x: x > 0) & (df["B"] > 0)]
Basically this displays bottom 4 results which is semi-correct. But I need to display everything BUT these results. So essentially, I'm looking for a way to use this filter but in a "not" version if that's possible. So if column A is greater than 0 AND column B is greater than 0 then we want to disqualify these values from the dataframe. Thanks
基本上这显示了半正确的后 4 个结果。但我需要显示除这些结果之外的所有内容。所以基本上，我正在寻找一种使用此过滤器的方法，但如果可能的话，使用“非”版本。因此，如果 A 列大于 0 且 B 列大于 0，那么我们希望从数据框中取消这些值。谢谢
回答by hhbilly
No need for map function call on Series "A".
不需要在系列“A”上调用 map 函数。
Apply De Morgan's Law:
应用德摩根定律：
"not (A and B)" is the same as "(not A) or (not B)"
"not (A and B)" 等同于 "(not A) or (not B)"
df2 = df[~(df.A > 0) | ~(df.B > 0)]
回答by ssm
There is no need for the mapimplementation. You can just reverse the arguments like ...
不需要map执行。你可以颠倒这样的论点......
df.ix[(df.A<=0)|(df.B<=0),:]
Or use boolean indexingwithout ix:
或boolean indexing不使用ix：
df[(df.A<=0)|(df.B<=0)]
回答by piRSquared
Try
尝试
df2 = df[df["A"].map(lambda x: x <= 0) | (df["B"] <= 0)]
