具有多个条件的布尔索引 [英] Boolean Indexing with multiple conditions

问题描述

我有一个 Pandas DF ，我需要过滤一些行，其中包含特征'a'和特征'b'的值== 0.

I have a Pandas DF where I need to filter out some rows that contains values == 0 for feature 'a' and feature 'b'.

为了检查这些值，我运行以下命令:

In order to inspect the values, I run the following:

DF1 = DF[DF['a'] == 0]

哪个返回正确的值.同样，通过执行以下操作:

Which returns the right values. Similarly, by doing this:

DF2 = DF[DF['b'] == 0]

我可以看到特征'b'的0值.

I can see the 0 values for feature 'b'.

但是，如果我尝试使用OR操作数将这2个代码合并在一行代码中:

However, if I try to combine these 2 in a single line of code using the OR operand:

DF3 = DF[DF['a'] == 0 |  DF['b'] == 0]

我明白了:

TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]

这是怎么回事?

推荐答案

您可以转换列'a'或'b'，使其均为float64或bool.但是，保留功能数据类型的更简单的解决方案是:

You can transform either column 'a' or 'b' so they are both either float64 or bool. However, an easier solution that preserves the data type of your features is this:

DF3 = DF[(DF['a'] == 0) | (DF['b'] == 0)]

一个常见的操作是使用布尔向量来过滤数据.操作员是:为或&对于and，〜不适用. 这些必须使用括号进行分组.

A common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses.

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