在构建Pandas DataFrame中使用逻辑运算符 [英] Using logical operators in building a Pandas DataFrame

问题描述

我有两段熊猫代码片段，我认为它们应该是等效的，但是第二段代码并没有达到我的期望.

I have two snippets of pandas code which I think should be equivalent, but the second one doesn't do what I expect.

# snippet 1
    data = all_data[[((np.isfinite(all_data[self.design_metric][i]) 
                    and all_data['Source'][i] == 2)) 
                    or ((np.isfinite(all_data[self.actual_metric][i]) 
                    and all_data['Source'][i] != 2))
                    for i in range(len(all_data))]]


# snippet 2
    data = all_data[(all_data['Source'] == 2 &
                    np.isfinite(all_data[self.design_metric])) |
                    (all_data['Source'] != 2 &
                    np.isfinite(all_data[self.actual_metric]))]

每个部分(例如all_data['Source'] == 2)单独执行我期望的操作，但是由于最终结果与列表理解版本的结果不同，因此我似乎对逻辑运算符做错了事. /p>

Each section (e.g. all_data['Source'] == 2 ) does what I expect on its own but it seems that I'm doing something wrong with the logical operators as the final result is coming out with a different result to the list comprehension version.

推荐答案

&运算符的绑定比==(或任何比较运算符)更紧密.请参见文档.一个简单的例子是:

The & operator binds more tightly than == (or any comparison operator). See the documentation. A simpler example is:

>>> 2 == 2 & 3 == 3
False

这是因为它被分组为2 == (2 & 3) == 3，然后调用了比较链.这就是您的情况.您需要在每个比较之间加上括号.

This is because it is grouped as 2 == (2 & 3) == 3, and then comparison chaining is invoked. This is what is happening in your case. You need to put parentheses around each comparison.

 data = all_data[((all_data['Source'] == 2) &
                np.isfinite(all_data[self.design_metric])) |
                ((all_data['Source'] != 2) &
                np.isfinite(all_data[self.actual_metric]))]

请注意==和!=比较周围的多余括号.

Note the extra parentheses around the == and != comparisons.

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