Python Pandas：多列的布尔索引 [英] Python Pandas: Boolean indexing on multiple columns

问题描述

尽管至少有两个 good 教程如何索引一个DataFrame在Python的 pandas 库，我仍然无法找出一个优雅的方式 SELECT 在多个列上。 p>

despite there being at least two good tutorials on how to index a DataFrame in Python's pandas library, I still can't work out an elegant way of SELECTing on more than one column.

>>> d = pd.DataFrame({'x':[1, 2, 3, 4, 5], 'y':[4, 5, 6, 7, 8]})
>>> d
   x  y
0  1  4
1  2  5
2  3  6
3  4  7
4  5  8
>>> d[d['x']>2] # This works fine
   x  y
2  3  6
3  4  7
4  5  8
>>> d[d['x']>2 & d['y']>7] # I had expected this to work, but it doesn't
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

我发现（我认为是）像这样一个相当不太实际的方式，像这样

I have found (what I think is) a rather inelegant way of doing it, like this

>>> d[d['x']>2][d['y']>7]

但是它不漂亮，它的可读性相当低（我认为）。

But it's not pretty, and it scores fairly low for readability (I think).

有更好的Python脚本吗？

Is there a better, more Python-tastic way?

推荐答案

这是一个优先运算符问题。

It is a precedence operator issue.

您应该添加额外的括号，使您的多条件测试工作：

You should add extra parenthesis to make your multi condition test working:

d[(d['x']>2) & (d['y']>7)]

您提到的教程的此部分显示了具有多个布尔条件的示例，并使用括号。

This section of the tutorial you mentioned shows an example with several boolean conditions and the parenthesis are used.

这篇关于Python Pandas：多列的布尔索引的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！