问题描述

在比较两个系列时，我遇到了 pandas 的意外行为.我想知道这是故意的还是错误的.

I ran up against unexpected behavior in pandas when comparing two series. I wanted to know if this is intended or a bug.

假设我:

import pandas as pd
x = pd.Series([1, 1, 1, 0, 0, 0], index=['a', 'b', 'c', 'd', 'e', 'f'], name='Value')
y = pd.Series([0, 2, 0, 2, 0, 2], index=['c', 'f', 'a', 'e', 'b', 'd'], name='Value')

x > y

产量:

a     True
b    False
c     True
d    False
e    False
f    False
Name: Value, dtype: bool

这不是我想要的.显然，我预计指数会排成一行.但是我必须明确地将它们排列起来才能得到想要的结果.

which isn't what I wanted. Clearly, I expected the indexes to line up. But I have to explicitly line them up to get the desired results.

x > y.reindex_like(x)

产量:

a     True
b     True
c     True
d    False
e    False
f    False
Name: Value, dtype: bool

这是我所期望的.

更糟糕的是，如果我:

x + y

我明白了:

a    1
b    1
c    1
d    2
e    2
f    2
Name: Value, dtype: int64

所以在操作时，索引排成一行.比较时，他们没有.我的观察准确吗?这是出于某种目的吗?

So when operating, the indexes line up. When comparing, they do not. Is my observation accurate? Is this intended for some purpose?

谢谢，

-PiR

推荐答案

Bug 与否.我建议制作一个数据框并比较数据框内的系列.

Bug or not. I would suggest to make a dataframe and compare the series inside the dataframe.

import pandas as pd
x = pd.Series([1, 1, 1, 0, 0, 0], index=['a', 'b', 'c', 'd', 'e', 'f'], name='Value_x')
y = pd.Series([0, 2, 0, 2, 0, 2], index=['c', 'f', 'a', 'e', 'b', 'd'], name='Value_y')

df = pd.DataFrame({"Value_x":x, "Value_y":y})
df['Value_x'] > df['Value_y']

Out[3]:

a     True
b     True
c     True
d    False
e    False
f    False
dtype: bool

这篇关于当你比较 2 个 pandas 系列时会发生什么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跟版网！