pandas 如何比较两个熊猫系列的两排？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/39928738/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How to compare two rows of two pandas series?
提问by Jiaqi
I have two python pandas series df10and df5. I want to compare their values.For example: df10[-1:]< df5[-1:],it returns true. df10[-2:-1] > df5[-2:-1], it returns false.
我有两个 python Pandas系列df10和df5. 我想比较它们的值。例如：df10[-1:]< df5[-1:]，它返回true。df10[-2:-1] > df5[-2:-1]，它返回假。
But if I combine them together, df10[-1:]< df5[-1:] and df10[-2:-1]>df5[-2:-1],it returns
但是如果我将它们组合在一起df10[-1:]< df5[-1:] and df10[-2:-1]>df5[-2:-1]，它会返回
The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()
系列的真值是不明确的。使用 a.empty、a.bool()、a.item()、a.any() 或 a.all()
But I expect it returns false. How can I solve this problem?
但我希望它返回 false。我怎么解决这个问题？
采纳答案by rofls
You can do this with the pandas Series valuesattribute:
您可以使用 pandas Seriesvalues属性执行此操作：
if (df10.values[-2:-1] > df5.values[-2:-1]) and\
(df10.values[-1:] < df5.values[-1:]):
print("we met the conditions!")
回答by blackeneth
Consider you have the two dataframes from this program:
假设您有来自该程序的两个数据框：
# Python 3.5.2
import pandas as pd
import numpy as np
# column names for example dataframe
cats = ['A', 'B', 'C', 'D', 'E']
df5 = pd.DataFrame(data = np.arange(25).reshape(5, 5), columns=cats)
print("Dataframe 5\n",df5,"\n")
df10=pd.DataFrame(data = np.transpose(np.arange(25).reshape(5, 5)), columns=cats)
print("Dataframe 10\n",df10)
The resulting data frames are:
生成的数据帧是：
Dataframe 5
A B C D E
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
Dataframe 10
A B C D E
0 0 5 10 15 20
1 1 6 11 16 21
2 2 7 12 17 22
3 3 8 13 18 23
4 4 9 14 19 24
Now let's look at the result of your first comparison:
现在让我们看看第一次比较的结果：
print(df5[-1:])
print(df10[-1:])
a=df10[-1:]< df5[-1:]
print("\n",a,"\n",type(a))
which results in:
这导致：
A B C D E
4 20 21 22 23 24
A B C D E
4 4 9 14 19 24
A B C D E
4 True True True True False
<class 'pandas.core.frame.DataFrame'>
Now the second comparison:
现在第二个比较：
print(df5[-2:-1])
print(df10[-2:-1])
b=df10[-2:-1]>df5[-2:-1]
print("\n",b,"\n",type(b))
which has results:
结果如下：
A B C D E
3 15 16 17 18 19
A B C D E
3 3 8 13 18 23
A B C D E
3 False False False False True
<class 'pandas.core.frame.DataFrame'>
The issue:
问题：
If we evaluate:
如果我们评估：
pd.Series([True, True, False, False]) and pd.Series([False, True, False, True])
What is the correct answer?:
正确答案是什么？：
pd.Series([False, True, False, False])FalseTrue- All of the above
- Any of the above
- It depends
pd.Series([False, True, False, False])FalseTrue- 上述所有的
- 以上任何一项
- 这取决于
The answer is: 6 - It depends. It depends on what you want.
答案是：6 - 这取决于。这取决于你想要什么。
First, we have to create boolean series for the comparison:
首先，我们必须为比较创建布尔系列：
a_new = (df10[-1:] < df5[-1:]).any()
print(a_new,"\n",type(a_new))
b_new = (df10[-2:-1] > df5[-2:-1]).any()
print("\n",b_new,"\n",type(b_new))
The results are:
结果是：
A True
B True
C True
D True
E False
dtype: bool
<class 'pandas.core.series.Series'>
A False
B False
C False
D False
E True
dtype: bool
<class 'pandas.core.series.Series'>
Now, we can compute 3 cases.
现在，我们可以计算 3 个案例。
Case 1: a.any() and b.any()
案例 1：a.any() 和 b.any()
a.any() = True if any item in a is True
b.any() = True if any item in b is True
print(a_new.any() and b_new.any())
The result is True.
结果为真。
Case 2: a.all() and b.all()
情况 2：a.all() 和 b.all()
a.all() = True if every item in a is True
b.all() = True if every item in b is True
print(a_new.all() and b_new.all())
The result is False.
结果是假的。
Case 3: Pairwise comparison
案例 3：成对比较
For this, you have to compare every element to each other.
为此，您必须将每个元素相互比较。
result_pairwise = [a_new and b_new for a_new, b_new in zip(a_new,b_new)]
print(result_pairwise,"\n",type(result_pairwise))
The result is:
结果是：
[False, False, False, False, False]
<class 'list'>
For more details:
更多细节：
