pandas 数据框中两列的相关系数与 .corr()
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/49350445/
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
Correlation coefficient of two columns in pandas dataframe with .corr()
提问by florence-y
I would like to calculate the correlation coefficient between two columns of a pandas data frame after making a column boolean in nature. The original tablehad two columns: a GroupColumn with one of two treatment groups, now boolean, and an AgeGroup. Those are the two columns I'm looking to calculate the correlation coefficient.
我想在本质上创建列布尔值后计算Pandas数据框的两列之间的相关系数。原来的table有两列：一个Group带有两个处理组之一的列，现在是布尔值，还有一个Age组。这些是我要计算相关系数的两列。
I tried the .corr()method, with:
我尝试了该.corr()方法，其中：
table.corr(method='pearson')
but have this returned to me:
但这是否还给我：
I have pasted the first 25 rows of boolean tablebelow. I don't know if I'm missing parameters, or how to interpret this result. It's also strange that it's 1 as well. Thanks in advance!
我在下面粘贴了前 25 行布尔值table。我不知道我是否缺少参数，或者如何解释这个结果。同样奇怪的是它也是1。提前致谢！
Group Age
0 1 50
1 1 59
2 1 22
3 1 48
4 1 53
5 1 48
6 1 29
7 1 44
8 1 28
9 1 42
10 1 35
11 0 54
12 0 43
13 1 50
14 1 62
15 0 64
16 0 39
17 1 40
18 1 59
19 1 46
20 0 56
21 1 21
22 1 45
23 0 41
24 1 46
25 0 35
回答by Brad Solomon
Calling .corr()on the entire DataFrame gives you a full correlation matrix:
调用.corr()整个 DataFrame 为您提供完整的相关矩阵：
>>> table.corr()
Group Age
Group 1.0000 -0.1533
Age -0.1533 1.0000
You can use the separate Series instead:
您可以改用单独的系列：
>>> table['Group'].corr(table['Age'])
-0.15330486289034567
This should be faster than using the full matrix and indexing it (with df.corr().iat['Group', 'Age']). Also, this should work whether Groupis bool or int dtype.
这应该比使用完整矩阵并对其进行索引（使用df.corr().iat['Group', 'Age']）更快。此外，无论Group是 bool 还是 int dtype ，这都应该有效。
