Pandas corr() 经常返回 NaN

Pandas corr() returning NaN too often(Pandas corr() 经常返回 NaN)

问题描述

我试图在数据帧上运行我认为应该是一个简单的相关函数，但它在我认为不应该的地方返回 NaN.

I'm attempting to run what I think should be a simple correlation function on a dataframe but it is returning NaN in places where I don't believe it should.

代码:

# setup
import pandas as pd
import io

csv = io.StringIO(u'''
id  date    num
A   2018-08-01  99
A   2018-08-02  50
A   2018-08-03  100
A   2018-08-04  100
A   2018-08-05  100
B   2018-07-31  500
B   2018-08-01  100
B   2018-08-02  100
B   2018-08-03  0
B   2018-08-05  100
B   2018-08-06  500
B   2018-08-07  500
B   2018-08-08  100
C   2018-08-01  100
C   2018-08-02  50
C   2018-08-03  100
C   2018-08-06  300
''')

df = pd.read_csv(csv, sep = '	')

# Format manipulation
df = df[df['num'] > 50]
df = df.pivot(index = 'date', columns = 'id', values = 'num')
df = pd.DataFrame(df.to_records())

# Main correlation calculations
print df.iloc[:, 1:].corr()

主题数据帧:

       A      B      C
0    NaN  500.0    NaN
1   99.0  100.0  100.0
2    NaN  100.0    NaN
3  100.0    NaN  100.0
4  100.0    NaN    NaN
5  100.0  100.0    NaN
6    NaN  500.0  300.0
7    NaN  500.0    NaN
8    NaN  100.0    NaN

corr() 结果:

    A    B    C
A  1.0  NaN  NaN
B  NaN  1.0  1.0
C  NaN  1.0  1.0

根据(有限的)文档 在函数上，它应该排除NA/空值".既然每一列都有重叠的值，那么结果不应该都是非NaN吗?

According to the (limited) documentation on the function, it should exclude "NA/null values". Since there are overlapping values for each column, should the result not all be non-NaN?

这里有很好的讨论和此处，但都没有回答我的问题.我已经尝试了 float64 的想法，讨论了 here，但是那也失败了.

There are good discussions here and here, but neither answered my question. I've tried the float64 idea discussed here, but that failed as well.

@hellpanderr 的评论提出了一个很好的观点，我使用的是 0.22.0

@hellpanderr's comment brought up a good point, I'm using 0.22.0

额外问题 - 我不是数学家，但在这个结果中 B 和 C 之间如何存在 1:1 的相关性?

推荐答案

结果似乎是您使用的数据的人工制品.在你写的时候，NAs 被忽略了，所以它基本上归结为:

The result seems to be an artefact of the data you work with. As you write, NAs are ignored, so it basically boils down to:

df[['B', 'C']].dropna()

       B      C
1  100.0  100.0
6  500.0  300.0

因此，每列只剩下两个值用于计算，因此应该 导致1的相关系数:

So, there are only two values per column left for the calculation which should therefore lead to to correlation coefficients of 1:

df[['B', 'C']].dropna().corr()

     B    C
B  1.0  1.0
C  1.0  1.0

那么，对于剩余的组合，NA 是从哪里来的?

So, where do the NAs then come from for the remaining combinations?

df[['A', 'B']].dropna()

       A      B
1   99.0  100.0
5  100.0  100.0


df[['A', 'C']].dropna()

       A      C
1   99.0  100.0
3  100.0  100.0

因此，在这里您最终每列只有两个值.不同之处在于 B 和 C 列只包含一个值 (100)，它给出了 0:

So, also here you end up with only two values per column. The difference is that the columns B and C contain only one value (100) which gives a standard deviation of 0:

df[['A', 'C']].dropna().std()

A    0.707107
C    0.000000

计算相关系数时，除以标准差，得到NA.

When the correlation coefficient is calculated, you divide by the standard deviation, which leads to a NA.

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