numpy corrcoef - 计算相关矩阵,同时忽略丢失的数据

Question

我正在尝试计算几个值的相关矩阵.这些值包括一些'nan'值.我正在使用numpy.corrcoef.对于输出相关矩阵的元素(i,j),我希望使用对于变量i和变量j都存在的所有值来计算相关性.

这就是我现在拥有的:

In[20]: df_counties = pd.read_sql("SELECT Median_Age, Rpercent_2008, overall_LS, population_density FROM countyVotingSM2", db_eng)
In[21]: np.corrcoef(df_counties, rowvar = False)
Out[21]: 
array([[ 1.        ,         nan,         nan, -0.10998411],
       [        nan,         nan,         nan,         nan],
       [        nan,         nan,         nan,         nan],
       [-0.10998411,         nan,         nan,  1.        ]])

太多的南瓜:(

Answer 1

其中一个主要特点pandas是NaN友好.要计算相关矩阵,只需调用即可df_counties.corr().下面是展示一个例子df.corr()是NaN宽容,而np.corrcoef不是.

import pandas as pd
import numpy as np

# data
# ==============================
np.random.seed(0)
df = pd.DataFrame(np.random.randn(100,5), columns=list('ABCDE'))
df[df < 0] = np.nan
df

         A       B       C       D       E
0   1.7641  0.4002  0.9787  2.2409  1.8676
1      NaN  0.9501     NaN     NaN  0.4106
2   0.1440  1.4543  0.7610  0.1217  0.4439
3   0.3337  1.4941     NaN  0.3131     NaN
4      NaN  0.6536  0.8644     NaN  2.2698
5      NaN  0.0458     NaN  1.5328  1.4694
6   0.1549  0.3782     NaN     NaN     NaN
7   0.1563  1.2303  1.2024     NaN     NaN
8      NaN     NaN     NaN  1.9508     NaN
9      NaN     NaN  0.7775     NaN     NaN
..     ...     ...     ...     ...     ...
90     NaN  0.8202  0.4631  0.2791  0.3389
91  2.0210     NaN     NaN  0.1993     NaN
92     NaN     NaN     NaN  0.1813     NaN
93  2.4125     NaN     NaN     NaN  0.2515
94     NaN     NaN     NaN     NaN  1.7389
95  0.9944  1.3191     NaN  1.1286  0.4960
96  0.7714  1.0294     NaN     NaN  0.8626
97     NaN  1.5133  0.5531     NaN  0.2205
98     NaN     NaN  1.1003  1.2980  2.6962
99     NaN     NaN     NaN     NaN     NaN

[100 rows x 5 columns]

# calculations
# ================================
df.corr()

        A       B       C       D       E
A  1.0000  0.2718  0.2678  0.2822  0.1016
B  0.2718  1.0000 -0.0692  0.1736 -0.1432
C  0.2678 -0.0692  1.0000 -0.3392  0.0012
D  0.2822  0.1736 -0.3392  1.0000  0.1562
E  0.1016 -0.1432  0.0012  0.1562  1.0000


np.corrcoef(df, rowvar=False)

array([[ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan]])

Answer 2

这将起作用，使用掩码数组 numpy模块：

import numpy as np
import numpy.ma as ma

A = [1, 2, 3, 4, 5, np.NaN]
B = [2, 3, 4, 5.25, np.NaN, 100]

print(ma.corrcoef(ma.masked_invalid(A), ma.masked_invalid(B)))

它输出：

[[1.0 0.99838143945703]
 [0.99838143945703 1.0]]

在此处阅读更多信息：https : //docs.scipy.org/doc/numpy/reference/maskedarray.generic.html