Python数据在两个不同的数组中随机丢失。希望将这些结合起来制作单个阵列

I've recently started using Python (and have also started looking at R).I came across an interesting example (copied below for reference) in R which I wanted to try and see if I could implement in Python (without using rpy or Pandas etc.).

我最近开始使用Python（并且也开始查看R）。我在R中遇到了一个有趣的例子（在下面复制以供参考）我想尝试看看我是否可以在Python中实现（不使用rpy或Pandas）等等。）。

R code-example

R代码示例

# Goal:
#       A stock is traded on 2 exchanges.
#       Price data is missing at random on both exchanges owing to non-trading.
#       We want to make a single price time-series utilising information
#       from both exchanges. I.e., missing data for exchange 1 will
#       be replaced by information for exchange 2 (if observed).
# Let's create some example data for the problem.
e1 <- runif(15)                         # Prices on exchange 1
e2 <- e1 + 0.05*rnorm(15)               # Prices on exchange 2.
cbind(e1, e2)
# Blow away 5 points from each at random.
e1[sample(1:15, 5)] <- NA
e2[sample(1:15, 5)] <- NA
cbind(e1, e2)
# Now how do we reconstruct a time-series that tries to utilise both?
combined <- e1                          # Do use the more liquid exchange here.
missing <- is.na(combined)
combined[missing] <- e2[missing]        # if it's also missing, I don't care.
cbind(e1, e2, combined)

I have tried

我努力了

import numpy as np 
e1=np.random.random((15,)).reshape(-1,1)
e2=e1+0.05*np.random.standard_normal(15).reshape(-1,1)
np.concatenate((e1,e2),axis=1) # cbind equivalent on two vectors

I have not managed to do the next section i.e.

我没有设法做下一节，即

# Blow away 5 points from each at random

I did try python's np.random.random_sample command but could not get it to work at all.
I would very much appreciate your assistance please with this and the last section i.e. reconstructing the timeseries that tries to utilise both data arrays as described above.

我确实尝试过python的np.random.random_sample命令，但根本无法使用它。我非常感谢您对此以及最后一节的帮助，即重建试图利用上述数据阵列的时间序列。

1 个解决方案

#1

You can use the "random" package

您可以使用“随机”包

import numpy as np 
import random
e1=np.random.random((15,)).reshape(-1,1)
e2=e1+0.05*np.random.standard_normal(15).reshape(-1,1)
e1[random.sample(range(e1.shape[0]), 5),:] = np.nan
e2[random.sample(range(e2.shape[0]), 5),:] = np.nan
np.concatenate((e1,e2),axis=1)