我最近开始使用 Python（并且也开始研究 R）。我在 R 中遇到了一个有趣的示例（复制如下以供参考），我想尝试看看我是否可以在 Python 中实现（不使用 rpy 或 Pandas ETC。）。
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)
我试过了
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
我还没有设法做下一部分，即
# Blow away 5 points from each at random
我确实尝试了 python 的np.random.random_sample命令，但根本无法让它工作。
我非常感谢您在本节和最后一节的帮助，即重建试图利用上述两个数据数组的时间序列。