如何将具有日分辨率的Numpy时间序列库存数据转换为月或周分辨率

Question

I am working with a python script from Harrison Kinsley to plot stock data ( Link )

The script was modified to accept data from a csv with very detailed time series data (day per day resolution timesteps). The csv is loaded into numpy on this line.

date, closep, highp, lowp, openp, volume = np.loadtxt(out,delimiter=',', unpack=True,
                                                      converters={ 0: mdates.strpdate2num('%Y%m%d')})

Is it somehow possible to convert the data to less datapoints? Month by month resolution or week by week resolution?

Answer 1

Well, you didn't say exactly what do you want to do. Less datapoints means that you either pick any random datapoint, of perform some operation: here you got many possibilities: get the median or average for a given day/week/month, or maybe max/min values, whatever you want, really. The code:

import numpy as np
share_prices  = np.array([100,102,104,106,104, 109, 110])
median_price = np.median(share_prices)
104.0
average_price = np.average(share_prices)
105.0
min_price = np.min(share_prices)
100
max_price = np.max(share_prices)
110

All of that would be much easier and more fun with awesome Pandas module written exactly for that purpose.