时间数据处理/数组的轴向汇总

案例：统计每个周一、周二、...、周五的收盘价的平均值，并放入一个数组。

# 统计周一至周五的收盘价的均值
import datetime as dt
import numpy as np


# 转换器函数：将日-月-年格式的日期字符串转换为星期
def dmy2wday(dmy):
  # 把日月年转周N
  dmy = str(dmy, encoding='utf-8')
  date = dt.datetime.strptime(dmy, '%d-%m-%Y').date()
  wday = date.weekday()  # 用 周日
  return wday


wdays, closing_prices = 
  np.loadtxt('aapl.csv', delimiter=',',
             usecols=(1, 6), unpack=True,
             converters={1: dmy2wday})
print(wdays)
"""
[4. 0. 1. 2. 3. 4. 0. 1. 2. 3. 4. 0. 1. 2. 3. 4. 1. 2. 3. 4. 0. 1. 2. 3.
 4. 0. 1. 2. 3. 4.]
"""
# 掩码[wdays==0]
ave_closing_prices = np.zeros(5)
for wday in range(ave_closing_prices.size):
  ave_closing_prices[wday] = np.mean(closing_prices[wdays == wday])
  # ave_closing_prices[wday] = closing_prices[wdays == wday].mean()
print(ave_closing_prices)
# [351.79       350.635      352.13666667 350.89833333 350.02285714]

# 数组的轴向汇总
prices = closing_prices.reshape(6, 5)
print(prices)
"""
[[336.1  339.32 345.03 344.32 343.44]
 [346.5  351.88 355.2  358.16 354.54]
 [356.85 359.18 359.9  363.13 358.3 ]
 [350.56 338.61 342.62 342.88 348.16]
 [353.21 349.31 352.12 359.56 360.  ]
 [355.36 355.76 352.47 346.67 351.99]]
"""


def func(ary):
  # 数组处理函数
  return np.mean(ary),np.std(ary)


r = np.apply_along_axis(func, 0, prices)
print(np.round(r, 2))
"""
[[349.76 349.01 351.22 352.45 352.74]  均值
 [  6.97   7.74   5.86   8.04   5.7 ]] 标准差
"""


for wday, ave_closing_price in zip(
        ['MON', 'TUE', 'WED', 'THU', 'FRI'],
        ave_closing_prices):
  print(wday, np.round(ave_closing_price, 2))

  """
  MON 351.79
  TUE 350.64
  WED 352.14
  THU 350.9
  FRI 350.02
  """

数组的轴向汇总

def func(data):
    pass
#func     处理函数
#axis     轴向 [0,1]
#array     数组
np.apply_along_axis(func, axis, array)

沿着数组中所指定的轴向，调用处理函数，并将每次调用的返回值重新组织成数组返回。