1. 读写文件（基本）
savetxt、loadtxt
i2 = np.eye(2) print(i2) np.savetxt(r"C:\Users\Thomas\Desktop\eye.txt",i2) c,v = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,7),unpack=True) print(c,v) #[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] [21144800. 13473000. 15236800. 9242600. 14064100. 11494200. 17322100. # 13608500. 17240800. 33162400. 13127500. 11086200. 10149000. 17184100. # 18949000. 29144500. 31162200. 23994700. 17853500. 13572000. 14395400. # 16290300. 21521000. 17885200. 16188000. 19504300. 12718000. 16192700. # 18138800. 16824200.]
delimiter=用什么进行分隔符，一般csv文件都是逗号
usecols=6,7，表示获取第七和第八字段数据，也就是股票的收盘价和成交量。
unpack变量为真：拆分存储不同列的数据，即分别将收盘价和成交量的数据赋值给c和v，也就是分开显示的意思。
2. 加权平均价格：average
VWAP
import numpy as np
# 加权平均价格
c,v = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,7),unpack=True)
vwap = np.average(c,weights=v)
print("VWAP = ", vwap)
#VWAP = 350.5895493532009
3. 算术平均值：mean
import numpy as np
# 加权平均价格
c,v = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,7),unpack=True)
mean = np.mean(c)
print("mean = ", mean)
#mean = 351.0376666666667
4. 时间加权平均价格：
TWAP
import numpy as np
# 加权平均价格
c,v = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,7),unpack=True)
t = np.arange(len(c))
twap = np.average(c,weights=t)
print("twap = ", twap)
#twap = 352.4283218390804
5. 最大值、最小值、极差值
max、min、ptp：
import numpy as np
# 最大值、最小值、极差值
h,l = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(4,5),unpack=True)
highest = np.max(h)
lowest = np.min(l)
spread_highest = np.ptp(h)
spread_lowest = np.ptp(l)
print("highest = ", highest)
print("lowest = ", lowest)
print("spread_highest = ", spread_highest)
print("spread_lowest = ", spread_lowest)
#highest = 364.9
#lowest = 333.53
#spread_highest = 24.859999999999957
#spread_lowest = 26.970000000000027
6. 中位数：median
排序函数：msort
方差：var
标准差：std
import numpy as np
# 中位数
c = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,),unpack=True)
print("median = ",np.median(c))
# 排序函数
print("sorted_close = ",np.msort(c))
# 方差函数
print("var = ",np.var(c))
# 标准差函数
print("std = ",np.std(c))
#median = 352.055
#sorted_close = [336.1 338.61 339.32 342.62 342.88 343.44 344.32 345.03 346.5 346.67
# 348.16 349.31 350.56 351.88 351.99 352.12 352.47 353.21 354.54 355.2
# 355.36 355.76 356.85 358.16 358.3 359.18 359.56 359.9 360. 363.13]
#var = 50.126517888888884
#std = 7.080008325481608
7. 差分函数：diff
条件选择函数：where
# 差分函数
c = np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv",delimiter=‘,‘,usecols=(6,),unpack=True)
print("diff = ",np.diff(c))
# 条件选择函数
print("price > 0",np.where(c > 0))
#diff = [ 3.22 5.71 -0.71 -0.88 3.06 5.38 3.32 2.96 -3.62 2.31
# 2.33 0.72 3.23 -4.83 -7.74 -11.95 4.01 0.26 5.28 5.05
# -3.9 2.81 7.44 0.44 -4.64 0.4 -3.29 -5.8 5.32]
#price > 0 (array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
# 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], dtype=int64),)
8. 日期分析：
import numpy as np
from datetime import datetime
def datestr2num(s):
return datetime.strptime(s.decode(‘ascii‘), "%d-%m-%Y").date().weekday()
dates, close=np.loadtxt(r"C:\Users\Thomas\Desktop\data.csv", delimiter=‘,‘, usecols=(1,6), converters={1: datestr2num}, unpack=True)
print("dates = ",dates)
#dates = [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.]
注意：这里的s要解析ascii码
9. summarize函数：对轴或者维度的编号进行定义
apply_along_axis：这个函数会调用另外一个有我们给出的函数，作用于每一个数组元素上。目前我们的数组总有3个元素，分别用于示例数据总的3个星期，元素中的索引值对应于实例数据中的1天。
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Python笔记_第五篇_Python数据分析基础教程_文件的读写
原文：https://www.cnblogs.com/noah0532/p/11273611.html