本文是我在学习期间的笔记，看的书是《Python数据分析基础教程 NumPy学习指南 》第二版。

读入csv文件

  • cvs Comma-Separated Value,逗号分割符
  • unpack= True 分拆存储不同的数据

平均值

vwap 成交量加权平均成绩

import numpy as np

def str2num(s):
    return float(s[1:-1])

def str2num_m(st):
    return float(st[1:-2])

c, v = np.loadtxt('data.csv',delimiter=',', usecols=(1,5),converters={1:str2num,5:str2num_m},unpack=True)
vwap = np.average(c, weights = v)
print("VWAP = ",vwap)

np.mean() 计算算术平均值

print("收盘价的平均值为： ",np.mean(c))
print("成交量的平均值是： ",np.mean(v))

时间加权平均价格

t = np.arange(len(c))
print("TWAP = ", np.average(c, weights=t))

取值范围

high_price, low_price = np.loadtxt('data.csv',delimiter=',', usecols=(3,4),converters={3:str2num,4:str2num},unpack=True)
print("Highest = ", np.max(high_price))
print("Lowest = ", np.min(low_price))
  • ptp() 返回数组最大值与最小值之间的差值
print("Spread high price: ", np.ptp(high_price))
print("Spread low price: ", np.ptp(low_price))

统计分析

print(high_price)
print(high_price.ndim)
print(len(high_price))
print(high_price.shape)
print(type(high_price))

获取收盘价

closing_price = np.loadtxt('data.csv',delimiter=',',usecols=(1,),converters={1:str2num})

np.median() 获取中位数

print(np.median(closing_price))

np.msort() 对数组排序

sorted_close = np.msort(closing_price)
print("sorted = ", sorted_close)

N = len(closing_price)
print("middle = ", sorted_close[int((N - 1) / 2)])

print("average middle = ",(sorted_close[int(N/2)] + sorted_close[int((N - 1)/2)]) / 2)

np.var(c) 方差

print("variance = ", np.var(closing_price))

print("Variance from definition = ", np.mean((closing_price - np.mean(closing_price)) ** 2))

专用函数

import numpy as np
import time

# 对应数据格式 ""2019年2月3日""
def datestr2num(s):
    s = s[1:-1]
    fmt = "%Y年%m月%d日"
    t1 = time.strptime(s,fmt)
    return time.strftime("%Y%m%d",t1)

dates = np.loadtxt("data.csv",delimiter = ',', usecols=(0,),converters={0:datestr2num},unpack=True)
print(date)

  • 未完成

对复数进行排序

  • sortcomplex 函数对生成的复数进行排序
import numpy as np

np.random.seed(42)
complex_numbers = np.random.random(5) + 1j * np.random.random(5)
print("Complex numbers\n ",complex_numbers)

print("Sorted\n", np.sort_complex(complex_numbers))

搜索

  • np.argmax() 返回数组中最大值对应的下标
  • np.nanargmax() 返回数组中最大值对应的下标，忽略Nan的值
  • np.argmin()
  • np.nanargmin()
  • np.argwhere() 根据条件搜索非0的元素，并分组返回对应的下标
a = np.array([2,3,4,1,3,5,0,6])
print("index of the min number: ",np.argmin(a))
print("index of the max number: ",np.argmax(a))

b = np.array([np.nan,3,5,6,7,8])
print("index of the min number: ",np.argmin(a))
print("index of the max number: ",np.argmax(a))

print("a = ",a)
print("np.argwhere(a <= 4): ",np.argwhere(a <= 4))
  • searchsorted() 为指定的插入值返回一个在有序数组中的索引位置，从这个位置插入可以保持数组的有序性
  • np.insert()
a = np.arange(9)
indices = np.searchsorted(a,[-2,6])
print("a = ",a)
print("indices :",indices)
print("The full array",np.insert(a,indices,[-2,6]))

数组元素抽取

  • extract() 根据某个条件在数组中抽取元素
  • nonzero() 抽取数组中的非0元素
a = np.arange(9)
condition = (a % 2) == 0
print("Even numbers: ",np.extract(condition, a))

print("non zero: ",np.nonzero(a))

金融函数

  • 未完成