numpy——ndarray运算（自生成股票数据案例）之逻辑运算、通用判断函数、三元运算符
一、逻辑运算
1.随机生成八只股票两周的交易日涨幅数据
>>> import numpy as np
>>> stock_change=np.random.normal(loc=0,scale=1,size=(8,10))
>>> stock_change
array([[-1.46202007, 0.95114736, 0.25165712, -1.80776718, -3.09513675,
-1.8709032 , 0.54687731, 0.2820287 , 0.97948358, 0.20339662],
[-1.27229295, -1.38005646, -0.60752081, 0.59927206, 0.53832373,
-1.80358735, -0.14047816, -1.09983869, 0.51795043, -1.24475096],
[-1.20619283, -1.99180927, 0.17533276, -0.19729808, 0.30560366,
-1.75770429, -0.69314496, -1.74585604, -1.10605684, 1.3995043 ],
[ 0.75374035, 1.76062662, -0.37416021, -0.4627814 , -1.28653669,
-0.34524617, -1.07785026, 1.71138629, 1.49583181, 0.0420216 ],
[-1.33479791, -0.93622978, 1.426623 , 1.51883403, -0.18363733,
0.08131291, -0.24989836, 0.15593843, 1.55480894, 1.31589355],
[ 0.44094674, -0.84947326, 0.0424738 , -0.33424018, 0.26616388,
-0.01324623, -0.3610848 , -1.17807392, 0.33983419, -0.7651107 ],
[ 0.1741568 , -1.56346108, 1.12376595, 1.44652797, 1.10749741,
-1.11022536, -0.83824722, 0.91788999, 0.82594165, 0.84196209],
[-0.51520268, -0.62884247, 1.09889016, -0.53605015, 1.31925862,
-0.73569019, 0.57139481, -0.43308124, 0.62886824, 0.75989891]])
>>>
2.逻辑判断，涨跌幅大于0.5标记为True，否则为False
>>> stock_change>0.5
array([[False, True, False, False, False, False, True, False, True,
False],
[False, False, False, True, True, False, False, False, True,
False],
[False, False, False, False, False, False, False, False, False,
True],
[ True, True, False, False, False, False, False, True, True,
False],
[False, False, True, True, False, False, False, False, True,
True],
[False, False, False, False, False, False, False, False, False,
False],
[False, False, True, True, True, False, False, True, True,
True],
[False, False, True, False, True, False, True, False, True,
True]])
3.赋值
>>> stock_change[stock_change>0.5]=1
#stock_change[stock_change>0.5]称为布尔索引，即满足这个条件的值，赋值为1
>>> stock_change
array([[-1.46202007, 1. , 0.25165712, -1.80776718, -3.09513675,
-1.8709032 , 1. , 0.2820287 , 1. , 0.20339662],
[-1.27229295, -1.38005646, -0.60752081, 1. , 1. ,
-1.80358735, -0.14047816, -1.09983869, 1. , -1.24475096],
[-1.20619283, -1.99180927, 0.17533276, -0.19729808, 0.30560366,
-1.75770429, -0.69314496, -1.74585604, -1.10605684, 1. ],
[ 1. , 1. , -0.37416021, -0.4627814 , -1.28653669,
-0.34524617, -1.07785026, 1. , 1. , 0.0420216 ],
[-1.33479791, -0.93622978, 1. , 1. , -0.18363733,
0.08131291, -0.24989836, 0.15593843, 1. , 1. ],
[ 0.44094674, -0.84947326, 0.0424738 , -0.33424018, 0.26616388,
-0.01324623, -0.3610848 , -1.17807392, 0.33983419, -0.7651107 ],
[ 0.1741568 , -1.56346108, 1. , 1. , 1. ,
-1.11022536, -0.83824722, 1. , 1. , 1. ],
[-0.51520268, -0.62884247, 1. , -0.53605015, 1. ,
-0.73569019, 1. , -0.43308124, 1. , 1. ]])
二、通用判断函数
1.np.all()
只要有一个False就返回False，只有全是True才返回True
判断stock_change[0:2,0:5]是否全是上涨的
>>> stock_change[0:2,0:5]>0
array([[False, False, True, False, True],
[ True, True, False, False, True]])
>>> np.all(stock_change[0:2,0:5]>0)
False
>>>
2.np.any()
只要有一个True就返回True
判断前五支股票是否上涨
>>> stock_change[0:5,:]>0
array([[False, False, True, False, True, False, False, True, True,
True],
[ True, True, False, False, True, False, False, True, False,
False],
[ True, True, False, True, False, True, False, False, True,
False],
[ True, True, True, False, False, True, True, False, True,
False],
[ True, True, False, False, True, True, False, True, True,
True]])
>>> np.any(stock_change[0:5,:]>0)
True
>>>
三、三元运算符
1.np.where(布尔值，True的位置的值，False的位置的值)
判断前四个股票前四天的涨跌幅，大于0的变为1，否则为0。
>>> temp = stock_change[0:4,0:4]
>>> temp
array([[-1.46647737, -0.85875713, 1.52534704, -0.50528693],
[ 0.34089158, 0.9204162 , -0.95259284, -0.49692041],
[ 0.4610343 , 0.6506377 , -0.89275792, 1.10992143],
[ 0.92498422, 1.05165063, 0.9136756 , -1.01392671]])
>>> np.where(temp>0,1,0)
array([[0, 0, 1, 0],
[1, 1, 0, 0],
[1, 1, 0, 1],
[1, 1, 1, 0]])
2.复杂判断
逻辑且，逻辑或
np.logical_and
np.logical_or
>>> np.logical_and(temp>0.5,temp<1)#大于0.5且小于1
array([[False, False, False, False],
[False, True, False, False],
[False, True, False, False],
[ True, False, True, False]])
>>> np.where(np.logical_and(temp>0.5,temp<1),1,0)#True的变成1，False的变成0
array([[0, 0, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[1, 0, 1, 0]])