Numpy入门（二）Numpy常用函数

常用函数

1 文件读写

import numpy as np

i2 = np.eye(2)

i2

array([[1., 0.],
       [0., 1.]])

i2.dtype

dtype('float64')

np.savetxt('eye.txt',i2)

2 CSV文件读写

可以看到,使用逗号分隔符,usecols指定了特定的列,unpack表示将选择的列拆分成多个数据,分别接收

c,v = np.loadtxt('data.csv',delimiter=',',usecols=(6,7),unpack = True)

c

336.1

v

21144800.0

3 成交量加权平均价格(VWAP)

vwap = np.average(c,weights =v)

vwap

336.1

mean = np.mean(c)

mean

336.1

4 取值范围

h,l = np.loadtxt('data.csv',delimiter=',',usecols =(4,5),unpack = True)

h

344.4

l

333.53

np.max(h)

344.4

np.min(l)

333.53

np.ptp(h)

0.0

median = np.median(c)

median

336.1

a = np.random.randn(10)

a

array([-0.91366364,  0.56874779,  1.93163306, -1.20680229,  0.61898735,
       -0.03009919,  1.0977881 , -1.69915867, -0.54960063, -0.38438985])

sorted_close = np.msort(a)

sorted_close

array([-1.69915867, -1.20680229, -0.91366364, -0.54960063, -0.38438985,
       -0.03009919,  0.56874779,  0.61898735,  1.0977881 ,  1.93163306])

np.median(a)

-0.20724452344036656

np.mean(a)

-0.05665579774572808

np.var(a)

1.123988115028372

np.diff(a)

array([ 1.48241142,  1.36288528, -3.13843535,  1.82578963, -0.64908654,
        1.1278873 , -2.79694677,  1.14955804,  0.16521078])

a

array([-0.91366364,  0.56874779,  1.93163306, -1.20680229,  0.61898735,
       -0.03009919,  1.0977881 , -1.69915867, -0.54960063, -0.38438985])

returns = np.diff(a)/a[:-1]

returns

array([ -1.62249143,   2.39629113,  -1.62475752,  -1.51291529,
        -1.04862651, -37.47234271,  -2.54780204,  -0.67654543,
        -0.30060151])

np.std(returns)

11.57815124733535

np.where(returns>0)

(array([1]),)

np.sqrt(1/12)

0.0

np.sqrt (1./12)

0.28867513459481287

a = np.arange(10)

indices = np.where(a>5)

np.take(a,indices)

array([[6, 7, 8, 9]])

np.argmin(a)

0

np.argmax(a)

9

np.ones(5)

array([1., 1., 1., 1., 1.])

a = np.ones(10)

a.fill(5)

a

array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])

np.ones(shape=(2,3),dtype = np.int32)

array([[1, 1, 1],
       [1, 1, 1]], dtype=int32)

np.dot 如果处理的是一维数据,那么就是点积,如果处理二维数据,就是矩阵的乘积

a = np.arange(9)

b = np.arange(9)

np.dot(a,b)

204

a = a.reshape(3,3)

a

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

b = b.reshape(3,3)

np.dot(a,b)

array([[ 15,  18,  21],
       [ 42,  54,  66],
       [ 69,  90, 111]])

b.clip(3,6)

array([[3, 3, 3],
       [3, 4, 5],
       [6, 6, 6]])

a = np.arange(9)

a.compress(a>2)

array([3, 4, 5, 6, 7, 8])

a = np.arange(1,9)

a.prod()

40320

a.cumprod()

array([    1,     2,     6,    24,   120,   720,  5040, 40320])