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day02_numpy_demo

news 2026/1/22 3:35:44

Numpy

Numpy的优势
ndarray属性
基本操作
ndarray.func()
numpy.func()
ndarray的运算：逻辑运算、统计运算、数组间运算
合并、分割、IO操作、数据处理,不过这个一般使用的是pandas

Numpy的优势

Numpy = numerical数值化 + python 数值计算的python库，用于快速处理任意维度的数组。
ndarrray = n任意个 + d(dimension维度) + array 任意维度的数组的意思
Numpy使用ndarray对象来处理多维数组，该对象是一个快速而灵活的大数据容器
Numpy提供了一个N维数组类型ndarray，他描述相同类型的items的集合

import numpy as npscore = np.array([[80, 89, 86, 67, 79],[78, 97, 89, 67, 81],[90, 94, 78, 67, 74],[91, 91, 90, 67, 69],[76, 87, 75, 67, 86],[70, 79, 84, 67, 84],[94, 92, 93, 67, 64],[86, 85, 83, 67, 80]])
score

array([[80, 89, 86, 67, 79],[78, 97, 89, 67, 81],[90, 94, 78, 67, 74],[91, 91, 90, 67, 69],[76, 87, 75, 67, 86],[70, 79, 84, 67, 84],[94, 92, 93, 67, 64],[86, 85, 83, 67, 80]])

type(score)

numpy.ndarray

## ndarray和list的效率的对比
import random
import time
import numpy as np
a = []
for i in range(5000000):a.append(random.random())
t1 = time.time()
sum1=sum(a)
t2 = time.time()b = np.array(a)
t4 = time.time()
sum3=np.sum(b)
t5 = time.time()
print('使用原生list的求和计算使用的时间：', t2-t1, "\t使用ndarry的时间计算：", t5-t4)

使用原生list的求和计算使用的时间： 0.03126645088195801 	使用ndarry的时间计算： 0.0027697086334228516

从上面的结果显示使用ndarray的时间处理和原生的list相比更加快速
Numpy专门的针对ndarray的操作和运算进行了设计，所以数组的存储效率和输入输出性能远远的高于Python中嵌套列表

第一个：内存块存储风格：ndarray必须要相同的类型，可以连续存储 list的通用性强，可以不同类型数据，所以list数据之间是依靠引用的形式存储
第二个：并行化处理形式：ndarray支持并行化运算
第三个：底层语言：Numpy底层语言是c，内部解除了GIL全局解释器的限制

ndarray属性

属性

ndarray.shape：数组维度的元组
ndarray.ndim：数组维度
ndarray.size：数组中元素的个数
ndarray.itemszie：一个数组元素的长度
ndarray.dtype：数组元素的类型

score
print(score.shape) #(8, 5) 8行5列
print(score.ndim) # 2
print(score.size) # 40
print(score.itemsize) # 4
print(score.dtype) # int32

(8, 5)
2
40
4
int32

## ndarray的形状
import numpy as np
a = np.array([1, 2, 3, 4])
b = np.array([[1, 2, 3], [3, 4, 5]])
c = np.array([[[1, 3, 4], [3, 4, 5]], [[1, 5, 7], [4, 7, 8]]])

print(a.shape, b.shape, c.shape)

(4,) (2, 3) (2, 2, 3)

print(a, '\n\n', b, '\n\n', c)

[1 2 3 4] [[1 2 3][3 4 5]] [[[1 3 4][3 4 5]][[1 5 7][4 7 8]]]

data = np.array([1.1, 2.2, 3.3], dtype=np.float32)
data2 = np.array([1.2, 2.2, 3.2], dtype='float32')

print(data, data.dtype, data2, data2.dtype)

[1.1 2.2 3.3] float32 [1.2 2.2 3.2] float32

生成数组

生成0和1的：
- np.ones(shape[, dtype, order]) np.zeros(shape[, dtype, order])
- np.ones(shape=(2, 3), dtype=‘int32’)
- np.zeros(shape=(2, 3), dtype=np.float32)
从现有数组中生成：
- np.array() np.copy() np.asarray()
- data1 = np.array(score) ## 深拷贝
- data2 = np.asarray(score) ## 浅拷贝
- data3 = np.copy(score) ## 深拷贝
生成固定范围的数组：
- np.linspace(satrt, stop, num, endpoint, restep, detype) np.arange()
- np.linspace(0, 10, 100) ## [0, 10]产生100个等距离的数组
- np.arange(a, b, c) ## 产生[a, b) 步长为c的数组
生成随机数组：
- np.random.rand(d0, d1, d2,....) 返回[0.0, 1.0]内的一组均匀分布的数组, d0, d1, d2表示维度的元组数据
- np.random.uniform(low=0.0, high=1.0, size=None) 均匀分布[low, high)，size-int类型表输出一位样本数，元组表输出的是对应维度数组
- np.random.normal(loc=0.0, scale=1.0, size=None) 正态分布均值loc 标准差scale 形状size

np.ones(shape=(2, 4))

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

np.zeros((4, 3))

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

data1 = np.array([1, 3, 4, 5])
data1

array([1, 3, 4, 5])

data2 = np.asarray(data1)
data2

array([1, 3, 4, 5])

data3 = np.copy(data1)
data3

array([1, 3, 4, 5])

np.linspace(0, 10, 100)

array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])

np.arange(0, 100, 2)

array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])

np.random.uniform(1, 2, 20)

array([1.08186729, 1.14786875, 1.70033877, 1.21356519, 1.80826522,1.82539046, 1.2411259 , 1.94754535, 1.26016768, 1.95195603,1.83118684, 1.93096164, 1.42540342, 1.01900246, 1.00777939,1.94587154, 1.30147204, 1.85872718, 1.51138215, 1.72144173])

np.random.rand(2, 3)

array([[0.93695681, 0.54056962, 0.05346231],[0.25430123, 0.4679477 , 0.42365386]])

data4 = np.random.normal(0, 1, 10000000)
data4

array([-1.37843425,  0.43112438,  0.74566392, ...,  1.11031839,-0.35627334, -0.49286865])

import matplotlib.pyplot as pltplt.figure(figsize=(15, 8), dpi=80)
plt.hist(data4, 1000)
plt.show()

在这里插入图片描述

数组的切片操作和数据索引

import numpy as np
stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
stock_change

array([[-0.0128315 ,  1.36389291,  1.67468755, -1.63839812,  0.50246918,0.40632079,  0.5468709 , -1.51506239, -0.95175431,  0.79676231],[-0.29024725, -0.85783328, -2.88228976,  0.09475102,  0.26886068,-0.72337737,  0.32906655,  1.38442008,  0.22017286,  0.11595155],[-1.48797053, -0.34888996, -0.46878054,  0.06614233, -1.2163201 ,-0.12437208, -0.48048511,  0.92053831,  1.37148844,  0.4052761 ],[-0.68483909,  1.45441467,  0.32439071,  2.09266866, -1.40087978,0.21482243,  1.06350017, -1.12371055, -0.21362273, -0.86489608],[-0.8955743 , -2.80666246, -1.81775787, -0.64719575, -1.03749633,-0.09075791,  0.04027887,  0.88156425, -0.38851649,  0.4366844 ],[-0.6112534 ,  0.20743331, -1.10785011, -1.94937533,  0.79183302,-1.43629441, -0.39276676,  1.43465142, -0.77917209,  0.75375268],[-0.45255197,  0.21874378,  0.74356075,  0.89123163,  0.80052696,0.07645454,  1.18475498,  1.21210169, -2.57089921, -0.04719686],[ 1.49996354,  1.73125796,  0.35972564, -0.31768555, -0.23859956,0.14878977,  1.78480518, -0.157626  ,  0.52180221,  1.53564593]])

stock_change[0, 0:3] # 二维数组中第一个一维数组中的第0到3个之间的数据，左闭右开

array([-1.23848824,  1.80273454,  0.48612183])

a1 = np.array([[[1, 2, 3],[4, 5, 6]], [[12, 3, 4], [5, 6, 7]]])
a1

array([[[ 1,  2,  3],[ 4,  5,  6]],[[12,  3,  4],[ 5,  6,  7]]])

a1[1, 0, 2]  ## 三维数组中第二个二维数组中的第一个一维数组的第三个数据

形状的修改

ndarray.reshape(shape)
ndarray.resize(shape)
ndarray.T

print(stock_change.shape)
print(stock_change)
data = stock_change.reshape(10, 8)  ## 有返回值 不修改对象stock_change的原始数据
print(data.shape)
print(data)

(10, 8)
[[-1.23848824  1.80273454  0.48612183 -0.72560924  0.70273282  1.0001417-1.50264292  0.07910228][ 0.50097203 -0.30643765 -2.06606864  1.06603865 -0.24707909 -0.435822391.40507793  0.16617008][ 0.90592803  0.42831191 -0.92043446 -0.86909989  1.86906101 -0.27504789-0.85507962 -0.06812796][-0.47386474 -0.12860694  0.78529739  0.6299527   1.35195163  0.52554048-1.44443021 -0.30228474][-2.00270709 -0.93547033 -1.91377025 -0.44282643  0.39398671 -1.157779111.06886255 -0.99258445][ 1.46011953  0.02989662 -0.57156073  0.33255032  1.10206919  1.10728184-0.2309872  -0.36046913][ 0.6419396   0.45193213 -0.28647482  2.35270101 -1.36580147 -0.3416711-0.68923525  0.40515396][-0.65856583 -0.80067154  1.00151152 -0.59024112  1.72517446  0.992832990.32894163  0.29112266][-0.02950995  1.00548516  0.28799688 -0.23560119 -0.27545952 -2.067568870.10599702  1.29010633][ 0.10229354 -1.61937238 -2.19289266 -2.0243394  -1.584921    1.15768340.11722609  1.00201755]]
(10, 8)
[[-1.23848824  1.80273454  0.48612183 -0.72560924  0.70273282  1.0001417-1.50264292  0.07910228][ 0.50097203 -0.30643765 -2.06606864  1.06603865 -0.24707909 -0.435822391.40507793  0.16617008][ 0.90592803  0.42831191 -0.92043446 -0.86909989  1.86906101 -0.27504789-0.85507962 -0.06812796][-0.47386474 -0.12860694  0.78529739  0.6299527   1.35195163  0.52554048-1.44443021 -0.30228474][-2.00270709 -0.93547033 -1.91377025 -0.44282643  0.39398671 -1.157779111.06886255 -0.99258445][ 1.46011953  0.02989662 -0.57156073  0.33255032  1.10206919  1.10728184-0.2309872  -0.36046913][ 0.6419396   0.45193213 -0.28647482  2.35270101 -1.36580147 -0.3416711-0.68923525  0.40515396][-0.65856583 -0.80067154  1.00151152 -0.59024112  1.72517446  0.992832990.32894163  0.29112266][-0.02950995  1.00548516  0.28799688 -0.23560119 -0.27545952 -2.067568870.10599702  1.29010633][ 0.10229354 -1.61937238 -2.19289266 -2.0243394  -1.584921    1.15768340.11722609  1.00201755]]

stock_change.resize((10, 8))  ## 无返回值 直接改变stock_change对象
stock_change

array([[-1.23848824,  1.80273454,  0.48612183, -0.72560924,  0.70273282,1.0001417 , -1.50264292,  0.07910228],[ 0.50097203, -0.30643765, -2.06606864,  1.06603865, -0.24707909,-0.43582239,  1.40507793,  0.16617008],[ 0.90592803,  0.42831191, -0.92043446, -0.86909989,  1.86906101,-0.27504789, -0.85507962, -0.06812796],[-0.47386474, -0.12860694,  0.78529739,  0.6299527 ,  1.35195163,0.52554048, -1.44443021, -0.30228474],[-2.00270709, -0.93547033, -1.91377025, -0.44282643,  0.39398671,-1.15777911,  1.06886255, -0.99258445],[ 1.46011953,  0.02989662, -0.57156073,  0.33255032,  1.10206919,1.10728184, -0.2309872 , -0.36046913],[ 0.6419396 ,  0.45193213, -0.28647482,  2.35270101, -1.36580147,-0.3416711 , -0.68923525,  0.40515396],[-0.65856583, -0.80067154,  1.00151152, -0.59024112,  1.72517446,0.99283299,  0.32894163,  0.29112266],[-0.02950995,  1.00548516,  0.28799688, -0.23560119, -0.27545952,-2.06756887,  0.10599702,  1.29010633],[ 0.10229354, -1.61937238, -2.19289266, -2.0243394 , -1.584921  ,1.1576834 ,  0.11722609,  1.00201755]])

stock_change.T  ## 转置

array([[-1.23848824,  0.50097203,  0.90592803, -0.47386474, -2.00270709,1.46011953,  0.6419396 , -0.65856583, -0.02950995,  0.10229354],[ 1.80273454, -0.30643765,  0.42831191, -0.12860694, -0.93547033,0.02989662,  0.45193213, -0.80067154,  1.00548516, -1.61937238],[ 0.48612183, -2.06606864, -0.92043446,  0.78529739, -1.91377025,-0.57156073, -0.28647482,  1.00151152,  0.28799688, -2.19289266],[-0.72560924,  1.06603865, -0.86909989,  0.6299527 , -0.44282643,0.33255032,  2.35270101, -0.59024112, -0.23560119, -2.0243394 ],[ 0.70273282, -0.24707909,  1.86906101,  1.35195163,  0.39398671,1.10206919, -1.36580147,  1.72517446, -0.27545952, -1.584921  ],[ 1.0001417 , -0.43582239, -0.27504789,  0.52554048, -1.15777911,1.10728184, -0.3416711 ,  0.99283299, -2.06756887,  1.1576834 ],[-1.50264292,  1.40507793, -0.85507962, -1.44443021,  1.06886255,-0.2309872 , -0.68923525,  0.32894163,  0.10599702,  0.11722609],[ 0.07910228,  0.16617008, -0.06812796, -0.30228474, -0.99258445,-0.36046913,  0.40515396,  0.29112266,  1.29010633,  1.00201755]])

类型的修改和数组去重

ndarray.astype(type)
ndarray序列化到本地
- ndarray.tostring()
- ndarray.tobytes()
np.unique() 去重

stock_change.astype(np.int32)

array([[ 0,  1,  1, -1,  0,  0,  0, -1,  0,  0],[ 0,  0, -2,  0,  0,  0,  0,  1,  0,  0],[-1,  0,  0,  0, -1,  0,  0,  0,  1,  0],[ 0,  1,  0,  2, -1,  0,  1, -1,  0,  0],[ 0, -2, -1,  0, -1,  0,  0,  0,  0,  0],[ 0,  0, -1, -1,  0, -1,  0,  1,  0,  0],[ 0,  0,  0,  0,  0,  0,  1,  1, -2,  0],[ 1,  1,  0,  0,  0,  0,  1,  0,  0,  1]])

stock_change.tobytes()  ## 之前可以使用tostring的方法

b"\x10\x83d\xcbfG\x8a\xbf\x06\xcb\n_\x81\xd2\xf5?\xf6i\x89+\x85\xcb\xfa?(\x9dK\xf1\xe06\xfa\xbf\x040\xd3<:\x14\xe0?\xf4\x96\xb4\xeb(\x01\xda?\x9b\xfe\x94e\xf7\x7f\xe1?\x80I\xb5\x10\xb2=\xf8\xbf\xf2\x01\xcbv\xc5t\xee\xbf\x92\xbe9\xac\x13\x7f\xe9?F\x98\xc71i\x93\xd2\xbf\xcf~\x07\xc6^s\xeb\xbf$a\xd4\xee\xed\x0e\x07\xc0\xf2\xf0\x87I\x9aA\xb8?/\x91\xedg\x035\xd1?\xc0\x85\xe6K\xe8%\xe7\xbf9\r\r*m\x0f\xd5?H\x8d\xcb\xab\x95&\xf6?A\xed \xca\x9f.\xcc?\xb0\xce\x0f;\x00\xaf\xbd?\xe4\xa3\x860\xba\xce\xf7\xbf\x9e5\x1b\x8c6T\xd6\xbfv\xdd\xc3\x15\x80\x00\xde\xbf\x19s/\x1c\xb4\xee\xb0?\x9c\xc7I\x11\x0cv\xf3\xbf`\xcb$A\xd9\xd6\xbf\xbf}\xbd\xa6\x99D\xc0\xde\xbf(\xedu\xc2\x0cu\xed?W\x04\xd2\xdd\x9d\xf1\xf5?MD\xf8)\x0b\xf0\xd9?[`\xc0\xaa3\xea\xe5\xbf6ozQHE\xf7?M*CB\xd1\xc2\xd4?{<!\x11\xc9\xbd\x00@\xb3\x0b\xb0\xeb\x00j\xf6\xbf\x86\xfc\xe7*M\x7f\xcb?_\xca\xdc\xbf\x18\x04\xf1?\x85]G\xea\xb7\xfa\xf1\xbfi4gX\xfdW\xcb\xbf\xc2g^\x8b:\xad\xeb\xbf\x06l\x0bo\x8b\xa8\xec\xbf{L4s\x0bt\x06\xc0;\xdd0F\x89\x15\xfd\xbf\xf6\x06\x03\xde\xd3\xb5\xe4\xbf}\x13\xc9\xc0\x95\x99\xf0\xbf\xfb\rF\x14\xe9;\xb7\xbf\xa1\xa1\x9fpn\x9f\xa4?\x19\xa3\x84<\xc65\xec?\xb9^\xa1Ft\xdd\xd8\xbf\x8b,N\x1f\xa3\xf2\xdb?\xe4@UJc\x8f\xe3\xbfC\x02\xa0\xbf,\x8d\xca?\xf5)\x82\t\xc1\xb9\xf1\xbf\xbdxl0\xa40\xff\xbfi\x02C3\xb2V\xe9?Q^\x8d\xd9\x0f\xfb\xf6\xbf\xb0\x9c\x914\x17#\xd9\xbfe\xdf\xd2\x0cU\xf4\xf6?\xf3\xf7\xf4M\xfa\xee\xe8\xbf\xb6R=\xee\xbd\x1e\xe8?\x84o$\x87\x9c\xf6\xdc\xbf\xf5\xc6$\xd6\xcb\xff\xcb?@\xf6@\xeb?\xcb\xe7?\\\xa311\xf8\x84\xec?S\xf6>\xb5\xea\x9d\xe9?\x06\x18\xed_\x86\x92\xb3?_\xaf\x14\xa2\xc1\xf4\xf2?O\xd2\x02\xbd\xc4d\xf3?p\xe7\x80\x9a3\x91\x04\xc0\xeb\xfe#\xf2/*\xa8\xbf\x9a\xfa\\\xc5\xd9\xff\xf7?\xf4\xfe\xb0\x8a;\xb3\xfb?\x97\x89l\xad\xbe\x05\xd7?\x1d\xc4\xce\xc6\xf5T\xd4\xbf\xfd\x99\xf0'n\x8a\xce\xbf:J\xe4\x15\x8b\x0b\xc3??UZ\xe1\x8f\x8e\xfc?\xebph\xb9\x16-\xc4\xbf\x87]\xab\x8b\x9a\xb2\xe0?\xb4\xa2\tw\x01\x92\xf8?"

temp = np.array([[1, 2, 3, 4], [3, 4, 5, 6]])
np.unique(temp)

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

ndarray的运算

逻辑运算：
- stock_change > 0.5 数据大于0.5的标记为True 否则为False
- stock_change[stock_change > 0.5] 返回所有大于0.5的数据
- stock_change[stock_change > 0.5] = 1.1 返回所有大于0.5的数据更改为1.1
- np.all(布尔值) 布尔值里面所有True才返回True, 只要有一个False就返回False
  - np.all(stock_change[0:2, 0:5] > 0) 判断里面数据是否全部大于0
- np.any(布尔值) 布尔值里面有一个True就返回True,只有全是False才会返回False
  - np.any(stock_change[0:2, 0:5] > 0) 判断里面是否有数据大于0
- 三元运算符：np.where(布尔值, True的位置的值, False位置的值)
  - np.where(stock_change>0, 1, 0) 将大于0的数据置为1 否则置为0
  - np.where(np.logical_and(stock_change > 0.5, stock_change < 1), 1, 0) 将大于0.5并且小于1的置为1，否则置为0
  - np.where(np.logical_or(stock_change > 0.5, stock_change < -0.5), 1, 0) 将大于0.5或者小于-0.5的置为1，否则置为0
统计运算:
- 统计指标函数：min,max,mean,median,var,std，函数其中有一个参数axis,为1代表使用行去进行统计，为0使用列进行统计计算。
  - np.max(a, axis=1) / ndarray.max(axis=1) / np.max(a) / adarray.max()
- 返回最大值、最小值的位置：
  - np.argmax(a. axis=) / np.argmin(a, axis=)
数组间运算:
- 数组与数的运算：arr ±*/等等直接对数组中的每个元素执行相同的操作
- 数组与数组的运算：需要满足广播机制
- 广播机制：当操作两个数组进行运算的时候，numpy会比较两个数组的shape,只有满足shape对应位置相等或者相对应的一个地方为1的数组才可以进行运算，结果对应shape取相应的位置的最大值。
- 矩阵运算：矩阵matrix 矩阵必须是二维的，但是数组可以是一位的。
  - np.mat() 将数组转换为矩阵
  - 有两种方法来存储矩阵：ndarray二维数组、matrix数据结构
  - 矩阵运算 (m, n) * (n , l) = (m, l) 也就是第一个矩阵的列数和第二个矩阵的行数要相等
  - np.matmul() numpy库中用于矩阵乘法的函数,它的作用是计算两个矩阵的乘积
  - np.dot() 向量点乘

逻辑运算

import numpy as np
stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
stock_change > 0.5

array([[False, False, False, False, False, False,  True,  True, False,False],[False, False, False, False, False, False, False,  True, False,False],[False,  True, False, False, False, False,  True,  True,  True,True],[False,  True,  True, False, False,  True, False, False, False,False],[False, False, False,  True, False, False, False,  True,  True,False],[False, False, False, False, False, False, False, False,  True,False],[False, False, False,  True, False,  True, False, False,  True,True],[False, False, False, False,  True, False,  True, False,  True,False]])

stock_change[stock_change > 0.5]

array([1.36389291, 1.67468755, 0.50246918, 0.5468709 , 0.79676231,1.38442008, 0.92053831, 1.37148844, 1.45441467, 2.09266866,1.06350017, 0.88156425, 0.79183302, 1.43465142, 0.75375268,0.74356075, 0.89123163, 0.80052696, 1.18475498, 1.21210169,1.49996354, 1.73125796, 1.78480518, 0.52180221, 1.53564593])

stock_change[stock_change > 0.5] = 1.1
stock_change

array([[-0.0128315 ,  1.1       ,  1.1       , -1.63839812,  1.1       ,0.40632079,  1.1       , -1.51506239, -0.95175431,  1.1       ],[-0.29024725, -0.85783328, -2.88228976,  0.09475102,  0.26886068,-0.72337737,  0.32906655,  1.1       ,  0.22017286,  0.11595155],[-1.48797053, -0.34888996, -0.46878054,  0.06614233, -1.2163201 ,-0.12437208, -0.48048511,  1.1       ,  1.1       ,  0.4052761 ],[-0.68483909,  1.1       ,  0.32439071,  1.1       , -1.40087978,0.21482243,  1.1       , -1.12371055, -0.21362273, -0.86489608],[-0.8955743 , -2.80666246, -1.81775787, -0.64719575, -1.03749633,-0.09075791,  0.04027887,  1.1       , -0.38851649,  0.4366844 ],[-0.6112534 ,  0.20743331, -1.10785011, -1.94937533,  1.1       ,-1.43629441, -0.39276676,  1.1       , -0.77917209,  1.1       ],[-0.45255197,  0.21874378,  1.1       ,  1.1       ,  1.1       ,0.07645454,  1.1       ,  1.1       , -2.57089921, -0.04719686],[ 1.1       ,  1.1       ,  0.35972564, -0.31768555, -0.23859956,0.14878977,  1.1       , -0.157626  ,  1.1       ,  1.1       ]])

print(np.all(stock_change[0:2, 0:5] > 0))
print(np.any(stock_change[0:2, 0:5] > 0))

False
True

print(np.where(stock_change>0, 1, 0))

[[0 1 1 0 0 0 1 1 1 0][0 1 0 0 0 1 1 0 0 1][0 1 0 0 0 0 0 1 0 0][1 0 1 0 1 1 0 0 0 1][1 0 0 1 0 1 0 0 1 0][1 1 1 1 0 1 1 1 0 1][0 1 0 1 0 0 1 0 1 0][1 1 1 0 1 1 1 0 0 1]]

print(np.where(np.logical_and(stock_change > 0.5, stock_change < 1), 1 , 0))

[[0 0 0 0 0 0 0 0 0 0][0 0 0 0 0 0 0 0 0 1][0 1 0 0 0 0 0 0 0 0][0 0 0 0 1 0 0 0 0 0][0 0 0 0 0 0 0 0 0 0][0 0 1 0 0 0 1 1 0 0][0 0 0 0 0 0 1 0 0 0][0 0 0 0 0 0 0 0 0 1]]

print(np.where(np.logical_or(stock_change > 0.5, stock_change < -0.5), 1 , 0))

[[1 0 0 0 1 0 0 1 1 0][1 1 1 1 0 1 1 1 0 1][0 1 1 1 1 0 1 0 1 0][1 0 1 0 1 1 1 0 0 1][0 1 0 0 0 1 0 1 1 0][0 1 1 0 0 0 1 1 1 1][1 0 0 0 0 0 1 1 0 0][1 0 1 0 1 0 0 1 1 1]]

统计运算

print(np.max(stock_change), stock_change.max())

2.837073584187165 2.837073584187165

print(np.mean(stock_change, axis=0), np.mean(stock_change, axis=1), np.mean(stock_change))

[-0.9652667  -0.15328082  0.08317861 -0.54300528 -0.42430401 -0.27689675-0.03939256  0.58928582  0.11866925  0.06092911] [-0.24814861 -0.59923979  0.47094442  0.21607003 -0.15542244 -0.36903679-0.12744662 -0.42778684] -0.15500833265906144

print(np.argmax(stock_change), np.argmax(stock_change, axis=1))

32 [7 7 7 2 3 8 5 8]

数组的运算

数组和数的运算

arr = np.array([[1, 2, 3, 2, 1, 4], [5,  6, 1, 2, 3, 1]])
arr

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

arr+1

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

arr*2

array([[ 2,  4,  6,  4,  2,  8],[10, 12,  2,  4,  6,  2]])

arr/2

array([[0.5, 1. , 1.5, 1. , 0.5, 2. ],[2.5, 3. , 0.5, 1. , 1.5, 0.5]])

arr-2

array([[-1,  0,  1,  0, -1,  2],[ 3,  4, -1,  0,  1, -1]])

数组和数组运算

arr1 = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr2 = np.array([[1], [3]])
print(arr1, "\n\n", arr2)

[[1 2 3 2 1 4][5 6 1 2 3 1]] [[1][3]]

print(arr1 * arr2, '\n\n', arr1 / arr2)

[[ 1  2  3  2  1  4][15 18  3  6  9  3]] [[1.         2.         3.         2.         1.         4.        ][1.66666667 2.         0.33333333 0.66666667 1.         0.33333333]]

矩阵运算

data = np.array([[80, 86],[82, 80],[85, 78],[90, 90],[86, 82],[82, 90],[78, 80],[92, 94]])
data

array([[80, 86],[82, 80],[85, 78],[90, 90],[86, 82],[82, 90],[78, 80],[92, 94]])

data2 = np.mat([[80, 86],[82, 80],[85, 78],[90, 90],[86, 82],[82, 90],[78, 80],[92, 94]])
print(data2, '\n\n', type(data2))

[[80 86][82 80][85 78][90 90][86 82][82 90][78 80][92 94]] <class 'numpy.matrix'>

data3 = np.mat([[0.3], [0.7]])
data3

matrix([[0.3],[0.7]])

print(data2 * data3, '\n\n', data @ np.array([[0.3], [0.7]]))  ## 计算成绩 第一列乘上0.3 第二列乘上0.7

[[84.2][80.6][80.1][90. ][83.2][87.6][79.4][93.4]] [[84.2][80.6][80.1][90. ][83.2][87.6][79.4][93.4]]

print(np.matmul(data2, data3), '\n\n', np.dot(data2, data3))

[[84.2][80.6][80.1][90. ][83.2][87.6][79.4][93.4]] [[84.2][80.6][80.1][90. ][83.2][87.6][79.4][93.4]]

合并和分割

合并：合并可以从水平的方向进行合并，也可以在垂直的方法进行合并
- numpy.hstack(tuple(column, wise)) 水平拼接
- numpy.vstack(tuple(row, wise)) 垂直拼接
- numpy.concatenate((a1, a2, a3…), axis=0) axis=1来表示水平,axis=0表示垂直
分割
- np.split(ary, indices_or_sections, axis=0)

合并

import numpy as np
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
np.hstack((a, b))

array([1, 2, 3, 2, 3, 4])

np.vstack((a, b))

array([[1, 2, 3],[2, 3, 4]])

np.concatenate((a, b), axis=0)

array([1, 2, 3, 2, 3, 4])

x = np.array([[1, 2], [3, 4]])
print(np.concatenate((x, x), axis=0))
print('\n\n', np.concatenate((x, x), axis=1))

[[1 2][3 4][1 2][3 4]][[1 2 1 2][3 4 3 4]]

分割

x1 = np.arange(9.0)
np.split(x1, 3)

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

x1 = np.arange(8.0)
np.split(x1, [3, 5, 6, 8])  ## 按照索引进行分割

[array([0., 1., 2.]),array([3., 4.]),array([5.]),array([6., 7.]),array([], dtype=float64)]

IO操作和数据处理

numpy数据读取：
- np.genfromtxt(path, delimiter=) ## 文件路径和分隔符号
- np.genfromtxt(‘tes.csv’, delimiter=‘,’)

import numpy as np
data = np.genfromtxt('gh.csv', delimiter=',')
data

array([[  nan,   nan,   nan],[  12.,  213.,  321.],[ 123.,  345., 1241.],[  14.,   24.,  123.]])

对于上面的数组中的nan值的类型是float64,对于这个的一般处理方式有两种
- 将数据存在nan的行删除
- 使用该列的平均值填充到nan的位置

总结

Numpy的优势：内存存储风格，ndrray存储相同数据，内存连续存储，底层c语言实现，支持多线程
ndarray的属性：shape、dtype、ndim、size、itemsize
基本操作：ndarray.方法() np.函数()
- 生成数组的方法：np.ones(shape) np.zeros(shape)
- 从现有数组中生成：np.array() np.copy() np.asarray()
- 生成固定范围的数组：np.linspace(a, b, c) np.arange(a, b, c)
- 生成随机数：均匀分布：np.random.uniform() 正态分布：np.random.normal()
- 切片索引
- 形状修改：ndarray.reshape((a, b)) ndarray.resize((a, b)) ndarray.T
- 类型修改：ndarray.astype(type) ndarray.tobytes()
- 数组去重：np.unique()
numpy的运算：
- 逻辑运算:
  - 布尔索引
  - np.all() np.any()
  - np.where(a, b, c) a是布尔值 b是true对应的值 c是false对应的值
- 统计运算
  - 统计指标：max min mean median var std
  - 最大值最小值位置：np.argmax() np.argmin()
- 数组间运算
  - 数组与数的运算：
  - 数组与数组的运算：要注意广播机制
  - 矩阵运算：np.mat() np.dot() np.matmul()