人工智能机器学习有关-Numpay(三)
作者：互联网
人工智能机器学习有关-Numpay(三)
1、Numpy介绍
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定义：
- Numpy是一个开源的Python科学计算库，用于快速处理任意维度的数组。
- Numpy中，存储对象是ndarray。
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创建Numpy：
import numpy as np np.array([]) -
numpy优势
- 内存块风格--一体式存储
- 支持并行化运算
- 效率高于纯python代码---底层使用了c，内部释放了GIL
2、N维数组
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ndarray的属性
- ndarray.shape：数组维度的元组
- ndarray.ndim：数组维度
- ndarray.size：数组中的元素数量
- ndarray.itemsize：一个数组元素的长度
- ndarray.dtype：数组元素的类型
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ndarray的形状
np.array()
三维数组不好理解---excel中有多个sheet
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ndarray的类型
bool
int
float
str.....
注意：如果不指定，正说默认为int64，小数默认为float64。
3、Numpy的基本操作
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生成数组的方法
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生成0和1数组
- np.ones()
- np.ones_like()
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从现有数组中生成
- np.array---深拷贝
- np.asarray--浅拷贝
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生成等间隔数组
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np.linspace(1,100,11) #从1到100，间隔是11;效果如下：
array([ 0., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100.]) -
np.arange(10,50,2) #从10到50，间隔是2；效果如下：
array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48]) -
np.logspace(1,3,1) #生成以10的N次幂的数据；效果如下：
array([ 10., 100., 1000.])
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生成随机数组
使用np.random
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均匀分布
- np.random.rand(2,3) #随机生成两行三列的数组，效果如下：
array([[0.35756598, 0.57026639, 0.02924949], [0.02111498, 0.72537111, 0.94669752]])-
np.random.uniform(low=1,high=10,size=([3,5]))#下限是1，上限是10，生成三行五列；效果如下
array([[3.15528825, 1.01357735, 4.72909696, 8.01292636, 4.8997339 ], [5.65180884, 4.07661969, 9.52166085, 4.26229383, 3.72778983], [8.32872314, 3.17203041, 2.16791937, 3.95843925, 8.59350188]])
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正态分布
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均值：图形的左右位置
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方差：图像是瘦还是胖
值越小，图形越瘦高，数据越集中
值越大，图形越矮胖，数据越分散
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正态分布创建方式
- np.random.randn(d0,d1,d2.....)
- np.random.normal(loc=0.0,scale=1.0,size=None)；loc代表比概率分布的均值；scale代表次概率分布的标准差；size表示输出的shape，默认为None，只输出一个值。
- np.random.standard_normal(size=None)：返回指定形状的标准正态分布的数组
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4、数组索引，切片
先对行进行索引，在进行列索引
高维数组索引，从宏观到微观
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代码如下
stock_change = np.random.normal(0,1,(8,10)) array([[-1.44426604, -0.35956676, 0.10022073, -0.37589995, 0.41068841, 0.35831041, -2.18716918, -0.27044373, -0.60649162, 0.74780256], [ 1.50355343, 0.00727481, 0.70220799, 0.11446953, 2.29954941, -0.06165946, 0.43377417, 2.66687501, 1.31977339, 0.39484888], [ 0.57895097, 0.75483241, 0.46582577, -1.70723503, -0.4722333 , 1.36472691, 0.31702275, 0.95247131, 1.13712267, -0.92562661], [ 0.42483005, -0.52735992, -1.22389302, -0.70718243, -0.74624277, -0.46608281, 2.3002048 , 0.55910289, 1.27616921, -0.22121508], [-0.07102279, 0.23494155, 0.35115159, 0.22186354, 0.85730874, 0.86016689, 0.42037822, -0.36173826, -1.26004742, 0.22148513], [-1.00150606, 0.32304105, -0.64324946, -0.15920028, -0.12329149, -0.91909789, -0.70919568, 0.76674683, -1.23383876, 1.57300598], [-0.40099816, -0.9621985 , -0.55014995, 0.0815694 , 1.16744742, -0.15184784, 0.08657591, -1.00556795, 0.06046253, 0.1674691 ], [ 0.55177698, -1.4687088 , -2.43569035, 1.31883436, -1.06246593, 0.44006957, -0.8233832 , -1.01700783, -0.98957025, 1.10041451]]) #进行切片 stock_change[0:2, 0:3] #从0到2两行，从0到3三列 array([[-1.44426604, -0.35956676, 0.10022073], [ 1.50355343, 0.00727481, 0.70220799]])
5、形状修改
stock_change = np.random.normal(0,1,(4,5))
stock_change
array([[-1.13690825, -0.86626341, 0.26315935, -1.50416658, -0.20551933],
[-0.62318598, 0.45052168, -1.31538088, -1.05299411, 0.98931701],
[-0.90524912, -1.27093755, 0.7382356 , 0.94703348, -0.05833449],
[-2.33703858, 0.05670275, 1.97424882, 0.09213721, 0.26717848]])
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对象.reshape：不进行行列互换，产生新变量
stock_change.resize([5,4]) array([[-0.56597865, 1.37304626, 0.88760232, 1.60456417], [ 0.42406144, 0.85342625, 0.17185211, 0.63999772], [ 0.63243256, -0.14288988, 0.51224131, 0.52807762], [-0.25435597, 1.08117107, -1.27219027, -0.96426461], [ 0.47159064, 1.19946091, 0.10747391, -0.34872273]]) -
对象.resize：进行行列互换，对原来的值进行修改
stock_change.resize([5,4])#不直接输出结果，改变原来数组的值 stock_change array([[-0.56597865, 1.37304626, 0.88760232, 1.60456417], [ 0.42406144, 0.85342625, 0.17185211, 0.63999772], [ 0.63243256, -0.14288988, 0.51224131, 0.52807762], [-0.25435597, 1.08117107, -1.27219027, -0.96426461], [ 0.47159064, 1.19946091, 0.10747391, -0.34872273]]) -
对象.T：进行行列互换
stock_change.T array([[-1.13690825, -0.62318598, -0.90524912, -2.33703858], [-0.86626341, 0.45052168, -1.27093755, 0.05670275], [ 0.26315935, -1.31538088, 0.7382356 , 1.97424882], [-1.50416658, -1.05299411, 0.94703348, 0.09213721], [-0.20551933, 0.98931701, -0.05833449, 0.26717848]])
6、类型修改
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对象.astype(np.int32)#括号里面是你要修改成的数据类型。例如：
stock_change array([[ 0.70391962, -0.64805071, 1.86832388, -0.99192503, -0.84167201], [-1.05297977, 0.29382444, -1.7573821 , 1.22405082, 1.05422963], [ 0.18694931, -0.54332531, -0.48083977, 0.73456394, 0.03539551], [ 1.98783588, 0.43443585, 0.61193212, -0.31977936, -1.12256928]]) stock_change.astype(np.int32) array([[ 0, 0, 1, 0, 0], [-1, 0, -1, 1, 1], [ 0, 0, 0, 0, 0], [ 1, 0, 0, 0, -1]]) -
对象.tostring()
arr = np.array([[1,2,3],[4,5,6],[7,8,9]]) arr array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) arr.tostring() b'\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00'
7、数组的去重
arr= np.array([[1,2,3,4,5,6],[4,5,6,7,8,9]])
arr
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
np.unique(arr)
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
8、ndarray运算
import numpy as np
stock_change = np.random.normal(0,1,(8,10))
stock_change
array([[ 0.04799655, 0.65765235, 0.42361094, 0.81425383, -0.14889895,
0.34435302, -0.89120847, 1.09553709, -0.39220518, 0.35681464],
[-0.1205083 , 1.04201374, -0.84020323, -0.76451012, 0.38678516,
-0.48928631, -0.03033363, -0.64034053, -0.31345406, 1.00351474],
[-1.18944149, -1.21265991, -0.27092964, 0.78723819, 0.86631678,
-0.41733842, -0.17263459, 0.57000637, -0.90242552, 1.1615763 ],
[ 0.20939948, 1.0627663 , -1.11295892, -2.20829153, -0.13560078,
0.04241456, 1.95342178, -1.52867478, -0.4256004 , 1.89226058],
[-1.14428267, -1.03230368, -0.64917991, 0.95959798, -0.1140127 ,
-0.60413769, 0.64979583, 1.96630929, -1.50833397, -0.18793 ],
[-0.16371276, -0.07527353, -1.07491874, -0.460191 , -0.82218305,
1.35116945, 0.0615233 , -1.2129384 , -1.21896538, 0.19445662],
[-2.24073749, -0.4371897 , -0.65786767, -1.20137206, -0.74103418,
0.03041893, 1.92970227, -1.74572423, 0.04164877, 1.58580713],
[ 0.04217637, -0.62788208, 0.15461643, -0.38924692, -0.71137869,
1.86107771, 0.63093733, 0.03397154, -0.02377345, 0.15119872]])
stock_c = stock_change[0:5,0:5]
stock_c
array([[ 0.04799655, 0.65765235, 0.42361094, 0.81425383, -0.14889895],
[-0.1205083 , 1.04201374, -0.84020323, -0.76451012, 0.38678516],
[-1.18944149, -1.21265991, -0.27092964, 0.78723819, 0.86631678],
[ 0.20939948, 1.0627663 , -1.11295892, -2.20829153, -0.13560078],
[-1.14428267, -1.03230368, -0.64917991, 0.95959798, -0.1140127 ]])
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逻辑运算
stock_c>1 array([[False, False, False, False, False], [False, True, False, False, False], [False, False, False, False, False], [False, True, False, False, False], [False, False, False, False, False]]) stock_c[stock_c>1] = 2 stock_c array([[ 0.04799655, 0.65765235, 0.42361094, 0.81425383, -0.14889895], [-0.1205083 , 2. , -0.84020323, -0.76451012, 0.38678516], [-1.18944149, -1.21265991, -0.27092964, 0.78723819, 0.86631678], [ 0.20939948, 2. , -1.11295892, -2.20829153, -0.13560078], -
通用判断函数
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np.all()：满足所有的要求才返回true
stock_d = stock_change[0:2,0:5] stock_d array([[ 0.04799655, 0.65765235, 0.42361094, 0.81425383, -0.14889895], [-0.1205083 , 2. , -0.84020323, -0.76451012, 0.38678516]]) np.all(stock_d>0) #全部大于0才返回true False -
np.any()：有一个满足要求就返回true
np.any(stock_d>0) #有一个大于0就返回true True
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三元运算符
- np.where()
np.where(stock_d>0,1,0) #在stock_d中大于0的赋值为1，小于0的赋值为0 array([[1, 1, 1, 1, 0], [0, 1, 0, 0, 1]])- np.logic_and()：并的意思
- np.logic_or：或的意思
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统计运算
- min：求最小值
- max：求最大值
- midian：求中位数
- mean：均值
- std：标准差
- var：方差
- argmax：最大值下标
- argmin：最小值下标
9、矩阵乘法
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np.matmul()：矩阵相乘，不支持矩阵与数字相乘
a = np.array([[80, 86], [82, 80], [85, 78], [90, 90], [86, 82], [82, 90], [78, 80], [92, 94]]) b = np.array([[0.7], [0.3]]) np.matmul(a,b) array([[81.8], [81.4], [82.9], [90. ], [84.8], [84.4], [78.6], [92.6]]) -
np.dot()：点乘，支持矩阵与数字相乘
np.dot(a,10) array([[81.8], [81.4], [82.9], [90. ], [84.8], [84.4], [78.6], [92.6]])两者在进行矩阵相乘的时候没有区别
标签：机器,人工智能,np,False,array,x00,Numpay,change,stock 来源： https://www.cnblogs.com/xzycoding/p/15434014.html