商务网站建设的步骤,西安团购网站建设,wordpress京东客,wordpress 文章页幻灯片3.1.2 ndarray介绍 点击标题即可获取文章的源代码和笔记 Numpy 高效的运算工具
Numpy的优势
ndarray属性
基本操作ndarray.方法()numpy.函数名()
ndarray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3.1 Numpy优势3.1.1 Numpy介绍 - 数值计算库num - numerica…3.1.2 ndarray介绍 点击标题即可获取文章的源代码和笔记 Numpy 高效的运算工具
Numpy的优势
ndarray属性
基本操作ndarray.方法()numpy.函数名()
ndarray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3.1 Numpy优势3.1.1 Numpy介绍 - 数值计算库num - numerical 数值化的py - pythonndarrayn - 任意个d - dimension 维度array - 数组3.1.2 ndarray介绍3.1.3 ndarray与Python原生list运算效率对比3.1.4 ndarray的优势1存储风格ndarray - 相同类型 - 通用性不强list - 不同类型 - 通用性很强2并行化运算ndarray支持向量化运算3底层语言C语言解除了GIL
3.2 认识N维数组-ndarray属性3.2.1 ndarray的属性shapendimsizedtypeitemsize在创建ndarray的时候如果没有指定类型默认整数 int64浮点数 float643.2.2 ndarray的形状[1, 2, 3, 4][[1, 2, 3, 4],[1, 2, 3, 4],[1, 2, 3, 4]][[[1, 2, 3, 4],[1, 2, 3, 4],[1, 2, 3, 4]],[[1, 2, 3, 4],[1, 2, 3, 4],[1, 2, 3, 4]],[[1, 2, 3, 4],[1, 2, 3, 4],[1, 2, 3, 4]]]3.2.3 ndarray的类型
3.3 基本操作adarray.方法()np.函数名()np.array()3.3.1 生成数组的方法1生成0和1np.zeros(shape)np.ones(shape)2从现有数组中生成np.array() np.copy() 深拷贝np.asarray() 浅拷贝3生成固定范围的数组np.linspace(0, 10, 100)[0, 10] 等距离np.arange(a, b, c)range(a, b, c)[a, b) c是步长4生成随机数组分布状况 - 直方图1均匀分布每组的可能性相等2正态分布σ 幅度、波动程度、集中程度、稳定性、离散程度3.3.2 数组的索引、切片3.3.3 形状修改ndarray.reshape(shape) 返回新的ndarray原始数据没有改变ndarray.resize(shape) 没有返回值对原始的ndarray进行了修改ndarray.T 转置 行变成列列变成行3.3.4 类型修改ndarray.astype(type)ndarray序列化到本地ndarray.tostring()3.3.5 数组的去重set()
3.4 ndarray运算逻辑运算布尔索引通用判断函数np.all(布尔值)只要有一个False就返回False只有全是True才返回Truenp.any()只要有一个True就返回True只有全是False才返回Falsenp.where三元运算符np.where(布尔值, True的位置的值, False的位置的值)统计运算统计指标函数min, max, mean, median, var, stdnp.函数名ndarray.方法名返回最大值、最小值所在位置np.argmax(temp, axis)np.argmin(temp, axis)数组间运算3.5.1 场景3.5.2 数组与数的运算3.5.3 数组与数组的运算3.5.4 广播机制3.5.5 矩阵运算1 什么是矩阵矩阵matrix 二维数组矩阵 二维数组两种方法存储矩阵1ndarray 二维数组矩阵乘法np.matmulnp.dot2matrix数据结构2 矩阵乘法运算形状(m, n) * (n, l) (m, l)运算规则A (2, 3) B(3, 2)A * B (2, 2)
3.6 合并、分割
3.7 IO操作与数据处理3.7.1 Numpy读取3.7.2 如何处理缺失值两种思路直接删除含有缺失值的样本替换/插补按列求平均用平均值进行填补
import numpy as np# 创建ndarray
score 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]])
scorearray([[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.ndarray3.1.3 ndarray与Python原生list运算效率对比
import random
import time
import numpy as np# 生成一个大数组
a []
for i in range(100000000):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(t2-t1,t5-t4)5.195146083831787 0.236427545547485353.2.1 ndarray的属性
score 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]])type(score)numpy.ndarrayscore.dtype # 数组元素的类型dtype(int32)score.shape # 数组维度的元组(8, 5)score.ndim # 数组维数2score.size # 数组中元素的数量40score.itemsize # 一个数组元素的长度字节43.2.2 ndarray的形状
#创建不同形状的数组
anp.array([[1,2,3],[4,5,6]])
bnp.array([1,2,3,4])
cnp.array([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])aarray([[1, 2, 3],[4, 5, 6]])a.shape # 二维数组(2, 3)barray([1, 2, 3, 4])b.shape # 一维数组(4,)carray([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6]]])c.shape # 三维数组(2, 2, 3)3.2.3 ndarray的类型
data np.array([1.1,2.2,3.3])
data.dtypedtype(float64)创建数组的时候指定类型
a np.array([[1,2,3],[4,5,6]],dtypenp.float32)
# a np.array([[1,2,3],[4,5,6]],dtypefloat32)
a.dtypedtype(float32)arr np.array([python,tensorflow,scikit-learn,numpy],dtypenp.string_)
arrarray([bpython, btensorflow, bscikit-learn, bnumpy], dtype|S12)3.3基本操作
1.生成0和1的数组
zero np.zeros([3,4])
zeroarray([[0., 0., 0., 0.],[0., 0., 0., 0.],[0., 0., 0., 0.]])zero np.zeros((3,4))
zeroarray([[0., 0., 0., 0.],[0., 0., 0., 0.],[0., 0., 0., 0.]])one np.ones([3,4])
# one np.ones((3,4))
onearray([[1., 1., 1., 1.],[1., 1., 1., 1.],[1., 1., 1., 1.]])np.ones(shape[3,4],dtypenp.int32)array([[1, 1, 1, 1],[1, 1, 1, 1],[1, 1, 1, 1]])2.从现有数组生成
scorearray([[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]])data1 np.array(score) # 深拷贝
data1array([[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]])data2 np.asarray(score) # 浅拷贝 原数据发生修改后也会跟着进行修改
data2array([[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]])data3 np.copy(score) # 深拷贝
data3array([[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[3,1]91score[3,1] 100000data1array([[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]])data2 # 原数组数据修改后也会跟着发生变化array([[ 80, 89, 86, 67, 79],[ 78, 97, 89, 67, 81],[ 90, 94, 78, 67, 74],[ 91, 100000, 90, 67, 69],[ 76, 87, 75, 67, 86],[ 70, 79, 84, 67, 84],[ 94, 92, 93, 67, 64],[ 86, 85, 83, 67, 80]])data3array([[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]])3.生成固定范围的数组
np.linspace(0,10,5) # 左闭右闭 等差数列范围在【010个数】个数为5个array([ 0. , 2.5, 5. , 7.5, 10. ])for i in range(0,10,1):print(i)
# range(0,10,1) 左闭右开 【010步长0
1
2
3
4
5
6
7
8
9np.arange(0,10,1) # 左闭右开 【010步长array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])4.生成随机数组
# 生成均匀分布的随机数
x1 np.random.uniform(-1,1,100000) # uniform(起始值,终点值,个数)
x1array([ 0.55046079, 0.37804729, -0.89677218, ..., 0.35451722,0.34995045, 0.01961797])import matplotlib.pyplot as plt
%matplotlib inline# 1. 创建画布
plt.figure(figsize(20,8),dpi100)# 2. 绘制直方图
plt.hist(x1,1000)# 3. 显示图像
plt.show() # 生成正态分布的随机数标准正态分布均值为0方差为1
# loc 均值 scale 标准差
data4 np.random.normal(loc1.75,scale0.1,size1000000)
data4array([1.82548844, 1.91684274, 1.48534258, ..., 1.75064937, 1.8181808 ,1.81005547])import matplotlib.pyplot as plt
%matplotlib inline# 1. 创建画布
plt.figure(figsize(20,8),dpi100)# 2. 绘制直方图
plt.hist(data4,1000)# 3. 显示图像
plt.show() 案例随机生成8只股票2周的交易日涨幅数据
8只股票两周10天的涨跌幅数据如何获取 两周的交易日数量为2 * 510 随机生成涨跌幅在某个正态分布内比如均值0方差1
stock_change np.random.normal(loc0,scale1,size(8,10))
stock_changearray([[-0.61330497, 0.55840141, 0.41709496, 1.27999683, -1.00183693,1.19508749, -1.30481202, -0.32462183, 0.1629303 , -0.37215778],[-0.67655708, -0.24960482, -0.26775897, -1.54340984, -1.7202066 ,1.38874363, -0.0149956 , 0.66870059, -0.04502848, 0.63144735],[-0.28952395, -1.70484263, 0.61871199, 0.61306774, 0.22872944,1.1493577 , 2.48623902, 0.18940315, -0.44105589, 1.49241966],[ 0.33087272, -0.67879541, -0.6040623 , -1.20256264, -0.76551783,1.31036346, -0.46289576, -0.44254887, -0.20934797, 0.13978528],[ 0.58783968, -2.67898464, -1.41139208, 1.07009707, -2.23082484,0.69616862, 0.38991086, -1.10458314, -1.85230749, -1.59066425],[ 1.46959111, -0.91715307, 0.08142567, 2.86350894, 0.83436522,-2.01224295, -0.28835842, -1.28407105, 1.52191189, -0.09642856],[-0.82991129, 0.83983885, -1.10666366, 0.06332958, 0.42674457,1.491716 , -0.81436095, -0.85603011, 0.72720565, -2.60215313],[ 0.42427358, 0.81760609, 2.48509044, 0.41373531, -0.5184894 ,0.76798932, 0.01676593, -1.35196338, 1.216088 , 0.39931822]])3.3.2数组的索引、切片
获取第一个股票的前3个交易日的涨跌幅数据
stock_change[0,0:3]array([-0.61330497, 0.55840141, 0.41709496])一维、二维、三维的数组如何索引
a1np.array([[[1,2,3],[4,5,6]],[[12,3,34],[5,6,7]]])
a1array([[[ 1, 2, 3],[ 4, 5, 6]],[[12, 3, 34],[ 5, 6, 7]]])a1.shape(2, 2, 3)a1[1,0,2]34a1[1,0,2] 1000000
a1array([[[ 1, 2, 3],[ 4, 5, 6]],[[ 12, 3, 1000000],[ 5, 6, 7]]])3.3.3形状修改
需求让刚才的股票行、日期列反过来变成日期行股票列
stock_change.shape(8, 10)stock_changearray([[-0.61330497, 0.55840141, 0.41709496, 1.27999683, -1.00183693,1.19508749, -1.30481202, -0.32462183, 0.1629303 , -0.37215778],[-0.67655708, -0.24960482, -0.26775897, -1.54340984, -1.7202066 ,1.38874363, -0.0149956 , 0.66870059, -0.04502848, 0.63144735],[-0.28952395, -1.70484263, 0.61871199, 0.61306774, 0.22872944,1.1493577 , 2.48623902, 0.18940315, -0.44105589, 1.49241966],[ 0.33087272, -0.67879541, -0.6040623 , -1.20256264, -0.76551783,1.31036346, -0.46289576, -0.44254887, -0.20934797, 0.13978528],[ 0.58783968, -2.67898464, -1.41139208, 1.07009707, -2.23082484,0.69616862, 0.38991086, -1.10458314, -1.85230749, -1.59066425],[ 1.46959111, -0.91715307, 0.08142567, 2.86350894, 0.83436522,-2.01224295, -0.28835842, -1.28407105, 1.52191189, -0.09642856],[-0.82991129, 0.83983885, -1.10666366, 0.06332958, 0.42674457,1.491716 , -0.81436095, -0.85603011, 0.72720565, -2.60215313],[ 0.42427358, 0.81760609, 2.48509044, 0.41373531, -0.5184894 ,0.76798932, 0.01676593, -1.35196338, 1.216088 , 0.39931822]])reshape_stock_change stock_change.reshape((10,8))
reshape_stock_change.shape# reshape(10,8)返回新的ndarray,但是没有修改原始的数据只是修改了数组的形状但并没有让数组的行列进行互换只是把数组单纯的重新进行了切割(10, 8)reshape_stock_changearray([[-0.61330497, 0.55840141, 0.41709496, 1.27999683, -1.00183693,1.19508749, -1.30481202, -0.32462183],[ 0.1629303 , -0.37215778, -0.67655708, -0.24960482, -0.26775897,-1.54340984, -1.7202066 , 1.38874363],[-0.0149956 , 0.66870059, -0.04502848, 0.63144735, -0.28952395,-1.70484263, 0.61871199, 0.61306774],[ 0.22872944, 1.1493577 , 2.48623902, 0.18940315, -0.44105589,1.49241966, 0.33087272, -0.67879541],[-0.6040623 , -1.20256264, -0.76551783, 1.31036346, -0.46289576,-0.44254887, -0.20934797, 0.13978528],[ 0.58783968, -2.67898464, -1.41139208, 1.07009707, -2.23082484,0.69616862, 0.38991086, -1.10458314],[-1.85230749, -1.59066425, 1.46959111, -0.91715307, 0.08142567,2.86350894, 0.83436522, -2.01224295],[-0.28835842, -1.28407105, 1.52191189, -0.09642856, -0.82991129,0.83983885, -1.10666366, 0.06332958],[ 0.42674457, 1.491716 , -0.81436095, -0.85603011, 0.72720565,-2.60215313, 0.42427358, 0.81760609],[ 2.48509044, 0.41373531, -0.5184894 , 0.76798932, 0.01676593,-1.35196338, 1.216088 , 0.39931822]])stock_change.resize((10,8)) # resize((10,8)) 没有返回值直接对原始的ndarray进行了修改
# 效果和 reshape一样只是修改了数组的形状但并没有让数组的行列进行互换只是把数组单纯的重新进行了切割
stock_changearray([[-0.61330497, 0.55840141, 0.41709496, 1.27999683, -1.00183693,1.19508749, -1.30481202, -0.32462183],[ 0.1629303 , -0.37215778, -0.67655708, -0.24960482, -0.26775897,-1.54340984, -1.7202066 , 1.38874363],[-0.0149956 , 0.66870059, -0.04502848, 0.63144735, -0.28952395,-1.70484263, 0.61871199, 0.61306774],[ 0.22872944, 1.1493577 , 2.48623902, 0.18940315, -0.44105589,1.49241966, 0.33087272, -0.67879541],[-0.6040623 , -1.20256264, -0.76551783, 1.31036346, -0.46289576,-0.44254887, -0.20934797, 0.13978528],[ 0.58783968, -2.67898464, -1.41139208, 1.07009707, -2.23082484,0.69616862, 0.38991086, -1.10458314],[-1.85230749, -1.59066425, 1.46959111, -0.91715307, 0.08142567,2.86350894, 0.83436522, -2.01224295],[-0.28835842, -1.28407105, 1.52191189, -0.09642856, -0.82991129,0.83983885, -1.10666366, 0.06332958],[ 0.42674457, 1.491716 , -0.81436095, -0.85603011, 0.72720565,-2.60215313, 0.42427358, 0.81760609],[ 2.48509044, 0.41373531, -0.5184894 , 0.76798932, 0.01676593,-1.35196338, 1.216088 , 0.39931822]])stock_change.shape(10, 8)stock_change.T # 转置行列互换array([[-0.61330497, 0.1629303 , -0.0149956 , 0.22872944, -0.6040623 ,0.58783968, -1.85230749, -0.28835842, 0.42674457, 2.48509044],[ 0.55840141, -0.37215778, 0.66870059, 1.1493577 , -1.20256264,-2.67898464, -1.59066425, -1.28407105, 1.491716 , 0.41373531],[ 0.41709496, -0.67655708, -0.04502848, 2.48623902, -0.76551783,-1.41139208, 1.46959111, 1.52191189, -0.81436095, -0.5184894 ],[ 1.27999683, -0.24960482, 0.63144735, 0.18940315, 1.31036346,1.07009707, -0.91715307, -0.09642856, -0.85603011, 0.76798932],[-1.00183693, -0.26775897, -0.28952395, -0.44105589, -0.46289576,-2.23082484, 0.08142567, -0.82991129, 0.72720565, 0.01676593],[ 1.19508749, -1.54340984, -1.70484263, 1.49241966, -0.44254887,0.69616862, 2.86350894, 0.83983885, -2.60215313, -1.35196338],[-1.30481202, -1.7202066 , 0.61871199, 0.33087272, -0.20934797,0.38991086, 0.83436522, -1.10666366, 0.42427358, 1.216088 ],[-0.32462183, 1.38874363, 0.61306774, -0.67879541, 0.13978528,-1.10458314, -2.01224295, 0.06332958, 0.81760609, 0.39931822]])stock_change.T.shape(8, 10)3.3.4类型修改
stock_change.astype(np.int32)array([[ 0, 0, 0, 1, -1, 1, -1, 0],[ 0, 0, 0, 0, 0, -1, -1, 1],[ 0, 0, 0, 0, 0, -1, 0, 0],[ 0, 1, 2, 0, 0, 1, 0, 0],[ 0, -1, 0, 1, 0, 0, 0, 0],[ 0, -2, -1, 1, -2, 0, 0, -1],[-1, -1, 1, 0, 0, 2, 0, -2],[ 0, -1, 1, 0, 0, 0, -1, 0],[ 0, 1, 0, 0, 0, -2, 0, 0],[ 2, 0, 0, 0, 0, -1, 1, 0]])type(stock_change)numpy.ndarray# 序列化转换成bytes
stock_change.tostring()b\x9a\xa38\xc11\xa0\xe3\xbf\x10\xa0\t\xa3l\xde\xe1?9\xfaO\x11\xaf\xb1\xda?~\xd3\xf4\xf3\xddz\xf4?\x0f\xae\xd2)\x86\x07\xf0\xbfO\xfb\x1b\x10\x14\x1f\xf3?\xd0d\x18\x92\x82\xe0\xf4\xbf\x0c\xc2\xa0\x9a\xc6\xd4\xbf\xdd\xfb{f\xe6\xda\xc4?\xc3\xa8\xec\xdbn\xd1\xd7\xbf\xe3\xb0z\t[\xa6\xe5\xbf\xb3\x9b\x01\xf5\x0c\xf3\xcf\xbf\xdd\xeeL\x83\xf6\xd1\xbf\xc5\xff\xd5\x84\xce\xb1\xf8\xbf\xcd\x92\xd6Y\xf7\x85\xfb\xbf\x1d#\xdeK8\xf6?[-\x15\xa2\x03\xb6\x8e\xbfC\xde \xc7\xfee\xe5?\xbb\x166\xeb\xf8\r\xa7\xbf|\xfd\xcb\x11\xd14\xe4?^\x9e\xdcr\x8f\x87\xd2\xbf\xfe\xa6\n\x12\tG\xfb\xbfa\xfc\xfe\x15}\xcc\xe3?S\xec\xb4\x9e\xe3?\x17y\xbb\x9d\x01G\xcd?,c\xe2\xe5\xc4c\xf2?\xa7\x1f,H\xd1\xe3\x03;\x0e\x9f\xc5\\\xc8?P\xc1\xcbyB:\xdc\xbf \xc3o\xf3\xe0\xf7?\x7fx\x8d\xc4\x04-\xd5?\x13BP\\xb1\xb8\xe5\xbfw3\xdauzT\xe3\xbfb\x0cQQ\xb2\xf3\xbf\x07\xd4\xee\x1f\x7f\xe8\xbf\xcd\xf4\t\xae?\xf7\xf4?G\xb3b\x8a\x15\xa0\xdd\xbf\xe9IV\x83\xb8R\xdc\xbf\xc7\x88\x96\x03\xea\xcb\xca\xbf\xc4q\xaf\xe1{\xe4\xc1?\x03$o(\x95\xcf\xe2?l\xb3\xa9\x7f\x8fn\x05\xc0NX/\xdc\x0f\x95\xf6\xbf\xbc\x0e\x1b\x1e\x1f\xf1?C\xe7\xf7\xb0\xba\xd8\x01\xc0\xdaKPg\x03G\xe6?/J\xbb\xa9L\xf4\xd8?\x7fV\x11_\xac\xf1\xbf\x7f\x94\xdf-\r\xa3\xfd\xbf\xb1\xe0~\\\\s\xf9\xbfl\xb7\n\xf8q\x83\xf7?4H\xe5fQY\xed\xbf\xdde\x96\x18P\xd8\xb4?\x02\x0c\x1cw\xe8\x06\xe8j\x9a\xb1\x1e\xb3\xea?R\D\xd5\x12\x19\x00\xc0]B\xc7\xdbvt\xd2\xbf\xcc\xf5\x16\x8e\x8b\xf4\xbfK\xdc)H\xc0Y\xf8?r\xc7\xbc\xba\x8a\xaf\xb8\xbf\xd5i \xa2\x8e\xea\xbf\x9d\x0b.\xb9\xf5\xdf\xea?\x81\xa6\x16\xf4\xe4\xb4\xf1\xbfEq\xf7\xf6]6\xb0?\xf7\x16_r\xc8O\xdb?\x80\xe8\x18\x99\x11\xde\xf7?\x04M\x16\xb1\x0f\xea\xbf\x85\x83D\x99d\xeb\xbf\xe0\x1e\xad\xcaDE\xe7?\xe6\xe6\x9c\xa85\xd1\x04\xc0\x90t\xebaL\\xdb?5w\xc0\xd4)\xea?\xce\xbe\x19w\xe1\x03\x94q\xdc\xab\xa3z\xda?\x08\xc0/\x16w\x97\xe0\xbf\t_)V^\x93\xe8??\x82\xfb\x82\x16\x91?\x10\x87\xf3Z\xa4\xa1\xf5\xbf\xd3\x8cX\xb1\x18u\xf3?\xdf\xc5\xb3\xffm\x8e\xd9?3.3.5数组的去重
temp np.array([[1,2,3,4],[3,4,5,6]])
temparray([[1, 2, 3, 4],[3, 4, 5, 6]])np.unique(temp)array([1, 2, 3, 4, 5, 6])temp.flatten() # 降为1维数组array([1, 2, 3, 4, 3, 4, 5, 6])type(temp.flatten())numpy.ndarrayset(temp.flatten()) # 再用set去重{1, 2, 3, 4, 5, 6}3.4 ndarray运算
3.4.1 逻辑运算
stock_change np.random.normal(loc0,scale1,size(8,10))
stock_changearray([[-1.28396641, -2.01191074, -0.18834465, 2.42922844, -0.70687122,0.58481125, 0.55148057, 1.28943409, -1.44445438, 0.87934969],[ 0.12013781, -1.43581686, -0.63207426, 1.63806518, 1.17037384,-0.44528328, 1.23718753, -1.08925098, -0.26050859, -0.69753153],[-2.36635008, -2.62254681, 0.22101136, 0.81108448, -0.66006311,-0.15948853, 1.58475241, -0.81268957, -1.45337789, -0.06213791],[ 0.45162183, 0.55933576, -0.065766 , -0.40962168, 2.08206249,-0.84223895, -0.57720066, 1.79367669, -0.97694251, -0.33250153],[ 0.60649904, -0.59661935, -0.90621156, 1.79910292, -1.20565147,0.08852257, -0.99133308, 0.96236294, -0.9192948 , -0.03587398],[ 0.43325825, 0.48811556, 1.12822497, -1.27967886, 0.7919012 ,-0.38423972, 0.72962012, 1.74817488, 1.56455728, -1.72640669],[-0.38688515, 0.40048111, 2.51085027, -0.61192208, 0.70982823,-0.14795647, 0.30593344, -0.06915128, -1.34996629, -1.08573709],[-0.04277865, 0.60692697, 0.90975811, -0.5889982 , 0.25598235,-0.88764388, 0.10974295, 0.45449013, -1.03761231, -2.7914244 ]])# 逻辑判断如果涨跌幅大于0.5就标记为True,否则标记为False
stock_change0.5array([[False, False, False, True, False, True, True, True, False,True],[False, False, False, True, True, False, True, False, False,False],[False, False, False, True, False, False, True, False, False,False],[False, True, False, False, True, False, False, True, False,False],[ True, False, False, True, False, False, False, True, False,False],[False, False, True, False, True, False, True, True, True,False],[False, False, True, False, True, False, False, False, False,False],[False, True, True, False, False, False, False, False, False,False]])stock_change[stock_change0.5] # 布尔索引array([2.42922844, 0.58481125, 0.55148057, 1.28943409, 0.87934969,1.63806518, 1.17037384, 1.23718753, 0.81108448, 1.58475241,0.55933576, 2.08206249, 1.79367669, 0.60649904, 1.79910292,0.96236294, 1.12822497, 0.7919012 , 0.72962012, 1.74817488,1.56455728, 2.51085027, 0.70982823, 0.60692697, 0.90975811])stock_change[stock_change0.5] 1.1 stock_changearray([[-1.28396641, -2.01191074, -0.18834465, 1.1 , -0.70687122,1.1 , 1.1 , 1.1 , -1.44445438, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 , 1.1 ,-0.44528328, 1.1 , -1.08925098, -0.26050859, -0.69753153],[-2.36635008, -2.62254681, 0.22101136, 1.1 , -0.66006311,-0.15948853, 1.1 , -0.81268957, -1.45337789, -0.06213791],[ 0.45162183, 1.1 , -0.065766 , -0.40962168, 1.1 ,-0.84223895, -0.57720066, 1.1 , -0.97694251, -0.33250153],[ 1.1 , -0.59661935, -0.90621156, 1.1 , -1.20565147,0.08852257, -0.99133308, 1.1 , -0.9192948 , -0.03587398],[ 0.43325825, 0.48811556, 1.1 , -1.27967886, 1.1 ,-0.38423972, 1.1 , 1.1 , 1.1 , -1.72640669],[-0.38688515, 0.40048111, 1.1 , -0.61192208, 1.1 ,-0.14795647, 0.30593344, -0.06915128, -1.34996629, -1.08573709],[-0.04277865, 1.1 , 1.1 , -0.5889982 , 0.25598235,-0.88764388, 0.10974295, 0.45449013, -1.03761231, -2.7914244 ]])3.4.2通用判断函数
stock_change[0:2,0:5]array([[-1.28396641, -2.01191074, -0.18834465, 1.1 , -0.70687122],[ 0.12013781, -1.43581686, -0.63207426, 1.1 , 1.1 ]])# 判断stock_change[0:2,0:5]是否全是上涨的
np.all(stock_change[0:2,0:5] 0)
# 只有有一个False就返回False,只有全都是True才返回TrueFalsestock_change[0:5,:]array([[-1.28396641, -2.01191074, -0.18834465, 1.1 , -0.70687122,1.1 , 1.1 , 1.1 , -1.44445438, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 , 1.1 ,-0.44528328, 1.1 , -1.08925098, -0.26050859, -0.69753153],[-2.36635008, -2.62254681, 0.22101136, 1.1 , -0.66006311,-0.15948853, 1.1 , -0.81268957, -1.45337789, -0.06213791],[ 0.45162183, 1.1 , -0.065766 , -0.40962168, 1.1 ,-0.84223895, -0.57720066, 1.1 , -0.97694251, -0.33250153],[ 1.1 , -0.59661935, -0.90621156, 1.1 , -1.20565147,0.08852257, -0.99133308, 1.1 , -0.9192948 , -0.03587398]])# 判断前5只股票这段期间是否有上涨的
np.any(stock_change[0:5,:] 0)
# 只要有一个是True就返回True全都是False才返回FalseTrue3.4.3 np.where三元运算符
stock_change[:4,:4]array([[-1.28396641, -2.01191074, -0.18834465, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 ],[-2.36635008, -2.62254681, 0.22101136, 1.1 ],[ 0.45162183, 1.1 , -0.065766 , -0.40962168]])#判断前四个股票前四天的涨跌幅大于0的置为1,否则为0
tempstock_change[:4,:4]
np.where(temp 0 ,1 ,0)array([[0, 0, 0, 1],[1, 0, 0, 1],[0, 0, 1, 1],[1, 1, 0, 0]])temparray([[-1.28396641, -2.01191074, -0.18834465, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 ],[-2.36635008, -2.62254681, 0.22101136, 1.1 ],[ 0.45162183, 1.1 , -0.065766 , -0.40962168]])#判断前四个服票前四天的涨跌幅大于0.5并且小于1的换为1否则为0
#判断前四个般票前四天的涨跌幅大于0.5或者小于-0.5的换为1否则为0np.logical_and(temp0.5,temp1)array([[False, False, False, False],[False, False, False, False],[False, False, False, False],[False, False, False, False]])np.where(np.logical_and(temp0.5,temp1),1,0)array([[0, 0, 0, 0],[0, 0, 0, 0],[0, 0, 0, 0],[0, 0, 0, 0]])np.logical_or(temp0.5,temp-0.5)array([[ True, True, False, True],[False, True, True, True],[ True, True, False, True],[False, True, False, False]])np.where(np.logical_or(temp0.5,temp-0.5),1,0)array([[1, 1, 0, 1],[0, 1, 1, 1],[1, 1, 0, 1],[0, 1, 0, 0]])3.4.4 统计运算
2.股票涨跌幅统计运算
进行统计的时候axis轴的取值并不一定Numpy中不同的API轴的值都不一样在这里axis 0代表列axis 1代表行去进行统计
temparray([[-1.28396641, -2.01191074, -0.18834465, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 ],[-2.36635008, -2.62254681, 0.22101136, 1.1 ],[ 0.45162183, 1.1 , -0.065766 , -0.40962168]])temp.max()1.1np.max(temp)1.1#接下来对于这4只股票的4天数据,进行一些统计运算
#指定行去统计
print(前四只股票前四天的是大涨幅{}.format(np.max(temp,axis1)))前四只股票前四天的是大涨幅[1.1 1.1 1.1 1.1]#使用min,std,mean
print(前四只股票前四天的最大跌幅{}.format(np.min(temp,axis1)))前四只股票前四天的最大跌幅[-2.01191074 -1.43581686 -2.62254681 -0.40962168]print(前四只股票前四天的波动程度{}.format(np.std(temp,axis1)))前四只股票前四天的波动程度[1.17480848 0.93619571 1.61034658 0.56932139]print(前四只股票前四天的平均涨跌幅{}).format(np.mean(temp,axis1)))前四只股票前四天的平均涨跌幅[-0.59605545 -0.21193833 -0.91697138 0.26905854])返回最大值、最小值所在位置
np.argmaxtempaxisnp.argmintempaxis
temparray([[-1.28396641, -2.01191074, -0.18834465, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 ],[-2.36635008, -2.62254681, 0.22101136, 1.1 ],[ 0.45162183, 1.1 , -0.065766 , -0.40962168]])np.argmax(temp, axis1)array([3, 3, 3, 1], dtypeint64)np.argmax(temp, axis-1)array([3, 3, 3, 1], dtypeint64)3.5.2 数组与数的运算
arrnp.array([[1,2,3,2,1,4],[5,6,1,2,3,111]])
arrarray([[ 1, 2, 3, 2, 1, 4],[ 5, 6, 1, 2, 3, 111]])arr 10array([[ 11, 12, 13, 12, 11, 14],[ 15, 16, 11, 12, 13, 121]])arr * 10array([[ 10, 20, 30, 20, 10, 40],[ 50, 60, 10, 20, 30, 1110]])3.5.3 数组与数组的运算
arr1 np.array([[1,2,3,2,1,4],[5,6,1,2,3,1]])
arr2 np.array([[1,2,3,4],[3,4,5,6]])
arr1array([[1, 2, 3, 2, 1, 4],[5, 6, 1, 2, 3, 1]])arr2array([[1, 2, 3, 4],[3, 4, 5, 6]])arr1 arr2 ---------------------------------------------------------------------------ValueError Traceback (most recent call last)ipython-input-93-d972d21b639e in module
---- 1 arr1 arr2ValueError: operands could not be broadcast together with shapes (2,6) (2,4) 广播机制判断两个数组能否进行运算的方法
维度相等 或者shape(每个维度对应的位置为1)
arr1np.array([[1,2,3,2,1,4],[5,6,1,2,3,1]])
arr2np.array([[1],[3]])arr1array([[1, 2, 3, 2, 1, 4],[5, 6, 1, 2, 3, 1]])arr1.shape(2, 6)arr2array([[1],[3]])arr2.shape(2, 1)arr1 arr2array([[2, 3, 4, 3, 2, 5],[8, 9, 4, 5, 6, 4]])(arr1 arr2).shape(2, 6)3.5.5 矩阵运算
# array存储矩阵
anp.array([[80,86],[82,80],[85,78],[90,90],[86,82],[82,98],[78,80],[92,94]])aarray([[80, 86],[82, 80],[85, 78],[90, 90],[86, 82],[82, 98],[78, 80],[92, 94]])b np.array([[0.3],[0.7]])
barray([[0.3],[0.7]])# matrix存储矩阵
a_mat np.mat([[80,86],[82,80],[85,78],[90,90],[86,82],[82,98],[78,80],[92,94]])a_matmatrix([[80, 86],[82, 80],[85, 78],[90, 90],[86, 82],[82, 98],[78, 80],[92, 94]])type(a_mat)numpy.matrixb_mat np.mat([[0.3],[0.7]])b_matmatrix([[0.3],[0.7]])a_mat * b_matmatrix([[84.2],[80.6],[80.1],[90. ],[83.2],[93.2],[79.4],[93.4]])type(a)numpy.ndarraynp.matmul(a,b) # np.matmul(a,b)用于两个array数组类型相乘array([[84.2],[80.6],[80.1],[90. ],[83.2],[93.2],[79.4],[93.4]])np.dot(a,b) # np.dot(a,b) 也可以用于两个array数组类型相乘array([[84.2],[80.6],[80.1],[90. ],[83.2],[93.2],[79.4],[93.4]])a barray([[84.2],[80.6],[80.1],[90. ],[83.2],[93.2],[79.4],[93.4]])3.6 合并、分割
a np.array((1,2,3))
aarray([1, 2, 3])b np.array((2,3,4))
barray([2, 3, 4])3.6.1 合并
np.hstack((a,b)) # 水平拼接array([1, 2, 3, 2, 3, 4])a np.array([1,2,3])
aarray([1, 2, 3])a1 np.array([[1],[2],[3]])
a1array([[1],[2],[3]])b1 np.array([[2],[3],[4]])
b1array([[2],[3],[4]])np.hstack((a1,b1))array([[1, 2],[2, 3],[3, 4]])np.vstack((a,b)) # 竖直拼接array([[1, 2, 3],[2, 3, 4]])anp.array([[1,2],[3,4]])
aarray([[1, 2],[3, 4]])bnp.array([[5,6]])
barray([[5, 6]])np.concatenate((a,b),axis0) # axis0 竖直拼接array([[1, 2],[3, 4],[5, 6]])b.Tarray([[5],[6]])aarray([[1, 2],[3, 4]])np.concatenate((a,b.T),axis1) # axis1 水平拼接array([[1, 2, 5],[3, 4, 6]])3.6.2 分割
x np.arange(9.0)
xarray([0., 1., 2., 3., 4., 5., 6., 7., 8.])np.split(x,3)[array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]np.split(x,[3,6])[array([0., 1., 2.]), array([3., 4., 5.]), array([6., 7., 8.])]3.7 IO操作与数据处理
3.7.1 Numpy读取
data np.genfromtxt(test.csv,delimiter,,dtypeU75) # dtype转换数据类型关键字设置为U75 不设置dtype输出数据类型为nan
# delimiter,表示数据由逗号分隔
dataarray([[id, value1.value2, value3, ],[1, 123, 1.4, 23],[2, 110, , 18],[3, , 2.1, 19]], dtypeU75)3.7.2 如何处理缺失值
data np.genfromtxt(test.csv,delimiter,)
dataarray([[ nan, nan, nan, nan],[ 1. , 123. , 1.4, 23. ],[ 2. , 110. , nan, 18. ],[ 3. , nan, 2.1, 19. ]])data[2,2]nantype(data[2,2])numpy.float64def fill_nan_by_column_mean(t):# 先遍历每一列for i in range(t.shape[1]):# 计算nan的个数nan_num np.count_nonzero(t[:,i][t[:,i] ! t[:,i]])if nan_num0:now_colt[:,i]# 求和now_col_not_nan now_col[np.isnan(now_col)False].sum()# 和/个数now_col_mean now_col_not_nan / (t.shape[0] - nan_num)# 赋值给now col now_col[np.isnan(now_col)] now_col_mean#赋值给t,即更新t的当前列t[:,i]now_col return tdataarray([[ nan, nan, nan, nan],[ 1. , 123. , 1.4, 23. ],[ 2. , 110. , nan, 18. ],[ 3. , nan, 2.1, 19. ]])fill_nan_by_column_mean(data)array([[ 2. , 116.5 , 1.75, 20. ],[ 1. , 123. , 1.4 , 23. ],[ 2. , 110. , 1.75, 18. ],[ 3. , 116.5 , 2.1 , 19. ]])data[0,0] np.nannan_num np.count_nonzero(data[:,0][data[:,0] ! data[:,0]]) # numpy.count_nonzero是用于统计数组中非零元素的个数
nan_num1data[:,0]array([nan, 1., 2., 3.])data[:,0] ! data[:,0]array([ True, False, False, False])np.nan ! np.nan # np.nan 原意为 not a number所以当然不能判断两个np.nan 是否相等啦Trueaarray([[-1.28396641, -2.01191074, -0.18834465, 1.1 ],[ 0.12013781, -1.43581686, -0.63207426, 1.1 ]])a.shape(2, 4)a.reshape(-1,2) # 自动计算功能不想指定的位置用-1来填补即可array([[-1.28396641, -2.01191074],[-0.18834465, 1.1 ],[ 0.12013781, -1.43581686],[-0.63207426, 1.1 ]])3.8 总结
