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numpy-科学计算基础库
例子：
import numpy as np #创建数组 a = np.arange(10) print(a) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0123456789] <class‘numpy.ndarray‘> Process finished with exit code 0
对列表中的元素开平方
之前的方法为：
import math
b = [3,4,9]
#定义存储开平方结果的列表
result = []
for i in b:
result.append(math.sqrt(i))
print(result)
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [1.7320508075688772, 2.0, 3.0] Process finished with exit code 0
现在使用numpy速度更快，更方便。对ndarray对象类型进行向量处理：
import numpy as np b = np.array([3,4,9]) print(np.sqrt(b))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [1.732050812. 3. ] Process finished with exit code 0
array进行创建数组
一维数组:
import numpy as np a = np.array([3,4,9]) print(a) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [349] <class‘numpy.ndarray‘> Process finished with exit code 0
a.shape 为(3,)
二维数组：
import numpy as np a = np.array([[1,2,3], [2,3,4], [3,4,5]]) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[123] [234] [345]] (3, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
三维数组：
import numpy as np a = np.array([[[1,2,3], [2,3,4], [3,4,5]]]) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[[123] [234] [345]]] (1, 3, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
array函数中dtype参数的使用，设置数组元素类型：
import numpy as np a = np.array([3,4,9], dtype=float) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [3. 4. 9.] (3,) <class‘numpy.ndarray‘> Process finished with exit code 0
array函数中ndmin参数的使用，说明最小维度为几，传入的值如果维度不够，就会在前面加维度1：
import numpy as np a = np.array([3,4,9], dtype=float, ndmin=3) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[[3. 4. 9.]]] (1, 1, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
arange函数：
import numpy as np a = np.arange(0, 6, dtype=float) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0. 1. 2. 3. 4. 5.] (6,) <class‘numpy.ndarray‘> Process finished with exit code 0
随机创建数组
import numpy as np a = np.random.random(10) #创建size=10的10个随机数[0.0, 1.0) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0.702246790.123333660.76152280.484887290.550499690.881890770.884483420.63407020.558463580.03856909] (10,) <class‘numpy.ndarray‘> Process finished with exit code 0
创建二维的：
import numpy as np a = np.random.random(size=(3,4)) #3行4列 print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[0.754527620.065117610.288767950.33917503] [0.700558530.058995910.69513740.48631801] [0.797255140.526458490.609551850.94158767]] (3, 4) <class‘numpy.ndarray‘> Process finished with exit code 0
三维的：
import numpy as np a = np.random.random(size=(3,4,2)) #3行4列 print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[[0.094590110.06400518] [0.639320670.90659996] [0.250105030.00512396] [0.935335790.15083294]] [[0.686090450.53156758] [0.717630290.43475711] [0.384470340.23069394] [0.488141150.65881832]] [[0.914885050.58573524] [0.731302860.89564597] [0.316572410.63555136] [0.608981150.71098613]]] (3, 4, 2) <class‘numpy.ndarray‘> Process finished with exit code 0
随机整数：
dtype参数默认为np.int, 也可以设置为np.int64
import numpy as np a = np.random.randint(1, 10, 5) print(a) print(a.shape) print(a.dtype) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [41338] (5,) int64 <class‘numpy.ndarray‘> Process finished with exit code 0
发现实际默认的跟讲的相反
import numpy as np a = np.random.randint(1, 10, (3,3)) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[453] [168] [276]] (3, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
import numpy as np a = np.random.randint(1, 10, (4,4)) print(a) print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[5531] [3816] [7722] [6469]] (4, 4) <class‘numpy.ndarray‘> Process finished with exit code 0
标准正态分布
一维：
import numpy as np a = np.random.randn(4) print(a) print(a.shape) print(a.dtype) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [-0.07124224 -0.23748904 -0.667593420.78374469] (4,) float64 <class‘numpy.ndarray‘> Process finished with exit code 0
二维：
import numpy as np a = np.random.randn(2,3) print(a) print(a.shape) print(a.dtype) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[-1.01226872 -1.32755441 -2.26288293] [ 0.941234711.046929860.85342488]] (2, 3) float64 <class‘numpy.ndarray‘> Process finished with exit code 0
三维：
import numpy as np a = np.random.randn(2,3,2) print(a) print(a.shape) print(a.dtype) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[[-0.10896308 -0.5064629 ] [-0.399167530.35598577] [-0.41677605 -0.41341541]] [[-1.129731980.26209766] [ 0.24671435 -0.2798904 ] [ 0.823667670.76207401]]] (2, 3, 2) float64 <class‘numpy.ndarray‘> Process finished with exit code 0
指定期望和方差的正太分布
默认期望为0.0，方差为1.0
import numpy as np a = np.random.normal(loc=3, scale=4, size=(2,3)) print(a) print(a.shape) print(a.dtype) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[-1.676151311.557906541.159349 ] [-0.842052853.530456531.2121123 ]] (2, 3) float64 <class‘numpy.ndarray‘> Process finished with exit code 0
ndarray对象的属性
import numpy as np a = np.random.normal(loc=3, scale=4, size=(2,3)) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 5.34933995 -1.681678264.93713342] [ 4.687251645.717888035.41723111]] 26 float64 8 (2, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
其他方式创建数组
import numpy as np a = np.zeros((5,)) #等价于a = np.zeros(5) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0. 0. 0. 0. 0.] 15 float64 8 (5,) <class‘numpy.ndarray‘> Process finished with exit code 0
import numpy as np a = np.ones((2,3)) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[1. 1. 1.] [1. 1. 1.]] 26 float64 8 (2, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
import numpy as np a = np.empty((2,3)) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[-3.10503618e+231 -2.32036278e+0771.48219694e-323] [ 0.00000000e+0000.00000000e+0004.17201348e-309]] 26 float64 8 (2, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
import numpy as np a = np.linspace(1, 10) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [ 1. 1.183673471.367346941.551020411.734693881.918367352.102040822.285714292.469387762.653061222.836734693.020408163.204081633.38775513.571428573.755102043.938775514.122448984.306122454.489795924.673469394.857142865.040816335.22448985.408163275.591836735.77551025.959183676.142857146.326530616.510204086.693877556.877551027.061224497.244897967.428571437.61224497.795918377.979591848.163265318.346938788.530612248.714285718.897959189.081632659.265306129.448979599.632653069.8163265310. ] 150 float64 8 (50,) <class‘numpy.ndarray‘> Process finished with exit code 0
上面注释写错了，是底数为10，但是倍数就不一定了，比如下面的例子的意思就是在值范围[10，10^10]中间取20个数，使他们之间的倍数是相同的：
import numpy as np a = np.logspace(1, 10, 20, dtype=int) print(a) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [ 1029882637842335695120691615841832985455591623776483293014384498428133231274274983792690191128837891335981828610000000000] 120 int64 8 (20,) <class‘numpy.ndarray‘> Process finished with exit code 0
一维数组的切片索引：
import numpy as np a = np.arange(10) print(a) print(a[0]) print(a[-1]) print(a[:]) print(a[2:6:2]) print(a[::-1]) print(a[-3:-1:1]) print(a[-1:-3:-1]) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0123456789] 09 [0123456789] [24] [9876543210] [78] [98] 110 int64 8 (10,) <class‘numpy.ndarray‘> Process finished with exit code 0
二维的切片和索引
[行的切片，列的切片 ] = [start：stop：step，start：stop：step]
import numpy as np a = np.arange(1, 13) a = a.reshape(4,3) print(a) #等价于 print(a[:,:]) print() print(a[0]) print(a[1][2]) print(a[:][2]) #得到第二行，等价于 print(a[2]) #也等价于下面的写法 print(a[2][:]) print() #想要得到第二列为： print(a[:,2]) #得到二三行的一二列 print(a[2:4,1:3]) print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] [[ 123] [ 456] [ 789] [101112]] [123] 6 [789] [789] [789] [ 36912] [[ 89] [1112]] 212 int64 8 (4, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
使用坐标获取：
import numpy as np a = np.arange(1, 13) a = a.reshape(4,3) print(a) #第三行第二列 print(a[2,1]) #等价于 print(a[2][1]) print() #同时获得第三行第二列，第四行第一列 print(np.array((a[2,1],a[3,0]))) #等价于 print(a[(2,3),(1,0)]) print() print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] 88 [ 810] [ 810] 212 int64 8 (4, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
索引为负数:
import numpy as np a = np.arange(1, 13) a = a.reshape(4,3) print(a) #获取最后一行 print(a[-1]) #行进行倒序 print(a[::-1, :]) #行列都倒序 print(a[::-1, ::-1]) print() print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] [101112] [[101112] [ 789] [ 456] [ 123]] [[121110] [ 987] [ 654] [ 321]] 212 int64 8 (4, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
数组的复制
浅拷贝：
import numpy as np a = np.arange(1, 13).reshape(4,3) print(a) print(id(a)) #获取一二行一二列 sub_a = a[:2,:2] print(sub_a) print(id(sub_a)) #修改切片的值 sub_a[0][0] = 100 print(a) print(sub_a)#结果可见会影响原来数组，浅拷贝 print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] 4495540752 [[12] [45]] 4496167680 [[10023] [ 456] [ 789] [ 101112]] [[1002] [ 45]] 212 int64 8 (4, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
深拷贝——copy方法
import numpy as np a = np.arange(1, 13).reshape(4,3) print(a) print(id(a)) #获取一二行一二列 sub_a = np.copy(a[:2,:2]) print(sub_a) print(id(sub_a)) #修改切片的值 sub_a[0][0] = 200 print(a) print(sub_a)#结果可见不会影响原来数组，深拷贝 print(a.ndim) print(a.size) print(a.dtype) #float64 = 8个字节 print(a.itemsize) #以字节为单位 print(a.shape) print(type(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] 4347974160 [[12] [45]] 4426006000 [[ 123] [ 456] [ 789] [101112]] [[2002] [ 45]] 212 int64 8 (4, 3) <class‘numpy.ndarray‘> Process finished with exit code 0
修改数组的维度
import numpy as np #一维成二维 a = np.arange(1, 13).reshape(4,3) print(a) #一维变三维 c = np.reshape(a, (2,2,3)) print(c) #多维成一维： d = a.reshape(12) print(d) e = a.reshape(-1) print(e) print() f = c.ravel() print(f) g = c.flatten() print(g) print()
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123] [ 456] [ 789] [101112]] [[[ 123] [ 456]] [[ 789] [101112]]] [ 123456789101112] [ 123456789101112] [ 123456789101112] [ 123456789101112]
数组的拼接
垂直的
import numpy as np #一维成二维 a = np.arange(1, 7).reshape(2,3) b = np.arange(7, 13).reshape(2,3) print(a) print(b) #水平拼接 c = np.hstack((a,b)) print(c) #垂直拼接 d = np.vstack((a,b)) print(d) print()
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[123] [456]] [[ 789] [101112]] [[ 123789] [ 456101112]] [[ 123] [ 456] [ 789] [101112]]
import numpy as np #一维成二维 a = np.arange(1, 7).reshape(2,3) b = np.arange(7, 13).reshape(2,3) print(a) print(b) #垂直方向 e = np.concatenate((a,b)) print(e) #水平方向 f = np.concatenate((a,b), axis=1) print(f)
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[123] [456]] [[ 789] [101112]] [[ 123] [ 456] [ 789] [101112]] [[ 123789] [ 456101112]]
三维数组有三个轴=0，1，2
import numpy as np #一维成二维 a = np.arange(1, 7).reshape(1,2,3) b = np.arange(7, 13).reshape(1,2,3) print(a) print(b) print() #垂直方向 e = np.concatenate((a,b)) print(e) print(e.shape) #水平方向 f = np.concatenate((a,b), axis=1) print(f) print(f.shape) g = np.concatenate((a,b), axis=2) print(g) print(g.shape) print()
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[[123] [456]]] [[[ 789] [101112]]] [[[ 123] [ 456]] [[ 789] [101112]]] (2, 2, 3) [[[ 123] [ 456] [ 789] [101112]]] (1, 4, 3) [[[ 123789] [ 456101112]]] (1, 2, 6)
数组的分隔
import numpy as np #一维成二维 x = np.arange(1, 7) a = np.split(x,3) #平均分割成3份,值个数够分隔成这么多，否则报错，返回一个列表对象 print(a) print(a[0]) print(type(a)) b = np.split(x,[3,5]) #以索引位置值3和值5作为分割线，按位置分割 print(b) print(type(b))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [array([1, 2]), array([3, 4]), array([5, 6])] [12] <class‘list‘> [array([1, 2, 3]), array([4, 5]), array([6])] <class‘list‘> Process finished with exit code 0
二维数组：
import numpy as np #一维成二维 x = np.arange(1, 17).reshape((4,4)) print(x) print() #垂直分隔，行分隔，平均分隔， a = np.split(x, 2, axis=0) #平均分割成2份,值个数够分隔成这么多，否则报错，返回一个列表对象 print(a) print(a[0]) print(type(a)) print() #垂直分隔，行分隔，行索引位置分隔， b = np.split(x,[1,2], axis=0) #以值3和值5作为分割线 print(b) print(type(b)) print() #水平方向，列分隔，平均分隔 c = np.split(x, 2, axis=1) #平均分割成2份,值个数够分隔成这么多，否则报错，返回一个列表对象 print(c) print(type(c)) print() #水平方向，列分隔，位置分隔 d = np.split(x,[2,3], axis=1) #以列索引值3和值5作为分割线 print(d) print(type(d)) print()
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 1234] [ 5678] [ 9101112] [13141516]] [array([[1, 2, 3, 4], [5, 6, 7, 8]]), array([[ 9, 10, 11, 12], [13, 14, 15, 16]])] [[1234] [5678]] <class‘list‘> [array([[1, 2, 3, 4]]), array([[5, 6, 7, 8]]), array([[ 9, 10, 11, 12], [13, 14, 15, 16]])] <class‘list‘> [array([[ 1, 2], [ 5, 6], [ 9, 10], [13, 14]]), array([[ 3, 4], [ 7, 8], [11, 12], [15, 16]])] <class‘list‘> [array([[ 1, 2], [ 5, 6], [ 9, 10], [13, 14]]), array([[ 3], [ 7], [11], [15]]), array([[ 4], [ 8], [12], [16]])] <class‘list‘> Process finished with exit code 0
hsplit()方法
也可以按位置分割，就是省略了axis参数：
vsplit()方法
上面结果有错，应为：
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12
上面的例子等价于：
import numpy as np #一维成二维 x = np.arange(1, 17).reshape((4,4)) print(x) print() #垂直分隔，行分隔，平均分隔， a = np.vsplit(x, 2) #平均分割成2份,值个数够分隔成这么多，否则报错，返回一个列表对象 print(a) print(a[0]) print(type(a)) print() #垂直分隔，行分隔，行索引位置分隔， b = np.vsplit(x,[1,2]) #以值3和值5作为分割线 print(b) print(type(b)) print() #水平方向，列分隔，平均分隔 c = np.hsplit(x, 2) #平均分割成2份,值个数够分隔成这么多，否则报错，返回一个列表对象 print(c) print(type(c)) print() #水平方向，列分隔，位置分隔 d = np.hsplit(x,[2,3]) #以列索引值3和值5作为分割线 print(d) print(type(d)) print()
数组的转置——transpose
import numpy as np a = np.arange(1,25).reshape((4,6)) print(a, a.shape) print() print(‘转置后a[i][j] -> a[j][i]‘) b = a.transpose() print(b, b.shape) print() #对二维来说，还可以使用.T print(a.T) print() #numpy中的transpose方法 print(np.transpose(a)) print() #多维数组进行转置 c = a.reshape((2,3,4)) print(c, c.shape) print() print(‘a[i][j][k] -> a[k][j][i]‘) d = np.transpose(c) print(d, d.shape) print() #指定维度位置的变换 e = np.transpose(c, (1,0,2)) #即a[i][j][k] -> a[j][i][k] print(e, e.shape) print()
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[ 123456] [ 789101112] [131415161718] [192021222324]] (4, 6) 转置后a[i][j] -> a[j][i] [[ 171319] [ 281420] [ 391521] [ 4101622] [ 5111723] [ 6121824]] (6, 4) [[ 171319] [ 281420] [ 391521] [ 4101622] [ 5111723] [ 6121824]] [[ 171319] [ 281420] [ 391521] [ 4101622] [ 5111723] [ 6121824]] [[[ 1234] [ 5678] [ 9101112]] [[13141516] [17181920] [21222324]]] (2, 3, 4) a[i][j][k] -> a[k][j][i] [[[ 113] [ 517] [ 921]] [[ 214] [ 618] [1022]] [[ 315] [ 719] [1123]] [[ 416] [ 820] [1224]]] (4, 3, 2) [[[ 1234] [13141516]] [[ 5678] [17181920]] [[ 9101112] [21222324]]] (3, 2, 4) Process finished with exit code 0
函数1
算术函数-广播机制
import numpy as np a = np.arange(9, dtype=float).reshape(3,3) b = np.array([10,10,10]) print(‘加法‘) print(np.add(a,b)) print(a+b) print() print(‘减法‘) print(np.subtract(b,a)) print(b-a) print() print(‘乘法‘) print(np.multiply(a,b)) print(a*b) print() print(‘除法‘) print(np.divide(a,b)) print(a/b)
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py 加法 [[10. 11. 12.] [13. 14. 15.] [16. 17. 18.]] [[10. 11. 12.] [13. 14. 15.] [16. 17. 18.]] 减法 [[10. 9. 8.] [ 7. 6. 5.] [ 4. 3. 2.]] [[10. 9. 8.] [ 7. 6. 5.] [ 4. 3. 2.]] 乘法 [[ 0. 10. 20.] [30. 40. 50.] [60. 70. 80.]] [[ 0. 10. 20.] [30. 40. 50.] [60. 70. 80.]] 除法 [[0. 0.10.2] [0.30.40.5] [0.60.70.8]] [[0. 0.10.2] [0.30.40.5] [0.60.70.8]] Process finished with exit code 0
使用函数的好处是可以指定输出结果
import numpy as np a = np.arange(9, dtype=float).reshape(3,3) print(a) y = np.empty((3,3)) print(y) #刚好保存的是之前的值 np.multiply(a,10, out=y) print(y)
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [[0. 1. 2.] [3. 4. 5.] [6. 7. 8.]] [[0. 1. 2.] [3. 4. 5.] [6. 7. 8.]] [[ 0. 10. 20.] [30. 40. 50.] [60. 70. 80.]] Process finished with exit code 0
数学函数
import numpy as np a = np.array([0,30,45,60,90]) print(np.sin(a*np.pi/180))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [0. 0.50.707106780.86602541. ] Process finished with exit code 0
四舍五入：
import numpy as np a = np.array([1.0, 4.55, 123, 0.567, 25.532]) print(np.around(a)) print(np.around(a, decimals=1)) print(np.around(a, decimals=-1)) print(np.floor(a)) print(np.ceil(a))
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py [ 1. 5. 123. 1. 26.] [ 1. 4.6123. 0.625.5] [ 0. 0. 120. 0. 30.] [ 1. 4. 123. 0. 25.] [ 1. 5. 123. 1. 26.] Process finished with exit code 0
统计函数
import numpy as np a = np.array([2,3,5,4]) b = np.array([2,2,3,3]) print(np.sum(a)) print(np.prod(a)) print(np.mean(a)) print(np.std(a)) print(np.var(a)) print() #多维的都可以指定轴 print(np.median(a)) #如果顺序是乱的，那么会自己排序 d = np.arange(1,13).reshape(3,4) print(d) print(np.median(d, axis=0)) #垂直轴 print(np.median(d, axis=1)) #水平轴 print() print(np.power(a,b)) print(np.power(a,2)) print(np.min(a)) print(np.max(a)) print(np.argmin(a)) print(np.argmax(a)) print(np.exp(a)) #e^a c = np.array([10,10,np.e]) print(np.log(c)) #以e为底数的对数 print() x = np.arange(5) print(x) y = np.zeros(10) print(y) np.power(x,2, out=y[1:6]) #指明存放位置 print(y)
返回：
/Users/user/PycharmProjects/python3/venv/bin/python /Users/user/PycharmProjects/python3/test.py 141203.51.1180339887498951.253.5 [[ 1234] [ 5678] [ 9101112]] [5. 6. 7. 8.] [ 2.56.510.5] [ 4912564] [ 492516] 2502 [ 7.389056120.08553692148.413159154.59815003] [2.302585092.302585091. ] [01234] [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 1. 4. 9. 16. 0. 0. 0. 0.] Process finished with exit code 0
原文：https://www.cnblogs.com/wanghui-garcia/p/11188702.html
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