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import numpy as np
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## 创建一个一维数组
np.array([1,2,3])array([1, 2, 3])
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np.array([1,2,3])array([1, 2, 3])
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np.array([[1,2,3]])array([[1, 2, 3]])
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np.array([[1,2,3]]).Tarray([[1],
[2],
[3]])
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np.ones(4)array([1., 1., 1., 1.])
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np.zeros(4)array([0., 0., 0., 0.])
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np.random.random(3)array([0.76998129, 0.65607576, 0.23038928])常见错误
1.保留中括号
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a = np.array([[1,2,3]])
a.dtypedtype('int32')
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a = np.array([[1.,2,3]])
a.dtypedtype('float64')
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a = np.array([[1,2,'3']])
a.dtypedtype('<U11')常用的api
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## 创建一个数组1-10
np.arange(1,11)array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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np.arange(-2,2,0.5)# 0.5是步长array([-2. , -1.5, -1. , -0.5, 0. , 0.5, 1. , 1.5])
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## 把数据切割几段
np.linspace(0,2,5)array([0. , 0.5, 1. , 1.5, 2. ])
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np.linspace(0,100,10)array([ 0. , 11.11111111, 22.22222222, 33.33333333,
44.44444444, 55.55555556, 66.66666667, 77.77777778,
88.88888889, 100. ])
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## 创建高维度数组
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np.ones([2,3])array([[1., 1., 1.],
[1., 1., 1.]])
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np.zeros([3,3])array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])创建对角矩阵
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a =np.eye(3)
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b = np.array([1,2,3])
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np.dot(a,b)array([1., 2., 3.])
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c = np.array([4,5,6])
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np.diag(c)array([[4, 0, 0],
[0, 5, 0],
[0, 0, 6]])numpy数组的属性
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c = np.array([1,2,3,4])
c.ndim1- 数组的形状
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a= np.array([
[2,3,4],
[1,3,7]
])
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a.shape(2, 3)
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a.size #数量6
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a.dtypedtype('int32')数组的运算
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data = np.array([1,2])
ones = np.ones(2)
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dataarray([1, 2])
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onesarray([1., 1.])
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data+onesarray([2., 3.])
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data - onesarray([0., 1.])
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data * onesarray([1., 2.])
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data/onesarray([1., 2.])
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data * 3array([3, 6])
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data ** 2array([1, 4], dtype=int32)
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np.sin(data)array([0.84147098, 0.90929743])
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np.exp(data)array([2.71828183, 7.3890561 ])
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np.sqrt(data)array([1. , 1.41421356])布尔过滤
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a = np.array([168,159,165,178,190])
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a >=175array([False, False, False, True, True])
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a = [10,20,30,40]
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a * 2[10, 20, 30, 40, 10, 20, 30, 40]索引入门
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a = np.array([1,2,3])
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a[1]2
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#索引范围
a[0:2]array([1, 2])
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a[1:30]array([2, 3])
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a[1:]array([2, 3])
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a[:5]array([1, 2, 3])
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a[:]array([1, 2, 3])
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a[-1]3
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a[-3:-1]array([1, 2])numpy数组的元素操作
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data = np.array([1,2,3,4,5,6,7,8,9,8,7,6,5,4])
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data.max()9
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data.min()1
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data.sum()75
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data.mean() #求平均值5.357142857142857二位数组切片
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data = np.array([
[1,2],
[3,4],
[5,6]
])
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dataarray([[1, 2],
[3, 4],
[5, 6]])
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data[0][1]2
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data[0,1]2
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data[1:3]array([[3, 4],
[5, 6]])
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data[0:2,0:1]array([[1],
[3]])numpy 数据形状的改变
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data= np.array([1,2,3,4,5,6])
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data.shape(6,)
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data.reshape(2,3)array([[1, 2, 3],
[4, 5, 6]])
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data.reshape(3,2)
## 注意行乘以列等于数据的总量array([[1, 2],
[3, 4],
[5, 6]])
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data.reshape(3,-1)array([[1, 2],
[3, 4],
[5, 6]])数据降维
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c = np.array([
[1,2,3],
[2,3,4]
])
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c.ravel()array([1, 2, 3, 2, 3, 4])
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a = np.array([
[3000,3500],
[4500,5000],
[10000,11000]]
)
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np.sum(a)37000
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np.sum(a,axis=0)array([17500, 19500])
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np.sum(a,axis=1)array([ 6500, 9500, 21000])
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np.max(a)11000
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np.min(a)3000
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np.mean(a)6166.666666666667
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np.median(a)4750.0方差
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np.var(a)9888888.888888888标准差
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np.std(a)3144.660377352201随机数
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np.random.rand(2,3)array([[0.14406139, 0.58255917, 0.56687269],
[0.45931544, 0.27684285, 0.53764072]])
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np.random.randint(2,6,5)array([2, 3, 2, 4, 5])
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np.random.randn(10)array([ 0.13193888, 0.09997268, -0.071677 , -0.08255967, -0.4487725 ,
-1.75318345, -0.46725208, 0.34519292, -0.30273565, -0.58354462])创建正太分布规律的数据
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np.random.normal(175,15,size=(4,4))array([[190.6869503 , 162.3662239 , 155.35991852, 185.87871385],
[179.38113865, 171.48428447, 179.59261062, 169.09543353],
[166.4724072 , 185.78899473, 168.35652325, 163.75069691],
[164.24513277, 186.98536138, 164.74146814, 176.38007905]])
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np.random.randn(4,4)array([[-3.08839751e-01, 7.66951959e-01, -4.02554299e-01,
6.24276206e-01],
[-6.56442394e-01, -5.90405577e-01, -2.12262531e+00,
-2.38134835e-01],
[-2.17110459e-01, -6.00174700e-01, 1.06536890e-01,
1.70518271e+00],
[ 2.33097080e-01, -4.03758442e-01, 2.64799870e-01,
6.67566195e-04]])
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np.average(np.random.randn(400,400))-0.005990795466101744
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np.std(np.random.randn(400,400))1.0005126387054453numpy数据打散
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a = np.arange(10)
aarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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np.random.permutation(a)array([6, 1, 7, 5, 9, 4, 2, 3, 8, 0])
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aarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])直接修改原始数组
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np.random.shuffle(a)
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aarray([2, 0, 9, 1, 7, 6, 4, 3, 8, 5])numpy指定随机数种子
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np.random.seed(3)
np.random.randint(24)10numpy逻辑操作
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a = np.array([True,False,True,True,False])
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np.all(a)False
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np.any(a)True
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a = np.array([50,55,65,57,58,66,100])
a >19array([ True, True, True, True, True, True, True])numpy数据排序
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b = np.random.randn(10)
barray([ 0.87226552, -0.01554175, 1.16668206, -2.15861048, 0.75620445,
-1.69049879, -0.34879409, -1.72951471, 0.69631161, 0.56496171])
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b.sort()
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barray([-2.15861048, -1.72951471, -1.69049879, -0.34879409, -0.01554175,
0.56496171, 0.69631161, 0.75620445, 0.87226552, 1.16668206])不修改元素族排序
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a = np.random.randn(10)
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aarray([ 0.47986141, -0.29594453, -0.42030926, -0.85961379, -0.40791451,
1.24757841, -1.82938368, -1.59651767, 0.69031204, 0.92812246])
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a.argsort()array([6, 7, 3, 2, 4, 1, 0, 8, 9, 5], dtype=int64)
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aarray([ 0.47986141, -0.29594453, -0.42030926, -0.85961379, -0.40791451,
1.24757841, -1.82938368, -1.59651767, 0.69031204, 0.92812246])
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a[a.argsort()]array([-1.82938368, -1.59651767, -0.85961379, -0.42030926, -0.40791451,
-0.29594453, 0.47986141, 0.69031204, 0.92812246, 1.24757841])二维数组
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b = np.random.randn(3,3)
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barray([[ 1.58653631, 0.40130586, -1.26968864],
[ 0.46920629, 1.30385884, 1.23128365],
[ 0.22948181, 0.27360762, -0.666784 ]])
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b.sort(axis=1)
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barray([[-1.26968864, 0.40130586, 1.58653631],
[ 0.46920629, 1.23128365, 1.30385884],
[-0.666784 , 0.22948181, 0.27360762]])
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b.sort(axis=0)
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barray([[-1.26968864, 0.22948181, 0.27360762],
[-0.666784 , 0.40130586, 1.30385884],
[ 0.46920629, 1.23128365, 1.58653631]])
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b.argsort(axis=1)array([[1, 0],
[1, 0],
[1, 0],
...,
[1, 0],
[0, 1],
[1, 0]], dtype=int64)
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np.set_printoptions(suppress=True)
totalpoint = 1000
b = np.random.rand(totalpoint,2)
barray([[0.03393228, 0.16615602],
[0.07044839, 0.25010443],
[0.37316487, 0.33790676],
...,
[0.2200427 , 0.60226645],
[0.04297848, 0.38231248],
[0.45174169, 0.16641803]])
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c = np.sqrt(np.sum(b**2,axis=1))
carray([0.73849201, 0.78324931, 0.2300633 , ..., 0.77652285, 0.78353274,
0.62629663])
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c < 1array([ True, True, True, ..., False, True, True])
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np.sum(c<=1)785401387行向量和列向量
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a = np.array([1,2,3])
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aarray([1, 2, 3])
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a.transpose()array([1, 2, 3])ID数组（一维数组）
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b = np.arange(10)
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barray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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b[::2]array([0, 2, 4, 6, 8])数组倒序排列
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barray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])b::-1
2D(二位数组)
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a = np.array([
[1,2],
[3,4],
[5,6]
])
aarray([[1, 2],
[3, 4],
[5, 6]])
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a[1,1]4
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data = np.arange(25).reshape(5,5)
dataarray([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
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data[1,2:4]array([7, 8])
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data[3:,3:]array([[18, 19],
[23, 24]])
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data[::2,2::2]array([[ 2, 4],
[12, 14],
[22, 24]])
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data[:,1:2]array([[ 1],
[ 6],
[11],
[16],
[21]])
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data[[0,2,4],[0,2,4]]array([ 0, 12, 24])
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data[[1,3,4],3:]array([[ 8, 9],
[18, 19],
[23, 24]])数组拆分和合并
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data = np.arange(9).reshape(3,3)
dataarray([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
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np.vsplit(data,3)[array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])]
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np.hsplit(data,3)[array([[0],
[3],
[6]]),
array([[1],
[4],
[7]]),
array([[2],
[5],
[8]])]数组的合并
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a = np.array([
[1,2],
[3,4]
])
b = np.array([
[7,8],
[9,10]
])
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np.vstack((a,b))array([[ 1, 2],
[ 3, 4],
[ 7, 8],
[ 9, 10]])
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np.hstack((a,b))array([[ 1, 2, 7, 8],
[ 3, 4, 9, 10]])numpy 高级操作，索引数组
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b = np.array([1,3,5,2,4,6])
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b[np.argsort(b)]array([1, 2, 3, 4, 5, 6])
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## mnumpy广播机制
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a = np.arange(3)
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a + 1array([1, 2, 3])其他常用的api
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b = np.array([1,2,3,4,5,6,7,8,9])
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np.argmax(b)8
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np.argmin(b)0
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np.nonzero(b)(array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=int64),)
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np.count_nonzero(b)9
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