1200字范文 > 利用Python进行数据分析的学习笔记——chap12

利用Python进行数据分析的学习笔记——chap12

时间：2024-05-27 19:16:38

NumPy高级应用

ndarray对象的内部机理

import numpy as npimport pandas as pdnp.ones((10,5)).shape

(10, 5)

np.ones((3,4,5),dtype=np.float64).strides

(160, 40, 8)

NumPy数据类型体系

ints = np.ones(10,dtype=np.uint16)floats = np.ones(10,dtype=np.float32)np.issubdtype(ints.dtype,np.integer)

True

np.issubdtype(floats.dtype,np.floating)

True

#调用dtype的mro方法即可查看所有的父类np.float64.mro()

[numpy.float64,numpy.floating,numpy.inexact,numpy.number,numpy.generic,float,object]

高级数组操作

数组重塑

arr = np.arange(8)arr

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

arr.reshape((4,2))

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

arr.reshape((4,2)).reshape((2,4))

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

#作为参数的形状的其中一维可以是-1，它表示该维度的大小由数据本身推断而来arr = np.arange(15)arr.reshape((5,-1))

array([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],[ 9, 10, 11],[12, 13, 14]])

other_arr = np.ones((3,5))other_arr.shape

(3, 5)

arr.reshape(other_arr.shape)

array([[ 0, 1, 2, 3, 4],[ 5, 6, 7, 8, 9],[10, 11, 12, 13, 14]])

arr = np.arange(15).reshape((5,3))arr

array([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],[ 9, 10, 11],[12, 13, 14]])

#扁平化或散开,不会产生数据的副本arr.ravel()

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

#会返回数据的副本arr.flatten()

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

C和Fortran顺序（行优先和列优先）

arr = np.arange(12).reshape((3,4))arr

array([[ 0, 1, 2, 3],[ 4, 5, 6, 7],[ 8, 9, 10, 11]])

arr.ravel()

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

arr.ravel('F')

array([ 0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11])

C/行优先顺序：先经过更高的维度（例如，轴1会先于轴0被处理）

Fortran/列优先顺序：后经过更高的维度（例如，轴0会先于轴1被处理）

数组的合并和拆分

arr1 = np.array([[1,2,3],[4,5,6]])arr2 = np.array([[7,8,9,],[10,11,12]])np.concatenate([arr1,arr2],axis=0)

array([[ 1, 2, 3],[ 4, 5, 6],[ 7, 8, 9],[10, 11, 12]])

np.concatenate([arr1,arr2],axis=1)

array([[ 1, 2, 3, 7, 8, 9],[ 4, 5, 6, 10, 11, 12]])

#比较方便的连接操作np.vstack((arr1,arr2))

array([[ 1, 2, 3],[ 4, 5, 6],[ 7, 8, 9],[10, 11, 12]])

np.hstack((arr1,arr2))

array([[ 1, 2, 3, 7, 8, 9],[ 4, 5, 6, 10, 11, 12]])

split用于将一个数组沿指定轴拆分为多个数组

from numpy.random import randnarr = randn(5,2)arr

array([[-1.15791597, 1.75726353],[-0.58322042, -0.4999356 ],[-0.45514448, -0.53653599],[ 0.09353445, -0.61268623],[ 1.48642482, 0.96240678]])

first,second,third = np.split(arr,[1,3])first

array([[-1.15791597, 1.75726353]])

second

array([[-0.58322042, -0.4999356 ],[-0.45514448, -0.53653599]])

third

array([[ 0.09353445, -0.61268623],[ 1.48642482, 0.96240678]])

#有个知识点import matplotlib.pyplot as pltfrom pylab import *img = plt.imread('数组连接函数.png')imshow(img)

堆叠辅助类：r_和c_

arr = np.arange(6)arr1 = arr.reshape((3,2))arr2 = randn(3,2)np.r_[arr1,arr2]

array([[ 0. , 1. ],[ 2. , 3. ],[ 4. , 5. ],[-0.15830216, -0.17789986],[ 0.92069385, -0.61503971],[ 0.0215242 , -0.31915158]])

np.c_[np.r_[arr1,arr2],arr]

array([[ 0. , 1. , 0. ],[ 2. , 3. , 1. ],[ 4. , 5. , 2. ],[-0.15830216, -0.17789986, 3. ],[ 0.92069385, -0.61503971, 4. ],[ 0.0215242 , -0.31915158, 5. ]])

#切片翻译为数组np.c_[1:6,-10:-5]

array([[ 1, -10],[ 2, -9],[ 3, -8],[ 4, -7],[ 5, -6]])

元素的重复操作：tile和repeat

arr = np.arange(3)arr.repeat(3)

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

arr.repeat([2,3,4])

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

arr = randn(2,2)arr

array([[-1.36101126, 1.20707733],[-2.02673963, -0.32688146]])

arr.repeat(2,axis=0)

array([[-1.36101126, 1.20707733],[-1.36101126, 1.20707733],[-2.02673963, -0.32688146],[-2.02673963, -0.32688146]])

arr.repeat([2,3],axis=0)

array([[-1.36101126, 1.20707733],[-1.36101126, 1.20707733],[-2.02673963, -0.32688146],[-2.02673963, -0.32688146],[-2.02673963, -0.32688146]])

arr.repeat([2,3],axis=1)

array([[-1.36101126, -1.36101126, 1.20707733, 1.20707733, 1.20707733],[-2.02673963, -2.02673963, -0.32688146, -0.32688146, -0.32688146]])

arr

array([[-1.36101126, 1.20707733],[-2.02673963, -0.32688146]])

#tile的功能是沿指定轴向堆叠数组的副本。np.tile(arr,2)

array([[-1.36101126, 1.20707733, -1.36101126, 1.20707733],[-2.02673963, -0.32688146, -2.02673963, -0.32688146]])

np.tile(arr,(2,1))#可看成两行一列

array([[-1.36101126, 1.20707733],[-2.02673963, -0.32688146],[-1.36101126, 1.20707733],[-2.02673963, -0.32688146]])

np.tile(arr,(3,2))#可看成三行两列

array([[-1.36101126, 1.20707733, -1.36101126, 1.20707733],[-2.02673963, -0.32688146, -2.02673963, -0.32688146],[-1.36101126, 1.20707733, -1.36101126, 1.20707733],[-2.02673963, -0.32688146, -2.02673963, -0.32688146],[-1.36101126, 1.20707733, -1.36101126, 1.20707733],[-2.02673963, -0.32688146, -2.02673963, -0.32688146]])

花式索引的等价函数：take和put

arr = np.arange(10)*100inds = [7,1,2,6]arr[inds]

array([700, 100, 200, 600])

arr.take(inds)

array([700, 100, 200, 600])

arr.put(inds,42)

arr

array([ 0, 42, 42, 300, 400, 500, 42, 42, 800, 900])

inds = [2,0,2,1]arr = randn(2,4)arr

array([[-0.50161986, -0.0522166 , 0.76367148, -0.55916362],[-1.00260667, 0.43677575, 0.59555349, 1.09068029]])

arr.take(inds,axis=1)

array([[ 0.76367148, -0.50161986, 0.76367148, -0.0522166 ],[ 0.59555349, -1.00260667, 0.59555349, 0.43677575]])

广播

广播指的是不同形状的数组之间的算术运算的执行方式。

将标量值跟数组合并时就会发生最简单的广播。

arr = np.arange(5)arr

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

arr * 4

array([ 0, 4, 8, 12, 16])

arr = randn(4,3)arr.mean(0)

array([ 0.20388605, 0.3803822 , -0.12810073])

demeaned = arr - arr.mean(0)demeaned

array([[ 1.78874981, 0.15783634, -0.50319093],[ 0.43109711, 0.46940631, -0.86418856],[-0.96776341, -0.21361791, 0.03363107],[-1.25208351, -0.41362474, 1.33374841]])

demeaned.mean(0)

array([ 0.00000000e+00, -2.77555756e-17, -5.55111512e-17])

arr

array([[ 1.99263586, 0.53821853, -0.63129166],[ 0.63498315, 0.84978851, -0.99228929],[-0.76387737, 0.16676429, -0.09446966],[-1.04819746, -0.03324254, 1.20564768]])

row_means = arr.mean(1)row_means.reshape((4,1))

array([[ 0.63318758],[ 0.16416079],[-0.23052758],[ 0.04140256]])

demeaned = arr - row_means.reshape((4,1))demeaned.mean(1)

array([1.48029737e-16, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])

沿其他轴向广播

arr - arr.mean(1).reshape((4,1))

array([[ 1.35944828, -0.09496904, -1.26447924],[ 0.47082236, 0.68562772, -1.15645008],[-0.53334979, 0.39729187, 0.13605792],[-1.08960002, -0.0746451 , 1.16424512]])

arr = np.zeros((4,4))arr_3d = arr[:,np.newaxis,:]arr_3d.shape

(4, 1, 4)

arr_1d = np.random.normal(size=3)arr_1d[:,np.newaxis]

array([[2.04800711],[0.52206057],[1.07754942]])

arr_1d[np.newaxis,:]

array([[2.04800711, 0.52206057, 1.07754942]])

#如果有一个三维数组，并希望对轴2进行距平化arr = randn(3,4,5)depth_means = arr.mean(2)depth_means

array([[ 0.03148651, 0.75426701, -0.09871035, -0.14189834],[-0.13307934, 0.43572469, -0.28305591, 0.11958806],[ 0.65974424, -0.51244655, 0.00827298, -0.7063478 ]])

demeaned = arr - depth_means[:,:,np.newaxis]demeaned.mean(2)

array([[-1.38777878e-18, 8.88178420e-17, 0.00000000e+00,4.44089210e-17],[ 0.00000000e+00, 4.44089210e-17, -2.22044605e-17,-2.22044605e-17],[-4.44089210e-17, 8.88178420e-17, -4.44089210e-17,4.44089210e-17]])

arr

array([[[-0.17944366, 0.26318479, -0.61798966, 0.61224075,0.07944035],[ 0.5355697 , 0.77165914, 2.35891609, 0.83188406,-0.72669392],[ 0.51017447, -0.3048777 , -0.69343153, 0.62523682,-0.63065379],[ 0.616 , 0.93732656, -0.90731587, -2.35714756,1.00562157]],[[-1.08873953, -0.09360078, -0.24163424, 0.88653706,-0.1279592 ],[-0.59340819, 1.22832916, -0.52064469, 1.46835124,0.59599591],[-0.6261998 , 0.29504874, -0.97687309, -0.56734596,0.46009055],[ 0.86167635, -1.17828692, -0.57720655, 1.86401846,-0.37226101]],[[ 0.7998406 , 0.33150187, 0.07888266, 1.99724594,0.09125012],[-0.51269232, -0.08079038, -0.69063807, 0.29238486,-1.57049686],[ 1.16061633, 0.34521942, -1.59255222, -0.1817776 ,0.30985897],[-0.77002413, 1.63996696, -1.66131826, -0.90786592,-1.83249766]]])

def demean_axis(arr,axis=0):means = arr.mean(axis)#下面这些一般化的东西类似于N维的[:,:,np.newaxis]indexer = [slice(None)]*arr.ndimindexer[axis] = np.newaxisreturn arr - means[indexer]

通过广播设置数组的值

arr = np.zeros((4,3))arr[:] = 5arr

array([[5., 5., 5.],[5., 5., 5.],[5., 5., 5.],[5., 5., 5.]])

col = np.array([1.28,-0.42,0.44,1.6])arr[:] = col[:,np.newaxis]arr

array([[ 1.28, 1.28, 1.28],[-0.42, -0.42, -0.42],[ 0.44, 0.44, 0.44],[ 1.6 , 1.6 , 1.6 ]])

arr[:2] = [[-1.37],[0.509]]arr

array([[-1.37 , -1.37 , -1.37 ],[ 0.509, 0.509, 0.509],[ 0.44 , 0.44 , 0.44 ],[ 1.6 , 1.6 , 1.6 ]])

ufunc高级应用

ufunc实例方法

arr = np.arange(10)np.add.reduce(arr)

45

arr.sum()

45

arr = randn(5,5)arr[::2].sort(1)#对部分行进行排序

arr[:,:-1] < arr[:,1:]

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

np.logical_and.reduce(arr[:,:-1]<arr[:,1:],axis=1)

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

arr = np.arange(15).reshape((3,5))np.add.accumulate(arr,axis=1)

array([[ 0, 1, 3, 6, 10],[ 5, 11, 18, 26, 35],[10, 21, 33, 46, 60]], dtype=int32)

arr = np.arange(3).repeat([1,2,2])arr

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

#outer用于计算两个数组的叉积，输出结果的维度是两个输入数据的维度之和np.multiply.outer(arr,np.arange(5))

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

result = np.subtract.outer(randn(3,4),randn(5))result.shape

(3, 4, 5)

arr = np.arange(10)np.add.reduceat(arr,[0,5,8])

array([10, 18, 17], dtype=int32)

arr = np.multiply.outer(np.arange(4),np.arange(5))arr

array([[ 0, 0, 0, 0, 0],[ 0, 1, 2, 3, 4],[ 0, 2, 4, 6, 8],[ 0, 3, 6, 9, 12]])

np.add.reduceat(arr,[0,2,4],axis=1)

array([[ 0, 0, 0],[ 1, 5, 4],[ 2, 10, 8],[ 3, 15, 12]], dtype=int32)

#有个知识点import matplotlib.pyplot as pltfrom pylab import *img = plt.imread('ufunc的方法.png')imshow(img)

自定义ufunc

def add_elements(x,y):return x+y#np.frompyfunc接受一个Python函数以及两个表示输入输出参数数量的整数add_them = np.frompyfunc(add_elements,2,1)add_them(np.arange(8),np.arange(8))

array([0, 2, 4, 6, 8, 10, 12, 14], dtype=object)

add_them = np.vectorize(add_elements,otypes=[np.float64])add_them(np.arange(8),np.arange(8))

array([ 0., 2., 4., 6., 8., 10., 12., 14.])

arr = randn(10000)%timeit add_them(arr,arr)

1.38 ms ± 12 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit np.add(arr,arr)

3.04 µs ± 37.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

结构化和记录式数组

dtype = [('x',np.float64),('y',np.int32)]sarr = np.array([(1.5,6),(np.pi,-2)],dtype=dtype)sarr

array([(1.5 , 6), (3.14159265, -2)],dtype=[('x', '<f8'), ('y', '<i4')])

sarr[0]

(1.5, 6)

sarr[0]['y']

6

#在访问结构化数组的某个字段时，返回的时该数据的视图，不会发生数据复制。sarr['x']

array([1.5 , 3.14159265])

嵌套dtype和多维字段

dtype = [('x',np.int64,3),('y',np.int32)]arr = np.zeros(4,dtype=dtype)arr

array([([0, 0, 0], 0), ([0, 0, 0], 0), ([0, 0, 0], 0), ([0, 0, 0], 0)],dtype=[('x', '<i8', (3,)), ('y', '<i4')])

#各个记录的x字段所表示的是一个长度为3的数组。arr[0]['x']

array([0, 0, 0], dtype=int64)

arr['x']

array([[0, 0, 0],[0, 0, 0],[0, 0, 0],[0, 0, 0]], dtype=int64)

dtype = [('x',[('a','f8'),('b','f4')]),('y',np.int32)]data = np.array([((1,2),5),((3,4),6)],dtype=dtype)data['x']

array([(1., 2.), (3., 4.)], dtype=[('a', '<f8'), ('b', '<f4')])

data['y']

array([5, 6])

data['x']['a']

array([1., 3.])

numpy.searchsorted:在有序数组中查找元素

arr = np.array([0,1,7,12,15])arr.searchsorted(9)

3

arr.searchsorted([0,8,11,16])

array([0, 3, 3, 5], dtype=int64)

#默认行为返回相等值组的左侧索引arr = np.array([0,0,0,1,1,1,1])arr.searchsorted([0,1])

array([0, 3], dtype=int64)

arr.searchsorted([0,1],side='right')

array([3, 7], dtype=int64)

data = np.floor(np.random.uniform(0,10000,size=50))bins = np.array([0,100,1000,5000,10000])data

array([7455., 8775., 5760., 4314., 9398., 3141., 4802., 5949., 7802.,8637., 3824., 812., 3940., 3743., 49., 3315., 884., 5795.,4014., 8971., 4366., 6258., 3544., 7797., 6004., 9342., 5054.,6352., 8438., 4590., 7793., 5600., 219., 4646., 5666., 6797.,1069., 1503., 3679., 8784., 6431., 9303., 652., 801., 8617.,1541., 2569., 555., 1122., 9156.])

labels = bins.searchsorted(data)labels

array([4, 4, 4, 3, 4, 3, 3, 4, 4, 4, 3, 2, 3, 3, 1, 3, 2, 4, 3, 4, 3, 4,3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 2, 3, 4, 4, 3, 3, 3, 4, 4, 4, 2, 2,4, 3, 3, 2, 3, 4], dtype=int64)

from pandas import DataFrame, SeriesSeries(data).groupby(labels).mean()

149.0000002653.8333333 3317.8888894 7437.360000dtype: float64

np.digitize(data,bins)

array([4, 4, 4, 3, 4, 3, 3, 4, 4, 4, 3, 2, 3, 3, 1, 3, 2, 4, 3, 4, 3, 4,3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 2, 3, 4, 4, 3, 3, 3, 4, 4, 4, 2, 2,4, 3, 3, 2, 3, 4], dtype=int64)

NumPy的matrix类

X = np.array([[8.82768214,3.82222409,-1.14276475,2.04411587],[3.82222409,6.75272284,0.83909108,2.08293758],[-1.14376475,0.83909108,5.01690521,0.79573241],[2.04411587,2.08293758,0.79573241,6.24095859]])X[:,0]#一维的

array([ 8.82768214, 3.82222409, -1.14376475, 2.04411587])

y = X[:,:1]#切片操作可产生二维结果X

array([[ 8.82768214, 3.82222409, -1.14276475, 2.04411587],[ 3.82222409, 6.75272284, 0.83909108, 2.08293758],[-1.14376475, 0.83909108, 5.01690521, 0.79573241],[ 2.04411587, 2.08293758, 0.79573241, 6.24095859]])

y

array([[ 8.82768214],[ 3.82222409],[-1.14376475],[ 2.04411587]])

#y^T*ynp.dot(y.T,np.dot(X,y))

array([[1195.50003771]])

NumPy提供了一个matrix类，单行或列会以二维形式返回，且使用星号（*）的乘法直接就是矩阵乘法

Xm = np.matrix(X)ym = Xm[:,0]Xm

matrix([[ 8.82768214, 3.82222409, -1.14276475, 2.04411587],[ 3.82222409, 6.75272284, 0.83909108, 2.08293758],[-1.14376475, 0.83909108, 5.01690521, 0.79573241],[ 2.04411587, 2.08293758, 0.79573241, 6.24095859]])

ym

matrix([[ 8.82768214],[ 3.82222409],[-1.14376475],[ 2.04411587]])

ym.T*Xm*ym

matrix([[1195.50003771]])

#返回矩阵的逆Xm.I * X

matrix([[ 1.00000000e+00, -1.33379915e-16, -3.03071941e-17,-5.78350775e-17],[ 1.86115454e-16, 1.00000000e+00, -4.20971699e-17,5.40747487e-17],[-6.89393057e-17, -1.67620379e-17, 1.00000000e+00,-1.66100444e-17],[-8.76871963e-18, 5.35448631e-17, 2.92310959e-17,1.00000000e+00]])

不建议用numpy.matrix替代正规的ndarray，因为它们的应用面较窄。对于个别带有大量线性代数运算的函数，可以将函数参数转换为matrix类型，然后在返回之前用np.asarray（不会复制任何数据）将其转换回正规的ndarray

高级数组输入输出

内存映像文件

mmap = np.memmap('mymmap',dtype='float64',mode='w+',shape=(10000,10000))mmap

memmap([[0., 0., 0., ..., 0., 0., 0.],[0., 0., 0., ..., 0., 0., 0.],[0., 0., 0., ..., 0., 0., 0.],...,[0., 0., 0., ..., 0., 0., 0.],[0., 0., 0., ..., 0., 0., 0.],[0., 0., 0., ..., 0., 0., 0.]])

section = mmap[:5]section[:] = np.random.randn(5,10000)mmap.flush()mmap

memmap([[ 2.19259494, 0.098218 , 1.22445449, ..., 0.86149985,-0.57312049, 0.07745414],[-0.57110137, -0.70372025, -0.26646839, ..., 0.28740281,-0.18572474, 0.57800705],[-0.57249472, -2.2577152 , 0.47813972, ..., -0.50423976,0.06229319, 1.21312965],...,[ 0. , 0. , 0. , ..., 0. ,0. , 0. ],[ 0. , 0. , 0. , ..., 0. ,0. , 0. ],[ 0. , 0. , 0. , ..., 0. ,0. , 0. ]])

del mmap

只要某个内存映像超出了作用域，它就会被垃圾回收器回收，之前对其所做的任何修改都会被写入磁盘。当打开一个已经存在的内存映像时，仍然需要指明数据类型和形状，因为磁盘上的那个文件只是一块二进制数据而已，没有任何元数据。

mmap = np.memmap('mymmap',dtype='float64',shape=(10000,10000))mmap

连续内存的重要性

arr_c = np.ones((1000,1000),order='C')arr_f = np.ones((1000,1000),order='F')arr_c.flags

C_CONTIGUOUS : TrueF_CONTIGUOUS : FalseOWNDATA : TrueWRITEABLE : TrueALIGNED : TrueWRITEBACKIFCOPY : FalseUPDATEIFCOPY : False

arr_f.flags

C_CONTIGUOUS : FalseF_CONTIGUOUS : TrueOWNDATA : TrueWRITEABLE : TrueALIGNED : TrueWRITEBACKIFCOPY : FalseUPDATEIFCOPY : False

arr_f.flags.f_contiguous

True

#arr_c会比arr_f快，因为它的行在内存中是连续的。可这结果相反啊%timeit arr_c.sum(1)

845 µs ± 38.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit arr_f.sum(1)

444 µs ± 7.97 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

arr_f.copy('C').flags

C_CONTIGUOUS : TrueF_CONTIGUOUS : FalseOWNDATA : TrueWRITEABLE : TrueALIGNED : TrueWRITEBACKIFCOPY : FalseUPDATEIFCOPY : False

arr_c[:50].flags.contiguous

True

arr_c[:,:50].flags

C_CONTIGUOUS : FalseF_CONTIGUOUS : FalseOWNDATA : FalseWRITEABLE : TrueALIGNED : TrueWRITEBACKIFCOPY : FalseUPDATEIFCOPY : False

其他加速手段：Cython、f2py、C

#这个Cython函数用于对一个一维数组的所有元素求和from numpy cimport ndarray, float64_tdef sum_elements(ndarray[float64_t] arr):cdef Py_ssize_t i ,n = len(arr)cdef float64_t result = 0for i in range(n):result += arr[i]return result

File "C:\windows", line 2from numpy cimport ndarray, float64_t^SyntaxError: invalid syntax

本内容不代表本网观点和政治立场，如有侵犯你的权益请联系我们处理。

网友评论

网友评论仅供其表达个人看法，并不表明网站立场。