前言:
目前我们对“python创建一个长度为10的空向量”大约比较注意,咱们都需要了解一些“python创建一个长度为10的空向量”的相关文章。那么小编在网摘上搜集了一些关于“python创建一个长度为10的空向量””的相关文章,希望我们能喜欢,咱们一起来学习一下吧!NumPy是Python语言的扩展库,支持许多高维数组和矩阵的操作。此外,它还为数组操作提供了许多数学函数库。机器学习涉及到对数组的大量转换和操作,这使得NumPy成为必不可少的工具之一。
下面的100个练习都是从numpy邮件列表、stack overflow和numpy文档中收集的。
1.以np的名称导入numpy包(★☆☆)
import numpy as np
2.打印numpy版本和配置(★☆☆)
print(np.__version__)np.show_config()
3.创建一个大小为10的空向量(★☆☆)
Z = np.zeros(10)print(Z)
4.如何找到任何数组的内存大小(★☆☆)
Z = np.zeros((10,10))print("%d bytes" % (Z.size * Z.itemsize))
5.如何从命令行获取numpy add函数的文档?(★☆☆)
%run `python -c "import numpy; numpy.info(numpy.add)"`
6.创建一个大小为10的空矢量,第五个值为1(★☆☆)
Z = np.zeros(10)Z[4] = 1print(Z)
7.创建一个向量,其值的范围是10到49(★☆☆)
Z = np.arange(10,50)print(Z)
8.反转向量(第一个元素变为最后一个)(★☆☆)
Z = np.arange(50)Z = Z[::-1]print(Z)
9.创建一个从0到8的3 * 3矩阵(★☆☆)
x = np.arange(0,9).reshape(3,3)print(x)
10.查找来自[1,2,0,0,4,0]的非零元素的索引(★☆☆)
nz = np.nonzero([1,2,0,0,4,0])print(nz)
11.创建一个3 * 3的单位矩阵(★☆☆)
Z = np.eye(3)print(Z)
12.创建一个具有随机值的3x3x3数组(★☆☆)
Z = np.random.random((3,3,3))print(Z)
13.创建一个具有随机值的10x10数组,并找到最小值和最大值(★☆☆)
Z = np.random.random((10,10))Zmin, Zmax = Z.min(), Z.max()print(Zmin, Zmax)
14.创建一个大小为30的随机向量,并找到平均值(★☆☆)
Z = np.random.random(30)m = Z.mean()print(m)
15.创建一个边界为1,内部为0的二维数组(★☆☆)
Z = np.ones((10,10))Z[1:-1,1:-1] = 0print(Z)
16.如何在现有数组周围添加边框(用0填充)?(★☆☆)
Z = np.ones((5,5))Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)print(Z)
17.以下表达式的结果是什么?(★☆☆)
0 * np.nannp.nan == np.nannp.inf > np.nannp.nan - np.nannp.nan in set([np.nan])0.3 == 3 * 0.1print(0 * np.nan)print(np.nan == np.nan)print(np.inf > np.nan)print(np.nan - np.nan)print(np.nan in set([np.nan]))print(0.3 == 3 * 0.1)
18.创建一个5x5矩阵,对角线正下方的值为(1、2、3、4)(★☆☆)
Z = np.diag(1+np.arange(4),k=-1)print(Z)
19.创建一个8x8矩阵,并用棋盘图案填充它(★☆☆)
Z = np.zeros((8,8),dtype=int)Z[1::2,::2] = 1Z[::2,1::2] = 1print(Z)
20.考虑一个形状为(6,7,8)的数组,第100个元素的索引(x,y,z)是什么?(★☆☆)
print(np.unravel_index(99,(6,7,8)))
21.使用tile函数创建一个棋盘格8x8矩阵(★☆☆)
Z = np.tile( np.array([[0,1],[1,0]]), (4,4))print(Z)
22.归一化一个5x5随机矩阵(★☆☆)
Z = np.random.random((5,5))Z = (Z - np.mean (Z)) / (np.std (Z))print(Z)
23.创建一个自定义dtype,将颜色描述为四个unsigned bytes(RGBA)(★☆☆)
color = np.dtype([("r", np.ubyte, 1), ("g", np.ubyte, 1), ("b", np.ubyte, 1), ("a", np.ubyte, 1)])
24.将5x3矩阵乘以3x2矩阵(实矩阵乘积)(★☆☆)
Z = np.dot(np.ones((5,3)), np.ones((3,2)))print(Z)# Alternative solution, in Python 3.5 and aboveZ = np.ones((5,3)) @ np.ones((3,2))print(Z)
25.给定一维数组,将3到8之间的所有元素乘以-1。(★☆☆)
Z = np.arange(11)Z[(3 < Z) & (Z < 8)] *= -1print(Z)
26.以下脚本的输出是什么?(★☆☆)
print(sum(range(5),-1))from numpy import *print(sum(range(5),-1))
27.考虑一个整数向量Z,以下哪个表达式是合法的?(★☆☆)
Z**Z2 << Z >> 2Z <- Z1j*ZZ/1/1Z<Z>ZZ**Z2 << Z >> 2Z <- Z1j*ZZ/1/1Z<Z>Z
28.以下表达式的结果是什么?
np.array(0) / np.array(0)np.array(0) // np.array(0)np.array([np.nan]).astype(int).astype(float)print(np.array(0) / np.array(0))print(np.array(0) // np.array(0))print(np.array([np.nan]).astype(int).astype(float))
29.如何round away from zero一个浮点数组(★☆☆)
Z = np.random.uniform(-10,+10,10)print (np.copysign(np.ceil(np.abs(Z)), Z))
30.如何找到两个数组之间的公共值?(★☆☆)
Z1 = np.random.randint(0,10,10)Z2 = np.random.randint(0,10,10)print(np.intersect1d(Z1,Z2))
31.如何忽略所有numpy警告(不建议)?(★☆☆)
# Suicide mode ondefaults = np.seterr(all="ignore")Z = np.ones(1) / 0# Back to sanity_ = np.seterr(**defaults)# Equivalently with a context managernz = np.nonzero([1,2,0,0,4,0])print(nz)
32.以下表达式是否正确?(★☆☆)
np.sqrt(-1) == np.emath.sqrt(-1)
33.如何获取昨天,今天和明天的日期?(★☆☆)
yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')today = np.datetime64('today', 'D')tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')
34.如何获取与2016年7月对应的所有日期?(★★☆)
Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')print(Z)
35.如何就地计算((A + B)*(-A / 2))(without copy)?(★★☆)
A = np.ones(3)*1B = np.ones(3)*2C = np.ones(3)*3np.add(A,B,out=B)np.divide(A,2,out=A)np.negative(A,out=A)np.multiply(A,B,out=A)
36.使用5种不同方法(★★☆)提取随机数组的整数部分
Z = np.random.uniform(0,10,10)print (Z - Z%1)print (np.floor(Z))print (np.ceil(Z)-1)print (Z.astype(int))print (np.trunc(Z))
37.创建一个5x5矩阵,其行值范围为0到4(★★☆)
Z = np.zeros((5,5))Z += np.arange(5)print(Z)
38.考虑一个生成器函数,该函数生成10个整数并使用它来构建数组(★☆☆)
def generate(): for x in range(10): yield xZ = np.fromiter(generate(),dtype=float,count=-1)print(Z)
39.创建一个大小为10的向量,其值的范围从0到1,0和1都排除在外(★★☆)
Z = np.linspace(0,1,11,endpoint=False)[1:]print(Z)
40.创建一个大小为10的随机向量并将其排序(★★☆)
Z = np.random.random(10)Z.sort()print(Z)
41.如何求和一个比np.sum快的小数组?(★★☆)
Z = np.arange(10)np.add.reduce(Z)
42.考虑两个随机数组A和B,检查它们是否相等(★★☆)
A = np.random.randint(0,2,5)B = np.random.randint(0,2,5)# Assuming identical shape of the arrays and a tolerance for the comparison of valuesequal = np.allclose(A,B)print(equal)# Checking both the shape and the element values, no tolerance (values have to be exactly equal)equal = np.array_equal(A,B)print(equal)
43.使数组不可变(只读)(★★☆)
Z = np.zeros(10)Z.flags.writeable = FalseZ[0] = 1
44.考虑一个表示笛卡尔坐标的随机10x2矩阵,将其转换为极坐标(★★☆)
Z = np.random.random((10,2))X,Y = Z[:,0], Z[:,1]R = np.sqrt(X**2+Y**2)T = np.arctan2(Y,X)print(R)print(T)
45.创建大小为10的随机向量,并将最大值替换为0(★★☆)
Z = np.random.random(10)Z[Z.argmax()] = 0print(Z)
46.创建一个结构化数组,x和y坐标覆盖[0,1]x[0,1]区域(★★☆)
Z = np.zeros((5,5), [('x',float),('y',float)])Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5), np.linspace(0,1,5))print(Z)
47.给定两个数组X和Y,构造柯西矩阵C(Cij = 1 /(xi_yj))
X = np.arange(8)Y = X + 0.5C = 1.0 / np.subtract.outer(X, Y)print(np.linalg.det(C))
48.打印每种numpy标量类型的最小和最大可表示值(★★☆)
for dtype in [np.int8, np.int32, np.int64]: print(np.iinfo(dtype).min) print(np.iinfo(dtype).max)for dtype in [np.float32, np.float64]: print(np.finfo(dtype).min) print(np.finfo(dtype).max) print(np.finfo(dtype).eps)
49.如何打印数组的所有值?(★★☆)
np.set_printoptions(threshold=np.nan)Z = np.zeros((16,16))print(Z)
50.如何找到向量中最接近给定标量的值?(★★☆)
Z = np.arange(100)v = np.random.uniform(0,100)index = (np.abs(Z-v)).argmin()print(Z[index])
51.创建一个结构化的数组,表示位置(x,y)和颜色(r,g,b)(★★☆)
Z = np.zeros(10, [ ('position', [ ('x', float, 1), ('y', float, 1)]), ('color', [ ('r', float, 1), ('g', float, 1), ('b', float, 1)])])print(Z)
52.考虑一个形状(100,2)表示坐标的随机向量,逐点查找距离(★★☆)
Z = np.random.random((10,2))X,Y = np.atleast_2d(Z[:,0], Z[:,1])D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)print(D)# Much faster with scipyimport scipyimport scipy.spatialZ = np.random.random((10,2))D = scipy.spatial.distance.cdist(Z,Z)print(D)
53.如何将浮点数(32位)数组转换为整数(32位)?
Z = (np.random.rand(10)*100).astype(np.float32)Y = Z.view(np.int32)Y[:] = Zprint(Y)54.如何读取以下文件?(★★☆)
1, 2, 3, 4, 56, , , 7, 8 , , 9,10,11from io import StringIO# Fake files = StringIO('''1, 2, 3, 4, 5 6, , , 7, 8 , , 9,10,11''')Z = np.genfromtxt(s, delimiter=",", dtype=np.int)print(Z)
55. numpy数组的枚举等效于什么?(★★☆)
Z = np.arange(9).reshape(3,3)for index, value in np.ndenumerate(Z): print(index, value)for index in np.ndindex(Z.shape): print(index, Z[index])
56.生成通用的类似于2D的高斯数组(★★☆)
X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))D = np.sqrt(X*X+Y*Y)sigma, mu = 1.0, 0.0G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )print(G)
57.如何在2D数组中随机放置p个元素?(★★☆)
n = 10p = 3Z = np.zeros((n,n))np.put(Z, np.random.choice(range(n*n), p, replace=False),1)print(Z)
58.减去矩阵的每一行的均值(★★☆)
X = np.random.rand(5, 10)# Recent versions of numpyY = X - X.mean(axis=1, keepdims=True)# Older versions of numpyY = X - X.mean(axis=1).reshape(-1, 1)print(Y)
59.如何按第n列对数组排序?(★★☆)
Z = np.random.randint(0,10,(3,3))print(Z)print(Z[Z[:,1].argsort()])
60.如何判断给定的2D数组是否有空列?(★★☆)
Z = np.random.randint(0,3,(3,10))print((~Z.any(axis=0)).any())
61.从数组中的给定值中找到最接近的值(★★☆)
Z = np.random.uniform(0,1,10)z = 0.5m = Z.flat[np.abs(Z - z).argmin()]print(m)
62.考虑两个形状为(1,3)和(3,1)的数组,如何使用迭代器计算它们的总和?(★★☆)
A = np.arange(3).reshape(3,1)B = np.arange(3).reshape(1,3)it = np.nditer([A,B,None])for x,y,z in it: z[...] = x + yprint(it.operands[2])
63.创建一个具有name属性的数组类(★★☆)
class NamedArray(np.ndarray): def __new__(cls, array, name="no name"): obj = np.asarray(array).view(cls) obj.name = name return obj def __array_finalize__(self, obj): if obj is None: return self.info = getattr(obj, 'name', "no name")Z = NamedArray(np.arange(10), "range_10")print (Z.name)
64.对于给定的向量,如何为第二个向量索引的每个元素添加1(注意重复索引)?(★★★)
Z = np.ones(10)I = np.random.randint(0,len(Z),20)Z += np.bincount(I, minlength=len(Z))print(Z)# Another solutionnp.add.at(Z, I, 1)print(Z)
65.如何基于索引列表(I)将向量(X)的元素累积到数组(F)中?(★★★)
X = [1,2,3,4,5,6]I = [1,3,9,3,4,1]F = np.bincount(I,X)print(F)
66.考虑一个(dtype = ubyte)的(w,h,3)图像,计算唯一颜色的数量(★★★)
w,h = 16,16I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)F = I[...,0]*256*256 + I[...,1]*256 +I[...,2]n = len(np.unique(F))print(np.unique(I))
67.考虑一个四维数组,如何一次获得最后两个轴的和?(★★★)
A = np.random.randint(0,10,(3,4,3,4))# solution by passing a tuple of axes (introduced in numpy 1.7.0)sum = A.sum(axis=(-2,-1))print(sum)# solution by flattening the last two dimensions into one# (useful for functions that don't accept tuples for axis argument)sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)print(sum)
68.考虑一维向量D,如何使用描述子集指标的相同大小的向量S来计算D子集的均值?(★★★)
D = np.random.uniform(0,1,100)S = np.random.randint(0,10,100)D_sums = np.bincount(S, weights=D)D_counts = np.bincount(S)D_means = D_sums / D_countsprint(D_means)# Pandas solution as a reference due to more intuitive codeimport pandas as pdprint(pd.Series(D).groupby(S).mean())
69.如何获得点积的对角线?(★★★)
A = np.random.uniform(0,1,(5,5))B = np.random.uniform(0,1,(5,5))# Slow version np.diag(np.dot(A, B))# Fast versionnp.sum(A * B.T, axis=1)# Faster versionnp.einsum("ij,ji->i", A, B)
70.考虑向量[1、2、3、4、5],如何构建一个在每个值之间有3个连续零的新向量?(★★★)
Z = np.array([1,2,3,4,5])nz = 3Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))Z0[::nz+1] = Zprint(Z0)
71.考虑一个维度为(5,5,3)的数组,如何将它与维度为(5,5)的数组相乘以?(★★★)
A = np.ones((5,5,3))B = 2*np.ones((5,5))print(A * B[:,:,None])
72.如何交换数组的两行?(★★★)
A = np.arange(25).reshape(5,5)A[[0,1]] = A[[1,0]]print(A)
73.考虑一组描述10个三角形(具有共享顶点)的10个三元组,找到组成所有三角形的唯一线段集(★★★)
faces = np.random.randint(0,100,(10,3))F = np.roll(faces.repeat(2,axis=1),-1,axis=1)F = F.reshape(len(F)*3,2)F = np.sort(F,axis=1)G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )G = np.unique(G)print(G)
74.给定一个数组C,为bincount,如何生成一个数组n使得np.bincount(A)== C?(★★★)
C = np.bincount([1,1,2,3,4,4,6])A = np.repeat(np.arange(len(C)), C)print(A)
75.如何使用数组上的滑动窗口计算平均值?(★★★)
def moving_average(a, n=3) : ret = np.cumsum(a, dtype=float) ret[n:] = ret[n:] - ret[:-n] return ret[n - 1:] / nZ = np.arange(20)print(moving_average(Z, n=3))
76.考虑一维数组Z,构建一个二维数组,其第一行是(Z [0],Z [1],Z [2]),随后的每一行都移位1(最后一行应为( Z [-3],Z [-2],Z [-1])(★★★)
def rolling(a, window): shape = (a.size - window + 1, window) strides = (a.itemsize, a.itemsize) return stride_tricks.as_strided(a, shape=shape, strides=strides)Z = rolling(np.arange(10), 3)print(Z)
77.如何取反布尔值,或如何改变浮点符号?(★★★)
Z = np.random.randint(0,2,100)np.logical_not(Z, out=Z)Z = np.random.uniform(-1.0,1.0,100)np.negative(Z, out=Z)
78.考虑描述线(2d)的2组点(P0、P1)和点P,如何计算从p到每条线i的距离(P0 [i],P1 [i])?(★★★)
def distance(P0, P1, p): T = P1 - P0 L = (T**2).sum(axis=1) U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L U = U.reshape(len(U),1) D = P0 + U*T - p return np.sqrt((D**2).sum(axis=1))P0 = np.random.uniform(-10,10,(10,2))P1 = np.random.uniform(-10,10,(10,2))p = np.random.uniform(-10,10,( 1,2))print(distance(P0, P1, p))
79.考虑描述线(2d)的2组点(P0、P1)和一组点P,如何计算从每个点j(P [j])到每个线i(P0 [i],P1 [i]的距离) )?(★★★)
# based on distance function from previous questionP0 = np.random.uniform(-10, 10, (10,2))P1 = np.random.uniform(-10,10,(10,2))p = np.random.uniform(-10, 10, (10,2))print(np.array([distance(P0,P1,p_i) for p_i in p]))
80.考虑一个任意的数组,编写一个函数来提取形状固定的子部分并以给定元素为中心(必要时使用fill值填充)(★★★)
Z = np.random.randint(0,10,(10,10))shape = (5,5)fill = 0position = (1,1)R = np.ones(shape, dtype=Z.dtype)*fillP = np.array(list(position)).astype(int)Rs = np.array(list(R.shape)).astype(int)Zs = np.array(list(Z.shape)).astype(int)R_start = np.zeros((len(shape),)).astype(int)R_stop = np.array(list(shape)).astype(int)Z_start = (P-Rs//2)Z_stop = (P+Rs//2)+Rs%2R_start = (R_start - np.minimum(Z_start,0)).tolist()Z_start = (np.maximum(Z_start,0)).tolist()R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()Z_stop = (np.minimum(Z_stop,Zs)).tolist()r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]R[r] = Z[z]print(Z)print(R)
81.考虑数组Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14],如何生成数组R = [[1,2, 3,4],[2,3,4,5],[3,4,5,6],…,[11,12,13,14]]?(★★★)
Z = np.arange(1,15,dtype=np.uint32)R = stride_tricks.as_strided(Z,(11,4),(4,4))print(R)
82.计算矩阵的Rank(★★★)
Z = np.random.uniform(0,1,(10,10))U, S, V = np.linalg.svd(Z) # Singular Value Decompositionrank = np.sum(S > 1e-10)print(rank)
83.如何在数组中查找最频繁的值?
Z = np.random.randint(0,10,50)print(np.bincount(Z).argmax())
84.从随机的10x10矩阵(★★★)中提取所有连续的3x3 blocks
Z = np.random.randint(0,5,(10,10))n = 3i = 1 + (Z.shape[0]-3)j = 1 + (Z.shape[1]-3)C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)print(C)
85.创建一个2D数组子类,使得Z [i,j] == Z [j,i](★★★)
# Note: only works for 2d array and value setting using indicesclass Symetric(np.ndarray): def __setitem__(self, index, value): i,j = index super(Symetric, self).__setitem__((i,j), value) super(Symetric, self).__setitem__((j,i), value)def symetric(Z): return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)S = symetric(np.random.randint(0,10,(5,5)))S[2,3] = 42print(S)
86.考虑一组形状为(n,n)的p个矩阵和一组形状为(n,1)的p个向量。如何一次计算p个矩阵乘积的总和?(结果的形状为(n,1))(★★★)
p, n = 10, 20M = np.ones((p,n,n))V = np.ones((p,n,1))S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])print(S)# It works, because:# M is (p,n,n)# V is (p,n,1)# Thus, summing over the paired axes 0 and 0 (of M and V independently),# and 2 and 1, to remain with a (n,1) vector.
87.考虑一个16x16的数组,如何获得块总和(块大小为4x4)?(★★★)
Z = np.ones((16,16))k = 4S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0), np.arange(0, Z.shape[1], k), axis=1)print(S)
88.如何使用numpy数组实现“生命游戏”?(★★★)
def iterate(Z): # Count neighbours N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] + Z[1:-1,0:-2] + Z[1:-1,2:] + Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:]) # Apply rules birth = (N==3) & (Z[1:-1,1:-1]==0) survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1) Z[...] = 0 Z[1:-1,1:-1][birth | survive] = 1 return ZZ = np.random.randint(0,2,(50,50))for i in range(100): Z = iterate(Z)print(Z)
89.如何获取数组的n个最大值(★★★)
Z = np.arange(10000)np.random.shuffle(Z)n = 5# Slowprint (Z[np.argsort(Z)[-n:]])# Fastprint (Z[np.argpartition(-Z,n)[:n]])
90.给定任意数量的向量,构建笛卡尔积(每一项的每一个组合)(★★★)
def cartesian(arrays): arrays = [np.asarray(a) for a in arrays] shape = (len(x) for x in arrays) ix = np.indices(shape, dtype=int) ix = ix.reshape(len(arrays), -1).T for n, arr in enumerate(arrays): ix[:, n] = arrays[n][ix[:, n]] return ixprint (cartesian(([1, 2, 3], [4, 5], [6, 7])))
91.如何从常规数组创建记录数组?(★★★)
Z = np.array([("Hello", 2.5, 3), ("World", 3.6, 2)])R = np.core.records.fromarrays(Z.T, names='col1, col2, col3', formats = 'S8, f8, i8')print(R)
92.考虑一个大向量Z,用3种不同的方法计算Z的3次方(★★★)
x = np.random.rand(int(5e7))%timeit np.power(x,3)%timeit x*x*x%timeit np.einsum('i,i,i->i',x,x,x)
93.考虑两个形状为(8,3)和(2,2)的数组A和B。如何找到A的行包含B的每一行的元素,而不考虑B中元素的顺序?(★★★)
A = np.random.randint(0,5,(8,3))B = np.random.randint(0,5,(2,2))C = (A[..., np.newaxis, np.newaxis] == B)rows = np.where(C.any((3,1)).all(1))[0]print(rows)
94.考虑一个10x3矩阵,提取不等值的行(例如[2,2,3])(★★★)
Z = np.random.randint(0,5,(10,3))print(Z)# solution for arrays of all dtypes (including string arrays and record arrays)E = np.all(Z[:,1:] == Z[:,:-1], axis=1)U = Z[~E]print(U)# soluiton for numerical arrays only, will work for any number of columns in ZU = Z[Z.max(axis=1) != Z.min(axis=1),:]print(U)
95.将整数向量转换为矩阵二进制表示形式(★★★)
# Author: Warren WeckesserI = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)print(B[:,::-1])# Author: Daniel T. McDonaldI = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)print(np.unpackbits(I[:, np.newaxis], axis=1))
96.给定二维数组,如何提取unique行?(★★★)
Z = np.random.randint(0,2,(6,3))T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))_, idx = np.unique(T, return_index=True)uZ = Z[idx]print(uZ)# NumPy >= 1.13uZ = np.unique(Z, axis=0)print(uZ)
97.考虑两个向量A和B,写出inner、outer、sum和mul函数的einsum等价(★★★)
A = np.random.uniform(0,1,10)B = np.random.uniform(0,1,10)np.einsum('i->', A) # np.sum(A)np.einsum('i,i->i', A, B) # A * Bnp.einsum('i,i', A, B) # np.inner(A, B)np.einsum('i,j->ij', A, B) # np.outer(A, B)
98.考虑由两个向量(X,Y)描述的路径,如何使用等距样本进行采样(★★★)
phi = np.arange(0, 10*np.pi, 0.1)a = 1x = a*phi*np.cos(phi)y = a*phi*np.sin(phi)dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengthsr = np.zeros_like(x)r[1:] = np.cumsum(dr) # integrate pathr_int = np.linspace(0, r.max(), 200) # regular spaced pathx_int = np.interp(r_int, r, x) # integrate pathy_int = np.interp(r_int, r, y)
99.给定一个整数n和一个二维数组X,从X中选择可以解释为n次多项式分布的行,即仅包含整数且总和为n的行。(★★★)
X = np.asarray([[1.0, 0.0, 3.0, 8.0], [2.0, 0.0, 1.0, 1.0], [1.5, 2.5, 1.0, 0.0]])n = 4M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)M &= (X.sum(axis=-1) == n)print(X[M])
100.计算一维数组X的平均值的自举95%置信区间(即,对数组中的元素重新取样N次,计算每个样本的平均值,然后计算平均值的百分比)。(★★★)
X = np.random.randn(100) # random 1D arrayN = 1000 # number of bootstrap samplesidx = np.random.randint(0, X.size, (N, X.size))means = X[idx].mean(axis=1)confint = np.percentile(means, [2.5, 97.5])print(confint)
谢谢阅读!!:)
标签: #python创建一个长度为10的空向量