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数据科学中100个Numpy代码技巧

林小婵的店 1789

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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