100个Numpy练习【5】

2018-03-01 11:05:45来源:网络收集作者:◇黑桃┈━═☆人点击

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接上文: 100个Numpy练习【1】


接上文: 100个Numpy练习【2】


接上文: 100个Numpy练习【3】


接上文: 100个Numpy练习【4】


Numpy是Python做数据分析必须掌握的基础库之一,非常适合刚学习完Numpy基础的同学,完成以下习题可以帮助你更好的掌握这个基础库。


Python版本:Python 3.6.2


Numpy版本:Numpy 1.13.1





81. 考虑一个数组 Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14] ,如何生成一个数组 R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ...,[11,12,13,14]] ? (★★★)

( 提示 : stride_tricks.as_strided)


# Author: Stefan van der Walt
Z = np.arange(1,15,dtype=np.uint32)
R = stride_tricks.as_strided(Z,(11,4),(4,4))
print(R)
82. 计算矩阵的秩(★★★)

( 提示 : np.linalg.svd)


# Author: Stefan van der Walt
Z = np.random.uniform(0,1,(10,10))
U, S, V = np.linalg.svd(Z) # Singular Value Decomposition
rank = np.sum(S > 1e-10)
print(rank)
83. 如何找出数组中出现频率最高的值?(★★★)

( 提示 : np.bincount, argmax)


Z = np.random.randint(0,10,50)
print(np.bincount(Z).argmax())
84. 从一个 10x10 的矩阵中提取出连续的 3x3 区块 (★★★)

( 提示 : stride_tricks.as_strided)


# Author: Chris Barker
Z = np.random.randint(0,5,(10,10))
n = 3
i = 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.创建一个满足 Z[i,j] == Z[j,i] 的二维数组子类 (★★★)

( 提示 : class method)


# Author: Eric O. Lebigot
# Note: only works for 2d array and value setting using indices
class 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] = 42
print(S)
86. 考虑p个 nxn 矩阵和一组形状为 (n,1) 的向量,如何直接计算p个矩阵的乘积 (n,1) ? (★★★)

( 提示 : np.tensordot)


# Author: Stefan van der Walt
p, n = 10, 20
M = 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. 对于一个 16x16 的数组,如何得到一个区域的和(区域大小为 4x4 )? (★★★)

( 提示 : np.add.reduceat)


# Author: Robert Kern
Z = np.ones((16,16))
k = 4
S = 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. 如何利用 numpy 数组实现Game of Life? (★★★)

( 提示 : Game of Life , Game of Life有哪些图形? )


# Author: Nicolas Rougier
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 Z
Z = np.random.randint(0,2,(50,50))
for i in range(100): Z = iterate(Z)
print(Z)
89. 如何找到一个数组的第n个最大值?(★★★)

( 提示 : np.argsort | np.argpartition)


Z = np.arange(10000)
np.random.shuffle(Z)
n = 5
# Slow
print (Z[np.argsort(Z)[-n:]])
# Fast
print (Z[np.argpartition(-Z,n)[:n]])
90. 给定任意个数向量,创建笛卡尔积(每一个元素的每一种组合)(★★★)

( 提示 : np.indices)


# Author: Stefan Van der Walt
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 ix
print (cartesian(([1, 2, 3], [4, 5], [6, 7])))
91. 如何从一个常规数组中创建记录数组( record array )? (★★★)

( 提示 : np.core.records.fromarrays)


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. 思考一个大向量 Z , 用三种不同的方法计算它的立方 (★★★)

( 提示 : np.power, *, np.einsum)


# Author: Ryan G.
x = np.random.rand(5e7)
%timeit np.power(x,3)
%timeit x*x*x
%timeit np.einsum('i,i,i->i',x,x,x)
93. 考虑两个形状分别为 (8,3) 和 (2,2) 的数组 A 和 B . 如何在数组 A 中找到满足包含 B 中元素的行?(不考虑 B 中每行元素顺序)? (★★★)

( 提示 : np.where)


# Author: Gabe Schwartz
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. 思考一个 10x3 的矩阵,如何分解出有不全相同值的行 (如 [2,2,3] ) (★★★)
# Author: Robert Kern
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 Z
U = Z[Z.max(axis=1) != Z.min(axis=1),:]
print(U)
95. 将一个整数向量转换为二进制矩阵(★★★)

( 提示 : np.unpackbits)


# Author: Warren Weckesser
I = 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. McDonald
I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)
print(np.unpackbits(I[:, np.newaxis], axis=1))
96. 给定一个二维数组,如何提取出唯一的行?(★★★)

( 提示 : np.ascontiguousarray)


# Author: Jaime Fernández del Río
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)
97. 考虑两个向量 A 和 B ,写出用 einsum 等式对应的 inner, outer, sum, mul 函数 (★★★)

( 提示 : np.einsum)


# Author: Alex Riley
# Make sure to read: http://ajcr.net/Basic-guide-to-einsum/
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 * B
np.einsum('i,i', A, B) # np.inner(A, B)
np.einsum('i,j->ij', A, B) # np.outer(A, B)
98. 考虑一个由两个向量描述的路径 (X,Y) ,如何用等距样例( equidistant samples )对其进行采样( sample ) (★★★)?

( 提示 : np.cumsum, np.interp)


# Author: Bas Swinckels
phi = np.arange(0, 10*np.pi, 0.1)
a = 1
x = a*phi*np.cos(phi)
y = a*phi*np.sin(phi)
dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths
r = np.zeros_like(x)
r[1:] = np.cumsum(dr) # integrate path
r_int = np.linspace(0, r.max(), 200) # regular spaced path
x_int = np.interp(r_int, r, x) # integrate path
y_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中选择可以被解释为从多n度的多项分布式的行,即这些行只包含整数对n的和.(★★★)

( 提示 : np.logical_and.reduce, np.mod)


# Author: Evgeni Burovski
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 = 4
M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)
M &= (X.sum(axis=-1) == n)
print(X[M])
100. 对于一个一维数组 X ,计算它boostrapped之后的95%置信区间的平均值. (★★★)

( 提示 : np.percentile)


# Author: Jessica B. Hamrick
X = np.random.randn(100) # random 1D array
N = 1000 # number of bootstrap samples
idx = 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)

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