接上文: 100个Numpy练习【1】 接上文: 100个Numpy练习【2】
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
(提示: np.add.reduce)
# Author: Evgeni Burovski
Z = np.arange(10)
np.add.reduce(Z)
# np.add.reduce 是numpy.add模块中的一个ufunc(universal function)函数,C语言实现
(提示: np.allclose, np.array_equal)
A = np.random.randint(0, 2, 5)
B = np.random.randint(0, 2, 5)
# 假设array的形状(shape)相同和一个误差容限(tolerance)
equal = np.allclose(A,B)
print(equal)
# 检查形状和元素值,没有误差容限(值必须完全相等)
equal = np.array_equal(A,B)
print(equal)
(提示: flags.writeable)
Z = np.zeros(5)
Z.flags.writeable = False
Z[0] = 1
(提示: np.sqrt, np.arctan2)
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)
(提示: argmax)
Z = np.random.random(10)
Z[Z.argmax()] = 0
print (Z)
x
和y
坐标覆盖[0, 1]x[1, 0]
区域 (★★☆)(提示: np.meshgrid)
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)
X
和Y
,构造柯西(Cauchy)矩阵C ($C_{ij}=\frac{1}{x_i-y_j}$) (★★☆)# Author: Evgeni Burovski
X = np.arange(8)
Y = X + 0.5
C = 1.0 / np.subtract.outer(X, Y)
print (C)
print(np.linalg.det(C)) # 计算行列式
(提示: np.iinfo, np.finfo, eps)
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)
(提示: np.set_printoptions)
np.set_printoptions(threshold=np.nan)
Z = np.zeros((16,16))
print(Z)
(提示: argmin)
Z = np.arange(100)
v = np.random.uniform(0, 100)
index = (np.abs(Z-v)).argmin()
print(Z[index])
(提示: dtype)
Z = np.zeros(10, [('position', [('x', float, 1),
('y', float, 1)]),
('color', [('r', float, 1),
('g', float, 1),
('b', float, 1)])])
print (Z)
(提示: np.atleast_2d, T, np.sqrt)
Z = np.random.random((100, 2))
X, Y = np.atleast_2d(Z[:, 0], Z[:, 1])
D = np.sqrt((X-X.T)**2 + (Y-Y.T)**2)
print (D)
# 使用scipy库可以更快
import scipy.spatial
Z = np.random.random((100,2))
D = scipy.spatial.distance.cdist(Z,Z)
print(D)
(提示: astype(copy=False))
Z = np.arange(10, dtype=np.int32)
Z = Z.astype(np.float32, copy=False)
print(Z)
(提示: np.genfromtxt)
1, 2, 3, 4, 5
6, , , 7, 8
, , 9,10,11
# 先把上面保存到文件example.txt中
# 这里不使用StringIO, 因为Python2 和Python3 在这个地方有兼容性问题
Z = np.genfromtxt("example.txt", delimiter=",")
print(Z)
(提示: np.ndenumerate, np.ndindex)
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])
(提示: np.meshgrid, np.exp)
X, Y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
D = np.sqrt(X**2 + Y**2)
sigma, mu = 1.0, 0.0
G = np.exp(-( (D-mu)**2 / (2.0*sigma**2) ))
print (G)
(提示: np.put, np.random.choice)
# Author: Divakar
n = 10
p = 3
Z = np.zeros((n,n))
np.put(Z, np.random.choice(range(n*n), p, replace=False),1)
print(Z)
(提示: mean(axis=,keepdims=))
# Author: Warren Weckesser
X = np.random.rand(5, 10)
# 新
Y = X - X.mean(axis=1, keepdims=True)
# 旧
Y = X - X.mean(axis=1).reshape(-1, 1)
print(Y)
(提示: argsort)
# Author: Steve Tjoa
Z = np.random.randint(0,10,(3,3))
print(Z)
print(Z[ Z[:,1].argsort() ])
(提示: any, ~)
# Author: Warren Weckesser
Z = np.random.randint(0,3,(3,10))
print((~Z.any(axis=0)).any())