Numpy闯关100题，我闯了95关，你呢？

import numpy as np

print(np.__version__)print(np.show_config())

Z = np.zeros(10)print(Z)

Z = np.zeros((10,10))print("%d bytes" % (Z.size * Z.itemsize))

import numpy as npnp.info(numpy.add)

Z = np.zeros(10)Z[4] = 1print(Z)

Z = np.arange(10,50)print(Z)

Z = np.arange(50)Z = Z[::-1]print(Z)

nz = np.nonzero([1,2,0,0,4,0])print(nz)

Z = np.linspace(0, 1, 12, endpoint=True)[1: -1]print (Z)

Z = np.random.random(10)Z.sort()print (Z)

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)

Z = np.zeros(5)Z.flags.writeable = FalseZ[0] = 1

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)

Z = np.random.random(10)Z[Z.argmax()] = 0print (Z)

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 = np.arange(8)Y = X + 0.5C = 1.0 / np.subtract.outer(X, Y)print (C)print(np.linalg.det(C)) # 计算行列式

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)

np.set_printoptions(threshold=np.nan)Z = np.zeros((16,16))print(Z)

Z = np.arange(100)v = np.random.uniform(0, 100)index = (np.abs(Z-v)).argmin()print(Z[index])

Z = np.zeros(10, [('position', [('x', float, 1),                                 ('y', float, 1)]),                  ('color',    [('r', float, 1),                                 ('g', float, 1),                                 ('b', float, 1)])])print (Z)

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)

Z = np.arange(10, dtype=np.int32)Z = Z.astype(np.float32, copy=False)print(Z)

'''1, 2, 3, 4, 56,  ,  , 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)

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])

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, 0.0G = np.exp(-( (D-mu)**2 / (2.0*sigma**2) ))print (G)

n = 10p = 3Z = np.zeros((n,n))np.put(Z, np.random.choice(range(n*n), p, replace=False),1)print(Z)

X = np.random.rand(5, 10)Y = X - X.mean(axis=1, keepdims=True)print(Y)

Z = np.random.randint(0,10,(3,3))print(Z)print(Z[ Z[:,1].argsort() ])

Z = np.random.randint(0,3,(3,10))print((~Z.any(axis=0)).any())

Z = np.random.uniform(0,1,10)z = 0.5m = Z.flat[np.abs(Z - z).argmin()]print(m)

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])

class NameArray(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 = NameArray(np.arange(10), "range_10")print (Z.name)

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)

X = [1,2,3,4,5,6]I = [1,3,9,3,4,1]F = np.bincount(I,X)print(F)

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))

A = np.random.randint(0,10,(3,4,3,4))# 传递一个元组(numpy 1.7.0)sum = A.sum(axis=(-2,-1))print(sum)
# 将最后两个维度压缩为一个# (适用于不接受轴元组参数的函数)sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)print(sum)

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())

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)

Z = np.array([1,2,3,4,5])nz = 3Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))Z0[::nz+1] = Zprint(Z0)

A = np.ones((5,5,3))B = 2*np.ones((5,5))print(A * B[:,:,None])

A = np.arange(25).reshape(5,5)A[[0,1]] = A[[1,0]]print(A)

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)

C = np.bincount([1,1,2,3,4,4,6])A = np.repeat(np.arange(len(C)), C)print(A)

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))

from numpy.lib import stride_tricks
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)

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)

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))

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%2
R_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)

Z = np.arange(1,15,dtype=np.uint32)R = stride_tricks.as_strided(Z,(11,4),(4,4))print(R)

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)

Z = np.random.randint(0,10,50)print(np.bincount(Z).argmax())

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)

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] = 42print(S)

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.

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)

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)

Z = np.arange(10000)np.random.shuffle(Z)n = 5
# Slowprint (Z[np.argsort(Z)[-n:]])
# Fastprint (Z[np.argpartition(-Z,n)[:n]])

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])))

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)

x = np.random.rand(5e7)
%timeit np.power(x,3)%timeit x*x*x%timeit np.einsum('i,i,i->i',x,x,x)

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)

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])
I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)print(np.unpackbits(I[:, np.newaxis], axis=1))

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)

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)

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)

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])

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)