NAIAHD

numpy 100제

76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z[0],Z[1],Z[2]) and each subsequent row is shifted by 1 (last row should be (Z[-3],Z[-2],Z[-1])

#hint: from numpy.lib import stride_tricks
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)

data = rolling(np.arange(10),3)
print(data)

77. How to negate a boolean, or to change the sign of a float inplace?

#hint: np.logical_not, np.negative
data = np.random.randint(0,2,100)
print(data)

print(np.logical_not(data, out = data))

data = np.random.uniform(-1.0, 1.0, 100)
print(data)

print(np.negative(data, out = data))

78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0[i],P1[i])? 

#No hints provided...
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. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P[j]) to each line i (P0[i],P1[i])?

#No hints provided..
P0 = 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. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a fill value when necessary)

#hint: minimum maximum
Z = np.random.randint(0, 10, (10,10))
print(Z)
shape = (5,5)
fill = 0
position = (1,1)
R = np.ones(shape)
P = 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)

81. Consider an array Z = [1,2,3,4,5,6,7,8,9,10,11,12,13,14], how to generate an array R = [[1,2,3,4], [2,3,4,5], [3,4,5,6], ..., [11,12,13,14]]?

#hint: stride_tricks.as_strided
from numpy.lib import stride_tricks
Z = np.arange(1,15, dtype = np.uint32)
print(Z)
R = stride_tricks.as_strided(Z, (11,4), (4,4))
print(R)

82. Compute a matrix rank 

#hint: np.linalg.svd
Z = np.random.uniform(0,1,(10,10))
U,S, V = np.linalg.svd(Z)
rank = np.sum(S > 1e-10)
print(rank)

83. How to find the most frequent value in an array?

#hint: np.bincount, argmax
data = np.random.randint(0,10,50)
print(data)
print(np.bincount(data))
print(np.bincount(data).argmax())

84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix

#hint: stride_tricks.as_strided
data = np.random.randint(0,5,(10,10))
n = 3
i = 1 + (data.shape[0]-3)
j = 1 + (data.shape[1]-3)
C = stride_tricks.as_strided(data, shape= (i,j,n,n), strides = data.strides+ data.strides)
print(C)

85. Create a 2D array subclass such that Z[i,j] == Z[j,i] 

#hint: class method
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. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1))

#hint: np.tensordot
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)

87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)?

#hint: np.add.reduceat
Z = np.ones((16,16))
print(Z)
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. How to implement the Game of Life using numpy arrays?

#No hints provided...
def iterate(Z):
    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:])

    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. How to get the n largest values of an array

#hint: np.argsort | np.argpartitio
data = np.arange(10000)
print(data)
np.random.shuffle(data)
n = 5
print(data[np.argsort(data)[-n:]])
print(data[np.argpartition(-data,n )[:n]])

90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item)

#hint: np.indices
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. How to create a record array from a regular array?

#hint: np.core.records.fromarrays
data = np.array([("Hello", 2.5, 3),("World", 3.6, 2)])
R = np.core.records.fromarrays(data.T, names='col1, col2, col3', formats='S8, f8, i8')
print(R)

92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods

#hint: np.power, *, np.einsum
x = np.random.rand(int(5e1))

%timeit np.power(x,3)
%timeit x*x*x
%timeit np.einsum('i,i,i->i',x,x,x)

93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B?

#hint: np.where
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. Considering a 10x3 matrix, extract rows with unequal values (e.g. [2,2,3])

#No hints provided...
data = np.random.randint(0,5,(10,3))
print(data)
E = np.all(data[:,1:] == data[:,:-1], axis = 1)
print(E)
U = data[~E]
print(U)

U = data[data.max(axis=1) != data.min(axis =1),:]
print(U)

95. Convert a vector of ints into a matrix binary representation

#hint: np.unpackbits
I = np.array([0,1,2,3,15,16,32,64,128])
print(I)
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))

96. Given a two dimensional array, how to extract unique rows?

#hint: np.ascontiguousarray | np.uniqu
data = np.random.randint(0,2,(6,3))
T = np.ascontiguousarray(data).view(np.dtype((np.void, data.dtype.itemsize * data.shape[1])))
_, idx = np.unique(T, return_index = True)
uZ = Z[idx]
print(uZ)

uZ = np.unique(Z, axis = 0)
print(uZ)

97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function

#hint: np.einsum
A = np.random.uniform(0,1 , 10)
print(A)
B = np.random.uniform(0,1,10)
print(B)

print(np.einsum('i->', A)) #np.sum(A)
print(np.einsum('i,i->i', A,B))# A * B
print(np.einsum('i,i', A,B))# np.inner(A, B)
print(np.einsum('i,j->ij', A,B))#np.outer(A, B)

A = [1,2,3]
B = [4,5,6]
print(np.inner(A, B))
# 1* 4+ 2* 5 + 3* 6
print(np.outer(A,B))
# 1   4, 5, 6 
# 2   4, 5, 6 
# 3   4, 5, 6 

np.diff() : 차분 

98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples

#hint: np.cumsum, np.interp
phi = np.arange(0, 10 * np.pi, 0.1)
a = 1
x = a* phi * np.cos(phi)
y = a* phi * np.sin(phi)
print(x)
dr = (np.diff(x) ** 2 + np.diff(y) ** 2) **.5
r = np.zeros_like(x)
r[1:] = np.cumsum(dr)
r_int = np.linspace(0, r.max(), 200)
x_int = np.interp(r_int, r, x)
y_int = np.interp(r_int, r, y)
print(r_int)
print(x_int)
print(y_int)

x = [1,3,6]
np.diff(x)  # (3-1, 6 - 3)

99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n.

#hint: np.logical_and.reduce, np.mod
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)
print(M)
M &= (X.sum(axis = -1) == n)
print(X[M])

100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means).

#hint: np.percentile
X = np.random.randn(100)
print(x)
N = 1000
idx = np.random.randint(0, X.size, (N, X.size))
print(idx)
means = X[idx].mean(axis = 1)
print(means)
confint = np.percentile(means, [2.5, 97.5])
print(confint)

51. Create a structured array representing a position (x,y) and a color (r,g,b)

#hint: dtype
position = ('position', [ ('x', float, 1),('y', float, 1)])
color = ('color', [('r', float), ('g', float), ('b', float)])
data = np.zeros(10,[position, color])
print(data)

[((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))

((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))

((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))

((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))

((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))]

52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances

#hint: np.atleast_2d, T, np.sqrt
data = np.random.random((10,2))
print(data)

X, Y = np.atleast_2d(data[:, 0], data[:, 1])
print(X,'----',y)

D = np.sqrt((X-X.T)**2 + (Y-Y.T)**2)
print(D)

print('-'*10)

import scipy 
import scipy.spatial
data = np.random.random((10,2))
print(data)
D = scipy.spatial.distance.cdist(data,data)
print(D)

53. How to convert a float (32 bits) array into an integer (32 bits) in place?

#hint: view and [:] =
data = np.arange(10, dtype = np.float32)
data = data.astype(np.int32, copy = False)
print(data)

54. How to read the following file?

1, 2, 3, 4, 5
6,  ,  , 7, 8
 ,  , 9,10,11

#1, 2, 3, 4, 5
#6,  ,  , 7, 8
# ,  , 9,10,11
#hint: np.genfromtxt
from io import StringIO
s = StringIO("""1, 2, 3, 4, 5\n
                6,  ,  , 7, 8\n
                 ,  , 9,10,11\n""")
data = np.genfromtxt(s, delimiter=",", dtype =np.int)
print(data)

55. What is the equivalent of enumerate for numpy arrays?

#hint: np.ndenumerate, np.ndindex
Z = np.arange(9).reshape(3,3)
print(Z)

for idx, val in np.ndenumerate(Z):
    print(idx, val)
    
for idx in np.ndindex(Z.shape):
    print(idx, Z[idx]) 

56. Generate a generic 2D Gaussian-like array?

#hint: np.meshgrid, np.exp
X,Y = np.meshgrid(np.linspace(-1,1, 10) , np.linspace(-1,1,10))
print(X,Y)
D = np.sqrt(X*X+ Y*Y)
sigma, mu = 1.0, 0.0
G = np.exp(-((D-mu) ** 2 / (2.0 * sigma**2)))
print(G)

57. How to randomly place p elements in a 2D array?

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

print(np.random.choice(range(n*n), p))

58. Subtract the mean of each row of a matrix

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

59. How to sort an array by the nth column?

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

60. How to tell if a given 2D array has null columns?

#hint: any, ~
data = np.random.randint(0,10,(3,3))
print((~data.any(axis=0)).any())

61. Find the nearest value from a given value in an array?

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

[0.07982761 0.33920184 0.76591757 0.06233438 0.90342925 0.83787931 0.34956868 0.6211812 0.06168744 0.51835467]

[0.42017239 0.16079816 0.26591757 0.43766562 0.40342925 0.33787931 0.15043132 0.1211812 0.43831256 0.01835467]

9

0.5183546659345304 => flat위의 9번 째 자리의 수

62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator?

#hint: np.nditer
A = np.arange(3).reshape(3,1)
B = np.arange(3).reshape(1,3)
print(A, B)

it = np.nditer([A,B, None])

for x,y,z in it:
    z[...] = x+y

print(it.operands[2])    

63. Create an array class that has a name attribute

#hint: class method
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')
data = NameArray(np.arange(10),'range_10')
print(data.name)

64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)?

#hint: np.bincount | np.add.at
data = np.ones(10)
I = np.random.randint(0, len(Z), 20)
print(I)

data += np.bincount(I, minlength = len(Z))
print(data)

np.add.at(data,I,1)
print(data)

65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)?

#hint: np.bincount
A = [1,2,3,4,5,6]
B = [1,3,9,3,4,1]
C = np.bincount(B,A)
print(C)

66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors

#hint: np.unique
w,h = 16,16
I = 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(n)
print(np.unique(I))

67. Considering a four dimensions array, how to get sum over the last two axis at once?

#hint: sum(axis=(-2,-1))
A = np.random.randint(0,10,(3,4,3,4))
sum = A.sum(axis= (-2,-1))
print(sum)

sum = A.reshape(A.shape[:-2] + (-1, )).sum(axis = -1)
print(sum)

68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices?

#hint: np.bincount
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_counts

import pandas as pd
print(pd.Series(D).groupby(S).mean())

69. How to get the diagonal of a dot product?

#hint: np.diag
import numpy as np
A= np.random.uniform(0,1,(5,5))
B= np.random.uniform(0,1,(5,5))

print(np.diag(np.dot(A,B)))
print(np.sum(A * B.T, axis = 1))
print(np.einsum("ij,ji->i",A,B))

70. Consider the vector [1, 2, 3, 4, 5], how to build a new vector with 3 consecutive zeros interleaved between each value?

각 요소 사이에 0을 3개 입력 

#hint: array[::4]
Z = np.array([1,2,3,4,5])
nz = 3
z0 = np.zeros(len(Z) + (len(Z)-1)*(nz)) 
z0[::nz+1] = Z
print(z0)

71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)?

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

72. How to swap two rows of an array?

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

73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles

#hint: repeat, np.roll, np.sort, view, np.unique
faces = np.random.randint(0,100,(10,3))
print(faces)
F = np.roll(faces.repeat(2, axis = 1), -1, axis = 1)
print(F)
F = F.reshape(len(F) * 3, 2)
print(F)
F = np.sort(F, axis = 1)
print(F)
G = F.view(dtype = [('p0', F.dtype),('p1',F.dtype)])
print(G)
G = np.unique(G)
print(G)

74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C?

A= np.bincount([1,1,2,3,4,4,6])

0 1 2 3 4 5 6 

0: 0은 없음 0

1: 1은 2번

2: 2는 1번

3: 3은 1번

4: 4은 2번

5: 5는 0번

6: 6은 1번

#hint: np.repeat
A= np.bincount([1,1,2,3,4,4,6])
print(A)
B = np.repeat(np.arange(len(A)),A)
print(B)

75. How to compute averages using a sliding window over an array? 

##hint: np.cumsum
def average(a, n = 3):
    ret = np.cumsum(a, dtype = float)
    ret[n:] = ret[n:]- ret[:-n]
    return ret[n-1:] / n
data = np.arange(20)
print(data)
print(average(data, n = 3))

26. What is the output of the following script?

print(sum(range(5),-1))
from numpy import *
print(sum(range(5),-1))

print(sum(range(5),-1))
from numpy import *
print(sum(range(5),-1))
import numpy as np
print(np.sum(range(5),-1))

python의 sum은 sum(sequence[,start]) python의 내장함수

-1+1+2+3+4=9

numpy.sum(a, axis=None),axis 축으로 더한다.

27. Consider an integer vector Z, which of these expressions are legal?

Z**Z
2 << Z >> 2
Z <- Z
1j*Z
Z/1/1
Z<Z>Z

import numpy as np
Z = np.random.randint(1,4,(5,5))
print(Z**Z)
print(2 << Z >> 2)
print(Z <- Z)
print(1j*Z)
print(Z/1/1)
print(Z<Z>Z) #오류가 난다.

[[27 1 1 1 4] [ 1 1 1 1 27] [ 4 1 4 4 1] [ 1 4 4 27 1] [ 4 27 1 1 1]] [[4 1 1 1 2] [1 1 1 1 4] [2 1 2 2 1] [1 2 2 4 1] [2 4 1 1 1]] [[False False False False False] [False False False False False] [False False False False False] [False False False False False] [False False False False False]] [[0.+3.j 0.+1.j 0.+1.j 0.+1.j 0.+2.j] [0.+1.j 0.+1.j 0.+1.j 0.+1.j 0.+3.j] [0.+2.j 0.+1.j 0.+2.j 0.+2.j 0.+1.j] [0.+1.j 0.+2.j 0.+2.j 0.+3.j 0.+1.j] [0.+2.j 0.+3.j 0.+1.j 0.+1.j 0.+1.j]] [[3. 1. 1. 1. 2.] [1. 1. 1. 1. 3.] [2. 1. 2. 2. 1.] [1. 2. 2. 3. 1.] [2. 3. 1. 1. 1.]]

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-b8e9b3bf440b> in <module> 6 print(1j*Z) 7 print(Z/1/1) ----> 8 print(Z<Z>Z) #오류가 난다. ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

28. What are the result of the following expressions?

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))

nan 0 [-2.14748365e+09]

29. How to round away from zero a float array ?

#hint: np.uniform, np.copysign, np.ceil, np.abs, np.where
data = np.random.random(5)*100
data1 = np.round(data+0.5)
data2 = np.copysign(np.ceil(np.abs(data)),data)
print(data)
print(data1)
print(data2)

[78.43772873 89.73307257 96.14244721 19.5070952 27.94266262]

[79. 90. 97. 20. 28.]

[79. 90. 97. 20. 28.]

30. How to find common values between two arrays? 

#hint: np.intersect1d
data1 = np.random.randint(1,5,10)
data2 = np.random.randint(3,10,10)
print(np.intersect1d(data1,data2))

31. How to ignore all numpy warnings (not recommended)?

#hint: np.seterr, np.errstate
data = np.seterr(all= "ignore")
data1 = np.ones(1) / 0

_ = np.seterr(**data)
with np.errstate(divide='ignore'):
    data = np.ones(1) / 0

numpy.org/doc/stable/reference/generated/numpy.errstate.html

32. Is the following expressions true? 

print(np.sqrt(-1) == np.emath.sqrt(-1))
print(np.sqrt(-1) )
print(np.emath.sqrt(-1))

33. How to get the dates of yesterday, today and tomorrow?

yesterday = np.datetime64('today','D') - np.timedelta64(1,'D')
print(yesterday)

today = np.datetime64('today','D')
print(today)

tomorrow = np.datetime64('today','D') + np.timedelta64(1,'D')
print(tomorrow)

34. How to get all the dates corresponding to the month of July 2016?

#np.arange(dtype=datetime64['D'])
data = np.arange('2016-07','2016-08', dtype='datetime64[D]')
print(data)

35. How to compute ((A+B)*(-A/2)) in place (without copy)?

#hint: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=)
A = np.ones(3)*1
B = np.ones(3)*2
np.add(A,B, out = B)
np.negative(A, out=A)
np.divide(A,2,out = A)
np.multiply(A,B,out = A)

array([-1.5, -1.5, -1.5])

36. Extract the integer part of a random array using 5 different methods 

#hint: %, np.floor, astype, np.trunc
data = np.random.uniform(0,10,10)

print (data - data%1)
print (np.floor(data))
print (np.ceil(data)-1)
print (data.astype(int))
print (np.trunc(data))

37. Create a 5x5 matrix with row values ranging from 0 to 4

#hint: np.arange
data = np.zeros((5,5))
print(data)

data += np.arange(5)
print(data)

38. Consider a generator function that generates 10 integers and use it to build an array

#hint: np.fromiter
def generate():
    for x in range(10):
        yield x

data = np.fromiter(generate(), dtype = float, count = -1)
print(data)

[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]

39. Create a vector of size 10 with values ranging from 0 to 1, both excluded

#hint: np.linspace
print(np.linspace(0,1,11))
print(np.linspace(0,1,11,endpoint=False))
print(np.linspace(0,1,11,endpoint=False)[1:])

40. Create a random vector of size 10 and sort it

#hint: sort
data = np.random.random(10)
data.sort()
print(data)

41. How to sum a small array faster than np.sum?

#hint: np.add.reduce
import datetime
t0 = datetime.datetime.now()
data = np.arange(10)
for i in range(999):
    np.sum(data)
t1 = datetime.datetime.now()
print(t1-t0)

t0 = datetime.datetime.now()
data = np.arange(10)
for i in range(999):
    np.add.reduce(data)
t1 = datetime.datetime.now()
print(t1-t0)

42. Consider two random array A and B, check if they are equal

#hint: np.allclose, np.array_equal
A = np.random.randint(0,2,5)
B = np.random.randint(0,2,5)
print(A)
print(B)
data = np.allclose(A,B)
print(data)

data = np.array_equal(A,B)
print(data)

43. Make an array immutable (read-only)

#hint: flags.writeable
data = np.zeros(10)
data.flags.writeable= False
data[0]=1

44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates

#hint: np.sqrt, np.arctan2
Z = np.random.random((10,2))
print(Z)
X,Y = Z[:,0], Z[:,1]
print(X)
print(Y)
R = np.sqrt(X**2 + Y**2)
T = np.arctan2(Y,X)
print(R)
print(T)

45. Create random vector of size 10 and replace the maximum value by 0

#hint: argmax
data = np.random.random(10)
print(data)
print(data.argmax())
print(data[data.argmax()])
data[data.argmax()] = 0
print(data)

46. Create a structured array with x and y coordinates covering the [0,1]x[0,1] area

#hint: np.meshgrid
Z = np.zeros((5,5))
print(Z)
Z = np.zeros((5,5), [('x',float),('y',float)])
print(Z)

Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),
                             np.linspace(0,1,5))
print(Z)

47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))

#hint: np.subtract.outer
X = np.arange(8)
print(X)

Y = X+0.5
print(Y)

C = 1.0 / np.subtract.outer(X, Y)
print(np.linalg.det(C))

48. Print the minimum and maximum representable value for each numpy scalar type

#hint: 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)

49. How to print all the values of an array?

np.set_printoptions(threshold=np.nan)

ValueError: threshold must be non-NAN, try sys.maxsize for untruncated representation

#hint: np.set_printoptions
#np.set_printoptions(threshold=np.nan)
data = np.zeros((10,10))
print(data)

50. How to find the closest value (to a given scalar) in a vector?

#hint: argmin
data = np.arange(100)
print(data)

v = np.random.uniform(0,100)
print(v)

index = (np.abs(data - v)).argmin()
print(data[index])

#1. Import the numpy package under the name np

import numpy as np

2. Print the numpy version and the configuration

import numpy as np
print(np.__version__)

3. Create a null vector of size 10

data = np.zeros(10)
data

4. How to find the memory size of any array

#메모리 크기 확인
data = np.zeros((5,5))
print(data.size)
print(data.itemsize)
print("%d bytes" % (data.size * data.itemsize))

5. How to get the documentation of the numpy add function from the command line?

np.info(np.add)

6. Create a null vector of size 10 but the fifth value which is 1

10게 에서 5번째는 1로 한다.

data = np.zeros(10)
data[4] = 1
data

7. Create a vector with values ranging from 10 to 49

data = np.arange(10,50)
data

8. Reverse a vector (first element becomes last)

data = np.arange(10)
data = data[::-1]
data

9. Create a 3x3 matrix with values ranging from 0 to 8

data = np.arange(9).reshape(3,3)
data

10. Find indices of non-zero elements from [1,2,0,0,4,0]

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

11. Create a 3x3 identity matrix 

data = np.eye(3)
print(data)

12. Create a 3x3x3 array with random values

data = np.random.random((3,3,3))
data

13. Create a 10x10 array with random values and find the minimum and maximum values

import numpy as np
data = np.random.random((10,10))
_min, _max = data.min(), data.max()
_min, _max

14. Create a random vector of size 30 and find the mean value

data = np.random.random(30)
print(data.mean())

15. Create a 2d array with 1 on the border and 0 inside

#외부는 1 내부는 0으로 해준다.
import numpy as np
data = np.ones((5,5))
data[1:-1, 1:-1] = 0
data

16. How to add a border (filled with 0's) around an existing array?

data = np.zeros((5,5))
print(data)
print('-'* 10)
# constant_values 주위의 값을 이것으로 한다.
data = np.pad(data,pad_width = 1, mode='constant', constant_values=0)
print(data)

17. What is the result of the following expression?

0 * np.nan
np.nan == np.nan
np.inf > np.nan
np.nan - np.nan
np.nan in set([np.nan])
0.3 == 3 * 0.1

print(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. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal 

#np.diag
data = np.diag(1+np.arange(4), k = -1)
data

19. Create a 8x8 matrix and fill it with a checkerboard pattern

data = np.zeros((8,8))
data[::2, 1::2] = 1
data[1::2,::2] = 1
data

20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?

#np.unravel_index
data = np.unravel_index(99, (6,7,8))
data

21. Create a checkerboard 8x8 matrix using the tile function

data = np.array([[0, 1],[1,0]]) 
data = np.tile(data, (4,4)) 
data

22. Normalize a 5x5 random matrix

#(x -mean)/std
data = np.random.random((5,5))
data = (data- np.mean(data)) / (np.std(data))
data

23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) 

#np.dtype
data = np.dtype([("r",np.ubyte, 1),("g",np.ubyte, 1),("b",np.ubyte, 1),("a",np.ubyte, 1)])
data

24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)

data = np.dot(np.ones((5,3)), np.ones((3,2)))
print(data)

25. Given a 1D array, negate all elements which are between 3 and 8, in place.

# 3부터 8까지 마이너스 로 바꾸기

data = np.arange(10)
data[(3<= data) & (data<=8)] *= -1
print(data)