100 numpy exercises 실행¶
In [2]:
!pip install numpy
1. Import the numpy package under the name np¶
numpy 패키지를 np 이름으로 사용
In [1]:
import numpy as np
2. Print the numpy version and the configuration¶
numpy 버전 및 설정 확인
In [4]:
print(np.__version__)
np.show_config()
3. Create a null vector of size 10¶
10개의 0으로 채워진 null vector 생성
In [5]:
Z = np.zeros(10)
print(Z)
4. How to find the memory size of any array¶
배열의 메모리 크기 확인
In [7]:
Z = np.zeros((10,10))
print(Z.size, Z.itemsize)
print("%d bytes" % (Z.size * Z.itemsize))
5. How to get the documentation of the numpy add function from the command line?¶
커맨드 라인에서 numpy 실행
In [ ]:
%run `python -c "import numpy; numpy.info(numpy.add)"`
6. Create a null vector of size 10 but the fifth value which is 1¶
5번째 값은 1인 10개의 null vector 생성
인덱스는 0부터 시작하므로 5번째는 [4]
In [9]:
Z = np.zeros(10)
Z[4] = 1
print(Z)
In [10]:
Z = np.arange(10,50)
print(Z)
8. Reverse a vector (first element becomes last)¶
vector 역순 (첫번째 요소가 마지막이 됨)
In [12]:
Z = np.arange(50)
Z = Z[::-1]
print(Z)
9. Create a 3x3 matrix with values ranging from 0 to 8¶
0 부터 8까지의 3x3 matrix 생성
In [33]:
Z = np.arange(9).reshape(3,3)
print(Z)
10. Find indices of non-zero elements from [1,2,0,0,4,0]¶
[1,2,0,0,4,0] 요소에서 0 이 아닌 인덱스 찾기
In [35]:
nz = np.nonzero([1,2,0,0,4,0])
print(nz)
11. Create a 3x3 identity matrix¶
3x3 identity matrix 생성
In [36]:
Z = np.eye(3)
print(Z)
12. Create a 3x3x3 array with random values¶
무작위 값으로 3x3x3 배열 생성
In [37]:
Z = np.random.random((3,3,3))
print(Z)
13. Create a 10x10 array with random values and find the minimum and maximum values¶
무작위 값으로 10x10 배열 생성하고 최소값, 최대값 찾기
In [39]:
Z = np.random.random((10,10))
Zmin, Zmax = Z.min(), Z.max()
print(Zmin, Zmax)
14. Create a random vector of size 30 and find the mean value¶
평균값 구하기
In [40]:
Z = np.random.random(30)
m = Z.mean()
print(m)
15. Create a 2d array with 1 on the border and 0 inside¶
- 2차원 배열 생성 (10 x 10)
- 전체값으로 1으로 채운다
- 내부만 0으로 채운다
In [41]:
Z = np.ones((10,10))
Z[1:-1, 1:-1] = 0
print(Z)
16. How to add a border (filled with 0's) around an existing array?¶
In [47]:
Z = np.ones((5,5))
print(Z)
In [48]:
Z = np.pad(Z, pad_width=1, mode='constant', constant_values =0)
print(Z)
17. What is the result of the following expression?¶
In [50]:
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¶
In [51]:
Z = np.diag(1+np.arange(4), k=-1)
print(Z)
19. Create a 8x8 matrix and fill it with a checkerboard pattern¶
- checkerboard 패턴으로 1을 채운다
In [54]:
Z = np.zeros((8,8), dtype=int)
Z[1::2, ::2] = 1
Z[::2, 1::2] = 1
print(Z)
20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?¶
- (6,7,8) 형태 배열에서
- 100번째 요소의 인덱스를 구한다
In [55]:
print(np.unravel_index(99, (6,7,8)))
21. Create a checkerboard 8x8 matrix using the tile function¶
- (2,2)단위 메트릭스를 만들고
- 단위 메트릭스를 (4,4)로 만든다.
In [60]:
np.array([[0,1], [1,0]])
Out[60]:
In [61]:
Z = np.tile(np.array([[0,1], [1,0]]), (4,4))
print(Z)
22. Normalize a 5x5 random matrix¶
In [62]:
Z = np.random.random((5,5))
Z = (Z - np.mean(Z)) / (np.std(Z))
print(Z)
23. Create a custom dtype that describes a color as four unsigned bytes (RGBA)¶
In [64]:
color = np.dtype([("r", np.ubyte, 1),
("g", np.ubyte, 1),
("b", np.ubyte, 1),
("a", np.ubyte, 1)])
print(color)
24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)¶
In [67]:
Z = np.dot(np.ones((5,3)), np.ones((3,2)))
print(Z)
In [69]:
Z = np.ones((5,3)) @ np.ones((3,2))
print(Z)
25. Given a 1D array, negate all elements which are between 3 and 8, in place.¶
- 0부터 10까지 1차원 배열 생성
- 3부터 8까지 음수로 변경
In [21]:
Z = np.arange(11)
Z[(3<=Z) & (Z<=8)] *= -1
print(Z)
26. What is the output of the following script?¶
- sum 에서 2번째 파라미터는 시작값을 지정 (기본: 0)
- np.sum에서 2번째 파라미터는 축을 지정
- If axis is negative it counts from the last to the first axis.
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print(sum(range(5), -1))
print(np.sum(range(5),-1))
In [8]:
?sum
27. Consider an integer vector Z, which of these expressions are legal?¶
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Z = np.arange(11)
# Z**Z
print(Z @ Z)
print(2 << Z >> 2)
print(Z <- Z)
print(1j*Z)
print(Z/1/1)
# print(Z<Z>Z)
28. What are the result of the following expressions?¶
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print(np.array(0))
In [38]:
print(np.array(0) / np.array(0))
print(np.array(0) // np.array(0))
print(np.array([np.nan]).astype(int).astype(float))
29. How to round away from zero a float array ?¶
In [40]:
Z = np.random.uniform(-10, +10, 10)
print(Z)
print(np.copysign(np.ceil(np.abs(Z)),Z))
30. How to find common values between two arrays?¶
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Z1 = np.random.randint(0,10,10)
Z2 = np.random.randint(0,10,10)
print(np.intersect1d(Z1, Z2))
In [43]:
np.random.randint(0,10,10)
Out[43]:
31. How to ignore all numpy warnings (not recommended)?¶
In [47]:
# Suicide mode on
defaults = np.seterr(all="ignore")
Z = np.ones(1) / 0
# Back to sanity
_ = np.seterr(**defaults)
# An equivalent way, with a context manager:
with np.errstate(divide='ignore'):
Z = np.ones(1) / 0
32. Is the following expressions true?¶
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np.sqrt(-1) == np.emath.sqrt(-1)
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33. How to get the dates of yesterday, today and tomorrow?¶
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yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')
print(yesterday)
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today = np.datetime64('today', 'D')
print(today)
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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¶
In [57]:
Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')
print(Z)
35. How to compute ((A+B)*(-A/2)) in place (without copy)?¶
In [59]:
A = np.ones(3)*1
B = np.ones(3)*2
C = np.ones(3)*3
np.add(A,B,out=B)
np.negative(A,out=A)
np.divide(A,2,out=A)
np.multiply(A,B,out=A)
Out[59]:
36. Extract the integer part of a random array using 5 different methods¶
In [60]:
Z = np.random.uniform(0,10,10)
print(Z)
In [62]:
print(Z - Z%1)
print(np.floor(Z))
print(np.ceil(Z) - 1)
print(Z.astype(int))
print(np.trunc(Z))
37. Create a 5x5 matrix with row values ranging from 0 to 4¶
In [69]:
Z = np.zeros((5,5))
print(Z)
In [70]:
Z += np.arange(5)
print(Z)
38. Consider a generator function that generates 10 integers and use it to build an array¶
In [72]:
def generate():
for x in range(10):
yield x
Z = np.fromiter(generate(), dtype=float, count=-1)
print(Z)
39. Create a vector of size 10 with values ranging from 0 to 1, both excluded¶
- np.linspace를 사용하여 벡터 생성
- [1:] 로 0 제외
In [77]:
np.linspace(0,1,11)
Out[77]:
In [78]:
np.linspace(0,1,11, endpoint=False)
Out[78]:
In [79]:
np.linspace(0,1,11, endpoint=False)[1:]
Out[79]:
40. Create a random vector of size 10 and sort it¶
In [82]:
Z = np.random.random(10)
Z.sort()
print(Z)
41. How to sum a small array faster than np.sum?¶
- 속도 비교를 위해 for 문을 추가하였습니다
In [103]:
%%time
Z = np.arange(10)
for i in range(999):
np.sum(Z)
In [104]:
%%time
Z = np.arange(10)
for i in range(999):
np.add.reduce(Z)
42. Consider two random array A and B, check if they are equal¶
- 무작위 배열 A, B를 만들고
- 같은지 비교
In [107]:
A = np.random.randint(0,2,5)
B = np.random.randint(0,2,5)
print(A)
print(B)
In [113]:
# Assuming identical shape of the arrays and a tolerance for the comparison of values
equal = np.allclose(A,B)
print(equal)
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# 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. Make an array immutable (read-only)¶
In [115]:
Z = np.zeros(10)
Z.flags.writeable = False
Z[0] = 1
44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates¶
- cartesian coordinates : 직교 좌표계
- polar coordinates : 극좌표계(極座標系)는 평면 위의 위치를 각도와 거리를 써서 나타내는 2차원 좌표계이다.
In [117]:
Z = np.random.random((10,2))
print(Z)
X, Y = Z[:,0], Z[:,1]
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¶
In [118]:
Z = np.random.random(10)
print(Z)
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Z.argmax()
Out[119]:
In [120]:
Z[Z.argmax()] = 0
print(Z)
46. Create a structured array with x and y coordinates covering the [0,1]x[0,1] area¶
In [122]:
Z = np.zeros((5,5))
print(Z)
In [123]:
Z = np.zeros((5,5), [('x', float), ('y', float)])
print(Z)
47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))¶
In [127]:
X = np.arange(8)
print(X)
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Y = X + 0.5
print(Y)
In [129]:
np.subtract.outer(X, Y)
Out[129]:
In [130]:
C = 1.0 / np.subtract.outer(X, Y)
print(C)
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print(np.linalg.det(C))
48. Print the minimum and maximum representable value for each numpy scalar type¶
In [132]:
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?¶
In [135]:
# np.set_printoptions(threshold=np.nan)
Z = np.zeros((16,16))
print(Z)
50. How to find the closest value (to a given scalar) in a vector?¶
In [136]:
Z = np.arange(100)
print(Z)
In [143]:
v = np.random.uniform(0, 100)
print(v)
In [144]:
index = (np.abs(Z-v)).argmin()
print(index)
51. Create a structured array representing a position (x,y) and a color (r,g,b)¶
In [150]:
position = ('position', [('x', float), ('y', float)])
color = ('color', [('r', float), ('g', float), ('b', float)])
Z = np.zeros(10, [position, color])
print(Z)
52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances¶
In [152]:
Z = np.random.random((10, 2))
print(Z)
In [154]:
X, Y = np.atleast_2d(Z[:, 0], Z[:, 1])
print(X, Y)
In [155]:
D = np.sqrt((X-X.T)**2 + (Y-Y.T)**2)
print(D)
In [157]:
# scipy 를 사용
!pip install scipy
In [158]:
import scipy
import scipy.spatial
Z = np.random.random((10,2))
D = scipy.spatial.distance.cdist(Z,Z)
print(D)
53. How to convert a float (32 bits) array into an integer (32 bits) in place?¶
In [5]:
Z = np.arange(10, dtype=np.float32)
Z = Z.astype(np.int32, copy=False)
print(X)
54. How to read the following file¶
- 파일에서 읽기
- StringIO를 사용하여 텍스트 생성
In [6]:
from io import StringIO
# Fake file
s = StringIO("""1, 2, 3, 4, 5\n
6, , , 7, 8\n
, , 9,10,11\n""")
Z = np.genfromtxt(s, delimiter=",", dtype=np.int)
print(Z)
55. What is the equivalent of enumerate for numpy arrays?¶
In [8]:
Z = np.arange(9).reshape(3,3)
print(Z)
In [9]:
for index, value in np.ndenumerate(Z):
print(index, value)
In [10]:
for index in np.ndindex(Z.shape):
print(index, Z[index])
56. Generate a generic 2D Gaussian-like array¶
In [13]:
X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))
print(X)
In [15]:
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?¶
In [43]:
n = 10
p = 3
Z = np.zeros((n,n))
np.put(Z, np.random.choice(range(n*n), p, replace=False),1)
print(Z)
In [72]:
np.random.choice(range(n*n), p)
Out[72]:
58. Subtract the mean of each row of a matrix¶
In [77]:
X = np.random.rand(5, 10)
print(X)
In [78]:
Y = X - X.mean(axis=1, keepdims=True)
print(Y)
59. How to sort an array by the nth column?¶
In [128]:
Z = np.random.randint(0,10,(3,3))
print(Z)
# 2번째 컬럼으로 정렬
print(Z[Z[:,1].argsort()])
60. How to tell if a given 2D array has null columns?¶
In [129]:
Z = np.random.randint(0,3,(3,10))
print((~Z.any(axis=0)).any())
61. Find the nearest value from a given value in an array¶
In [130]:
Z = np.random.uniform(0,1,10)
z = 0.5
m = Z.flat[np.abs(Z - z).argmin()]
print(m)
62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator?¶
In [133]:
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¶
In [134]:
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)
64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)?¶
In [136]:
Z = np.ones(10)
I = np.random.randint(0, len(Z), 20)
print(I)
In [137]:
Z += np.bincount(I, minlength=len(Z))
print(Z)
In [138]:
np.add.at(Z, I, 1)
print(Z)
65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)?¶
In [141]:
X = [1,2,3,4,5,6]
I = [1,3,9,3,4,1]
F = np.bincount(I,X)
print(F)
66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors¶
In [154]:
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)
67. Considering a four dimensions array, how to get sum over the last two axis at once?¶
In [157]:
A = np.random.randint(0,10,(3,4,3,4))
In [158]:
sum = A.sum(axis=(-2,-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?¶
In [160]:
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
print(D_means)
In [165]:
!pip install pandas
In [166]:
import pandas as pd
print(pd.Series(D).groupby(S).mean())
69. How to get the diagonal of a dot product?¶
In [168]:
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 version
np.sum(A * B.T, axis=1)
# Faster version
np.einsum("ij,ji->i", A, B)
Out[168]:
70. Consider the vector [1, 2, 3, 4, 5], how to build a new vector with 3 consecutive zeros interleaved between each value?¶
- [1,2,3,4,5] 벡터에서
- 각 요소 사이에 0 을 3개씩 연속으로 넣은 새로운 벡터 생성
In [170]:
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)?¶
In [171]:
A = np.ones((5,5,3))
B = 2*np.ones((5,5))
print(A * B[:,:,None])
72. How to swap two rows of an array?¶
In [175]:
A = np.arange(25).reshape(5,5)
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¶
In [176]:
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. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C?¶
In [177]:
C = np.bincount([1,1,2,3,4,4,6])
A = np.repeat(np.arange(len(C)), C)
print(A)
75. How to compute averages using a sliding window over an array?¶
In [178]:
def moving_average(a, n=3):
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n-1:] / n
Z = np.arange(20)
print(moving_average(Z, n=3))
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])¶
In [179]:
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)
77. How to negate a boolean, or to change the sign of a float inplace?¶
In [2]:
Z = np.random.randint(0, 2, 100)
print(Z)
In [3]:
np.logical_not(Z, out=Z)
Out[3]:
In [4]:
Z = np.random.uniform(-1.0, 1.0, 100)
print(Z)
In [5]:
np.negative(Z, out=Z)
Out[5]:
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])?¶
In [6]:
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])?¶
In [7]:
# based on distance function from previous question
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)¶
In [9]:
Z = np.random.randint(0, 10, (10,10))
shape = (5,5)
fill = 0
position = (1,1)
In [10]:
R = np.ones(shape, dtype = Z.dtype) * fill
P = np.array(list(position)).astype(int)
Rs = np.array(list(R.shape)).astype(int)
Zs = np.array(list(Z.shape)).astype(int)
In [11]:
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
In [12]:
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]]?¶
In [14]:
from numpy.lib import stride_tricks
Z = np.arange(1,15,dtype=np.uint32)
R = stride_tricks.as_strided(Z,(11,4),(4,4))
print(R)
82. Compute a matrix rank¶
In [23]:
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. How to find the most frequent value in an array?¶
- 가장 빈도수가 높은 값은?
In [69]:
Z = np.random.randint(0, 10, 50)
print(np.bincount(Z).argmax())
84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix¶
In [70]:
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. Create a 2D array subclass such that Z[i,j] == Z[j,i]¶
In [71]:
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))¶
In [72]:
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)?¶
In [73]:
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. How to implement the Game of Life using numpy arrays?¶
In [76]:
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
In [79]:
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¶
In [81]:
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. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item)¶
In [82]:
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?¶
In [83]:
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. Consider a large vector Z, compute Z to the power of 3 using 3 different methods¶
In [94]:
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?¶
In [95]:
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])¶
In [96]:
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)
In [ ]:
출처: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb
이것은 numpy 메일 링리스트, 스택 오버플로 및 numpy 문서에서 수집된 연습 모음입니다. 이 모음의 목표는 기존 및 신규 사용자 모두를 위한 빠른 참조를 제공하는 것 뿐만 아니라 가르치는 사람들을 위한 일련의 연습을 제공하는 것입니다.
오류가 있거나 문제를 해결할 수 있는 더 나은 방법이 있다고 생각되면 https://github.com/rougier/numpy-100 에서 이슈를 열어보십시오.
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