100 numpy exercises

100 numpy exercises

This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercices for those who teach.

If you find an error or think you've a better way to solve some of them, feel free to open an issue atGitHub - rougier/numpy-100: 100 numpy exercises (100% complete)

1. Import the numpy package under the name np (★☆☆)

import numpy as np

2. Print the numpy version and the configuration (★☆☆)

print(np.__version__)

np.show_config()

3. Create a null vector of size 10 (★☆☆)

Z = np.zeros(10)

print(Z)

4. How to find the memory size of any array (★☆☆)

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

print("%d bytes" % (Z.size * Z.itemsize))

5. How to get the documentation of the numpy add function from the command line? (★☆☆)

%run `python -c "import numpy; http://numpy.info(numpy.add)"`

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

Z = np.zeros(10)

Z[4] = 1

print(Z)

7. Create a vector with values ranging from 10 to 49 (★☆☆)

Z = np.arange(10,50)

print(Z)

8. Reverse a vector (first element becomes last) (★☆☆)

Z = np.arange(50)

Z = Z[::-1]

print(Z)

9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)

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

print(Z)

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

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

print(nz)

11. Create a 3x3 identity matrix (★☆☆)

Z = np.eye(3)

print(Z)

12. Create a 3x3x3 array with random values (★☆☆)

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

print(Z)

13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)

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 (★☆☆)

Z = np.random.random(30)

m = Z.mean()

print(m)

15. Create a 2d array with 1 on the border and 0 inside (★☆☆)

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? (★☆☆)

Z = np.ones((5,5))

Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)

print(Z)

17. What is the result of the following expression? (★☆☆)

print(0 * np.nan)

print(np.nan == np.nan)

print(np.inf > np.nan)

print(np.nan - np.nan)

print(0.3 == 3 * 0.1)

18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)

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

print(Z)

19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)

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?

print(np.unravel_index(100,(6,7,8)))

21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)

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

print(Z)

22. Normalize a 5x5 random matrix (★☆☆)

Z = np.random.random((5,5))

Zmax, Zmin = Z.max(), Z.min()

Z = (Z - Zmin)/(Zmax - Zmin)

print(Z)

23. Create a custom dtype that describes a color as four unisgned bytes (RGBA) (★☆☆)

color = np.dtype([("r", np.ubyte, 1),

("g", np.ubyte, 1),

("b", np.ubyte, 1),

("a", np.ubyte, 1)])

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

Z = np.dot(np.ones((5,3)), np.ones((3,2)))

print(Z)

# Alternative solution, in Python 3.5 and above

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. (★☆☆)

# Author: Evgeni Burovski

Z = np.arange(11)

Z[(3 < Z) & (Z <= 8)] *= -1

print(Z)

26. What is the output of the following script? (★☆☆)

# Author: Jake VanderPlas

print(sum(range(5),-1))

from numpy import *

print(sum(range(5),-1))

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

28. What are the result of the following expressions?

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 ? (★☆☆)

# Author: Charles R Harris

Z = np.random.uniform(-10,+10,10)

print (np.copysign(np.ceil(np.abs(Z)), Z))

30. How to find common values between two arrays? (★☆☆)

Z1 = np.random.randint(0,10,10)

Z2 = np.random.randint(0,10,10)

print(np.intersect1d(Z1,Z2))

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

# 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? (★☆☆)

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

33. How to get the dates of yesterday, today and tomorrow? (★☆☆)

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

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

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

34. How to get all the dates corresponding to the month of July 2016? (★★☆)

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

print(Z)

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

A = np.ones(3)*1

B = np.ones(3)*2

C = np.ones(3)*3

np.add(A,B,out=B)

np.divide(A,2,out=A)

np.negative(A,out=A)

np.multiply(A,B,out=A)

36. Extract the integer part of a random array using 5 different methods (★★☆)

Z = np.random.uniform(0,10,10)

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 (★★☆)

Z = np.zeros((5,5))

Z += np.arange(5)

print(Z)

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

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 (★★☆)

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

print(Z)

40. Create a random vector of size 10 and sort it (★★☆)

Z = np.random.random(10)

Z.sort()

print(Z)

41. How to sum a small array faster than np.sum? (★★☆)

# Author: Evgeni Burovski

Z = np.arange(10)

np.add.reduce(Z)

42. Consider two random array A anb B, check if they are equal (★★☆)

A = np.random.randint(0,2,5)

B = np.random.randint(0,2,5)

# Assuming identical shape of the arrays and a tolerance for the comparison of values

equal = np.allclose(A,B)

print(equal)

# 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) (★★☆)

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 (★★☆)

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)

45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)

Z = np.random.random(10)

Z[Z.argmax()] = 0

print(Z)

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

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)

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

# Author: Evgeni Burovski

X = np.arange(8)

Y = X + 0.5

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 (★★☆)

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)

Z = np.zeros((16,16))

print(Z)

50. How to find the closest value (to a given scalar) in an array? (★★☆)

Z = np.arange(100)

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

index = (np.abs(Z-v)).argmin()

print(Z[index])

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

Z = np.zeros(10, [ ('position', [ ('x', float, 1),

('y', float, 1)]),

('color', [ ('r', float, 1),

('g', float, 1),

('b', float, 1)])])

print(Z)

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

Z = np.random.random((10,2))

X,Y = np.atleast_2d(Z[:,0], Z[:,1])

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

print(D)

# Much faster with scipy

import scipy

# Thanks Gavin Heverly-Coulson (#issue 1)

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?

Z = np.arange(10, dtype=np.int32)

Z = Z.astype(np.float32, copy=False)

print(Z)

54. How to read the following file? (★★☆)

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=http://np.int)

print(Z)

55. What is the equivalent of enumerate for numpy arrays? (★★☆)

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])

56. Generate a generic 2D Gaussian-like array (★★☆)

X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))

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? (★★☆)

# 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)

58. Subtract the mean of each row of a matrix (★★☆)

# Author: Warren Weckesser

X = np.random.rand(5, 10)

# Recent versions of numpy

Y = X - X.mean(axis=1, keepdims=True)

# Older versions of numpy

Y = X - X.mean(axis=1).reshape(-1, 1)

print(Y)

59. How to I sort an array by the nth column? (★★☆)

# Author: Steve Tjoa

Z = np.random.randint(0,10,(3,3))

print(Z)

print(Z[Z[:,1].argsort()])

60. How to tell if a given 2D array has null columns? (★★☆)

# Author: Warren Weckesser

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 (★★☆)

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? (★★☆)

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 + y

print(it.operands[2])

63. Create an array class that has a name attribute (★★☆)

class NamedArray(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

http://self.info = getattr(obj, 'name', "no name")

Z = NamedArray(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)? (★★★)

# Author: Brett Olsen

Z = np.ones(10)

I = np.random.randint(0,len(Z),20)

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

print(Z)

# Another solution

# Author: Bartosz Telenczuk

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)? (★★★)

# Author: Alan G Isaac

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 (★★★)

# Author: Nadav Horesh

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(np.unique(I))

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

A = np.random.randint(0,10,(3,4,3,4))

# solution by passing a tuple of axes (introduced in numpy 1.7.0)

sum = A.sum(axis=(-2,-1))

print(sum)

# solution by flattening the last two dimensions into one

# (useful for functions that don't accept tuples for axis argument)

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? (★★★)

# Author: Jaime Fernández del Río

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)

# Pandas solution as a reference due to more intuitive code

import pandas as pd

print(pd.Series(D).groupby(S).mean())

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

# Author: Mathieu Blondel

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)

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

# Author: Warren Weckesser

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)? (★★★)

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? (★★★)

# Author: Eelco Hoogendoorn

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 (★★★)

# Author: Nicolas P. Rougier

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? (★★★)

# Author: Jaime Fernández del Río

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? (★★★)

# Author: Jaime Fernández del Río

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]) (★★★)

# Author: Joe Kington / Erik Rigtorp

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? (★★★)

# Author: Nathaniel J. Smith

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)

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])? (★★★)

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])? (★★★)

# Author: Italmassov Kuanysh

# 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) (★★★)

# Author: Nicolas Rougier

Z = np.random.randint(0,10,(10,10))

shape = (5,5)

fill = 0

position = (1,1)

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)

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]]? (★★★)

# 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. Compute a matrix rank (★★★)

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

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

print(np.bincount(Z).argmax())

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

# 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. Create a 2D array subclass such that Z[i,j] == Z[j,i] (★★★)

# 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. 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)) (★★★)

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

# 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. How to implement the Game of Life using numpy arrays? (★★★)

# 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. How to get the n largest values of an array (★★★)

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) (★★★)

# 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. How to create a record array from a regular array? (★★★)

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 (★★★)

# 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. 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? (★★★)

# 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 = (C.sum(axis=(1,2,3)) >= B.shape[1]).nonzero()[0]

print(rows)

94. Considering a 10x3 matrix, extract rows with unequal values (e.g. [2,2,3]) (★★★)

# Author: Robert Kern

Z = np.random.randint(0,5,(10,3))

E = np.logical_and.reduce(Z[:,1:] == Z[:,:-1], axis=1)

U = Z[~E]

print(Z)

print(U)

95. Convert a vector of ints into a matrix binary representation (★★★)

# 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. Given a two dimensional array, how to extract unique rows? (★★★)

# 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. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)

# Author: Alex Riley

# Make sure to read: A basic introduction to NumPy's 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', A, B) # np.outer(A, B)

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

# 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. 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. (★★★)

# 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. 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). (★★★)

# 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)