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

(hint: import … as …)

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import numpy as np

(hint: np.__version__)

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np.__version__

Create a vector of zeros of size 10 (★☆☆)

(hint: np.zeros)

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vec = np.zeros(10)

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

(hint: size, itemsize)

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print(vec.size, vec.itemsize)
print(vec.size * vec.itemsize)

Create am zero vector of size 10 but the fifth value which is 1 (★☆☆)

(hint: array[4])

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vec[4] = 1

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

(hint: np.arange)

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vec_range = np.arange(10, 50)

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

(hint: array[::-1])

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vec_range = vec_range[::-1]

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

(hint: reshape)

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mat = np.arange(0, 9)
mat = mat.reshape(3, 3)

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

(hint: np.nonzero)

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

Create a 3x3 identity matrix (★☆☆)

(hint: np.eye)

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identity_mat = np.eye(3)

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

(hint: np.random.random)

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tensor = np.random.random(27)
tensor = tensor.reshape(3, 3, 3)

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

(hint: min, max)

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arr = np.random.random(100).reshape(10, 10)
print(arr)
print(np.max(arr), np.min(arr))

Create a random vector of size 30 and find the mean value (★☆☆)

(hint: mean)

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

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

(hint: array[1:-1, 1:-1])

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arr_2D = np.ones(100).reshape(10, 10)
arr_2D[1:-1, 1:-1] = 0

How to add a border (filled with 0’s) around an existing array? (★☆☆)

(hint: np.pad)

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arr_2D = np.pad(arr_2D, 1)

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

(hint: NaN = not a number, inf = infinity)

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0 * np.nan
np.nan == np.nan
np.inf > np.nan
np.nan - np.nan
0.3 == 3 * 0.1

note: np.isclose is useful when rounding error occurs

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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)

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

(hint: np.diag)

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mat2 = np.zeros(25).reshape(5, 5)
mat2[0][0] = 1
mat2[0][4] = 2
mat2[4][0] = 3
mat2[4][4] = 4

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

(hint: array[::2])

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checkerboard = np.zeros(81)
checkerboard[::2] = 1
checkerboard = checkerboard.reshape(9, 9)
checkerboard = np.delete(checkerboard, 8, axis = 1)
checkerboard = np.delete(checkerboard, 8, axis = 0)

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

(hint: np.unravel_index)

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rk100 = np.unravel_index(100, (6, 7, 8))

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

(hint: np.tile)

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checkerboard2 = np.eye(2)
checkerboard2 = np.tile(checkerboard2, (4, 4))

Normalize a 5x5 random matrix (★☆☆)

(hint: (x - min) / (max - min))

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Nmat = np.random.random(25)
maxn = np.max(Nmat)
minn = np.min(Nmat)
Nmat = (Nmat-minn)/(maxn-minn)

Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)

(hint: np.dtype)

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RGBA = np.dtype([('R', np.ubyte), ('G', np.ubyte), ('B', np.ubyte), ('A', np.ubyte)])

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

(hint: np.dot | @)

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mat1 = np.random.random(15).reshape(5, 3)
mat2 = np.random.random(6).reshape(3, 2)
mat3 = mat1 @ mat2

Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)

(hint: >, <=)

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arr38 = np.random.randint(1, high = 10, size = 10)
arr38[((arr38<8) & (arr38>3))] *= -1

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

(hint: np.sum)

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# Author: Jake VanderPlas

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

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print(sum(range(5), -1))
print(np.sum(range(5), -1))
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Z**Z
2 << Z >> 2
Z <- Z
1j*Z
Z/1/1
Z<Z>Z
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Z = np.array([1, 2, 3])
print(Z**Z)
print(2 << Z >> 2)
print(Z < -Z)
print(1j*Z)
print(Z/1/1)\

What are the result of the following expressions?

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np.array(0) / np.array(0)
np.array(0) // np.array(0)
np.array([np.nan]).astype(int).astype(float)
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print(np.array(0) / np.array(0))
print(np.array(0) // np.array(0))
print(np.array([np.nan]).astype(int).astype(float))

How to round away from zero a float array ? (★☆☆)

(hint: np.uniform, np.copysign, np.ceil, np.abs)

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roundarr = np.random.uniform(-10, 10, 10)
print(roundarr)
roundarr = np.copysign(np.ceil(np.abs(roundarr)), roundarr)

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

(hint: np.intersect1d)

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arr1 = np.random.randint(1, 10, 8)
arr2 = np.random.randint(1, 10, 8)
arr3 = np.intersect1d(arr1, arr2)
print(arr1, arr2, arr3)

(hint: np.seterr, np.errstate)

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settings = np.seterr(all = 'ignore')

Is the following expressions true? (★☆☆)

(hint: imaginary number)

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

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

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

(hint: np.datetime64, np.timedelta64)

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yesterday = np.datetime64("Today", "D") - np.timedelta64(1, "D")
today = np.datetime64("Today", "D")
tomorrow = np.datetime64("Today", "D") + np.timedelta64(1, "D")
print(yesterday, today, tomorrow)

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

(hint: np.arange(dtype=datetime64[‘D’]))

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

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

(hint: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=))

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A = np.eye(3)
B = np.eye(3)
np.add(A, B, out = B)
np.divide(A, 2, out = A)
np.negative(A, out = A)
np.multiply(B, A, out = A)

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

(hint: %, np.floor, np.ceil, astype, np.trunc)

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intarr = np.random.uniform(-10, 10, 10)
#intarr = intarr//1
#intarr = np.floor(intarr)
intarr = intarr.astype('int64')
intarr

Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)

(hint: np.arange)

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mat04 = np.zeros([5, 5])
mat04 += np.arange(0, 5)

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

(hint: np.fromiter)

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def func():
    for i in range(10):
        yield i
        
arr = np.fromiter(func(), dtype = 'int64')

Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)

(hint: np.linspace)

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arr = np.linspace(0, 1, 11, endpoint = False)[1:]

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

(hint: sort)

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St = np.random.uniform(-10, 10, 10)
#St = np.fromiter(St, np.dtype(St[0]))
St.sort()

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

(hint: np.add.reduce)

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arr = np.arange(10)
res = np.add.reduce(arr)

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

(hint: np.allclose, np.array_equal)

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arr1 = np.arange(10)
arr2 = np.arange(10)
#res = np.allclose(arr1, arr2)
res = np.array_equal(arr1, arr2)

Make an array immutable (read-only) (★★☆)

(hint: flags.writeable)

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arr = np.arange(10)
arr.flags.writeable = 0

Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)

(hint: np.sqrt, np.arctan2)

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arr = np.random.randint(-10, 10, (10, 2))
x = arr[:, 0]
y = arr[:, 1]
r = np.sqrt(x**2+y**2, dtype = 'float16')
theta = np.arctan2(x, y)
arr = np.array([r, theta]).T

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

(hint: argmax)

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mat = np.random.random(10)
mat[np.argmax(mat)] = 0

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

(hint: np.meshgrid)

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[[(0.  , 0.  ) (0.25, 0.  ) (0.5 , 0.  ) (0.75, 0.  ) (1.  , 0.  )]
 [(0.  , 0.25) (0.25, 0.25) (0.5 , 0.25) (0.75, 0.25) (1.  , 0.25)]
 [(0.  , 0.5 ) (0.25, 0.5 ) (0.5 , 0.5 ) (0.75, 0.5 ) (1.  , 0.5 )]
 [(0.  , 0.75) (0.25, 0.75) (0.5 , 0.75) (0.75, 0.75) (1.  , 0.75)]
 [(0.  , 1.  ) (0.25, 1.  ) (0.5 , 1.  ) (0.75, 1.  ) (1.  , 1.  )]]

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mat = np.zeros((5,5), [('x',float),('y',float)])
mat['x'], mat['y'] = np.meshgrid(np.linspace(0,1,5),np.linspace(0,1,5))

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

(hint: np.subtract.outer)

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matx = np.arange(5)
maty = matx - 0.5
matc = 1/np.subtract.outer(matx, maty)

(hint: np.iinfo, np.finfo, eps)

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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)

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

(hint: np.atleast_2d, T, np.sqrt)

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vec = np.random.rand(5, 2)
vecx, vecy = np.atleast_2d(vec[:,0], vec[:,1])
dis = np.sqrt((vecx.T - vecx) ** 2 + (vecy.T - vecy) ** 2)

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

(hint: astype(copy=False))

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

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

(hint: np.ndenumerate, np.ndindex)

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mat = np.arange(9).reshape(3,3)
for index, value in np.ndenumerate(mat):
    print(index, value)
for index in np.ndindex(mat.shape):
    print(index, mat[index])

How to randomly place p elements in a 2D array? (★★☆)

(hint: np.put, np.random.choice)

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p = 10
mat = np.arange(100).reshape(10, 10)
print(mat)
np.put(mat, np.random.choice(range(10 * 10), p, replace = False), 1)
print(mat)

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

(hint: mean(axis=,keepdims=))

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mat = np.random.randint(1, 10, (5, 10))
print(mat)
mat = mat - mat.mean(axis=1, keepdims=True)

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

(hint: argsort)

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

Find the nearest value from a given value in an array (★★☆)

(hint: np.abs, argmin, flat)

in array([0.28428161, 0.25229009, 0.80514467, 0.76970704, 0.17243769, 0.31141961, 0.24836806, 0.63500349, 0.41708633, 0.87628688])

the nearest value to 0.5 is 0.41708633

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mat = np.array([0.28428161, 0.25229009, 0.80514467, 0.76970704, 0.17243769,0.31141961, 0.24836806, 0.63500349, 0.41708633, 0.87628688])
num = 0.4
ans = mat.flat[np.abs(mat - num).argmin()]
print(ans)

How to count elements of an integer vector (X) to an array (F) based on an index list (I)? (★★★)

(hint: np.bincount)

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X = [1,2,3,4,5,6]
I = [1,3,9,3,4,1]
# your code
print(F)

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[0. 7. 0. 6. 5. 0. 0. 0. 0. 3.]

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

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

(hint: sum(axis=(-2,-1)))

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tensor = np.random.randint(1, 10, (4, 4, 4, 4))
tensor = tensor.sum(axis = (-2, -1))