Numpy

Question 1

create identity matrix of dimension n

Answer 1

a=np.eye(n)

Question 2

convert a 1D array to a 3D array

Answer 2

a=np.array([i for i in range(27)]

y=a.reshape((3,3,3))

Question 3

convert all elements of array from float to int, from binary to boolean

Answer 3

y=a.astype(‘int’)

y=a.astype(‘bool’)

Question 4

From 2 numpy arrays, extract the indexes in which the elements in the 2 arrays match

Answer 4

print(np.where(a == b))

Question 5

Output a sequence of equally gapped 5 numbers in the range 0 to 100

Answer 5

o = np.linspace(0, 100, 5)

Question 6

Output a matrix (numpy array) of dimension 2-by-3 with each and every value equal to 5

Answer 6

o = np.full((2, 3), 5)

o=5*np.ones((2,3))

Question 7

Output an array by repeating a smaller array of 2 dimensions, 10 times

Answer 7

o = np.tile(a, 10)

Question 8

Output a 5-by-5 array of random integers between 0 (inclusive) and 10 (exclusive)

Answer 8

o=np.random.randint(0,10,(5,5))

Question 9

Output a 3-by-3 array of random numbers following normal distribution

Answer 9

o = np.random.normal(size = (3,3))
#normal takes (mu,sigma,size)

Question 10

Given 2 numpy arrays as matrices, output the result of multiplying the 2 matrices

Answer 10

o = a@b
also possible:
o = np.matmul(a, b)

Question 11

Create a 1D array of numbers from 0 to 9

Answer 11

a=np.arange(10)

Question 12

Create a 3×3 numpy array of all True’s

Answer 12

a=np.full((3,3), True, dtype=bool)

np.ones((3,3), dtype=bool)

Question 13

Extract all odd numbers from arr

Answer 13

arr[arr % 2 == 1]

Question 14

Replace all odd numbers in arr with -1

Answer 14

arr[arr % 2 == 1] = -1

Question 15

Replace all odd numbers in arr with -1 without changing arr

Answer 15

out = np.where(arr % 2 == 1, -1, arr)

Question 16

Convert a 1D array to a 2D array with 2 rows

Answer 16

arr.reshape(2, -1)  
# Setting to -1 automatically decides the number of rows or cols !!!!!!

Question 17

Stack arrays a and b vertically

Answer 17

np. concatenate([a, b], axis=0)
np. vstack([a, b])
np. r_[a,b]

Question 18

Stack arrays a and b horizontally

Answer 18

np. concatenate([a, b], axis=1)
np. hstack([a, b])
np. c_[a,b]

Question 19

a = np.array([1,2,3])
Desired output:
array([1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])

Answer 19

np.r_[np.repeat(a,3),np.tile(a,3)]

Question 20

Get the common items between a and b

Answer 20

np.intersect1d(a,b)

Question 21

From array a remove all items present in array b

Answer 21

np.setdiff1d(a,b)

Question 22

Get the positions where elements of a and b match

Answer 22

np.where(a == b)

a[a==b]

Question 23

Convert the function maxx that works on two scalars, to work on two arrays.

Answer 23

func=np.vectorize(scalar_func, otypes=[float])
func(a,b)

Question 24

Swap columns 1 and 2 in the array arr.

Answer 24

arr[:, [1,0,2]]

Question 25

Swap rows 1 and 2 in the array arr:

Answer 25

arr[[1,0,2], :]

Question 26

Reverse the rows of a 2D array arr

Answer 26

arr[::-1]

Question 27

Reverse the columns of a 2D array arr.

Answer 27

arr[:, ::-1]

Question 28

Create a 2D array of shape 5x3 to contain random decimal numbers between 5 and 10.

Answer 28

# Solution Method 1:
rand_arr = np.random.randint(low=5, high=10, size=(5,3)) + np.random.random((5,3))

# Solution Method 2:
rand_arr = np.random.uniform(5,10, size=(5,3))

Question 29

Print or show only 3 decimal places of the numpy array rand_arr

Answer 29

np.set_printoptions(precision=3)

Question 30

Pretty print rand_arr by suppressing the scientific notation (like 1e10)

Answer 30

np.set_printoptions(suppress=True, precision=6) # precision is optional

Question 31

Limit the number of items printed in python numpy array a to a maximum of 6 elements, show all

Answer 31

np. set_printoptions(threshold=6)

np. set_printoptions(threshold=np.nan)

Question 32

Find the mean, median, standard deviation of iris’s sepallength

Answer 32

np. mean(sepallength)
np. median(sepallength)
np. std(sepallength)

Question 33

Flatten array a into one D

Answer 33

a.ravel(), np.ravel(a)

Question 34

Compute the maximum for each row in the given array.

Answer 34

np.amax(a, axis=1)

Question 35

Compute the min-by-max for each row for given 2d numpy array.

Answer 35

np.apply_along_axis(lambda x: np.min(x)/np.max(x), arr=a, axis=1)