Lesson 6 Numpy

Question 1

Import numpy and check what version you have

Answer 1

import numpy as np
numpy.__version__

Question 2

Create an array from this list my_list = [1,2,3]

Answer 2

np.array(my_list)

Question 3

Create an array from the matrix my_matrix = [[1,2,3],[4,5,6],[7,8,9]]

Answer 3

np.array(my_matrix)

Question 4

my_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

Add the values 1,2 to the array above.

Answer 4

np.append(my_arr, [1,2])

Question 5

my_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])

Delete index 1 from my array

Answer 5

np.delete(my_arr, 1)

Question 6

Arrange the numbers in ascending order: another_arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])

Answer 6

np.sort(another_arr)

Question 7

Return evenly spaced values within a given interval. Do this for the values from 0-9

Answer 7

np.arange(0,10)

Question 8

Return evenly spaced values within a given interval. Do this for the values from 0-10 every 2 values

Answer 8

np.arange(0,11,2)

Question 9

Generate a matrix of zeros - one row, 3 columns

Answer 9

np.zeros(3)

Question 10

Generate a matrix of zeros - 10 rows, 10 cols

Answer 10

np.zeros((10,10))

Question 11

Generate a matrix of ones, 5 rows, 5 cols

Answer 11

np.ones((5,5))

Question 12

Return evenly spaced numbers over a specified interval. Do this for the numbers 0-10 with 3 numbers.

Answer 12

np.linspace(0,10,3)

Question 13

Return evenly spaced numbers over a specified interval. Do this for the numbers 0-20 with 50 numbers.

Answer 13

np.linspace(0,20,50)

Question 14

Create an identity matrix for 8 rows.

Answer 14

np.eye(8)

Question 15

Return 3 random numbers (0-1) in an array.

Answer 15

np.random.rand(3)

Question 16

Return random numbers (0-1) in a matrix with 4 rows and 4 cols.

Answer 16

np.random.rand(4,4)

Question 17

Return a random sample from the “standard normal” distribution. Do this for 3 numbers.

Answer 17

np.random.randn(3)

Question 18

Return a random sample from the “standard normal” distribution. Do this for 3 numbers.

Answer 18

np.random.randn(3)

Question 19

Return random integers from 1-99

Answer 19

np.random.randint(1,100)

Question 20

Return random integers from 1-99 as an array for 10 numbers.

Answer 20

np.random.randint(1,100,10)

Question 21

Return an array containing the same data with a new shape. Do this for the array arr = np.arange(30). Reshape the array into 5 rows and 6 cols.

Answer 21

arr.reshape(5,6)

Question 22

Return an array containing the same data with a new shape. Do this for the array arr = np.arange(30). Reshape the array into 5 rows and 2 cols.

Answer 22

rand_arr.reshape(5,2)

Question 23

find the index for the max and min value in the array rand_arr = np.random.randint(0,100,10)

Answer 23

rand_arr.argmax()
rand_arr.argmin()

Question 24

Find the max and min value for the array rand_arr = np.random.randint(0,100,10)

Answer 24

rand_arr.max()
rand_arr.min()

Question 25

Tell me how to find out the number of elements in a dimension

Answer 25

arr.shape

Question 26

How do I find out the number of axes or dimensions in an array.

Answer 26

arr.ndim

Question 27

How do I find out the total number of elements of the array.

Answer 27

arr.size

Question 28

Tell me the datatype of the object in the array.

Answer 28

arr.dtype

Question 29

Specify the datatype to be an integer when making the following matrix. arr_2 = np.ones(2) and then check the datatype.

Answer 29

arr_2 = np.ones(2, dtype=int) arr_2.dtype

Question 30

x = np.array([1, 2, 3, 4, 5, 6]) add a new axis

Answer 30

x2 = x[np.newaxis] x2.shape

Question 31

For the array x = np.array([1, 2, 3, 4, 5, 6]), convert the 1D array to a col vector by inserting an axis along the first dimension. Then show the number of elements in each row and col.

Answer 31

col_vector = x[np.newaxis, :] col_vector.shape

Question 32

For the array x = np.array([1, 2, 3, 4, 5, 6]), convert the 1D array to a row vector by inserting an axis along the first dimension. Then show the number of elements in each row and col.

Answer 32

row_vector = x[:, np.newaxis] row_vector.shape

Question 33

Select the 8th index in this array new_arr = np.arange(0,110,10)

Answer 33

new_arr[8]

Question 34

Select the numbers 10,20,40,40 in this array by splicing new_arr = np.arange(0,110,10)

Answer 34

new_arr[1:5]

Question 35

new_arr = np.arange(0,110,10) convert everything from 0-40 to 100.

Answer 35

new_arr[0:5]=100

Question 36

Multiply everything in this array by 2 new_arr = np.arange(0,110,10)

Answer 36

new_arr * 2

Question 37

Change everything in the array new_arr = np.arange(0,110,10) to 88

Answer 37

arr_slice[:]=88 #Show Slice again arr_slice

Question 38

Create a copy of the array.

Answer 38

new_arr_copy = new_arr.copy()

Question 39

a = np.array([[1,1], [2,2]]) b = np.array([[3,3], [4,4]]) Combine the two arrays vertically

Answer 39

np.vstack((a, b))

Question 40

Combine the two arrays horizontally a = np.array([[1,1], [2,2]]) b = np.array([[3,3], [4,4]])

Answer 40

np.hstack((a, b))

Question 41

c = np.arange(1, 25) Make a new array and split it 3 ways

Answer 41

np.hsplit(c,3)

Question 42

np.hsplit(c,3) split your array after the second column here three ways.

Answer 42

np.hsplit(c,(2,3))

Question 43

Add, subtract and multiply these two arrays arr_1 = np.array([2,4]) arr_2 = np.ones(2)

Answer 43

arr_1 + arr_2 arr_1 - arr_2 arr_1 * arr_2

Question 44

Add all of the elements in the array arr_3 = np.array([1, 2, 3, 4, 5, 6, 7, 8])

Answer 44

arr_3.sum()

Question 45

Add the rows in a 2d array arr_4 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

Answer 45

arr_4.sum(axis=0)

Question 46

Add the columns in a 2d array. arr_4 = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

Answer 46

arr_4.sum(axis=1)

Question 47

Find the unique elements in an array easily mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])

Answer 47

mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5]) unique_vals = np.unique(mixed_array)

Question 48

Find the index positions of unique values in an array mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])

Answer 48

unique_vals_idx = np.unique(mixed_array, return_index=True)

Question 49

Count the number of unique values in an array. mixed_array = np.array([5,5,11,11,2,3,4,8,14,14,15,5])

Answer 49

unique_vals_idx = np.unique(mixed_array, return_counts=True)