Python Numpy Question Exercise Practice Solutions | Data Science and Machine Learning with Python

Question

Assignment/Task 4

Question on Numpy-

1. Import the numpy package under the name np and Print the numpy version and the configuration 
2. Create a null vector of size 10
3. Create Simple 1-D array and check type and check data types in array
4. How to find number of dimensions, bytes per element and bytes of memory used?
5. Create a null vector of size 10 but the fifth value which is 1
6. Create a vector with values ranging from 10 to 49
7. Reverse a vector (first element becomes last)
8. Create a 3x3 matrix with values ranging from 0 to 8
9. Find indices of non-zero elements from [1,2,0,0,4,0]
10. Create a 3x3 identity matrix
11. Create a 3x3x3 array with random values
12. Create a 10x10 array with random values and find the minimum and maximum values
13. Create a random vector of size 30 and find the mean value
14. Create a 2d array with 1 on the border and 0 inside
15. How to add a border (filled with 0's) around an existing array? 

16. How to Accessing/Changing specific elements, rows, columns, etc in Numpy array?

    Example -
    [[ 1 2 3 4 5 6 7] [ 8 9 10 11 12 13 14]]

    Get 13, get first row only, get 3rd column only, get [2, 4, 6], replace 13 by 20

17. How to Convert a 1D array to a 2D array with 2 rows

18. Create the following pattern without hardcoding. Use only numpy functions and the below input array a.

    Input:

    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])

19. Write a program to show how Numpy taking less memory compared to Python List?

20. Write a program to show how Numpy taking less time compared to Python List?

Answer 1

1. Import the NumPy package under the name np and Print the NumPy version and the configuration

import numpy as np

# NumPy version

np.__version__

# NumPy configuration

np.show_config()

2. Create a null vector of size 10

Answer: np.zeros(10)

3. Create Simple 1-D array and check type and check data types in array

Answer:

a = np.arange(0, 10)

# type

type(a)

# data type

a.dtype

4. How to find number of dimensions, bytes per element and bytes of memory used?

Answer:

a = np.arange(0, 10)

# No of dimensions

a.ndim

# No of bytes per element

a.itemsize

# No of bytes

a.nbytes

5. Create a null vector of size 10 but the fifth value which is 1

Answer:

null = np.zeros(10)

null[4] = 1

6. Create a vector with values ranging from 10 to 49

Answer: np.arange(10,50)

7. Reverse a vector (first element becomes last)

Answer:

b = np.arange(10,50)

b[::-1]

8. Create a 3x3 matrix with values ranging from 0 to 8

Answer:

c = np.arange(0,9)

c.reshape(3,3)

9. Find indices of non-zero elements from [1,2,0,0,4,0]

Answer:

d = np.array([1, 2, 0, 0, 4, 0])

np.nonzero(d)

10. Create a 3x3 identity matrix

Answer:

# identity() function

np.identity(3)

# eye() function

np.eye(3, 3)

11. Create a 3x3x3 array with random values

Answer: np.random.random((3, 3, 3))

12. Create a 10x10 array with random values and find the minimum and maximum values

Answer:

e = np.random.random((10,10))

# minimum value

np.min(e)

# maximum values

np.max(e)

13. Create a random vector of size 30 and find the mean value

Answer:

f = np.random.random((5,6))

# mean values of matrix

np.mean(f)

14. Create a 2d array with 1 on the border and 0 inside

Answer:

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

g[1:4, 1:4] = 0

15. How to add a border (filled with 0's) around an existing array?

Answer:

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

h[:1, :] = 0

h[:, :1] = 0

h[4:, :] = 0

h[:, 4:] = 0

16. How to Accessing/Changing specific elements, rows, columns, etc in Numpy array?

Example - [[ 1 2 3 4 5 6 7] [ 8 9 10 11 12 13 14]]

Get 13, get first row only, get 3rd column only, get [2, 4, 6], replace 13 by 20

Answer:

i = np.arange(1,15).reshape(2,7)

# To get 13

i[1,5]

# To get first row only

i[0,:]

# to get third column only

i[:,2]

# To get [2, 4, 6]

i[0,1::2]

# replace 13 by 20

i[1,5] = 20

17. How to Convert a 1D array to a 2D array with 2 rows

Answer:

j = np.arange(1,17)

# Dimension of j

j.ndim

# convert 1D to 2D

j.reshape((2,8))

18. Create the following pattern without hardcoding. Use only numpy and the below input array a.

Input:

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:

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

np.append(np.repeat(a,3), np.tile(a,3))

19. Write a program to show how Numpy taking less memory compared to Python List?

Answer:

l = range(100)

a = 2

import sys

# Size of Python list

sys.getsizeof(a) * len(l)

# Size of numpy array

al = np.arange(100)

al.size * al.itemsize)

20. Write a program to show how Numpy taking less time compared to Python List?

Answer:

import time

import sys

# Python List

size = 10000000

l1 = range(size)

l2 = range(size)

start = time.time()

result = [(x * y) for x,y in zip(l1, l2)]

print("Time taken by List: ", (time.time() - start), "seconds")

# Numpy Array

nl1 = np.arange(size)

nl2 = np.arange(size)

start1 = time.time()

res = nl1 * nl2

print("Time taken by numpy array: ", (time.time() - start1), "seconds")

Answer 2

1.Import the numpy package under the name np and Print the numpy version and the configuration 

ans)import numpy as np

       a=np.array([1,2,3,4,5])

       print(a)

2.Create a null vector of size 10

   import numpy as np

   a=np.array(10)

   print(a)

3.Create Simple 1-D array and check type and check data types in array

    a=np.array([1,2,3,4,5])

    print(a)

    print(a,ndim)

    print(a[0])

    print(a[1])

    print(type(a))

4.How to find number of dimensions, bytes per element and bytes of memory used?

Answer 3

1.Import the numpy package under the name np and Print the numpy version and the configuration.

import numpy as np

print(np.__version__)

print(np.show_config())

2. Create a null vector of size 10.

x = np.zeros(10)

print(x)

3.Create Simple 1-D array and check type and check data types in array.

x = np.arange(5)

print(x)

print(type(x))

x.dtype

4. How to find number of dimensions, bytes per element and bytes of memory used?

x = np.arange(10)

print(x.size)

print(x.ndim)

print(x.itemsize)

print(x.nbytes)

5. Create a null vector of size 10 but the fifth value which is 1

x = np.zeros(10)

x[4] = 1

print(x)

6. Create a vector with values ranging from 10 to 49.

x = np.arange(10, 50)

print(x)

7. Reverse a vector (first element becomes last)

x = np.arange(10)

print(x[::-1])

8. Create a 3x3 matrix with values ranging from 0 to 8

x = np.arange(9)

x = x.reshape(3,3)

print(x)

9. Find indices of non-zero elements from [1,2,0,0,4,0]

x = [1,2,0,0,4,0]

arr = np.array(x)

res = np.nonzero(arr)

res

10. Create a 3x3 identity matrix

id = np.identity(3)

print(id)

11. Create a 3x3x3 array with random values

x = np.random.random((3,3,3))

print(x)

12. Create a 10x10 array with random values and find the minimum and maximum values

x = np.random.random((10,10))

print(x)

print(np.min(x))

print(np.max(x))

13. Create a random vector of size 30 and find the mean value

x = np.random.random(30)

print(x)

print(np.mean(x))

14. Create a 2d array with 1 on the border and 0 inside.

x = np.ones((4,4))

print(x)

print('2d array with 1 on the border and 0 inside')

x[1:-1,1:-1] = 0

print(x)

15. How to add a border (filled with 0's) around an existing array? 

x = np.ones((4,4))

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

print(x)

16. How to Accessing/Changing specific elements, rows, columns, etc in Numpy array?

x = np.array([[ 1, 2, 3, 4, 5, 6, 7], [ 8, 9, 10, 11, 12, 13, 14]])

print(x)

print(x[1,-2])

print('1st Row Only',x[0,:])

print('3rd Column Only',x[:,2])

print(x[0,:][1:6:2])

x[1,5]=20

print(x)

17. How to Convert a 1D array to a 2D array with 2 rows.

x= np.arange(4)

print(x)

x.reshape(2,2)

18. Create the following pattern without hardcoding. Use only numpy functions and the below input array a.

a = np.array([1,2,3])

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

19. Write a program to show how Numpy taking less memory compared to Python List?

l = range(1000)

import sys

a = 10

print('Size Of List : ', sys.getsizeof(a)*len(l))

a1 = np.arange(1000)

print('Size Of Numpy Array : ', a1.size*a1.itemsize)

20.Write a program to show how Numpy taking less time compared to Python List?

import time

import sys

size = 10000

list1 = range(size)

list2 = range(size)

n1 = np.arange(size)

n2 = np.arange(size)

start = time.time()

x = time.time()

res = [x+y for x,y in zip(list1,list2)]

print('Time Of List Is : ', (time.time()-start)*1000)

start = time.time()

res1 = n1+n2

print('Time Of Numpy Array Is : ', (time.time()-start)*1000)

Answer 4

GO_STP_6266

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6803989457809879040-tKDE

Answer 5

GS_STP_4359

Q1. Import the numpy package under the name np the numpy version and Print and the configuration 

Ans:

import numpy as np

print(np.__version__)

print(np.show_config())

Q2.Create a null vector of size 10 

Ans:

import numpy as np

a=np.zeros([10])

print(a)

Output: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

3. Create Simple 1-D array and check type and check data types in array 

Ans:

import numpy as np

a=np.array([10,20,30,40,50])

b=type(a)

c=a.dtype

print("type",b)

print("dtype=",c)

Output: 

type=<class 'numpy.ndarray'>

 dtype=int64

Q4. How to find number of dimensions, bytes per element and bytes of memory used? 

Ans:

a=np.array([10,20,30,40,50])

a.nbytes

np.shape

Output: nbytes 40

              shape(5, )

Q5. Create a null vector of size 10 but the fifth value which is 1 

Ans:

import numpy as np

a=np.zeros([0])

a[5]=1

print(a)

Output:

[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]

  

Q6. Create a vector with values ranging from 10 to 49

Ans:

import numpy as np

a=np.arange(10,49)

print(a)

Output:

 [10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48]

Q7. Reverse a vector (first element becomes last) 

Ans:

import numpy as np

a=np.array([10,20,30,40])

print("array=",a)

a=a[::-1]

print("reverse array=",a)

Output:

array=[10 20 30]

reverse array=[30 20 10]

Q8.Create a 3x3 matrix with values ranging from 0 to 8

Ans:

Cannot create 3*3 array using ranging 0 to 8.

0 to 8 only 7 vales but 3*3 array consist of 9 values

Q 9. Find indices of non-zero elements from [1,2,0,0,4,0] 

Ans:

Q10.Create a 3x3 identity matrix

Ans:

import numpy as np

a=np.array([[10,20,30],[40,50,60],[70,80,90]])

print(a)

Output:

[[10,20,30],

[40,50,60],

[70,80,90]]

Q11. Create a 3x3x3 array with random values

Ans:

import numpy as np

a=np.arange(0,28)

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

Output:

[[[0 1 2]

 [3 4 5 ]

 [ 6 4 7 ]]

[[9 10 11] 

[12 13 14] 

[15 16 17]] 

[[18 19 20]

 [21 22 23] 

[24 25 26]]]

Q12. Create a 10x10 array with random values and find the minimum and maximum values 

Ans:

a=np.arange(0,100)

a=a.reshape(10,10)

a.min()

a.max()

Output:

[[0 1 2 3 4 5 6 7 8 9] 

[10 11 12 13 14 15 16 17 18 19]

 [20 21 22 23 24 25 26 27 28 29] 

[30 31 32 33 34 35 36 37 38 39] 

[40 41 42 43 44 45 46 47 48 49]

 [50 51 52 53 54 55 56 57 58 59] 

[60 61 62 63 64 65 66 67 68 69] 

[70 71 72 73 74 75 76 77 78 79] 

[80 81 82 83 84 85 86 87 88 89] 

[90 91 92 93 94 95 96 97 98 99]]

min 0

max 99

Q13. Create a random vector of size 30 and find the mean value

import numpy as np

a=np.arange(0,29)

a.mean()

Output:

mean 14.5

Q14. Create a 2d array with 1 on the border and 0 inside

Ans:

a=np.ones(5,5)

a=a[1:-1,1:-1]=0

print(a)

Output:

[[1 1 1 1 1 ]

[1 0 0 0 1]

[1 0 0 0 1]

[1 0 0 0 1]

[1 1 1 1 1]]

Q15. How to add a border (filled with O's) around an existing array? 

Q16. How to Accessing/Changing specific elements, rows, columns, etc in Numpy array? Example - [[1234567] [89 10 11 12 13 14]] Get 13, get first row only, get 3rd column only, get [2, 4, 6], replace 13 by 20

Answer 6

GO_STP_6734

Here is the answer for task 4 in my linkedin : https://www.linkedin.com/pulse/assignmenttask-4-aswin-kumar

Answer 7

ma'am

how we can get [2,4,6] in question 16.

explain the Question 18,15.

Comments

You can use Slicing ..
i[0,1::2]

For Question 18

Create the following pattern without hardcoding. Use only numpy and the below input array a.

Input:

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:

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

np.append(np.repeat(a,3), np.tile(a,3))

15. How to add a border (filled with 0's) around an existing array?

Answer:

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

h[:1, :] = 0

h[:, :1] = 0

h[4:, :] = 0

h[:, 4:] = 0