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Ultimate NumPy
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Course Content

Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays
2. Indexing and Slicing
Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays
3. Commonly used NumPy Functions
Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation
4. Math with NumPy
BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

bookRandom Arrays

It is often the case that we need to generate a random number or an array of random numbers. Luckily, NumPy has a module named random specifically for this purpose.

Note

random generates pseudo-random numbers, not truly random numbers. However, for most purposes, this is more than enough.

The two most commonly used functions of the random module are:

  • rand();
  • randint().

rand()

The numpy.random.rand() function is used to generate either a random float number or an array of random floats from a uniform distribution over [0, 1).

Its only possible arguments are the dimensions of the array. If no argument is passed, rand() generates a random float number (scalar).

Let’s clarify with an example:


              
123456789101112

            
import numpy as np
# Generating a random number
random_number = np.random.rand()
print(random_number)
print('-' * 56)
# Generating a random 1D array with 5 elements
random_array = np.random.rand(5)
print(random_array)
print('-' * 56)
# Generating a random 2D array (matrix) of shape 4x3
random_matrix = np.random.rand(4, 3)
print(random_matrix)
copy

First, we generated a random float number. Next, we generated a random 1D array with 5 elements by setting its size to 5. Finally, we generated a random 2D array of shape 3x4.

Note

Dimensions in the rand() function should be specified as separate integer parameters, not a tuple of integers.

randint()

The numpy.random.randint function is used to generate either a random integer or an array of random integers from a discrete uniform distribution within a specified interval.

Its three most important parameters are low (the only required parameter), high, and size. The interval is [low, high) (from low inclusive to high exclusive). However, if high is not specified, then the interval is [0, low).

Let’s look at an example:


              
12345678910111213141516

            
import numpy as np
# Generating a random integer from 0 to 3 exclusive
random_integer = np.random.randint(3)
print(random_integer)
print('-' * 10)
# Generating a 1D array of random integers in [0, 5) with 4 elements
random_int_array = np.random.randint(5, size=4)
print(random_int_array)
print('-' * 10)
# Generating a 1D array of random integers in [2, 5) with 4 elements
random_int_array_2 = np.random.randint(2, 5, size=4)
print(random_int_array_2)
print('-' * 10)
# Generating a random 2D array of random integers in [1, 6) of shape 4x2
random_int_matrix = np.random.randint(1, 6, size=(4, 2))
print(random_int_matrix)
copy

There is actually nothing complicated here; everything is rather straightforward.

Note

Unlike rand(), we specify the dimensions of the array via a single parameter size, passing either an integer or a tuple of integers.

As always, here is the documentation for you: numpy.random.rand and numpy.random.randint.

Task
test

Swipe to show code editor

  1. Create a 1D array of random floats from a uniform distribution in [0, 1) with 4 elements for random_floats_array.
  2. Create a 2D array of random floats from a uniform distribution in [0, 1) with a shape of 3x2 for random_floats_matrix.
  3. Use the correct function to create a 2D array of random integers for random_integers_matrix.
  4. Set the interval to [10, 21) (from 10 to 21 exclusive) by specifying the first two arguments of the function.
  5. Set the shape of the random_integers_matrix to 3x2 by specifying the third keyword argument of the function.

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Section 1. Chapter 7
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bookRandom Arrays

It is often the case that we need to generate a random number or an array of random numbers. Luckily, NumPy has a module named random specifically for this purpose.

Note

random generates pseudo-random numbers, not truly random numbers. However, for most purposes, this is more than enough.

The two most commonly used functions of the random module are:

  • rand();
  • randint().

rand()

The numpy.random.rand() function is used to generate either a random float number or an array of random floats from a uniform distribution over [0, 1).

Its only possible arguments are the dimensions of the array. If no argument is passed, rand() generates a random float number (scalar).

Let’s clarify with an example:


              
123456789101112

            
import numpy as np
# Generating a random number
random_number = np.random.rand()
print(random_number)
print('-' * 56)
# Generating a random 1D array with 5 elements
random_array = np.random.rand(5)
print(random_array)
print('-' * 56)
# Generating a random 2D array (matrix) of shape 4x3
random_matrix = np.random.rand(4, 3)
print(random_matrix)
copy

First, we generated a random float number. Next, we generated a random 1D array with 5 elements by setting its size to 5. Finally, we generated a random 2D array of shape 3x4.

Note

Dimensions in the rand() function should be specified as separate integer parameters, not a tuple of integers.

randint()

The numpy.random.randint function is used to generate either a random integer or an array of random integers from a discrete uniform distribution within a specified interval.

Its three most important parameters are low (the only required parameter), high, and size. The interval is [low, high) (from low inclusive to high exclusive). However, if high is not specified, then the interval is [0, low).

Let’s look at an example:


              
12345678910111213141516

            
import numpy as np
# Generating a random integer from 0 to 3 exclusive
random_integer = np.random.randint(3)
print(random_integer)
print('-' * 10)
# Generating a 1D array of random integers in [0, 5) with 4 elements
random_int_array = np.random.randint(5, size=4)
print(random_int_array)
print('-' * 10)
# Generating a 1D array of random integers in [2, 5) with 4 elements
random_int_array_2 = np.random.randint(2, 5, size=4)
print(random_int_array_2)
print('-' * 10)
# Generating a random 2D array of random integers in [1, 6) of shape 4x2
random_int_matrix = np.random.randint(1, 6, size=(4, 2))
print(random_int_matrix)
copy

There is actually nothing complicated here; everything is rather straightforward.

Note

Unlike rand(), we specify the dimensions of the array via a single parameter size, passing either an integer or a tuple of integers.

As always, here is the documentation for you: numpy.random.rand and numpy.random.randint.

Task
test

Swipe to show code editor

  1. Create a 1D array of random floats from a uniform distribution in [0, 1) with 4 elements for random_floats_array.
  2. Create a 2D array of random floats from a uniform distribution in [0, 1) with a shape of 3x2 for random_floats_matrix.
  3. Use the correct function to create a 2D array of random integers for random_integers_matrix.
  4. Set the interval to [10, 21) (from 10 to 21 exclusive) by specifying the first two arguments of the function.
  5. Set the shape of the random_integers_matrix to 3x2 by specifying the third keyword argument of the function.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 7
toggle bottom row

bookRandom Arrays

It is often the case that we need to generate a random number or an array of random numbers. Luckily, NumPy has a module named random specifically for this purpose.

Note

random generates pseudo-random numbers, not truly random numbers. However, for most purposes, this is more than enough.

The two most commonly used functions of the random module are:

  • rand();
  • randint().

rand()

The numpy.random.rand() function is used to generate either a random float number or an array of random floats from a uniform distribution over [0, 1).

Its only possible arguments are the dimensions of the array. If no argument is passed, rand() generates a random float number (scalar).

Let’s clarify with an example:


              
123456789101112

            
import numpy as np
# Generating a random number
random_number = np.random.rand()
print(random_number)
print('-' * 56)
# Generating a random 1D array with 5 elements
random_array = np.random.rand(5)
print(random_array)
print('-' * 56)
# Generating a random 2D array (matrix) of shape 4x3
random_matrix = np.random.rand(4, 3)
print(random_matrix)
copy

First, we generated a random float number. Next, we generated a random 1D array with 5 elements by setting its size to 5. Finally, we generated a random 2D array of shape 3x4.

Note

Dimensions in the rand() function should be specified as separate integer parameters, not a tuple of integers.

randint()

The numpy.random.randint function is used to generate either a random integer or an array of random integers from a discrete uniform distribution within a specified interval.

Its three most important parameters are low (the only required parameter), high, and size. The interval is [low, high) (from low inclusive to high exclusive). However, if high is not specified, then the interval is [0, low).

Let’s look at an example:


              
12345678910111213141516

            
import numpy as np
# Generating a random integer from 0 to 3 exclusive
random_integer = np.random.randint(3)
print(random_integer)
print('-' * 10)
# Generating a 1D array of random integers in [0, 5) with 4 elements
random_int_array = np.random.randint(5, size=4)
print(random_int_array)
print('-' * 10)
# Generating a 1D array of random integers in [2, 5) with 4 elements
random_int_array_2 = np.random.randint(2, 5, size=4)
print(random_int_array_2)
print('-' * 10)
# Generating a random 2D array of random integers in [1, 6) of shape 4x2
random_int_matrix = np.random.randint(1, 6, size=(4, 2))
print(random_int_matrix)
copy

There is actually nothing complicated here; everything is rather straightforward.

Note

Unlike rand(), we specify the dimensions of the array via a single parameter size, passing either an integer or a tuple of integers.

As always, here is the documentation for you: numpy.random.rand and numpy.random.randint.

Task
test

Swipe to show code editor

  1. Create a 1D array of random floats from a uniform distribution in [0, 1) with 4 elements for random_floats_array.
  2. Create a 2D array of random floats from a uniform distribution in [0, 1) with a shape of 3x2 for random_floats_matrix.
  3. Use the correct function to create a 2D array of random integers for random_integers_matrix.
  4. Set the interval to [10, 21) (from 10 to 21 exclusive) by specifying the first two arguments of the function.
  5. Set the shape of the random_integers_matrix to 3x2 by specifying the third keyword argument of the function.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

It is often the case that we need to generate a random number or an array of random numbers. Luckily, NumPy has a module named random specifically for this purpose.

Note

random generates pseudo-random numbers, not truly random numbers. However, for most purposes, this is more than enough.

The two most commonly used functions of the random module are:

  • rand();
  • randint().

rand()

The numpy.random.rand() function is used to generate either a random float number or an array of random floats from a uniform distribution over [0, 1).

Its only possible arguments are the dimensions of the array. If no argument is passed, rand() generates a random float number (scalar).

Let’s clarify with an example:


              
123456789101112

            
import numpy as np
# Generating a random number
random_number = np.random.rand()
print(random_number)
print('-' * 56)
# Generating a random 1D array with 5 elements
random_array = np.random.rand(5)
print(random_array)
print('-' * 56)
# Generating a random 2D array (matrix) of shape 4x3
random_matrix = np.random.rand(4, 3)
print(random_matrix)
copy

First, we generated a random float number. Next, we generated a random 1D array with 5 elements by setting its size to 5. Finally, we generated a random 2D array of shape 3x4.

Note

Dimensions in the rand() function should be specified as separate integer parameters, not a tuple of integers.

randint()

The numpy.random.randint function is used to generate either a random integer or an array of random integers from a discrete uniform distribution within a specified interval.

Its three most important parameters are low (the only required parameter), high, and size. The interval is [low, high) (from low inclusive to high exclusive). However, if high is not specified, then the interval is [0, low).

Let’s look at an example:


              
12345678910111213141516

            
import numpy as np
# Generating a random integer from 0 to 3 exclusive
random_integer = np.random.randint(3)
print(random_integer)
print('-' * 10)
# Generating a 1D array of random integers in [0, 5) with 4 elements
random_int_array = np.random.randint(5, size=4)
print(random_int_array)
print('-' * 10)
# Generating a 1D array of random integers in [2, 5) with 4 elements
random_int_array_2 = np.random.randint(2, 5, size=4)
print(random_int_array_2)
print('-' * 10)
# Generating a random 2D array of random integers in [1, 6) of shape 4x2
random_int_matrix = np.random.randint(1, 6, size=(4, 2))
print(random_int_matrix)
copy

There is actually nothing complicated here; everything is rather straightforward.

Note

Unlike rand(), we specify the dimensions of the array via a single parameter size, passing either an integer or a tuple of integers.

As always, here is the documentation for you: numpy.random.rand and numpy.random.randint.

Task
test

Swipe to show code editor

  1. Create a 1D array of random floats from a uniform distribution in [0, 1) with 4 elements for random_floats_array.
  2. Create a 2D array of random floats from a uniform distribution in [0, 1) with a shape of 3x2 for random_floats_matrix.
  3. Use the correct function to create a 2D array of random integers for random_integers_matrix.
  4. Set the interval to [10, 21) (from 10 to 21 exclusive) by specifying the first two arguments of the function.
  5. Set the shape of the random_integers_matrix to 3x2 by specifying the third keyword argument of the function.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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