Mastering the Randint Python Function for Random Integer Generation

Did You Know? In Python 3.12, the randrange() function has been improved to produce more uniformly distributed values. Previously, it used a method like int(random() * n), which could lead to slightly uneven distributions. 

This change enhances the quality of random number generation, which also benefits other functions like randint(), ensuring more reliable and even random integer outputs.

The randint() function in Python's random module generates random integers within a specified range, both inclusive. It’s useful for tasks like simulating random events in games, creating randomized test cases, and modeling uncertainty in simulations. Understanding how to use randint() effectively can add controlled randomness to your programming projects.

This blog offers a comprehensive exploration of the randint() function, covering its syntax, parameters, return values, and practical applications.

Understanding the randint Python Function

The randint() function is part of Python's random function module and is used to generate random integers within a specified range. This function plays a significant role in applications like gaming, simulations, statistical sampling, and randomized testing, where the element of chance is essential. 

The Python random randint function is particularly useful when you need a predictable way to generate numbers in a range, but with randomness. This functionality makes it an essential part of Python's randomization tools, particularly for beginners in programming.

For instance, in game development, randint() can be used to spawn enemies at random intervals, adding unpredictability to the gameplay.

Understanding this function is foundational for anyone learning Python, especially for beginners interested in random number generation.

Syntax of the randint Python Function

The syntax of the randint() function is simple and intuitive, making it easy to use in your code. Here’s the basic structure:

random.randint(a, b)

In this syntax:

• a: The lower bound (inclusive) of the range from which the random number will be selected.
• b: The upper bound (inclusive) of the range from which the random number will be selected.

Both a and b must be integers, and the function will return a random integer that can range anywhere from a to b, including both a and b. This inclusive behavior makes it a versatile function for scenarios where exact boundaries need to be set.

For example, random.randint(1, 10) would produce a random integer anywhere between 1 and 10, including both of those endpoints. Understanding this syntax is essential for implementing randomness correctly in your Python programs.

Key Parameters of the randint() Function Explained

The two parameters of the randint() function, a and b, are both crucial for controlling the randomness of the output. Here's a closer look at each:

1. Lower Bound (a): This is the minimum value in the range. The number generated can never be less than a.
2. Upper Bound (b): This is the maximum value in the range. The number generated can never exceed b.

The power of the randint() function lies in these parameters, allowing you to create different random number ranges depending on your needs. For example, you can generate:

• random.randint(1, 5), which will return any integer from 1 to 5, inclusive.
• random.randint(100, 1000), which will return any integer from 100 to 1000.

This flexibility allows you to control randomness in many different contexts, from simulations to game development to random testing.

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Return Value of randint Python Function

The return value of the randint() function is always a random integer that falls within the specified range, inclusive of both bounds. Understanding the return value's behavior is crucial for leveraging the function in your projects.

For instance, when you run the following code:

import random
number = random.randint(1, 100)
print(number)

Output: Any integer from 1 to 100, and it will vary each time the program is run. 

The function's return value is useful for a wide range of applications, including generating random indices for lists, creating random test cases, and even simulating events in games.

Why It’s Important:
This demonstrates how randint() offers true randomness, in contrast to deterministic functions that always produce the same result. The variability of randint() is what makes it valuable for tasks requiring unpredictability, such as game mechanics or randomized data generation.

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A Practical Example: Using randint() with Output

Here’s a more practical example of how randint() can be used in a real-world situation. 

Let’s simulate the randomness of rolling a dice, a common use case for this function:

import random
# Simulate a dice roll
dice_roll = random.randint(1, 6)
# Print the result of the dice roll
print(f"You rolled a {dice_roll}")

Output: In this case, the output could be any integer between 1 and 6, simulating the randomness of rolling a dice. For example:

You rolled a 4

This simple example demonstrates how you can use randint() to simulate randomness in a game or testing environment. By adjusting the range, you can simulate various types of games or scenarios. 

For instance, you could extend this example to simulate multiple dice rolls or create random events for game mechanics, such as loot drops or random enemy behavior.

Why It’s Important:
This illustrates the power of randomness in generating unpredictable yet controlled outcomes, which is crucial in game development and simulations. Randomness enhances user experience, making games and simulations more engaging and dynamic by ensuring that no two experiences are identical.

Also Read: Generating Random Numbers in Python: A Quick Guide

Having learned how randint() functions, let’s see how it’s used in real-world contexts to solve various challenges.

 Real-World Applications of the randint Python Function

The randint() function in Python is an essential tool for generating random integers within a defined range. Its ability to produce randomness makes it indispensable in fields that require unpredictability, from gaming to data analysis. 

In this section, we’ll explore some of the critical ways that randint() is used across different industries and applications, adding real-world value by driving simulations, enhancing testing, and supporting complex algorithms.

1. Game Development

The randint() function is widely used in game development. It simulates random events, such as dice rolls, random enemy spawn points, or loot drops. Games often rely on randomness to create dynamic and unpredictable environments, enhancing the user experience.

For example, imagine you are creating a simple dice game where the player rolls a virtual die. The python random randint function can simulate this:

import random
# Simulate a dice roll
dice_roll = random.randint(1, 6)
# Print the result of the dice roll
print(f"You rolled a {dice_roll}")

Example Output:

You rolled a 3

This application is a basic example, but the concept extends to much larger games. In complex games, randint() can control random player actions, item drops, or even procedurally generated worlds. For instance, you could use randint() to decide the outcome of random player decisions or create environments with randomly generated obstacles or resources.

• Best Practice Tip: When creating procedural worlds or simulations in games, use randint() to ensure the game world is dynamic and always offers new challenges.
• Real-World Example: In open-world games, randint() can generate random events, such as rare item drops or dynamic enemy behavior, ensuring each player’s experience is unique.

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 2. Simulation and Modeling

Random number generation is essential for simulations in fields like economics, biology, and physics. Using the randint() function, developers can create models to simulate real-world phenomena such as traffic flow, stock market behavior, or even the spread of diseases.

For example, in simulating disease spread, you can use randint() to randomly determine who gets infected next by simulating contact with an infected person.

import random
# Simulate the probability of getting infected
infection_probability = random.randint(0, 100)
if infection_probability < 30:
   print("You got infected!")

else:
   print("You're safe!")

Example Output:

You got infected!

In this simulation, there's a 30% chance of getting infected, represented by a random number between 0 and 100. The randint() function allows the simulation to change each time it runs, making it more dynamic and realistic.

• Best Practice Tip: When building simulations that require randomness, use randint() to introduce variability and mimic real-world unpredictability.
• Real-World Example: In financial simulations, randint() can be used to model random stock price fluctuations for risk assessment, helping to predict market shifts and make informed decisions.

3. Randomized Testing and Debugging

In software testing, particularly unit tests, randint() aids in randomized testing by generating random test cases. This helps ensure the software handles all inputs, including edge cases.

For example, in testing a sorting algorithm, you can generate random lists and pass them to the sorting function:

import random
# Generate a random list of numbers
random_list = [random.randint(1, 100) for _ in range(10)]
import random
# Generate a random list of numbers
random_list = [random.randint(1, 100) for _ in range(10)]
print("Random List:", random_list)

Example Output:

Random List: [82, 55, 61, 91, 37, 48, 11, 65, 79, 2]

By using the randint() function, the list will change each time, providing different inputs for the testing scenario. This ensures that your code is robust enough to handle any data.

• Best Practice Tip: Randomized testing helps uncover edge cases and improve code reliability by introducing variability in test data.
• Real-World Example: In large-scale software testing, randint() can simulate a variety of inputs, ensuring your algorithms function correctly across diverse datasets.

Also Read: Python Cheat Sheet: From Fundamentals to Advanced Concepts for 2025

4. Cryptography and Security

The randint() function is used in cryptography for non-sensitive randomness, like simulations or lower-stakes security. However, it’s not secure enough for generating cryptographic keys or tokens.

For example, generating a random password with a specific length using randint() can be achieved as follows:

import random
import string
def generate_password(length=8):
   characters = string.ascii_letters + string.digits + string.punctuation
password = ''.join(random.choice(characters) for i in range(length))
   return password
print(generate_password(12))

Output:

P#kL$w9xGz#M

This script generates a random password that consists of letters, digits, and punctuation. It uses randint() to select characters randomly from a predefined set of options. However, for cryptographic applications, you should use Python's secrets module for more secure random numbers.

• Best Practice Tip: For high-security applications like password generation, always use the secrets module instead of randint() to ensure cryptographic security.
• Real-World Example: In token generation for authentication systems, randint() might be used for non-sensitive randomness, while the secrets module is preferred for more sensitive tasks like generating secure session keys.

 5. Machine Learning and Data Science

In the world of data science, randomization techniques like bootstrapping or Monte Carlo simulations heavily rely on random number generation. The randint() function can be used in these algorithms to generate random samples or to simulate uncertainty in models. 

Random sampling is crucial in many machine learning techniques like cross-validation, where different subsets of data are randomly selected for training and testing.

Here’s an example where randint() generates a random sample from a list:

import random
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sample = random.sample(data, 5)
print("Random Sample:", sample)

Output:

Random Sample: [3, 7, 2, 8, 4]

This sample allows for testing a machine learning model with different data each time, improving model generalization.

• Best Practice Tip:
  When using randomization techniques like cross-validation, always apply randint() to ensure diverse subsets. This minimizes bias and strengthens your model's reliability.
• Real-World Example:
  In finance, randint() can simulate random stock price fluctuations in Monte Carlo simulations. Financial analysts use random sampling to model future stock prices and predict volatility, helping assess investment risks. Similarly, in healthcare, randint() is used to simulate disease spread or generate random patient data for testing models, enhancing predictive accuracy.

Also Read: 12 Amazing Real-World Applications of Python

Having seen how randint() is applied, let's move on to some key tips to help you use it correctly and avoid potential pitfalls.

Best Practices for Using randint() and Avoiding Errors

The randint() function in Python is a powerful tool for generating random integers, but it requires proper use to avoid errors. This section covers best practices for using randint() effectively and avoiding common pitfalls.

Following these best practices will make your code more efficient, reliable, and easier to debug. This will help you build robust programs for simulations, games, or any project needing random numbers! 

1. Ensure Proper Range of Values

A common mistake when using randint() is incorrectly specifying the range. Since the function requires two arguments—a and b—the lower bound (a) must be less than or equal to the upper bound (b). Otherwise, Python will throw a ValueError, which can halt your program unexpectedly. This can easily happen if you manually calculate the bounds or receive dynamic input for the range.

To ensure correctness, always check that the lower bound is less than or equal to the upper bound before calling randint(). 

For example:

import random
# Correct usage
number = random.randint(1, 10)
print(number)  # Output: A random integer between 1 and 10
# Incorrect usage - This will raise a ValueError
number = random.randint(10, 1)  # ValueError: empty range for randrange()
print(number)

Fix: Always ensure that a <= b when using random.randint(a, b) to avoid unexpected errors.

Best Practice Tip: When generating random numbers with dynamic ranges, consider implementing a validation function to check if the bounds are correct before calling randint().

Real-world Example: In a game where a random number determines the number of items a player receives, passing an invalid range (e.g., randint(10, 1)) could lead to unexpected outcomes, halting the game or causing an error in item distribution.

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2. Avoid Repeated Random Values

In simulations or games, repeated random values can create biased outcomes, affecting fairness. This is fine for single instances but problematic when fairness relies on true randomness.

To avoid repeated random values, you can use the random.seed() function, which initializes the random number generator with a specific state. While this ensures reproducibility (useful for testing), in general applications where true randomness is required, you should avoid seeding unless necessary.

For example, seeding the random number generator ensures that your random numbers will be predictable across multiple runs, which is useful when debugging:

import random
# Seed for repeatable results (only for testing)
random.seed(1)
number = random.randint(1, 100)
print(number)  # Output: 63 (same number each time with this seed)

However, in typical randomization tasks, avoid using random.seed() unless repeatability is a requirement. Let Python’s random number generator handle the seed automatically to ensure proper randomness.

Best Practice Tip: Only seed the random number generator if you need to reproduce a sequence of random numbers for testing purposes. For most other applications, let Python handle the seeding automatically.

Real-world Example: In a game where random loot drops occur, repeated random values may result in biased or repetitive outcomes, diminishing the player’s experience. By ensuring varied randomness, we can enhance the gaming experience.

3. Using randint() for Random Choices

While randint() is a simple and effective way to generate random numbers, it’s not always the most efficient or readable approach, especially when you need to select random items from a list or other collection. Using randint() for random selection may work, but it involves additional code and isn’t as clear as alternatives provided by the random module.

For example, you can use random.choice() to select a random item from a list without the need to calculate an index manually:

import random
# Using randint() for random selection from a list
items = ['apple', 'banana', 'cherry', 'date']
index = random.randint(0, len(items) - 1)
print(items[index])  # Output: random item from the list
# A more Pythonic way
random_item = random.choice(items)
print(random_item)  # Output: random item from the list

The second approach, using random.choice(), is both more concise and easier to read. It is highly recommended for selecting random items from a list, tuple, or other collection types.

Best Practice Tip: Whenever possible, use random.choice() or random.sample() for tasks that involve selecting random elements from a collection. These methods are more readable, efficient, and less error-prone than using randint() for indexing.

4. Avoiding Common Errors with randint Python Functions

There are several other common errors that Python developers can run into when using randint(). Being aware of these mistakes and knowing how to avoid them can help you create more robust and secure applications.

1.Generating Random Numbers Outside the Expected Range: It’s important to always double-check your range values to ensure they are valid. Generating random numbers outside the expected range can cause your program to behave unpredictably.

• Solution: Validate that your range values are within a valid range. For example, ensure that the lower bound is less than or equal to the upper bound, and that both bounds are integers.
  2.Using randint() in Security-Sensitive Applications: The randint() function is not suitable for cryptographic applications, as it doesn’t generate cryptographically secure random numbers. For cryptographic purposes, you should use the secrets module instead of random.

• Solution: If you're working with security-sensitive applications, such as generating random tokens or passwords, always use secrets:

  import secrets
  # Secure random integer generation for cryptographic use
  secure_number = secrets.randbelow(100)  # Generates a random number below 100
  print(secure_number)

• 3. Using the Same Seed for Every Execution: When testing or debugging, it might be tempting to use a fixed seed for the random number generator every time. While this ensures repeatability, it can lead to biased random number generation in scenarios where unpredictability is key.
• Solution: Only use a fixed seed when you need reproducibility. For most use cases, it’s best to let Python handle the seeding automatically.
• 4. Not Importing the Random Module: One of the simplest but often overlooked mistakes is forgetting to import the random module at the start of your script. Without importing this module, you’ll get an ImportError when trying to use randint().
• Solution: Always import the random module at the beginning of your Python script to avoid this error.
• Also Read: Top 15 Python Challenges for Beginners with Examples
• After exploring best practices, we’ll explore handling common errors and exceptions that may arise when using randint().

Handling Errors and Exceptions with randint Python Function

The randint() function is a powerful tool for generating random integers in Python, but errors can arise from incorrect inputs or misuse. To avoid crashes and ensure smooth execution, it's essential to handle errors effectively. This section covers common exceptions with randint() and how to resolve them for more robust applications. 

By applying error-handling practices, you can make your code more resilient and easier to maintain. Let’s explore common exceptions and how to address them!

 1. ValueError: Incorrect Range

One of the most frequent errors that occurs with the randint() function is the ValueError, which happens when you provide an invalid range. Specifically, this error arises if the lower bound is greater than the upper bound. Python will raise a ValueError if the bounds are reversed, and this can cause your program to fail unexpectedly.

Example:

import random
# Incorrect usage - lower bound is greater than upper bound
random_number = random.randint(10, 1)
print(random_number)

Output:

ValueError: empty range for randrange() (10, 2, -8)

To prevent this error, always ensure that the lower bound is less than or equal to the upper bound. You can handle this with a simple check and raise a custom error if needed:

import random
# Safe use of randint with validation
def safe_randint(a, b):
   if a > b:
       raise ValueError(f"Lower bound {a} cannot be greater than upper bound {b}.")
   return random.randint(a, b)
# Correct range
print(safe_randint(1, 10))  # Output: A random integer between 1 and 10

Best Practice Tip: Always validate the input range before using randint(). This is especially important when bounds are generated dynamically, ensuring that invalid values do not slip through unnoticed.

Clarification: A ValueError might occur if you mistakenly swap the lower and upper bounds when calculating ranges.

Also Read: Exception Handling in Python

2. TypeError: Non-Integer Inputs

The randint() function is strictly for integers—both the lower and upper bounds must be integers. If you attempt to pass non-integer values, such as strings or floats, Python will raise a TypeError.

For example:

import random
# Incorrect usage - passing a string as input
random_number = random.randint("1", "10")
print(random_number)

Output:

 TypeError: '>' not supported between instances of 'str' and 'int'

To handle this error, it’s crucial to validate that the inputs are integers. The isinstance() function is useful for this check:

import random
# Safe usage with input validation
def safe_randint(a, b):
   if not isinstance(a, int) or not isinstance(b, int):
       raise TypeError("Both bounds must be integers.")
   return random.randint(a, b)
# Correct usage
print(safe_randint(1, 10))  # Output: A random integer between 1 and 10

Best Practice Tip: Always check the data types of inputs before passing them to randint(). This ensures that the function operates as expected without errors. In user-driven applications, consider using try-except blocks to catch unexpected input types gracefully.

Clarification: A TypeError may arise if you mistakenly pass a float or string instead of integers as the bounds.

3. Randomness Bias and Predictability

One potential challenge when using randint() is ensuring that the generated random numbers are truly random. While Python's random module is generally sufficient for most applications, the numbers generated may show bias or patterns, particularly when the range is small or when random numbers are generated repeatedly over large simulations.

For example, with a very small range like 1 to 3, the randomness may not be significant enough for large-scale simulations, leading to less variation:

import random
# Small range example
random_number = random.randint(1, 3)
print(random_number)

Output:

 2

For high-quality randomness in cryptographic applications, security, or simulations, use Python's secrets module, which provides cryptographically secure random numbers. This is particularly important for scenarios that require unpredictability, such as generating random passwords, security tokens, or securely simulating random events.

import secrets
# Generating a secure random number
secure_number = secrets.randbelow(10)  # Returns a number from 0 to 9
print(secure_number)

Output:

7

Best Practice Tip: For security-sensitive applications or when true randomness is required, always prefer the secrets module over randint() from the random module. The secrets module generates numbers that are less predictable and more suitable for tasks like token generation or password creation.

Clarification: Using randint() for cryptographic purposes can lead to predictable results. Always opt for the secrets module for better security.

4. Handling Multiple Random Number Generations

When generating multiple random numbers, especially in loops or recursive functions, the bounds might change dynamically. This can lead to unexpected behavior or errors if the upper bound becomes smaller than the lower bound after an iteration.

For example, generating random numbers with dynamic bounds can lead to issues if the bounds change unexpectedly:

import random
# Example where bounds change dynamically
for i in range(5):
   lower = random.randint(1, 10)
   upper = random.randint(5, 15)
   print(random.randint(lower, upper))

Output:

7
10
13
9
6

If lower exceeds upper, it can raise an error. To avoid this issue, ensure that the upper bound is always greater than the lower bound, even when they are dynamically generated:

import random
# Handling dynamic bounds safely
def generate_safe_random():
   for i in range(5):
       lower = random.randint(1, 10)
       upper = random.randint(5, 15)
       if lower > upper:
           lower, upper = upper, lower  # Swap if bounds are invalid
       print(random.randint(lower, upper))
generate_safe_random()

Output:

7
12
10
6
13

Best Practice Tip: When using randint() inside loops or functions with dynamic bounds, always check that the bounds are valid before generating random numbers. Adjust the bounds if necessary to avoid errors and maintain the integrity of your randomness.

Clarification: When using dynamic bounds, ensure that the upper bound is always greater than the lower bound to avoid errors or unexpected behavior.

Also Read: Top 50 Python Project Ideas with Source Code in 2025

With error handling covered, let’s move on to examining the behavior of randint() when called multiple times.

What Happens When Calling the randint Python Function Multiple Times?

When calling randint() multiple times in a program, you are essentially generating multiple random integers within a specified range. Each call produces a new result based on the range provided. However, understanding how randint() behaves across multiple calls and how its results are generated is key to ensuring that your applications function correctly, especially when working with simulations, games, or randomized processes.

It’s important to note that while the results from multiple calls to randint() may appear random, the behavior of the function can be influenced by various factors such as seeding and the size of the range. In this section, we'll explore what happens when randint() is called multiple times and how to interpret these results effectively.

1. Multiple Calls to randint() Without a Seed

By default, the randint() function in Python generates pseudo-random numbers. This means the numbers are determined based on a seed value, which is typically the system time, and therefore the sequence of random numbers is different each time the program runs. 

If you don’t explicitly set a seed, Python automatically uses the system time to initialize the random number generator, ensuring that the results vary between different runs of the program.

For example, when you call randint() multiple times in one program without setting a seed, each call will produce a new number, and these numbers will appear random based on the system time at the moment the program starts:

import random
# Calling randint() multiple times
print(random.randint(1, 10))  # Random number between 1 and 10
print(random.randint(1, 10))  # Random number between 1 and 10
print(random.randint(1, 10))  # Random number between 1 and 10

Output:

4
2
7

Each time the random randint function in python is called, it generates a new random integer within the specified range. These values are independent, meaning that one call does not influence the others. 

However, the randomness is determined by the state of the random number generator, which is initially seeded using the system time. Hence, the sequence of random numbers will differ each time the program is executed.

2. Impact of Seeding on Multiple Calls

If you want to produce the same sequence of random numbers every time you run your program—such as for debugging or testing—you can manually set the seed using the random.seed() function. This ensures that the sequence of random numbers is reproducible, meaning that every time you run the program with the same seed, you’ll get the same sequence of numbers.

Here’s an example of how setting a seed affects the output:

import random
# Setting the seed for reproducibility
random.seed(42)
# Calling randint() multiple times
print(random.randint(1, 10))  # Random number between 1 and 10
print(random.randint(1, 10))  # Random number between 1 and 10
print(random.randint(1, 10))  # Random number between 1 and 10

Output:

3
8
9

As you can see, each time you run the program with the seed set to 42, you will get the same sequence of numbers: 3, 8, and 9. This is because the seed ensures the random number generator starts from the same state every time, producing the same results on each run.

3. What Happens If the Range is Too Small?

One issue with using randint() is when the range provided is too small. If the range between the lower and upper bounds is too limited, the randomness of the results becomes constrained, and you may see repetitive or non-varying results. For example, generating random numbers between 1 and 2 will only result in 1 or 2, limiting the variability.

Example:

import random
# Generating random numbers between 1 and 2
print(random.randint(1, 2))  # Random number between 1 and 2
print(random.randint(1, 2))  # Random number between 1 and 2
print(random.randint(1, 2))  # Random number between 1 and 2

Output:

2
1
1

As seen in this example, with such a small range, the randomness becomes less meaningful, and multiple calls to randint() produce the same numbers repeatedly. While this may be acceptable in some cases, it’s often better to use a larger range for more meaningful randomization, especially if you require variability in the results.

To avoid this, always ensure that the range is large enough to allow for meaningful randomness in your results.

 4. Using randint() in a Loop

When calling randint() multiple times within a loop, it’s important to remember that each call generates a new random number, but the sequence can be influenced by the surrounding code and logic. If the loop runs for a large number of iterations, the randomness becomes more apparent, and you’ll see a wide range of values.

Here’s an example of generating random numbers within a loop:

import random
# Generating 5 random numbers between 1 and 100
for _ in range(5):
   print(random.randint(1, 100))

Output:

47
3
92
57
8

In this example, each iteration of the loop generates a new random number between 1 and 100. This approach is often used to create randomized results for simulations or data generation.

5. Interpreting the Results of Multiple Calls

When calling a random randint function in python multiple times, especially in large applications or simulations, it's important to understand how to interpret the results. Here are a few key concepts to keep in mind:

1. Independence of Results: Each call to randint() is independent of the others. This means that the result of one call doesn’t influence the result of the next. This is a critical property when working with random events in simulations or games where independence is required.
2. Range Impact: The size of the range you provide to randint() greatly affects the distribution of random numbers. A larger range allows for more diverse results, while a smaller range limits the possible outcomes. Be mindful of the range’s size when designing your randomization logic to ensure it meets your application’s needs.
3. Seeded vs. Unseeded: If the random number generator is not explicitly seeded, the numbers will be unpredictable, based on system time. However, if you need reproducible results, such as in testing or simulations, setting a specific seed will ensure that the sequence of random numbers remains consistent across multiple runs of the program.

Also Read: Top 70 Python Interview Questions & Answers

Having learned about multiple calls, discover how upGrad can help you further hone your Python expertise to take on more complex challenges.

How upGrad Can Help Builds Your Python Expertise

Functions like randint() are key for generating random integers, which have applications in areas like simulations, gaming, and testing. Mastering such Python functions is essential for a rewarding career in the programming field.  

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References:

• https://docs.python.org/3/library/random.html