Updated November 30, 2023
What is Random Uniform in Python?
You played or will play with numbers. But have you ever wondered how these numbers can cause an adrenaline rush? Let’s learn how the casinos, lotteries, and your daily redeem reward games work.
These numbers are shuffled by a pattern using the Python programming language using the random.uniform() function.
Python’s random.uniform() function is part of the random module and generates random numbers from a uniform distribution. The uniform distribution ensures that each value in the specified range has an equal chance of being generated. Let’s break down the syntax and parameters of this function.
Learn how to use Python’s random.uniform() function to generate random floats uniformly distributed between two values. This article explains the syntax and parameters and provides code examples for using random.uniform() to add randomness to your Python programs.
Table of Contents
- Introduction
- Syntax of uniform() function
- How does random.uniform() generate a random number?
- Examples of Random Uniform Python
- Advanced Techniques to Find Random Uniform in Python
- Cases Where random.uniform() Implements
- Comparison with Other Random Functions
- Best Practices to Use Random Uniform in Python
- Pitfalls of Using Random Uniform in Python
Key Takeaways
- The function has two parameters: `low` and `high`.
- For integer random numbers, use random.randint() instead to specify a range. random.random() will generate 0-1 floats.
- Use the random.uniform() function to generate random floats between two values. Specify the min and max values as arguments to set the range.
Syntax of uniform() function
random.uniform(a, b)random.uniform(low, high)Parameters:
- a: Specifies the lowest possible outcome
- b: Specifies the highest possible outcome
- low: The lower bound of the range.
- high: The upper bound of the range.
How does random.uniform() generate a random number?
The random.uniform() function uses the underlying random() function, which produces pseudo-random numbers between 0 and 1 with a uniform distribution. The generated number is scaled to fit the specified range [low, high].
The random.uniform() function generates random floating-point numbers between two specified values, including the lower bound but exclusive of the upper bound. It utilizes a pseudo-random number generator (PRNG) to generate a sequence of numbers that simulate randomness. While PRNGs are not genuinely random, they are engineered to create a numerical sequence that satisfies different randomness criteria.
The random.uniform() function takes two arguments:
- a: The lower bound of the range of random numbers to generate.
- b: The upper bound of the range of random numbers to generate.
The function generates a random number x such that:
a <= x < b
The random.uniform() function uses the following algorithm to generate a random number. For instance, we perform the below example.
Generate a random floating-point number r between 0 and 1.
Calculate x = a + (b – a) * r.
Return x.
The random.uniform() function is useful for generating random floating-point numbers for various purposes, such as simulation, testing, and game development.
Examples of Random Uniform Python
Let’s explore some examples to understand how the random.uniform() function works.
Example 1: Generating a Single Random Number
Code:
import random
random_number = random.uniform(1, 10)
print(random_number)In this example, a random float between 1 and 10 (inclusive) will be generated and printed.
Output:
Example 2: Generating Random Numbers Array
Code:
import random
random_array = [random.uniform(5, 285) for _ in range(5)]
print(random_array)Output:
This example creates an array of 5 random floats between 5 and 285.
Advanced Techniques to Find Random Uniform in Python
Here are some advanced techniques for generating random uniform numbers in Python:
1. Use the numpy.random module
Code:
import numpy as np
rand_nums = np.random.uniform(low=0.0, high=1.0, size=10)
print(rand_nums)Output:
Use Cases- The numpy random module is optimized for speed and provides many distribution options.
2. Use the secrets module (Python 3.6+)
Code:
import secrets
rand_nums = [secrets.randbelow(10) / 10 for _ in range(10)]
print(rand_nums)Output:
Use Cases- The secrets module is cryptographically secure and better for security/cryptography purposes.
3. Use the SystemRandom class to access OS-provided randomness
Code:
from random import SystemRandom
rand_nums = SystemRandom().uniform(0.0, 1.0)
print(rand_nums)Output:
Use Cases- SystemRandom uses operating system entropy sources to generate more secure, unpredictable random numbers critical for cryptography.
4. Seed and re-seed the random number generator
Code:
import random
random.seed(1024)
rand_nums = [random.uniform(0,10) for _ in range(10)]
random.seed(1024)
print(rand_nums)Output:
Use Cases – Seeding produces consistent random numbers; re-seeding increases unpredictability and randomness.
5. Use third-party libraries like NumPy’s PCG random state
Code:
from numpy.random import Generator, PCG64
rng = Generator(PCG64())
rand_nums = rng.uniform(0, 10, size=10)
print(rand_nums)Output:
Use Cases – These libraries provide various advanced random number generation algorithms.
Cases Where random.uniform() Implements
The random.uniform() function finds applications in various scenarios, including:
- Simulation: Random process simulation in the real world.
- Sampling: Selecting a random subset from a larger dataset where each item has an equal probability of being chosen. random.uniform() helps create test sets for machine learning models.
- Data Augmentation: Adding diversity to training data for machine learning models by applying random rotations, shifts, etc. The augmentation degree can be randomly chosen.
- Gaming: Providing variation in-game by randomizing characteristics like damage inflicted, loot dropped by characters, map or level generation, etc., based on uniform distributions.
- Statistical Analysis: Implementing hypothesis testing, bootstrapping methods, and Monte Carlo statistical techniques that rely on uniform random number generation
- Randomized Algorithm: Algorithms like Treap, Rabin’s algorithm, and Skip List use randomization based on uniform deviations for improved efficiency.
- Cryptography: Generating keys, initializing vectors, and primes that benefit from randomness provided by uniform number generation.
Comparison with Other Random Functions
While random.uniform() provides a uniform distribution, other functions like random.randint() generate integers with equal probability. Understanding the distribution’s nature helps to choose the appropriate function for specific use cases.
Example #1 – random.uniform()
Returns a random float between specified min and max range.
Code:
import random
print(random.uniform(0, 1))
print(random.uniform(-5, 5))Output:
Example #2 – random.randint()
Returns a random integer between the ranges [a, b], including endpoints.
Code:
import random
print(random.randint(0,5))
print(random.randint(-3,3))Output:
Example #3 – random.random()
Returns a float between 0.0 and 1.0.
Code:
import random
print(random.random())Output:
Example #4 – random.normalvariate()
Floating point from Gaussian distribution with specified mean and standard deviation
Code:
import random
print(random.normalvariate(0, 1))Output:
Best Practices to Use Random Uniform in Python
Below are some best practices for using random.uniform() effectively in Python:
- Set the seed: Initialize the random number generator with a seed value using random.seed() before generating any random numbers. This ensures reproducibility and control over the pseudo-random sequence.
- Understand the range bounds: Note that random.uniform() may or may not return the min and max range values, depending on floating point precision. Adjust the range accordingly.
- Handle outliers: When using uniform randoms for simulations, plan for handling rare outliers if the range is wide, as all values have equal probability.
- Visualize first: Plot out some dummy uniform random data first to verify it looks evenly distributed before using it in code. The distribution should appear flat across the specified range.
- Check integral usage: If your use case requires only integers, use random.randint() rather than converting floats from random.uniform() to integers. It will be more robust.
- Explore statistical power: Determine appropriate sample sizes when analyzing uniform random numbers to achieve sufficient statistical power.
- Profile generation speed: random.uniform() usage at large scales can become expensive. Profile code to isolate bottlenecks when performance matters.
Pitfalls of Using Random Uniform in Python
Below are some pitfalls for using random.uniform() effectively in Python:
- Not setting seed: Without setting a random seed, results are generated at random.uniform() will change with each run, making it hard to replicate code experiments.
- Assuming inclusion of range endpoints; Random.uniform() may exclude one or both endpoints depending on floating point precision. Adjust ranges carefully.
- Using only a few random values: Taking a sample that is too small may not properly represent the underlying distribution.
- Correlating random values: Successive random values from uniform() do not correlate. Refrain from making incorrect assumptions in simulations.
Conclusion
Python’s random.uniform() function is a versatile tool for generating random floating-point numbers within a specified range. Its simplicity and ability to customize the range make it versatile for various applications. Understanding its nuances and considering best practices ensures effective use in various scenarios.
FAQs
Q1. What is the difference between random.uniform() and random.randint()?
Answer: random.uniform() generates random floating-point numbers with a uniform distribution, while random.randint() generates random integers within a specified range.
Q2. Can I use random.uniform() for cryptographic purposes?
Answer: No, the random module should not be used for cryptographic purposes. Instead, it is recommended to use the secrets module for such applications.
Q3. How can I ensure reproducibility in random number generation?
Answer: Use the random.seed() function with a fixed seed value to ensure reproducibility.
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