The random.uniform() function in Python is part of the built-in random module, and it is used to generate a random floating-point number within a specified range. Unlike random.randint(), which returns integers, random.uniform() returns a float that can be either greater than or less than the specified bounds.

Syntax

import random
random_float = random.uniform(a, b)

• Parameters:
  • a: The lower bound of the range (can be any float).
  • b: The upper bound of the range (can be any float).

Return Value

• Returns a random float $N$ such that $a \leq N \leq b$ if $a < b$, or $b \leq N \leq a$ if $b < a$.

Example Usage

1. Basic Example:

   import random
   
   random_float = random.uniform(1.0, 10.0)  # Random float between 1.0 and 10.0
   print(random_float)  # Example output: 6.23741098850783
   

2. Generating Multiple Random Floats: You can generate multiple random floats using a loop:

   for _ in range(5):
       print(random.uniform(5.0, 15.0))  # Random float between 5.0 and 15.0
   

   This could output something like:

   11.285455204164003
   7.187957800678712
   12.141405165649837
   8.927038204140303
   14.209638145231943
   

3. Using with Other Functions: You can use random.uniform() in calculations:

   a = random.uniform(1, 5)
   b = random.uniform(1, 5)
   result = a + b  # Adding two random floats
   print("Sum of random floats:", result)
   

4. Seeding the Random Number Generator: You can use random.seed() to initialize the random number generator for reproducibility:

   random.seed(42)  # Seed the random number generator
   print(random.uniform(1, 10))  # Output will be the same each time the seed is set
   

Summary

• random.uniform(a, b) generates a random floating-point number within the range $[a, b]$ (inclusive).
• It is useful in simulations, modeling, and any situation requiring random float values, especially when precise decimal values are needed.
• The ability to seed the random number generator allows for reproducibility, which can be beneficial in testing and debugging scenarios.