Uniform Distribution

🎲 Uniform Distribution in Python

The Uniform Distribution generates random numbers that are equally likely to occur within a specified range.

It is widely used in simulations, random sampling, and scenarios where every outcome is equally probable.

 1. Characteristics of Uniform Distribution

  • Continuous or discrete (NumPy supports continuous by default)

  • Parameters:

    • low → minimum value

    • high → maximum value

    • size → number of random samples

  • All values between low and high have equal probability

 2. Generate Uniform Data Using NumPy


 

Output (example):

[1.23, 7.45, 3.67, 0.89, 9.12, 4.56, 2.78, 6.12, 8.90, 5.34]

3. Visualize


  • Histogram appears flat

  • kde=True adds smooth density curve

 4. Change Range and Size


 

 5. Generate 2D Uniform Array

Output (example):

[[0.12 0.56 0.34 0.78]
[0.45 0.23 0.89 0.67]
[0.34 0.12 0.90 0.11]]

🧠 Summary Table

Function Parameters Description
np.random.uniform() low, high, size Generates continuous uniform random numbers
Continuous low=0, high=1 Default random floats between 0 and 1
2D array size=(rows, cols) Generates matrix of random numbers

🎯 Practice Exercises

  1. Generate 1000 numbers uniformly between 50–100 and plot histogram.

  2. Generate 5×5 matrix of uniform random numbers between 0–1.

  3. Create uniform data between -10 and 10 and compute mean and standard deviation.

You may also like...