At times, it is necessary to introduce randomness according to a particular distribution when implementing various machine learning models. In this post, we’ll examine how to use the NumPy’s `random.uniform()`

function, which will generate `n`

values from a uniform distribution within a specified range.

## Basic Syntax: np.random.uniform(low, high, size)

```
from numpy.random import default_rng
# Instantiate NumPy Generator instance, setting random seed
rng = default_rng(seed = 1)
# Sample 1: [0, 1), 200 numbers
sample1 = rng.uniform(low = 0, high = 1, size = 200)
print("Sample 1:")
print(sample1)
# Sample 2: [-10, 100), 200 numbers
sample2 = rng.uniform(low = -10, high = 10, size = 200)
print("Sample 2:")
print(sample2)
```

**Output:**

```
Sample 1:
[0.1719594 0.22005712 0.6320633 0.58757608 0.36199078 0.06035538
0.19761037 0.65092953 0.23661011 0.62843592 0.32415973 0.45364117
...]
Sample 2:
[-3.42699535 5.59605115 -0.21797547 -0.50691541 -9.76982705 -1.19263974 -1.5363445 7.52296261 2.43838142 -3.51784328
...]
```

**NOTE:** since NumPy version 1.17.0, it has been recommended to first instantiate a Generator, such as `default_rng()`

, and then call the `numpy.random.uniform()`

function on the generator object. In this case, we've set the random seed for the Generator so that the results are reproducible.

The `uniform()`

function takes three arguments:

`low`

: the lowest value that can be generated, NumPy’s range for the function is inclusive of this value`high`

: the upper boundary for the function, NumPy’s range for the function is exclusive of this value`size`

: the number of values to generate

If we use `seaborn's histplot()`

function to create histograms of the distribution of our two samples, we'll see that they are roughly uniform, as expected.

`np.random.uniform(low = 0, high = 1, size = 200)`

Sample 1: `np.random.uniform(low = -10, high = 10, size = 200)`

Sample 2: From the above plots, we can see the distributions are roughly uniform!

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