## Random Float Numbers in Python: How to Generate Them

Generating random float numbers in Python is essential when working with data analysis, simulations, and modeling. Random float numbers can be generated using the built-in random module or by using the popular data-science module NumPy. This article will provide an overview of the steps to generate random float numbers using Python, as well as the different methods to get specific results.

### Method 1: Using the Random Module

The easiest and fastest way to generate a random float number in Python is by using the built-in random module. The random module provides different functions to generate a variety of numerical results.

Specifically, the `random.uniform()`

method generates random float numbers between two values with uniform distribution. The syntax of the `random.uniform()`

method is as follows:

```
import random
random.uniform(a, b)
```

Where `a`

and `b`

are the lower and upper bounds of the floating-point values, respectively. For instance, the code snippet below generates a random float within the range of 0 and 1:

```
import random
x = random.uniform(0, 1)
print(x)
```

### Rounding the Random Float to N Decimal Places

Sometimes, it is crucial to round the random float to a specific number of decimal places. The `round()`

function built into Python 3 allows you to perform the rounding operation.

The syntax of the `round()`

function is as follows:

```
round(number, ndigits)
```

`number`

is the float number to be rounded, while `ndigits`

is the number of digits to round to. For example, using the code below, you can round the previously generated random float number to two decimal places:

```
rounded_num = round(x, 2)
print(rounded_num)
```

### Generating N Random Floats rounded to N Decimal Places

To generate a sequence of random floats rounded to specific decimal places, a list comprehension can be used in combination with the `random.uniform()`

and `round()`

functions. The `range()`

function can also be used to specify the number of items to generate.

#### Here is an example:

```
import random
n = 5
decimal_places = 2
random_floats = [round(random.uniform(0, 1), decimal_places) for _ in range(n)]
print(random_floats)
```

### Method 2: Using NumPy

NumPy is a Python library used extensively in data-science for numerical calculations. NumPy provides additional functions that can help generate random float numbers.

To use NumPy, simply import the library and its random module. Using NumPy.random.uniform() Method

The NumPy library has an improved version of the `random.uniform()`

method.

The `NumPy.random.uniform()`

method generates random float numbers with uniform distribution using the same syntax as the random module.

```
import numpy as np
np.random.uniform(low, high, size)
```

where `low`

and `high`

are the lower and upper bounds of the floating-point values, and `size`

is an integer or tuple that specifies the dimensions of the output. For example, the code below generates random float numbers within the range of 0.5 to 5.9:

```
import numpy as np
low, high = 0.5, 5.9
n = 5
random_floats = np.random.uniform(low, high, n)
print(random_floats)
```

### Generating a List of Random Floating-Point Numbers Using NumPy

To generate a list of random float numbers using NumPy, you can use list comprehension in combination with the `NumPy.random.uniform()`

function. Here is an example:

```
import numpy as np
low, high = 0.5, 5.9
n = 5
decimal_places = 2
random_floats = [round(x, decimal_places) for x in np.random.uniform(low, high, n)]
print(random_floats)
```

### Converting NumPy Array to a Python List using tolist()

`NumPy.max()`

, `Numpy.min()`

, and other NumPy functions can result in an array type of data that doesn’t necessarily work best with other Python functions. In that case, you need to convert the array to a Python list using the `tolist()`

method.

#### Here is an example:

```
import numpy as np
x = np.array([1.2, 2.0, 3.8])
x_lst = x.tolist()
print(x_lst)
```

## Conclusion

In this article, we have discussed the different ways to generate random float numbers in Python. While the random module is built-in and easy to utilize, it may not be ideal for specific use cases.

NumPy, on the other hand, provides extra functionality, including generating arrays of random floats with uniform distribution. Both methods can be used in combination with list comprehension for more complex use cases.

By now, the reader should have a basic understanding of how to generate random floats using Python. Generating random float numbers in Python is crucial for many data science applications, including simulations, modeling, and analysis.