Numpy Tutorial

Creating NumPy Array

NumPy Array Manipulation

Matrix in NumPy

Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPy Array

Linear Algebra in NumPy Array

NumPy and Random Data

Sorting and Searching in NumPy Array

Universal Functions

Working With Images

Projects and Applications with NumPy

Random sampling in numpy | sample() function

In the numpy library, numpy.random.sample() is another function that generates random floats in the half-open interval [0.0, 1.0). In essence, it's an alias for the functions numpy.random.random_sample(), numpy.random.random(), and numpy.random.ranf(). This means that all these functions provide the exact same functionality, and the choice among them is often a matter of personal or team preference.

`numpy.random.sample()` function tutorial

Function Signature:

numpy.random.sample(size=None)

Parameters:

size: The shape of the output. It could be an integer or a tuple of integers. If size is not provided, a single float is returned.

Examples:

Generate a single random float between 0 and 1:

import numpy as np
random_float = np.random.sample()
print(random_float)

Generate an array of 5 random floats between 0 and 1:

random_array = np.random.sample(5)
print(random_array)

Generate a 3x3 matrix of random floats between 0 and 1:

random_matrix = np.random.sample((3, 3))
print(random_matrix)

Generate random floats in a different range, say between 5 and 10:
You can adjust the range with the following formula:
```
a, b = 5, 10
random_floats = (b - a) * np.random.sample(5) + a
print(random_floats)
```
This formula changes the samples from the range [0, 1) to any range [a, b).

Note:

You can set a seed using numpy.random.seed(seed_value) for reproducibility. This becomes especially useful during debugging or when you want consistent random values over multiple runs.
As highlighted earlier, numpy.random.sample(), along with numpy.random.random(), numpy.random.ranf(), and numpy.random.random_sample(), are functionally the same. Your choice of function name can be based on personal or code readability preferences.

In summary, numpy.random.sample() provides an easy and efficient means to produce random floating-point numbers in the range [0, 1). With some simple arithmetic operations, you can tailor these numbers to fit various ranges and distributions, adapting them for diverse applications.

1. Random sampling in Python with NumPy:

Random sampling involves selecting random values from a distribution, and in Python, this can be achieved using NumPy's random module.

import numpy as np

# Generate a random value between 0 and 1
random_value = np.random.random()

# Print the random value
print("Random Value:", random_value)

2. How to use `numpy.random` for random sampling:

Learn how to use NumPy's random module for generating random values.

import numpy as np

# Generate a random value between 0 and 1
random_value = np.random.random()

# Print the random value
print("Random Value:", random_value)

3. Numpy `random` module functions for sampling:

Explore the various functions provided by NumPy's random module for random sampling.

import numpy as np

# Generate a random value between 0 and 1
random_value = np.random.random()

# Print the random value
print("Random Value:", random_value)

4. Python `numpy` random sampling techniques:

Understand the techniques for random sampling using the numpy library in Python.

import numpy as np

# Generate an array of 5 random values between 0 and 1
random_values = np.random.random(size=5)

# Print the random values
print("Random Values:", random_values)

5. Sample code for random sampling with NumPy:

A sample code demonstrating random value sampling using NumPy in Python.

import numpy as np

# Generate an array of 5 random values between 0 and 1
random_values = np.random.random(size=5)

# Print the random values
print("Random Values:", random_values)

6. Generating random values with NumPy in Python:

Understand the process of generating random values using NumPy in Python.

import numpy as np

# Generate an array of 3 random values between 0 and 1
random_values = np.random.random(size=3)

# Print the random values
print("Random Values:", random_values)

7. Numpy random sampling vs Python random module:

Compare NumPy's random sampling with the random module in Python.

import numpy as np
import random

# Using NumPy random
random_values_np = np.random.random(size=5)

# Using Python random module
random_values_python = [random.random() for _ in range(5)]

# Print the random values
print("Random Values (NumPy):", random_values_np)
print("Random Values (Python):", random_values_python)

8. Python `numpy` random sampling for array manipulation:

Utilize numpy random sampling to manipulate the order of elements in a NumPy array.

import numpy as np

# Create an array to be randomized
original_array = np.array([10, 20, 30, 40, 50])

# Randomize the order of elements using NumPy random
randomized_array = np.random.permutation(original_array)

# Print the randomized array
print("Original Array:", original_array)
print("Randomized Array:", randomized_array)

Random sampling in numpy | sample() function

numpy.random.sample() function tutorial

1. Random sampling in Python with NumPy:

2. How to use numpy.random for random sampling:

3. Numpy random module functions for sampling:

4. Python numpy random sampling techniques: