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 | random() function

The numpy.random.random() function is used in the numpy library to generate random floats in the half-open interval [0.0, 1.0). In essence, it's an alias for the numpy.random.random_sample() function, which means they both provide the same functionality and can be used interchangeably.

`numpy.random.random()` function tutorial

Function Signature:

numpy.random.random(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.random()
print(random_float)

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

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

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

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

Generate random floats in a different range, say between 5 and 10:
To achieve this, you can use the following formula:
```
a, b = 5, 10
random_floats = (b - a) * np.random.random(5) + a
print(random_floats)
```
This formula scales the random samples from the range [0, 1) to the range [a, b).

Note:

You can set a seed using numpy.random.seed(seed_value) if you want reproducible results. This is useful especially when debugging or when you want consistent random values across multiple runs.
As mentioned before, numpy.random.random() and numpy.random.random_sample() are essentially the same in terms of functionality. The difference is just in the name.

In summary, numpy.random.random() is a straightforward and useful function for generating random floating-point values. By using arithmetic operations, you can adjust and mold these values to fit different ranges and distributions as required for your 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() function.

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.random()` for random sampling:

Learn how to use NumPy's random() function 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()` function for random value generation:

Explore the usage of NumPy's random() function, which generates random values in the half-open interval [0.0, 1.0).

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()` usage for random sampling:

Learn how to use the numpy.random() function in Python for random value 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)

5. Sample code for random value sampling in 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` vs `random_sample` for random sampling:

Compare NumPy's random with random_sample for random value sampling.

import numpy as np
import random

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

# Using random_sample
random_values_sample = np.random.random_sample(size=5)

# Print the random values
print("Random Values (NumPy):", random_values_np)
print("Random Values (random_sample):", random_values_sample)

8. Python `numpy.random.random()` for array randomization:

Utilize numpy.random.random() to randomize 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.random(size=len(original_array))
np.random.shuffle(randomized_array)

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

Random sampling in numpy | random() function

numpy.random.random() function tutorial

1. Random sampling in Python with NumPy:

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

3. Numpy random() function for random value generation:

4. Python numpy.random() usage for random sampling: