Numpy Tutorial

Creating NumPy Array

NumPy Array Manipulation

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Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPy Array

Linear Algebra in NumPy Array

NumPy and Random Data

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Random sampling in numpy | random_sample() function

numpy.random.random_sample() is a function in the numpy library that returns random floats in the half-open interval [0.0, 1.0). It can be very useful when you need continuous random samples.

`numpy.random.random_sample()` function tutorial

Function Signature:

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

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

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

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

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

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

Alternative Functions:

The following functions provide similar functionality:

numpy.random.rand(): This function also generates random samples from a uniform distribution over [0, 1), but its syntax is slightly different.
numpy.random.random(): An alias to random_sample.
numpy.random.ranf(): Another alias to random_sample.

Note: For reproducibility, you might want to use numpy.random.seed(seed_value) to set a seed for the random number generator.

The numpy.random.random_sample() function is valuable when you need to generate random floating-point samples quickly. Combine it with arithmetic operations to adapt it for various ranges and distributions.

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_sample() function.

import numpy as np

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

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

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

Learn how to use NumPy's random_sample() function for generating random values.

import numpy as np

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

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

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

Explore the usage of NumPy's random_sample() 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_sample()

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

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

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

import numpy as np

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

# 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_sample(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_sample(size=3)

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

7. Numpy `random_sample` vs `random.uniform` for random sampling:

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

import numpy as np
import random

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

# Using random.uniform
random_values_uniform = [random.uniform(0, 1) for _ in range(5)]

# Print the random values
print("Random Values (NumPy):", random_values_np)
print("Random Values (uniform):", random_values_uniform)

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

Utilize numpy.random.random_sample() 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_sample
randomized_array = np.random.random_sample(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_sample() function

numpy.random.random_sample() function tutorial

1. Random sampling in Python with NumPy:

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

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

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