Random sampling in numpy | randint() function

The randint() function is part of the numpy.random module, which provides various functions for generating random numbers. The randint() function returns random integers from a specified range.

Tutorial: Random Sampling using `randint()` in NumPy

Initialization
Start by importing NumPy:
```
import numpy as np
```
Basic Usage of randint()
Generate a single random integer between 0 (inclusive) and 5 (exclusive):
```
print(np.random.randint(5))  # Possible outputs: 0, 1, 2, 3, or 4
```
Specify Range
Generate a single random integer between 10 (inclusive) and 50 (exclusive):
```
print(np.random.randint(10, 50))
```
Generate Multiple Integers
Create an array of shape (5,) with random integers between 1 (inclusive) and 10 (exclusive):
```
print(np.random.randint(1, 10, 5))
```
Generate 2D Array of Integers
Create a 2D array of shape (3, 4) with random integers between 1 and 100:
```
print(np.random.randint(1, 100, (3, 4)))
```
Example output:
```
[[45 83 21 64]
 [ 8  7 32  2]
 [16 56 30 50]]
```
Control Randomness with Seed
If you want to reproduce the same set of random numbers in future runs, you can set a seed using np.random.seed():
```
np.random.seed(0)  # Setting seed to 0
print(np.random.randint(1, 100, 5))
```
By setting the seed, the same sequence of numbers will be generated every time you run the code. This is useful in scenarios where reproducibility is crucial.
Conclusion
The randint() function is powerful and versatile, offering the ability to generate single integers, arrays of random integers, and even multidimensional arrays of random integers within a specified range. This function is particularly useful in simulations, data analysis, machine learning, and many other areas where random number generation is crucial.

Remember, the numbers generated are pseudo-random, meaning they appear random but are generated by a deterministic algorithm. Using a seed makes this process reproducible.

1. Random integer sampling in Python with NumPy:

Random integer sampling involves generating random integers using NumPy's randint() function.

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 1
upper_bound = 10
sample_size = 5

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

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

Learn how to use NumPy's randint() function for generating random integers.

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 5
upper_bound = 20
sample_size = 8

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

3. Numpy `randint()` function for random integer generation:

Explore the usage of NumPy's randint() function specifically designed for random integer generation.

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 10
upper_bound = 50
sample_size = 6

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

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

Learn how to use the numpy.randint() function in Python for random integer sampling.

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 1
upper_bound = 100
sample_size = 10

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

5. Sample code for random integer sampling in NumPy:

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

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 0
upper_bound = 1000
sample_size = 3

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

6. Generating random integers with NumPy in Python:

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

import numpy as np

# Specify the range and size for random integer sampling
lower_bound = 50
upper_bound = 100
sample_size = 4

# Generate random integers using NumPy
random_integers = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Print the random integers
print("Random Integers:", random_integers)

7. Numpy `randint` vs `random.choices` for random sampling:

Compare NumPy's randint with random.choices for random integer sampling.

import numpy as np
import random

# Specify the range and size for random integer sampling
lower_bound = 1
upper_bound = 10
sample_size = 5

# Using NumPy randint
random_integers_np = np.random.randint(lower_bound, upper_bound + 1, size=sample_size)

# Using random.choices
random_integers_choices = random.choices(range(lower_bound, upper_bound + 1), k=sample_size)

# Print the random integers
print("Random Integers (NumPy):", random_integers_np)
print("Random Integers (choices):", random_integers_choices)

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

Utilize numpy.random.randint() 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 randint
randomized_array = np.random.randint(original_array.min(), original_array.max() + 1, 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 | randint() function

Tutorial: Random Sampling using randint() in NumPy

1. Random integer sampling in Python with NumPy:

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

3. Numpy randint() function for random integer generation:

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