Selecting Random Rows from a NumPy Array: Exploring Different Methods

Import NumPy:

import numpy as np

Create a 2D array:

This array can contain any data type. For instance, you can create an array of integers:

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])

Determine the number of random rows:

Define how many rows you want to select from the original array.

num_random_rows = 2  # Change this value to get a different number of rows

Generate random indices:

• Use np.random.choice to generate random indices without replacement. This ensures you don't pick the same row multiple times.
• Set the size parameter in np.random.choice to the number of desired random rows (num_random_rows).
• Set replace to False to avoid selecting the same row more than once.

num_rows = arr.shape[0]  # Get the total number of rows in the array
random_indices = np.random.choice(num_rows, size=num_random_rows, replace=False)

Select random rows:

Use these random indices to extract the desired rows from the original array using slicing.

random_rows = arr[random_indices]

print(random_rows)

This code will output a subset of the original array containing the specified number of randomly chosen rows.

Complete Example:

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
num_random_rows = 2

num_rows = arr.shape[0]
random_indices = np.random.choice(num_rows, size=num_random_rows, replace=False)
random_rows = arr[random_indices]

print(random_rows)

This example will print two randomly chosen rows from the array arr.

import numpy as np

# Create a 2D array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])

# Define the number of random rows to select
num_random_rows = 2

# Get the total number of rows in the array
num_rows = arr.shape[0]

# Generate random indices without replacement (unique rows)
random_indices = np.random.choice(num_rows, size=num_random_rows, replace=False)

# Select the random rows from the original array using indexing
random_rows = arr[random_indices]

# Print the randomly chosen rows
print("Original array:\n", arr)
print("\nRandomly chosen rows:\n", random_rows)

This code first creates a sample 2D array. Then, it defines how many rows you want to select randomly (changeable with num_random_rows). It retrieves the total number of rows in the array and uses np.random.choice to generate unique indices for those rows. Finally, it uses these indices to extract the desired rows and prints both the original and the randomly chosen subset.

Method 1: Shuffling and Slicing

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
num_random_rows = 2

np.random.shuffle(arr)  # Shuffle the rows
random_rows = arr[:num_random_rows]  # Slice the first 'num_random_rows' elements

print(random_rows)

Method 2: Boolean Masking

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
num_random_rows = int(arr.shape[0] * 0.25)  # Select ~25% of rows (adjustable)

mask = np.random.rand(arr.shape[0]) < 0.25  # Random mask with probability threshold
random_rows = arr[mask]

print(random_rows)

These methods offer different approaches:

• Shuffling and Slicing is simpler but might not be suitable for very large arrays due to the shuffling overhead.
• Boolean Masking avoids shuffling but requires creating a temporary mask array.

Choose the method that best suits your specific needs and array size.