Python’s Faker library to augment text data

Here’s an example of how you can generate a fake text column with some data augmentation technique using the Faker library in Python:

import csv
from faker import Faker
import random

fake = Faker()

# Read the input CSV file
with open('input_file.csv', 'r') as file:
    reader = csv.reader(file)
    header = next(reader)
    data = list(reader)

# Define a function to generate augmented data for the text column
def augment_text(text):
    # Split the text into words
    words = text.split()
    # Randomly capitalize some words
    for i in range(len(words)):
        if random.random() < 0.3:
            words[i] = words[i].upper()
    # Join the words back into a sentence
    augmented_text = ' '.join(words)
    return augmented_text

# Generate fake text data with data augmentation
for row in data:
    # Get the original text from the input CSV file
    original_text = row[1]
    # Augment the original text
    augmented_text = augment_text(original_text)
    # Generate fake text data using the augmented text
    fake_text = fake.text(max_nb_chars=500, ext_word_list=None, variable_nb_sentences=True, 
                          ext_stop_words=None)
    # Replace the original text with the fake text
    row[1] = fake_text.replace('.', ' ') + augmented_text

# Write the augmented data to a new CSV file
with open('output_file.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(header)
    writer.writerows(data)

In this example, we read the input CSV file and define a function augment_text that randomly capitalizes some words in the input text. We then loop through the data rows, augment the text column of each row using the augment_text function, and generate fake text data using the Faker.text() function with a maximum length of 500 characters. Finally, we write the augmented data to a new CSV file.

Note that this is just one example of how you can do data augmentation for the text column. There are many other techniques you can use to generate augmented text data, such as adding noise or synonyms, or replacing some words with their antonyms. The choice of technique depends on the specific task and the nature of the text data.