Creating a Fake Data Generator with Python 🤖📅

In the world of data science and machine learning, having access to diverse datasets for experimentation is crucial. However, real-world data can be scarce, sensitive, or simply not available. This is where generating fake data becomes invaluable. In this post, I’ll walk you through creating a fake data generator using Python’s Faker library and displaying this data in a well-styled table using pandas.

Python output

HTML Output

What You’ll Need

Before we dive in, ensure you have the following installed:

• Python 3.x
• pandas library: pip install pandas
• Faker library: pip install faker

Step-by-Step Guide

Let’s build a script that generates a set of fake personal data and displays it in a structured format.

1. Import Necessary Libraries

First, import the Faker and pandas libraries:

from faker import Faker
import pandas as pd

2. Initialize Faker

Create an instance of the Faker class:

fake = Faker()

3. Generate Fake Data

Define a dictionary to store the fake data:

data = { 
    'First Name': [],
    'Last Name': [],
    'DOB': [],
    'Email': [],
    'City': [],
    'Country': [],
    'ZipCode': [],
}

Use a loop to populate the dictionary with fake data: say 10 records

for i in range(11):
    data['First Name'].append(fake.first_name())
    data['Last Name'].append(fake.last_name())
    data['DOB'].append(fake.date_of_birth())
    data['Email'].append(fake.email())
    data['City'].append(fake.city())
    data['ZipCode'].append(fake.zipcode())
    data['Country'].append(fake.country())

4. Create a DataFrame

Convert the dictionary to a pandas DataFrame:

df = pd.DataFrame(data)

Print the DataFrame to see the generated data:

print(df)

5. Style the DataFrame (Optional)

To display the DataFrame with borders, define a style and save the styled DataFrame to an HTML file:

styles = [
    {'selector': 'table', 'props': [('border-collapse', 'collapse')]},
    {'selector': 'th, td', 'props': [('border', '1px solid black'), ('padding', '5px')]},
]

styled_df = df.style.set_table_styles(styles)
styled_df.to_html('styled_table.html')

6. Open the Styled Table (Optional)

The styled DataFrame is saved as styled_table.html. You can open this file in a web browser to view the data in a neat, tabular format with borders.

print("Styled table saved as 'styled_table.html'. Open this file in a web browser to view the table with borders.")

Full Code

Here’s the full script:

from faker import Faker
import pandas as pd

fake = Faker()

data = { 
    'First Name': [],
    'Last Name': [],
    'DOB': [],
    'Email': [],
    'City': [],
    'Country': [],
    'ZipCode': [],
}

for i in range(11):
    data['First Name'].append(fake.first_name())
    data['Last Name'].append(fake.last_name())
    data['DOB'].append(fake.date_of_birth())
    data['Email'].append(fake.email())
    data['City'].append(fake.city())
    data['ZipCode'].append(fake.zipcode())
    data['Country'].append(fake.country())

df = pd.DataFrame(data)

print(df)

# Generate a styled DataFrame and view it by saving the DataFrame to an HTML file and then opening that file in a web browser
# Define the table style to add borders
styles = [
    {'selector': 'table', 'props': [('border-collapse', 'collapse')]},
    {'selector': 'th, td', 'props': [('border', '1px solid black'), ('padding', '5px')]},
]

styled_df = df.style.set_table_styles(styles)
styled_df.to_html('styled_table.html')

print("Styled table saved as 'styled_table.html'. Open this file in a web browser to view the table with borders.")

Conclusion

Using Python’s Faker library, you can easily generate realistic fake data for various purposes, from testing to developing machine learning models. By combining it with pandas, you can structure this data in a DataFrame and apply styles to make it more readable and visually appealing. This approach is not only practical but also enhances the presentation of your data, making it easier to analyze and share.

Open the styled_table.html file in your web browser, and you'll see a beautifully formatted table containing the fake data, ready for use in your projects. Happy coding!