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Looking for synthetic data for your next project?
Learn how to use the Faker Python library to generate realistic synthetic data.
This could be bootstrapping a database or creating pandas dataframe to run sample analysis.
In this tutorial, well learn all about generating synthetic data with theFaker library.
Well start by installing Faker in our working environment.
Then, dive into the basics of data generation with Faker.
Introduction to Python Faker
Faker is a Python library for synthetic data generation.
Now that we have installed Faker lets see how we can generate synthetic data using it.
Lets instantiate aFakerobject calledfake.
you’ve got the option to use this to spin up geographical data.
Now, lets code a couple of practical examples to see how synthetic data generation with Faker is helpful.
This includes the following steps:
First, lets create a database that we can connect to.
Well useSQLitebecause we can use Pythons built-inSQLite moduleto work with SQLite databases.
We can use theconnect()function to connect to the database (fake_data.db).
This will createfake_data.dbif it does not exist already.
Next, well generate synthetic records with Faker and insert them into theuserstable in thefake_data.dbdatabase.
Now, lets run a simple select query to retrieve all the records from theuserstable.
This helps get an idea of the representation of each department within the organization.
Visualizing Salary Distribution by Department
Next, lets visualize the salary distribution by the department.
Weve used a box plot as it helps understand the spread of values.
Conclusion
In this tutorial, we learned all about generating synthetic data with Faker.
Then, we looked at using Faker to populate databases and pandas data frames with sample data.
So, are you ready to use Faker in your next project?
