I just released my 49th course on the learning platform Pluralsight. Building this new course Validate Data Cleanliness Using Asserts in Python, was fun. I have been consulting with Python for quite a while. Python is a popular high-level programming language that is known for its simplicity, readability, and versatility. It has a large and supportive community, which has contributed to a vast collection of libraries and tools that make it easy to develop a wide range of applications, from web development to scientific computing and machine learning.
Validate Data Cleanliness Using Asserts in Python
The second module, “Using Assert-based Tests for Data Cleaning,” is a 12-minute video clip that delves deeper into the use of asserts for data cleaning. The module begins with an introduction to assert-based tests and their importance in data cleaning. Viewers are then shown how to create quantitative and logical tests for clean data using asserts. The module concludes with a summary of the key concepts covered in the video clip. By the end of the module, viewers will have a solid understanding of how to use assert-based tests to ensure that their data is clean, reliable, and ready for analysis.
Why Watch this course?
If you’ve ever had to make important decisions based on inaccurate or inconsistent data, then you know how frustrating and costly it can be. It’s like trying to navigate through a dense fog with no compass – you’re bound to get lost and make mistakes.
Fortunately, there’s a way to avoid these pitfalls and ensure that your data is clean and reliable. It’s called “asserts” and it’s a powerful tool that can help you automate the process of validating and cleaning data.
And now, with the Validate Data Cleanliness Using Asserts in Python course, you can learn how to harness the power of asserts and take your data analysis to the next level. This course will teach you everything you need to know about using asserts in Python to verify the tidiness, equality, and cleanliness of your data.
You’ll start by discovering the numpy.testing module and how it can be used to verify data tidiness. Then, you’ll dive into the various testing functions available in the numpy.testing module, which you can use to compare two indexes, two Series, and two DataFrames.
And that’s not all! You’ll also learn how to compose quantitative and logical tests for clean data using asserts. This will enable you to create your own custom tests and apply them for data cleaning.
By the end of this course, you’ll be equipped with the skills and confidence needed to use asserts to validate data cleanliness in Python. You’ll be able to save time, avoid errors, and make better decisions based on reliable data. So what are you waiting for? Enroll now and take the first step toward data excellence!
Reference: Pinal Dave (http://blog.SQLAuthority.com)