This course teaches how to use asserts in Python to validate data cleanliness. Learn to compare indexes, Series, and DataFrames, compose quantitative and logical tests, and apply them for data cleaning.
Inaccurate or inconsistent data can lead to poor business decisions. However, manually validating data can be time-consuming and error-prone. Tools and technologies available today can help automate the process of validating and cleaning data. In this course, Validate Data Cleanliness Using Asserts in Python, you will learn how to use asserts in Python to validate the cleanliness of data. First, you will be introduced to the numpy.testing module and how it can be used to verify data tidiness. Next, you will discover how to verify the equality of two indexes, two Series, and two DataFrames using the various testing functions available in the numpy.testing module. Finally, you will explore how to compose quantitative and logical tests for clean data using asserts and apply them for data cleaning. When you are finished with this course, you will have the skills needed to use asserts to validate data cleanliness in Python.
Inaccurate or inconsistent data can lead to poor business decisions. However, manually validating data can be time-consuming and error-prone. Tools and technologies available today can help automate the process of validating and cleaning data. In this course, Validate Data Cleanliness Using Asserts in Python, you will learn how to use asserts in Python to validate the cleanliness of data. First, you will be introduced to the numpy.testing module and how it can be used to verify data tidiness. Next, you will discover how to verify the equality of two indexes, two Series, and two DataFrames using the various testing functions available in the numpy.testing module. Finally, you will explore how to compose quantitative and logical tests for clean data using asserts and apply them for data cleaning. When you are finished with this course, you will have the skills needed to use asserts to validate data cleanliness in Python.
