# Introduction In this lab, we will learn how to handle missing data in pandas, a common issue in data analysis. We'll cover how to identify missing data, fill in missing values, and drop data that's not needed. We will also discuss the experimental `NA` scalar in pandas that can be used to denote missing values. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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