Python Pandas Tutorial - Handling Missing Data Using Pandas

Welcome back to week 5 of Pandas Zero to Hero, a video series where I teach beginner-friendly ways of using pandas that will take your data analysis to the next level.

In this tutorial, I cover the concept of data types as well as how to deal with missing data using pandas. Missing data is one of the most common problems that you will face in almost all data science projects. Therefore, it is important that you not only know how to detect those missing data in your dataset but also the techniques to properly handle them.

There are mainly two ways to deal with missing data. The easiest and most straightforward one is by dropping rows or columns that contain missing data. However, in doing so, we run into the risk of removing potentially useful information and features from our data frame that will negatively impact our model and the predictions made by our model. Alternatively, we can use a method called imputation which is essentially filling our missing data with some substituted values. Although this is the more preferable way to handle missing data, the process of imputation does require more time, consideration and experience.

I hope you will find my tutorial helpful in your learning. Stay tuned for my tutorial next week where we will learn about combining data frames using pandas functions, concat and merge.

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