Used to commit all the ‘sins’ - lots of intermediate dataframes, lots of single cell calculations/transformations in the notebook. We will demonstrate how to do this in Pandas as well as show some of the situations you might want to look out for. Do you present through independent small examples through the whole book or, you start with small examples and then build up to a big project towards the end? You can use Pandas for that but as you mention things like transpose aren’t really meant for hetergenous data types.

Most of the examples of pandas code on blogs, videos, books, or otherwise push learners to use poor coding practices. In this article, we will explore 5 advanced functions in Pandas to help you improve your efficiency when working with Pandas library. Pandas has extensive API documentation and guides for getting started, but the documentation doesn’t always feature realistic use cases.This book contains best practices with Pandas, essential for anyone who wants to improve their data manipulation skills. The book is full of useful information and practical knowledge for the daily use of pandas, and also contains interesting focus on some external features such as Cython, CatBoost or sparklines. In this video course, you’ll learn how to sort data in a pandas DataFrame using the pandas sort functions sort_values() and sort_index().

I like the way how Matt have described the each scenario, graphical/pictorial representation of each topic to give you a visual clarity on what is happening underneath. There is a lot of bouncing around, which is necessary but the context switches slow down the process.The course is served on the Podia platform, which has a decent interface for presenting the course materials and a video player with standard controls. The basic, lowest cost version of the course does not include the exercises or other downloadable extras.

Opinionated” is important in the context of pandas: it’s a very flexible library that gives you rope to hang yourself, often contradicting the zen of Python: “There should be one — and preferably only one — obvious way to do it”. Anyone with rudimentary programming skills should be able to work through the exercises, and doing the exercises would be a good way to develop those skills further.Why do you think pandas is a better choice for data manipulation when compared with other Libraries in python? The Python world (which is arguable 10-100X bigger than the R world) has converged on the Pandas API. It is one of the most widely used libraries for data analysis and has been adopted by many organizations due to its simplicity and versatility. One of the best investments that you can make when becoming a data scientist is to become a Pandas expert, and there is no better book than Harrison’s to help you get there. Reading the book you can feel that the author has poured in his two plus decades of experience and want to share the most with his audience so that they can implement each scenarios.

It is also a legit hands-on material, and even the session on debugging chains that starts with commenting commands before applying more robust debugging methods mimics what data scientists are really doing in the wild. He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, USENIX, and OSCON as well as local user conferences. Learning Python for Data focuses on the Python language and fills in knowledge gaps for those who wish to learn Python and then move on to ML, AI, DL, etc. In reference to Alexey’s comment below about dplyr, I have barely used it, but many of my teammates use it, and they have shown me the basics. You've had a chance to work on the material, it is fresh on your mind, and you have used a different part of your brain by working on the labs.

I'm only a few pages into the Effective Pandas book and so far I've found it to be very enjoyable even though the material is mostly known to me.