The course also has solid, representative examples of using both Pandas built-in plotting functions and the Matplotlib visualization library to produce high quality data visualizations. 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.

Pandas is built for working with this type of data and also leverages NumPy so it is speedy and memory efficient. Also, does your book address best practices around inplace oprerations, or does it “completely” ban its use? I just can not say it enough, but I bought a lot of books the last 2 years regarding python but Effective Pandas is my most valued one. However, they are fine for string operations (though you may want to consider conversion to categoricals) as strings are already on the “slow path”.To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average.

You've heard that data processing, machine learning, all takes 80% munging or data cleansing, So doesn't it make sense to get really good at that one activity that takes up most of your time? It is by far, the best book you can get yourself if you want to take your data science skills to the next level, after all, they say modern data science is 90% data cleaning. Tips and insights on things like effectively chaining pandas methods, improving memory usage through manipulation of data types, making sure that appropriate constructs are used for accessing intermediate data frames in chained sequences (see notes on lambda functions in ch 10 and on the differences btw . I've been using Pandas for about 10 years, and I still improved my Pandas skill working through the Effective Pandas.Some of this is quite important, as there are some methods in pandas that create copies of objects but others that modify objects in place. Read more about the condition Very Good: A book that has been read and does not look new, but is in excellent condition. At the end of each chapter, Matt asks you to reinforce what you learned by practicing on a dataset of your choice.