Concatenating, Merging, and Joining

Merging and joining tables or datasets are highly common operations in the day-to-day job of a data wrangling professional. These operations are akin to the JOIN query in SQL for relational database tables. Often, the key data is present in multiple tables, and those records need to be brought into one combined table that matches on that common key. This is an extremely common operation in any type of sales or transactional data, and therefore must be mastered by a data wrangler. The pandas library offers nice and intuitive built-in methods to perform various types of JOIN queries involving multiple DataFrame objects.

Exercise 4.07: Concatenation in Datasets

In this exercise, we will concatenate DataFrames along various axes (rows or columns).

Note

The superstore dataset file can be found here: https://packt.live/3dcVnMs.

This is a very useful operation as it allows you to grow a DataFrame as the new data comes in or new feature...