We have already covered most of the fundamentals of working with data using the Pandas library. There is one more topic I'd like to discuss before concluding the series: The Apply function.

In the previous article, we learned how to create subgroups of data using the groupby function. This is

Sometimes you need to perform operations on subsets of data. Your rows might have attributes in common or somehow form logical groups based on other properties. Common operations like finding the average, maximum, count, or standard deviation of values from groups of data is a really common task, and Pandas

Merge/join operations in Pandas let you gather information from many tables into a single dataframe for further processing or analysis. This is another important skill that you will probably use a lot when working with data.

If you have some experience with relational databases you can recognize the analogous

In an ideal world, all the data you need is available in the right format and with complete content.

In the real world, you will probably need to scrape data from lots of different and incomplete sources. That's why it's important to learn how to clean your data before analyzing

Data analysis usually starts by loading data into the structures of your library/tools of choice. Almost always this data will either come from a database, the web, or a collection of files.

The files that contain your data can come in many different formats: Comma-separated values in a text