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
Pandas provides many options for calculating descriptive statistics and other reduction operations with just a simple function call. You might want to calculate these values as part of a ML/Data Analysis pipeline, or just because you want to get a better understanding of the data you are dealing with.
In this article, we will learn about mapping and the apply and applymap functions.
This technique will help you manipulate your data in very convenient ways, and is another important addition to your toolbox.
As always, we will explore the topic with examples that will help you understand what's going
Arithmetic operations are some of the most fundamental (and important) things you can do with series and dataframes. In this article, we will learn how to perform basic operations using both series and dataframes.
We are interested in the following scenarios:
Today we will deal with two techniques we need to cover before moving to more advanced Pandas topics: Reindexing and element deletion.
It will be a bit shorter than the first two articles in the series, but that doesn't mean it's not important. Both techniques are very useful, and you