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
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: