It also offers methods to do basic calculations such as count, mean, max, min, cumsum, imax, imin. Pandas offers some methods to get information of a data structure: info, index, columns, axes, where you can see the memory usage of the data, information about the axes such as the data types involved, and the number of not-null values. Getting information and basic calculations Keep in mind that anything that applies to Series probably applies to DataFrames too, but it may not be the case the other way around. Update (): Panels were removed in release 0.25.0.įrom here on, we will use the Series/DataFrame as the data structure of choice in the examples when explaining things. We'll see its usage in the following examples. The axes distinction is vital, since a lot of methods need to have this specified properly in order to work as expected. Instead of "index" and "columns", Panels' axes are named as follow: These are 3-dimensional data structures, that are rarely used, in comparison with DataFrames.Īnalogously to DataFrames, they can be thought of as Python dictionaries of DataFrames. We'll use the well known Titanic dataset (available in Seaborn), which holds data of the Titanic passengers, such as their age, paid fare, and if they survived or not. The overview is divided into sections, each one with code examples and an explanation of what is being done.įirst, we'll need to make some imports, which will be necessary through all the examples. Almost every Pandas method returns a (modified) copy of the data, which allows you to chain transformations, and perform complex modifications in one line. It uses its multi-dimensional arrays and fast operations internally to provide higher level methods for manipulation and analysis. Why is Pandas great? It is built on top of NumPy. We hope this post serves as a first guide for diving into them and kickstart your data handling & visualization journey. From our experience, they complement each other really well, and are worth learning together. Up next you'll find an overview of Pandas, a Python library which is old but gold and a must-know if you're attempting to do any work with data while living in the Python world, and a glance of Seaborn, a Python library for making statistical visualizations. In this post we'll show how we have been doing this lately. To understand the data, we need to manipulate it, clean it, make calculations and see how variables behave independently, and how they relate to one another. These kind of problems always involve working with large amounts of data which is key to understand before applying any machine learning technique. Here at Tryolabs we love Python almost as much as we love machine learning problems.
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