Matplotlib is a Python package for data visualization. It allows easy creation of various plots, including line, scattered, bar, box, and radial plots, with high flexibility for refined styling and customized annotation. The versatile artist module allows developers to define basically any kind of visualization. For regular usage, Matplotlib offers a simplistic object-oriented interface, the pyplot module, for easy plotting.

Besides generating static graphics, Matplotlib also supports an interactive interface which not only aids in creating a wide variety of plots but is also very useful in creating web-based applications.

Matplotlib is readily integrated into popular development environments, such as Jupyter Notebook, and it supports many more advanced data visualization packages.

There are many advantages in creating data visualization with code so that the visualization streamlines into part of the result generation pipeline. Let's have a look at some of the key advantages of the Matplotlib library. 

The Matplotlib plotting library is easy to use in several ways:

• Firstly, the object-oriented module structures simplify the plotting process. More often than not, we're only required to call import maplotlib.pyplot as plt to import the plotting API to create and customize many basic plots.
• Matplotlib is highly integrated with two common data analytics packages, pandas and NumPy. For example, we can simply append .plot() to a pandas DataFrame such as by df.plot() to create a simple plot, and customize its styling with Matplotlib syntax.
• For styling, Matplotlib offers functions to alter the appearance of each feature, and ready-made default style sheets are also available to avoid these extra steps when refined aesthetics is not required.

Often in data analytics, we need sophisticated plots to express our data. Matplotlib offers numerous plotting APIs natively, and is also the basis for a collection of third-party packages for additional functionalities, including:

• Seaborn: Provides simple plotting APIs, including some advanced plot types, with aesthetically appealing default styling
• HoloViews: Creates interactive plots with metadata annotation from bundled data 
• Basemap/GeoPandas/Canopy: Maps data values to colors on geographical maps

We would learn some of the applications of these third-party packages in later chapters on advanced plotting.

When we want to go beyond the default settings to ensure that the resultant figure meets our specific purpose, we can customize the appearance and behaviors of each plot feature:

• Per-element styling is possible
• The ability to plot data values as colors and draw any shape of patches allows the creation of almost any kind of visualization
• Useful in customizing plots created by extensions such as Seaborn

As Matplotlib is open source, it enables developers and data analysts to use it for free. The users also have the freedom to improve and contribute to the Matplotlib library. As part of the open source experience, the users get prompt online support from the members of the global community on various platforms and forums.

Matplotlib supports Python 3 since version 1.2, released in 2013. The Matplotlib 2.0 release introduced a number of changes and upgrades to improve data visualization projects. Let us look at some of the key improvements and upgrades. 

Matplotlib 2.0 presents new features that improve user experience, including speed, and output quality, as well as resource usage.

The alpha channel that specifies the transparency level is fully supported in Matplotlib 2.0.

Matplotlib 2.0 now resamples images with less memory and less data type conversion.

Community developers claim that the speed of text rendering by the Agg backend has improved by 20%.

A very efficient codec, H.264, is now used as the default, which replaces MPEG-4, to generate video output for animated plots. With H.264, we can now have longer video record time and lesser data traffic and loading time thanks to the higher compression rate and smaller output file size. It is also noted that real-time playback of H.264 videos is better than those encoded in MPEG-4.

There are a number of style changes for improved visualization, such as more intuitive colors by default. We will discuss more in the chapter on figure aesthetics.

For details on all Matplotlib updates, you may visit http://matplotlib.org/devdocs/users/whats_new.html.

As developers, you probably recognize the importance of reading documentation and manuals to get acquainted with syntax and functionality. We would like to reiterate the importance of reading the library documentation and encourage you to do the same. You can find the documentation here: https://matplotlib.org. On the official Matplotlib website, you would find the documentation for each function, news of latest releases and ongoing development, and a list of third-party packages, as well as tutorials and galleries of example plots.

However, building advanced and sophisticated plots by reading through documentation from scratch means a much steeper learning curve and a lot more time spent, especially when the documentation is regularly updated for better comprehension. This book aims to provide the reader with a guided road-map to accelerate the learning process, save time and effort, and put theory into practice. The online manuals can serve as the atlases you can turn to whenever you want to explore further.

The Matplotlib source code is available on GitHub at https://github.com/matplotlib/matplotlib. We encourage our readers to fork it and add their ideas!

Matplotlib enables users to obtain output plots as static figures. The plots can also be piped and made responsive through interactive backends. 

Static images are the most commonly used output format for reporting and presentation purposes, and for our own quick inspection of data. Static images can be classified into two distinct categories.

Raster is the classic image format that provides support to a wide variety of image files, including PNG, JPG and BMP. Each raster image can be seen as a dense array of color values. For raster images, resolution matters.

The amount of image details kept is measured in dots per inch (DPI). The higher the DPI value (that is, the more pixel dots kept in it), the clearer the resultant image would be, even when stretched to a larger size. Of course, the file size and computational resources needed for the rendering would increase accordingly.

For vector images, instead of a matrix of discrete color dots, information is saved as paths, which are lines joining dots. They scale without losing any details:

• SVG
• PDF
• PS