global parameters to maintain consistencywe usually try to keep the plot sizesfontsand colors the same across the visual story setting each of these attributes creates complexity and difficulties in maintaining the code base to overcome these issueswe can set formatting settings globallyas shown in the following snippet global formatting params params {'legend fontsize''large''figure figsize'( )'axes labelsize''large''axes titlesize':'large''xtick labelsize':'large''ytick labelsize':'large'plt rcparams update(paramsonce set using rcparams update()the attributes provided in the params dictionary are applied to every plot generated you are encouraged to apply these setting and generate the plots discussed in this section again to understand the difference python visualization ecosystem the matplotlib library is very powerful and popular visualization/plotting library without any doubts it provides most of the tools and tricks required to plot any type of data with capability to control even the finest elements yet matplotlib leaves lot more to be desired by even pro-users being low-level apiit requires lot of boilerplate codeinteractivity is limitedand styling and other formatting defaults seem dated to address these issues and provide high-level interfaces and ability to work with current python ecosystemthe python universe has quite few visualization libraries to choose from some of the most popular and powerful ones are bokehseabornggplotand plotly each of these libraries builds on the understanding and feature set of matplotlibwhile providing their own set of features and easy-to-use wrappers to plug the gaps you are encouraged to explore these libraries and understand the differences we will introduce some of these in the coming as and when required though differentmost libraries work on concepts similar to matplotlib and hence the learning curve is shorter if you're well versed in matplotlib summary this covered quite lot of ground in terms of understandingprocessingand wrangling data we covered major data formats like flat files (csvjsonxmlhtmletc )and used standard libraries to extract/collect data we touched on standard data types and their importance in the overall process of data science major part of this covered data wrangling tasks to transformcleanand process data so as to bring it into usable form though the techniques were explained using the pandas librarythe concepts are universal and applied in most data science related use cases you may use these techniques as pointers that can be easily applied using different libraries and programming/scripting languages we covered major plots using sample datasets describing their usage we also touched on the basics and the powerful tricks of matplotlib we strongly encourage you to read the referred links for in-depth understanding this covers the initial steps in the crisp dm model of data collectionprocessingand visualization in the coming we build on these concepts and apply them for solving specific real-world problems stay tuned