the parameters are self-explanatory-- represents color while stands for size of the bubble such plots are also called bubble charts the output generated is shown in figure - figure - scatter plot visualizing multi dimensional data in this sectionwe utilized pandas to plot all sorts of visualizations these were some of the most widely used visualizations and pandas provides lot of flexibility to do more with these alsothere is an extended list of plots that can be visualized using pandas the complete information is available on the pandas documentation visualizing with matplotlib matplotlib is popular plotting library it provides interfaces and utilities to generate publication quality visualizations since its first version in until todaymatplotlib is being continuously improved by its active developer community it also forms the base and inspiration of many other plotting libraries as discussed in the previous sectionpandas along with scipy (another popular python library for scientific computingprovide wrappers over matplotlib implementations for ease of visualizing data matplotlib provides two primary modules to work withpylab and pyplot in this sectionwe will concentrate only on pyplot module (the use of pylab is not much encouragedthe pyplot interface is an object oriented interface that favors explicit instantiations as opposed to pylab' implicit ones in the previous sectionwe briefly introduced different visualizations and saw few ways of tweaking them as well since pandas visualizations are derived from matplotlib itselfwe will cover additional concepts and capabilities of matplotlib this will enable you to not only use matplotlib with ease but also provide with tricks to improve visualizations generated using pandas