#### Overview of this book

In this book, you’ll get hands-on with customizing your data plots with the help of Matplotlib. You’ll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You’ll explore non-trivial layouts, Pylab customization, and more about tile configuration. You’ll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you’ll explore them further in this book. You’ll delve into niche plots and visualize ordinal and tabular data. In this book, you’ll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter. Learn expert techniques for effective data visualization using Matplotlib 3 and Python with our latest offering -- Matplotlib 3.0 Cookbook
Preface
Free Chapter
Heavy Customization
Drawing on Plots
Special Purpose Plots
3D and Geospatial Plots
Interactive Plotting
Other Books You May Enjoy

# Plotting on map projections

This section describes how to plot on the map projections and how to draw a day/night terminator.

# How to add simple points and lines to our plots

We can see, from the preceding map, that we have Europe in the middle. We will plot some data points and some curves with lines all over Europe. We will begin by putting down a cross or a point. So the latitude and longitude of Heidelberg is 8.7 degrees east and 49.5 degrees north.

We will start with the scatter method. So the scatter behaves just like we are used to it behaving from the standard Cartesian Euclidean projections. Input the code as follows:

`# Projecting with and without latlonm = Basemap(width=1.2e7,height=9e6,projection='lcc&apos...`