Python for Finance

Chapter 5. Data Visualization

Use a picture. It’s worth a thousand words.

— Arthur Brisbane (1911)

This chapter is about basic visualization capabilities of the matplotlib library. Although there are many other visualization libraries available, matplotlib has established itself as the benchmark and, in many situations, a robust and reliable visualization tool. It is both easy to use for standard plots and flexible when it comes to more complex plots and customizations. In addition, it is tightly integrated with NumPy and the data structures that it provides.

This chapter mainly covers the following topics:

2D plotting

From the most simple to some more advanced plots with two scales or different subplots; typical financial plots, like candlestick charts, are also covered.

3D plotting

A selection of 3D plots useful for financial applications are presented.

This chapter cannot be comprehensive with regard to data visualization with Python and matplotlib, but it provides a number of examples for the most basic and most important capabilities for finance. Other examples are also found in later chapters. For instance, Chapter 6 shows how to visualize time series data with the pandas library.

Two-Dimensional Plotting

To begin with, we have to import the respective libraries. The main plotting functions are found in the sublibrary matplotlib.pyplot:

In [1]: import numpy as np
        import matplotlib as mpl
        import matplotlib.pyplot as plt
        %matplotlib inline

One-Dimensional Data Set

In all that follows, ...