December 2018
Beginner
720 pages
15h 32m
English
Content preview from Python for Finance, 2nd Edition
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Chapter 7. Data Visualization
Use a picture. It’s worth a thousand words.
Arthur Brisbane (1911)
This chapter is about the basic visualization capabilities of the matplotlib and plotly packages.
Although there are more visualization packages 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 pandas and the data structures they provide.
matplotlib only allows for the generation of plots in the form of bitmaps (for example, in PNG or JPG format). On the other hand, modern web technologies—based, for example, on the Data-Driven Documents (D3.js) standard—allow for nice interactive and also embeddable plots (interactive, for example, in that one can zoom in to inspect certain areas in greater detail). A package that makes it convenient to create such D3.js plots with Python is plotly. A smaller additional library, called Cufflinks, tightly integrates plotly with pandas DataFrame objects and allows for the creation of popular financial plots (such as candlestick charts).
This chapter mainly covers the following topics:
- “Static 2D Plotting”
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This section introduces
matplotliband presents a selection of typical 2D plots, from the most simple to some more advanced ones with two scales or different subplots. - “Static 3D Plotting”
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Based on
matplotlib ...
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