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I am trying to create a figure containing several plots, and normalising them all so that their features can all be easily distinguished I’ll share practical examples you can relate to, especially if you’re working with us economic or business data. I'm having some trouble wording what it is i'm trying to do, but the example code below should help clarify.

The trick is to use two different axes that share the same x axis As mentioned earlier, we will now have a look at plotting multiple curves by superimposing them. You can use separate matplotlib.ticker formatters and locators as desired since the two axes are independent.

This means you work with axes objects directly and set the axis limits on them.

Some powerful dataviz techniques require splitting the chart into multiple sections This can be achieved by creating multiple axes within a figure using the plt.subplots() function This lesson explains how it works. Matplotlib has a variety of tools for working with grids of axes that have evolved over the history of the library

Here we will discuss the tools we think users should use most often, the tools that underpin how axes are organized, and mention some of the older tools. In this tutorial, we’ll explore how to create and customize multiple subplots within a single figure, and then dive into advanced plot customization techniques, including adding error bars,. We have now learnt about plotting multiple graphs using subplot and subplot2grid function of matplotlib library

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