#PYTHON RETURN MULTIPANEL FIGURE HOW TO#

axvline ( x = avg, color = 'b', label = 'Average', linestyle = '-', linewidth = 1 ) # Annotate the new customers for cust in : ax. set ( title = '2014 Revenue', xlabel = 'Total Revenue', ylabel = 'Customer' ) # Add a line for the average ax. plot ( kind = 'barh', y = 'Sales', x = 'Name', ax = ax ) avg = top_10. subplots () # Plot the data and get the average top_10. # Create the figure and the axes fig, ax = plt. To access this import matplotlib as follows: Matplotlib keeps a global reference to the global figure and axes objects which can be modified by the pyplot API.

If you take the time to understand this point, the rest of the matplotlib API will start to make sense. The other benefit of this knowledge is that you have a starting point when you see things on the web.

#PYTHON RETURN MULTIPANEL FIGURE HOW TO#

Once you understand what these are and how to access them through the object oriented API, the rest of the process starts to fall into place. Most of the terms are straightforward but the main thing to remember is that the Figure is the final image that may contain 1 or more axes. Keep it handy to understand the different terminology of a plot. This graphic from the matplotlib faq is gold. Rougier’s Matplotlib tutorial for an in-depth dive. For a complete example, please visit his excellent post on creating a visual sales analysis! This post was also adapated for matplotlib’s documentation, The Lifecycle of a Plot, which gives another good perspective. With permission, we have included part of Chris Moffitt’s Effectively Using Matplotlib as a crash course into Matplotlib terminology and usage. However, to customize figures or roll your own visualizers, a strong background in using matplotlib is required. Yellowbrick strives to provide well-styled visual diagnostic tools and complete information.

Because of this, Yellowbrick is able to generate publication-ready images for a variety of GUI backends, image formats, and Jupyter notebooks.

Yellowbrick generates visualizations by wrapping matplotlib, the most prominent Python scientific visualization library.

Python return multipanel figure