{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# The Lifecycle of a Plot\n\nThis tutorial aims to show the beginning, middle, and end of a single\nvisualization using Matplotlib. We'll begin with some raw data and\nend by saving a figure of a customized visualization. Along the way we try\nto highlight some neat features and best-practices using Matplotlib.\n\n.. currentmodule:: matplotlib\n\n
This tutorial is based on\n `this excellent blog post\n
In general, try to use the object-oriented interface over the pyplot\n interface.
Figures can have multiple axes on them. For information on how to do this,\n see the :doc:`Tight Layout tutorial\n `.
While indexing in NumPy follows the form (row, column), the *figsize*\n keyword argument follows the form (width, height). This follows\n conventions in visualization, which unfortunately are different from those\n of linear algebra.