{ "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\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'll try\nto highlight some neat features and best-practices using Matplotlib.\n\n.. currentmodule:: matplotlib\n\n
This tutorial is based off of\n `this excellent blog post
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 kwarg follows the form (width, height). This follows conventions in\n visualization, which unfortunately are different from those of linear\n algebra.