Matplotlib-Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is a data visualization library built on NumPy arrays, and designed to work with the broader SciPy stack.\n",
    "\n",
    "One of Matplotlib’s most important features is its ability to play well with many operating systems and graphics backends. It supports dozens of backends and output types, which means you can count on it to work regardless of which operating system you are using or which output format you wish.\n",
    "\n",
    "In recent years the interface and style of Matplotlib have begun to show their age. Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned.\n",
    "\n",
    "Still, we cannot ignore Matplotlib's strength as a well-tested, cross-platform graphics engine. Recent updates make it  easy to set new global plotting styles (see [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb)), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)), [ggpy](http://yhat.github.io/ggpy/), [HoloViews](http://holoviews.org/), [Altair](http://altair-viz.github.io/), and even Pandas itself can be used as wrappers around Matplotlib's API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Matplotlib Tips\n",
    "\n",
    "Before we dive into the details of creating visualizations with Matplotlib, there are a few useful things you should know about using the package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Matplotlib\n",
    "\n",
    "Just as we use the ``np`` shorthand for NumPy and the ``pd`` shorthand for Pandas, we will use some standard shorthands for Matplotlib imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``plt`` interface is what we will use most often, as we shall see throughout this chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Styles\n",
    "\n",
    "We will use the ``plt.style`` directive to choose appropriate aesthetic styles for our figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this section, we will adjust this style as needed.\n",
    "Note that the stylesheets used here are supported as of Matplotlib version 1.5; if you are using an earlier version of Matplotlib, only the default style is available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show() or No show()? How to Display Your Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How you view your Matplotlib plots depends on the context. The three applicable contexts are using Matplotlib in a script, in an IPython terminal, or in an IPython notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting from a script\n",
    "\n",
    "If you are using Matplotlib from within a script, the function ``plt.show()`` is your friend.\n",
    "``plt.show()`` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures.\n",
    "\n",
    "So, for example, you may have a file called *myplot.py* containing the following:\n",
    "\n",
    "```python\n",
    "# ------- file: myplot.py ------\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x))\n",
    "\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "You can then run this script from the command-line prompt, which will result in a window opening with your figure displayed:\n",
    "\n",
    "```\n",
    "$ python myplot.py\n",
    "```\n",
    "\n",
    "The ``plt.show()`` command does a lot under the hood, as it must interact with your system's interactive graphical backend.\n",
    "The details of this operation can vary greatly from system to system and even installation to installation, but matplotlib does its best to hide all these details from you.\n",
    "\n",
    "Note: the ``plt.show()`` command __should be used *only once* per Python session__. It is most often seen at the very end of the script. Multiple ``show()`` commands can lead to unpredictable backend-dependent behavior, and should mostly be avoided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting from an IPython shell\n",
    "\n",
    "It can be very convenient to use Matplotlib interactively within an IPython shell (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)). IPython is built to work well with Matplotlib if you specify Matplotlib mode.\n",
    "__To enable this mode, you can use the ``%matplotlib`` magic command after starting ``ipython``:__\n",
    "\n",
    "```ipython\n",
    "In [1]: %matplotlib\n",
    "Using matplotlib backend: TkAgg\n",
    "\n",
    "In [2]: import matplotlib.pyplot as plt\n",
    "```\n",
    "\n",
    "Any ``plt`` plot command will cause a figure window to open, and further commands can be run to update the plot.\n",
    "Some changes (such as modifying properties of lines that are already drawn) will not draw automatically: to force an update, use ``plt.draw()``. Using ``plt.show()`` in Matplotlib mode is not required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting from an IPython notebook\n",
    "\n",
    "The IPython notebook is a browser-based interactive data analysis tool that can combine narrative, code, graphics, HTML elements, and much more into a single executable document (see [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb)).\n",
    "\n",
    "Plotting interactively within an IPython notebook can be done with the ``%matplotlib`` command, and works in a similar way to the IPython shell.\n",
    "In the IPython notebook, you also have the option of embedding graphics directly in the notebook, with two possible options:\n",
    "\n",
    "- ``%matplotlib notebook`` will lead to *interactive* plots embedded within the notebook\n",
    "- ``%matplotlib inline`` will lead to *static* images of your plot embedded in the notebook\n",
    "\n",
    "For this book, we will generally opt for ``%matplotlib inline``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running this command (it needs to be done only once per kernel/session), any cell within the notebook that creates a plot will embed a PNG image of the resulting graphic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2Tj1CySkwGuHsbiWpzdbltG1NRzOsuwuOvv7Fosstnbx1uITV6VFO3UHN28OVr38ljt2F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\nAp2aEbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40832545c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(x, np.sin(x), '-')\n",
    "plt.plot(x, np.cos(x), '--');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Figures to File\n",
    "\n",
    "Saving a figure using the ``savefig()`` command. To save the previous figure as a PNG file, you can run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig('my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a file called ``my_figure.png`` in the current working directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 bjpcjp bjpcjp 23K Nov 28 16:40 my_figure.png\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh my_figure.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying this Image File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oqQxeDoMT75smmIWlxAdwtqaV3AoxkMkSVIZheqmg1WrJzMy02Ot9c66OVRu+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\nGyVqLRbF+mEwwC5LLXCq74aMf0LNacu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cSQanowk6L4JaDRp7Hp4lbZ3y1gFlbBl/8Ewd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\nxTl+/I8dO4a/vz+TJ08mOjqalJQU6urqtI6lqq5du/LUU08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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('my_figure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get List of supported file types for your System\n",
    "In ``savefig()``, the file format is inferred from the filename extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eps': 'Encapsulated Postscript',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'ps': 'Postscript',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when saving your figure, it's not necessary to use ``plt.show()`` or related commands discussed earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MATLAB-style Interface\n",
    "\n",
    "Matplotlib was originally written as a Python alternative for MATLAB users, and much of its syntax reflects that fact.\n",
    "The MATLAB-style tools are contained in the pyplot (``plt``) interface.\n",
    "For example, the following code will probably look quite familiar to MATLAB users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+FqiU+k4ptf/Ef9ta4th2U+xKKRPwBnAR0AuYppTqZWwqi2sEZmmtewKDgFud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\nUK2p1cWulDIBbwAXAb2AaUq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6+8R7OkMptVgp5XCL9SulPlRKFSqlMk76WaBS6jul1P4T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\n8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40804132b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()  # create a plot figure\n",
    "\n",
    "# create the first of two panels and set current axis\n",
    "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "# create the second panel and set current axis\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This interface is *stateful*. (It tracks the \"current\" figure and axes, which are where all ``plt`` commands are applied.) You access state info via the ``plt.gcf()`` (get current figure) and ``plt.gca()`` (get current axes) routines.\n",
    "\n",
    "While this is fast and convenient for simple plots, it is easy to run into problems. For example, once the second panel is created, how can we go back and add something to the first? Fortunately, there is a better way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object-oriented interface\n",
    "\n",
    "The object-oriented interface is available for more complicated situations, and for when you want more control over your figure. It uses plotting functions that are *methods* of explicit ``Figure`` and ``Axes`` objects. To re-create the previous plot using this style of plotting, you might do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+FqiU+k4ptf/Ef9ta4th2U+xKKRPwBnAR0AuYppTqZWwqi2sEZmmtewKDgFud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\n8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40801000b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create a grid of plots\n",
    "# ax will be an array of two Axes objects\n",
    "fig, ax = plt.subplots(2)\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Further Resources](03.13-Further-Resources.ipynb) | [Contents](Index.ipynb) | [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}