{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n", "\n", "*No changes were made to the contents of this notebook from the original.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Line Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n", "Here we will take a first look at creating a simple plot of this type.\n", "As with all the following sections, we'll start by setting up the notebook for plotting and importing the packages we will use:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('seaborn-whitegrid')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For all Matplotlib plots, we start by creating a figure and an axes.\n", "In their simplest form, a figure and axes can be created as follows:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = plt.axes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Matplotlib, the *figure* (an instance of the class plt.Figure) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n", "The *axes* (an instance of the class plt.Axes) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n", "Throughout this book, we'll commonly use the variable name fig to refer to a figure instance, and ax to refer to an axes instance or group of axes instances.\n", "\n", "Once we have created an axes, we can use the ax.plot function to plot some data. Let's start with a simple sinusoid:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = plt.axes()\n", "\n", "x = np.linspace(0, 10, 1000)\n", "ax.plot(x, np.sin(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n", "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to create a single figure with multiple lines, we can simply call the plot function multiple times:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3b7o6fO18EZPGzkxERwfw8pLHA0qDo7eOolW9VmhWpxltKaIhhQmTvbk9DHUM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\niLrxJyZ1+Yi2FNFITQUuXAD8/Ggr4bw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.plot(x, np.cos(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's all there is to plotting simple functions in Matplotlib!\n", "We'll now dive into some more details about how to control the appearance of the axes and lines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adjusting the Plot: Line Colors and Styles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n", "The plt.plot() function takes additional arguments that can be used to specify these.\n", "To adjust the color, you can use the color keyword, which accepts a string argument representing virtually any imaginable color.\n", "The color can be specified in a variety of ways:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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xR5W7Gc6ekZFBmzZt3I4QsEfz4ObM6jnL/QVadVJRiCHXeywDQNu2bcnKynI/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x - 0), color='blue') # specify color by name\n", "plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)\n", "plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1\n", "plt.plot(x, np.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF)\n", "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n", "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n", "\n", "Similarly, the line style can be adjusted using the linestyle keyword:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ff/7Z4UFKxDBggJoyCODpqTowRWee2ImIiGDBggUcOnSIv/3tb0LGVBSF4uLU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\njfuSk3kvMpIOvr4E6nT8XqnvqMNRFNi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, x + 0, linestyle='solid')\n", "plt.plot(x, x + 1, linestyle='dashed')\n", "plt.plot(x, x + 2, linestyle='dashdot')\n", "plt.plot(x, x + 3, linestyle='dotted');\n", "\n", "# For short, you can use the following codes:\n", "plt.plot(x, x + 4, linestyle='-') # solid\n", "plt.plot(x, x + 5, linestyle='--') # dashed\n", "plt.plot(x, x + 6, linestyle='-.') # dashdot\n", "plt.plot(x, x + 7, linestyle=':'); # dotted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you would like to be extremely terse, these linestyle and color codes can be combined into a single non-keyword argument to the plt.plot() function:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, x + 0, '-g') # solid green\n", "plt.plot(x, x + 1, '--c') # dashed cyan\n", "plt.plot(x, x + 2, '-.k') # dashdot black\n", "plt.plot(x, x + 3, ':r'); # dotted red" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n", "\n", "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the plt.plot() function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adjusting the Plot: Axes Limits\n", "\n", "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n", "The most basic way to adjust axis limits is to use the plt.xlim() and plt.ylim() methods:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "\n", "plt.xlim(-1, 11)\n", "plt.ylim(-1.5, 1.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "\n", "plt.xlim(10, 0)\n", "plt.ylim(1.2, -1.2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A useful related method is plt.axis() (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n", "The plt.axis() method allows you to set the x and y limits with a single call, by passing a list which specifies [xmin, xmax, ymin, ymax]:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.axis([-1, 11, -1.5, 1.5]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plt.axis() method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.axis('tight');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in x is equal to one unit in y:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.axis('equal');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information on axis limits and the other capabilities of the plt.axis method, refer to the plt.axis docstring." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Labeling Plots\n", "\n", "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n", "\n", "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.title(\"A Sine Curve\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"sin(x)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n", "For more information, see the Matplotlib documentation and the docstrings of each of these functions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n", "Again, Matplotlib has a built-in way of quickly creating such a legend.\n", "It is done via the (you guessed it) plt.legend() method.\n", "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the label keyword of the plot function:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n", "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n", "plt.axis('equal')\n", "\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the plt.legend() function keeps track of the line style and color, and matches these with the correct label.\n", "More information on specifying and formatting plot legends can be found in the plt.legend docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aside: Matplotlib Gotchas\n", "\n", "While most plt functions translate directly to ax methods (such as plt.plot() → ax.plot(), plt.legend() → ax.legend(), etc.), this is not the case for all commands.\n", "In particular, functions to set limits, labels, and titles are slightly modified.\n", "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n", "\n", "- plt.xlabel() → ax.set_xlabel()\n", "- plt.ylabel() → ax.set_ylabel()\n", "- plt.xlim() → ax.set_xlim()\n", "- plt.ylim() → ax.set_ylim()\n", "- plt.title() → ax.set_title()\n", "\n", "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ax.set() method to set all these properties at once:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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i7Y7UVMDBQXYaKo8lSwA/P7EM7OOPa7PPUhWI//znP+bOQTbslVfE3DJJScDg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\nIiJSxQJBRESqWCCIiEgVCwQREan6f8M6gCXBCtQMAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.axes()\n", "ax.plot(x, np.sin(x))\n", "ax.set(xlim=(0, 10), ylim=(-2, 2),\n", " xlabel='x', ylabel='sin(x)',\n", " title='A Simple Plot');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }