{ "metadata": { "name": "matplotlib" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## An Introduction to Graphing in Python\n", "The core plotting library in Python is [matplotlib](http://matplotlib.sourceforge.net/). The [matplotlib gallery](http://matplotlib.sourceforge.net/gallery.html) is a great way to figure out how to make the kind of plots you want. Just look for a plot of the right basic style and click on the image to see the code that generated it.\n", "\n", "Two things that may differ a bit from other graphing programs that you are used to:\n", "\n", "* Making a plot will return a plot object. If you assign this to something you can use it to modify the figure later. This is why in some of the examples below you will see some output before the figure.\n", "* In some Python interpreters you will need to explicitly tell Python to display the figures. This is done using the show() function. Several examples of using show() are included below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic plots of two variables\n", "----------------------------\n", "Generating basic bivariate plots is done using the plot() function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "#generate some data\n", "x = np.array(range(20))\n", "y = 3 + 0.5 * x + np.random.randn(20)\n", "\n", "#plot the data\n", "plt.plot(x, y, 'bo')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also scale any of the axes logarithmically." ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.loglog(x, y, 'rs')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.semilogx(x, y, 'g^')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histograms\n", "----------\n", "Histograms are made using the hist() function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#generate some random numbers from a normal distribution\n", "data = 100 + np.random.randn(500)\n", "\n", "#make a histogram with 20 bins\n", "plt.hist(data, 20)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Labels\n", "------\n", "We can add axis labels to a figure using the xlabel() and ylabel() functions." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hist(data, 20)\n", "plt.xlabel('Body Mass (g)', fontsize=20)\n", "plt.ylabel('Number of Individuals', fontsize= 20)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 5, "text": [ "" ] }, { "output_type": "display_data", "png": 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mReci4ezsjF27dmmdL4TAr7/+CmdnZ6MEIyIi+elcJIYPH45Tp07h1VdfrdJauHr1KoYP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\nAAAASUVORK5CYII=\n" } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Axis Limits\n", "-----------\n", "Axis limits are changed using the axis([xmin, xmax, ymin, ymax]) function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.hist(data, 20)\n", "plt.axis([90, 110, 0, 100])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 6, "text": [ "[90, 110, 0, 100]" ] }, { "output_type": "display_data", "png": 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} ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting multiple sets of data together\n", "---------------------------------------\n", "To plot multiple datasets together we tell Python not to overwrite the previous data using hold(True). Running hold(False) will cause Python to start overwriting the figure again." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.array(range(20))\n", "y = 3 + 0.5 * x + np.random.randn(20)\n", "z = 2 + 0.9 * x + np.random.randn(20)\n", "\n", "#plot the data\n", "plt.plot(x, y, 'bo')\n", "plt.hold(True)\n", "plt.plot(x, z, 'r^')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subplots\n", "--------\n", "Subplots are generated using subplot(#ofRows, #ofCols, Position)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.subplot(1, 2, 1)\n", "plt.plot(x, y, 'rs')\n", "plt.subplot(1, 2, 2)\n", "plt.hist(data, 10)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "New Figures\n", "-----------\n", "Plotting multiple figures in the same script requires that we create new figures, which is done using figure(). In this example the two figures are different figures rather than subplots of a single figure." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(z, x, 'go')\n", "plt.figure()\n", "plt.plot(z, y, 'rs')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 9, "text": [ "[]" ] }, { "output_type": "display_data", "png": 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} ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Want to know more?\n", "------------------\n", "Matplotlib is very powerful and there are lots of functions and arugments to create different kinds of figures and modify them as much as you would like.\n", "Check out the [website](http://matplotlib.sourceforge.net/) or the [gallery](http://matplotlib.sourceforge.net/gallery.html) to learn more." ] } ], "metadata": {} } ] }