{ "metadata": { "name": "", "signature": "sha256:c83d2afaebba14973f54e9becb6f89e1eb3e7b6e8f51deea7fda77183365484e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": [], "source": [ "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6.2. Creating beautiful statistical plots with seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Let's import NumPy, matplotlib, and seaborn." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# !pip install seaborn" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Downloading/unpacking seaborn\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " Downloading seaborn-0.5.1.tar.gz (104kB): " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 3% 4.1kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 7% 8.2kB" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 11% 12kB \r", " Downloading seaborn-0.5.1.tar.gz (104kB): 15% 16kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 19% 20kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 23% 24kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 27% 28kB" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 31% 32kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 35% 36kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 39% 40kB" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 43% 45kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 47% 49kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 50% 53kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 54% 57kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 58% 61kB" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 62% 65kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 66% 69kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 70% 73kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 74% 77kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 78% 81kB" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 82% 86kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 86% 90kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 90% 94kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 94% 98kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 98% 102kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): 100% 104kB\r", " Downloading seaborn-0.5.1.tar.gz (104kB): \r", " Downloading seaborn-0.5.1.tar.gz (104kB): 104kB downloaded\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " Running setup.py egg_info for package seaborn\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Installing collected packages: seaborn\r\n", " Running setup.py install for seaborn\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " \r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Successfully installed seaborn\r\n", "Cleaning up...\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. We generate a random dataset (following this example on seaborn's website: http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/linear_models.ipynb)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x1 = np.random.randn(80)\n", "x2 = np.random.randn(80)\n", "x3 = x1 * x2\n", "y1 = .5 + 2 * x1 - x2 + 2.5 * x3 + 3 * np.random.randn(80)\n", "y2 = .5 + 2 * x1 - x2 + 2.5 * np.random.randn(80)\n", "y3 = y2 + np.random.randn(80)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Seaborn implements many easy-to-use statistical plotting functions. For example, here is how to create a violin plot (showing the distribution of several sets of points)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(10,8));\n", "sns.violinplot([x1,x2,x3]);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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+bhBERKJs26E1Bqgcs3nzZgjmfAjCnkO+kiW5JmrnTq6DotwRCPgAAKIswe/3aVwNUXrF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\nHkd833azyzkkPTaARP87WLrsBMyf32l2OWRRbW0dWNi5CLH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"text": [ "" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Seaborn also implement all-in-one statistical visualization functions. For example, one can use a single function (`regplot`) to perform *and* display a linear regression between two variables." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(4,3));\n", "sns.regplot(x2, y2);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. Seaborn has built-in support for Pandas data structures. Here, we display the pairwise correlations between all variables defined in a `DataFrame`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.DataFrame(dict(x1=x1, x2=x2, x3=x3, \n", " y1=y1, y2=y2, y3=y3))\n", "sns.corrplot(df);\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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r9Tz4YD1GjBiZ43nsNYxkg2n/SGD6XesBmAr5Z6GUagQsBDpqmpZ9mrYA8vwq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\n5uZOu3YdCA8/YPMYc2I0GC0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"text": [ "" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n", "\n", "> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)." ] } ], "metadata": {} } ] }