From 7ccdbee826f761d09e0fb6bf54db7c75e3203d6e Mon Sep 17 00:00:00 2001 From: Guy Allard <w.g.allard@lumc.nl> Date: Mon, 18 Sep 2017 15:10:03 +0200 Subject: [PATCH] working on data visualization 1 notebook --- visualization/DataVisualization1.ipynb | 1074 ++++++++++++++++++++++++ 1 file changed, 1074 insertions(+) create mode 100644 visualization/DataVisualization1.ipynb diff --git a/visualization/DataVisualization1.ipynb b/visualization/DataVisualization1.ipynb new file mode 100644 index 0000000..bdb893b --- /dev/null +++ b/visualization/DataVisualization1.ipynb @@ -0,0 +1,1074 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<style>/* Remove the vertical scrollbar added by nbconvert. */\n", + ".reveal {\n", + " overflow: hidden;\n", + "}\n", + "\n", + "/* Workaround some highlight.js bugs in language autodetection. */\n", + "code.objectivec *,\n", + "code.perl *,\n", + "code.cs *,\n", + "code.javascript *,\n", + "code.http * {\n", + " color: black ! important;\n", + " font-weight: normal ! important;\n", + "}\n", + "span.title {\n", + " color: black ! important;\n", + "}\n", + "span.tag {\n", + " color: black ! important;\n", + "}\n", + "span.attribute {\n", + " color: black ! important;\n", + "}\n", + "</style>" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "def css_styling():\n", + " styles = open('styles/custom.css', 'r').read()\n", + " return HTML('<style>' + styles + '</style>')\n", + "css_styling()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Data Visualization with Python\n", + "\n", + "## Part 1: Python + Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# This lesson\n", + "\n", + "- A 'taster'\n", + " - a small subset of what is possible with matplotlib\n", + "- Worked examples" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Matplotlib\n", + "- Plotting library for Python\n", + "- High quality figures suitable for publication\n", + "- Integrates with IPython, Jupyter and NumPy (in PyLab mode)\n", + "- Established and robust\n", + "- Large community / user base" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Interfaces\n", + "1. Object Oriented \n", + " - Best for larger development projects\n", + " - Have to keep track of figures and axes\n", + " - Steep learning curve\n", + "<br><br>\n", + "2. Pyplot State Machine \n", + " - For interactive plotting\n", + " - Takes care of many housekeeping tasks\n", + " - Easier to learn than the OO interface\n", + "<br><br>\n", + "3. Pylab\n", + " - Modelled on matlab\n", + " - Imports common modules\n", + " - Handles most housekeeping tasks\n", + " - Easiest to learn\n", + " - The one we will be using!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Interfaces Example\n", + "\n", + "1. Object-oriented interface\n", + "```python\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x = np.arange(0, 10, 0.2)\n", + "y = np.sin(x)\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(x, y)\n", + "```\n", + "\n", + "2. State-machine environment (pyplot)\n", + "```python\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x = np.arange(0, 10, 0.2)\n", + "y = np.sin(x)\n", + "plt.plot(x, y)\n", + "```\n", + "\n", + "3. PyLab mode\n", + "```python\n", + "%pylab\n", + "x = arange(0, 10, 0.2)\n", + "y = sin(x)\n", + "plot(x, y)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Getting help\n", + "\n", + "Consult the built-in documentation, for example:\n", + "```\n", + ">>> help(subplot)\n", + "Help on function subplot in module matplotlib.pyplot:\n", + "\n", + "subplot(*args, **kwargs)\n", + " Return a subplot axes positioned by the given grid definition.\n", + "...\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Useful Resources\n", + "- Matplotlib Homepage\n", + " - https://matplotlib.org/\n", + "<br><br>\n", + "- Gallery\n", + " - https://matplotlib.org/gallery.html\n", + " - Many examples with source code\n", + "<br><br>\n", + "- Online documentation\n", + " - https://matplotlib.org/contents.html\n", + " - Full API documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# First Steps\n", + "\n", + "## Preparing the Jupyter Notebook\n", + "1. Open a new Jupyter Notebook\n", + "2. Run this code in the first empty cell:\n", + "```\n", + "%pylab inline\n", + "```\n", + "3. Now any pylab plotting commands will display in the notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Grab some data\n", + "Use Pandas to load a dataset which contains population data for four countries" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "populations = pd.DataFrame.from_csv(\n", + " \"https://git.lumc.nl/courses/programming-course/raw/master/visualization/data/populations.csv\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "Take a quick look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style>\n", + " .dataframe thead tr:only-child th {\n", + " text-align: right;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Year</th>\n", + " <th>Belgium</th>\n", + " <th>Denmark</th>\n", + " <th>Netherlands</th>\n", + " <th>Sweden</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1950</td>\n", + " <td>17.2786</td>\n", + " <td>8.5627</td>\n", + " <td>20.2273</td>\n", + " <td>14.0332</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1951</td>\n", + " <td>17.3564</td>\n", + " <td>8.6074</td>\n", + " <td>20.5288</td>\n", + " <td>14.1408</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1952</td>\n", + " <td>17.4608</td>\n", + " <td>8.6676</td>\n", + " <td>20.7642</td>\n", + " <td>14.2489</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1953</td>\n", + " <td>17.5555</td>\n", + " <td>8.7386</td>\n", + " <td>20.9860</td>\n", + " <td>14.3429</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1954</td>\n", + " <td>17.6388</td>\n", + " <td>8.8114</td>\n", + " <td>21.2307</td>\n", + " <td>14.4272</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Year Belgium Denmark Netherlands Sweden\n", + "0 1950 17.2786 8.5627 20.2273 14.0332\n", + "1 1951 17.3564 8.6074 20.5288 14.1408\n", + "2 1952 17.4608 8.6676 20.7642 14.2489\n", + "3 1953 17.5555 8.7386 20.9860 14.3429\n", + "4 1954 17.6388 8.8114 21.2307 14.4272" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "populations.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Plot it!\n", + "Let's make a plot the population of the Netherlands on the y-axis, and the year on the x-axis" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e4d4b3c90>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Add titles and label the axes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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PZq9nQGYz/npxXw5pXCfRYYlUSUoEklTcnXdmrOOe9+ayZ38+N518GNcf10VV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\nBc6IiSt48KMF1KtVnacuOYo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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49f5bf10>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"])\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "\n", + "xlabel(\"Year\")\n", + "\n", + "ylabel(\"Population (Millions)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Change some properties of the line\n", + "\n", + "How about a 5px thick orange line?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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WHZUERERSyGgiMLOmhCTwiLs/Ey3+zMy6ROVdgKWZjEFERJLL5FlD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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49e70ad0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], \n", + " linewidth=5, color=\"orange\")\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "\n", + "xlabel(\"Year\")\n", + "\n", + "ylabel(\"Population (Millions)\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Change some properties of the x-axis\n", + "\n", + "Label at five-year intervals \n", + "Display the label vertically" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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4S5xJSRGZ+Qi8dlLmInDgD+HEESoCIo0onz6CPdz9KymP70iZelIks0/+DGMH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\noqJuO9/j4ro9T0SkhMR6QZm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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49e63110>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], \n", + " linewidth=5, color=\"orange\")\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "\n", + "xticks(range(1950, 2016, 5), rotation=90);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Change which years are displayed" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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Db8uPyRHxWO3R3XKSZki6QdISSfdJ+lin+WdJCklT+vtHmJlZ//SYDHIlfgmw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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49efadd0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], \n", + " linewidth=5, color=\"orange\")\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "xticks(range(1950, 2016, 5), rotation=90)\n", + "\n", + "xlim(1970, 1990);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Change the y-axis scale" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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PnDA67gKHT4CBZ3nCcE1WkivVlwPnm9kUSe2ByZLGAVcDl5nZs5JGRs8PTj1Q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\nAZgwCsrXpd/fsjOM+BuUfi+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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49ba1450>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], \n", + " linewidth=5, color=\"orange\")\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "xticks(range(1950, 2016, 5), rotation=90)\n", + "xlim(1970, 1990)\n", + "\n", + "ylim(26,30);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Clean up the number formatting on the y-axis\n", + "\n", + "Integer tick labels" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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onJQMRKQ0rZwUuoRWjMteru85MOxmaN45mbgKlJKBiJSWLeUw5Vr4+A+hZZBJ+6GhS6jz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\nyUBERFAyEBERlAxERAQlAxERAf4fle+VxmcgEyEAAAAASUVORK5CYII=\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49ace6d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], \n", + " linewidth=5, color=\"orange\")\n", + "\n", + "title(\"Historical Population of The Netherlands\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "xticks(range(1950, 2016, 5), rotation=90)\n", + "xlim(1970, 1990)\n", + "ylim(26,30)\n", + "\n", + "yticks(range(26,31));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Plot multiple series\n", + "\n", + "Calling **plot** multiple times within the same cell will add multiple series to the chart\n", + "\n", + "Let's compare the Dutch with the Danes" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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uvjEn0RWDlmZYdA+8/rNwMbiq4TDph7Dzl6H30HxHJyLSrqw6rLv7PGBerJEU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\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e49760dd0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"], color=\"orange\")\n", + "plot(populations[\"Year\"], populations[\"Denmark\"], color=\"red\")\n", + "\n", + "title(\"Historical Populations of The Netherlands and Denmark\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "xticks(range(1950, 2016, 5), rotation=90);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Add a legend\n", + "\n", + "1. Give each plotted line a label\n", + "2. Add a legend to the figure" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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HoDWQ0cHt8AdOgh7HuS6fxpgGWSIwiWfHBlj2LJT8E9a+5+Z1PgD2vdJV93Qd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\n+1Q14hvSqOp7IlLY0gCNMcb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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e4972af50>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(populations[\"Year\"], populations[\"Netherlands\"],\n", + " color=\"orange\", label=\"The Netherlands\")\n", + "\n", + "plot(populations[\"Year\"], populations[\"Denmark\"],\n", + " color=\"red\", label=\"Denmark\")\n", + "\n", + "legend(loc=\"upper left\")\n", + "\n", + "title(\"Historical Populations of The Netherlands and Denmark\")\n", + "xlabel(\"Year\")\n", + "ylabel(\"Population (Millions)\")\n", + "xticks(range(1950, 2016, 5), rotation=90);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Other plot types\n", + "\n", + "Let's load a different dataset and take a look at some different plot types" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style>\n", + " .dataframe thead tr:only-child th {\n", + " text-align: right;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>sepal_length</th>\n", + " <th>sepal_width</th>\n", + " <th>petal_length</th>\n", + " <th>petal_width</th>\n", + " <th>species</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>5.1</td>\n", + " <td>3.5</td>\n", + " <td>1.4</td>\n", + " <td>0.2</td>\n", + " <td>setosa</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>4.9</td>\n", + " <td>3.0</td>\n", + " <td>1.4</td>\n", + " <td>0.2</td>\n", + " <td>setosa</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>4.7</td>\n", + " <td>3.2</td>\n", + " <td>1.3</td>\n", + " <td>0.2</td>\n", + " <td>setosa</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>4.6</td>\n", + " <td>3.1</td>\n", + " <td>1.5</td>\n", + " <td>0.2</td>\n", + " <td>setosa</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>5.0</td>\n", + " <td>3.6</td>\n", + " <td>1.4</td>\n", + " <td>0.2</td>\n", + " <td>setosa</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flowers = pd.DataFrame.from_csv(\"https://git.lumc.nl/courses/programming-course/raw/master/visualization/data/iris.csv\")\n", + "\n", + "flowers.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Boxplots\n", + "\n", + "A simple boxplot of the sepal-length distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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ujcBZzAU6VXUNMAP8RlWdVFVPdHM8WFUnA1cAHxqjPumnuCyjFamqHk3yZuBtwDuAfwT+\nFHg98KUkAAcA9w+87OrutTcnOSzJEcDLgE8mOR4o4KAxynkn8LrumACHJXlpt/2F7q+Lp5I8ALwK\neAvwuap6EngyyT+PmP+z3fM24FfHqE/6KYa7Vqyq+jFwE3BTktuBC4GvV9Vp+3rJPPt/AvxbVf1K\nkjXdfAv1IuDULqx/ogv7pwaafsx4/089M8e4r5d+issyWpGSnNCdbT/jJOBOYKr7sJVuCeTEgTG/\n3rW/FdhbVXuBw4E9Xf/5Y5bzReCigdpOGjH+K8AvJ3lxd4Z/zkDfD5n7a0JaUp4laKV6KXBZt7Ty\nNLAL2MjczwJcmuRw5v77/Rjw9e41Tya5jbmll9/s2v6CuWWZPwC+MGYt7wf+OsnO7pg3A5v2Nbiq\nvpbkemAn8H3gdmBv1/13wOYkTwD7+gtEWjR/fkBNSHIT8KGqmlnuWgCSvLT73OAQ5t4MNlbV9uWu\nSy8cnrlLS2NLktcBLwY+abDr+eaZu17QkmwAPjDU/JWqunA56pEmxXCXpAZ5tYwkNchwl6QGGe6S\n1CDDXZIaZLhLUoP+H7OlGZAWAlAbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e4a0f9110>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "boxplot(flowers[\"sepal_length\"], labels=[\"Sepal_length\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Boxplots\n", + "\n", + "Distributions of multiple features" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make a list containing the numeric feature column names\n", + "features = list(flowers.columns[:-1])\n", + "features" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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WXUCLoT2LRXsWq6Xac9H2cQNAqhbzHjcAJIngBoDEtGxw2z7L9k2zPL7a9ica8LqrbS+v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\nRU5ErkJggg==\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e498a30d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data\n", + "boxplot([flowers[f] for f in features], labels=features);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Controlling the size of the plot\n", + "\n", + "Let's change the shape of the boxplot" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7f2e499694d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# make the figure 10 'units' wide and 5 'units' high\n", + "figsize(10, 5)\n", + "\n", + "# plot the data\n", + "boxplot([flowers[f] for f in features], labels=features);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- GitLab