{ "cells": [ { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting set up to export text correctly\n", "\n", "If you're exporting text, you need to make sure `matplotlib` is exporting **editable** text, otherwise Illustrator will treat every single character as a shape instead of text. By default `matplotlib` exports \"Type 3 fonts\" which Adobe Illustrator doesn't understand, so you need to change matplotlib to export **Type 2/TrueType fonts**.\n", "\n", "This setting is, for some reason, the number `42`. Run this once at the top of your code and you'll be set for everything else in the script/notebook." ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib\n", "matplotlib.rcParams['pdf.fonttype'] = 42\n", "matplotlib.rcParams['ps.fonttype'] = 42" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't want to type this all of the time (which you shouldn't), there is a solution! Run the following code **from the command line** - it creates a `matplotlib` startup file that will run the above commands every time `matplotlib` is started.\n", "\n", "````bash\n", "mkdir -p ~/.matplotlib\n", "echo 'pdf.fonttype: 42' >> ~/.matplotlib/matplotlibrc\n", "echo 'ps.fonttype: 42' >> ~/.matplotlib/matplotlibrc\n", "````" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading in our data" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CountryContinentGDP_per_capitalife_expectancyPopulation
0AfghanistanAsia66354.86322856302
1AlbaniaEurope419574.2003071856
2AlgeriaAfrica509868.96330533827
3AngolaAfrica244645.23413926373
4Antigua and BarbudaN. America1273873.54477656
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" ], "text/plain": [ " Country Continent GDP_per_capita life_expectancy \\\n", "0 Afghanistan Asia 663 54.863 \n", "1 Albania Europe 4195 74.200 \n", "2 Algeria Africa 5098 68.963 \n", "3 Angola Africa 2446 45.234 \n", "4 Antigua and Barbuda N. America 12738 73.544 \n", "\n", " Population \n", "0 22856302 \n", "1 3071856 \n", "2 30533827 \n", "3 13926373 \n", "4 77656 " ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"countries.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting\n", "\n", "**Be sure to read the section above above about exporting text.**\n", "\n", "After you make your graphic, use `plt.savefig(\"filename.pdf\")` to save it as a vector-graphic `.pdf`. Do not save as `png` or `jpg` or anythign else. You *could* save as `svg` but I've found `pdf` generally works better.\n", "\n", "You'll also need to pass `transparent=True` when using `.savefig` to get rid of white backgrounds. It makes your file much easier to work with in Illustrator. \n", "\n", "Even though it's just `plt.savefig` again and again, I've included several examples below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1, bar chart\n", "\n", "Setting `linewidth=0` allows you to easily remove the lines around bars when in Illustrator. `plt.savefig` is the bit that saves your graph." ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.groupby(\"Continent\")['life_expectancy'].median().sort_values().plot(kind='barh', linewidth=0)\n", "ax.set_xlabel(\"Life expectancy\")\n", "\n", "# Remember: transparent=True\n", "plt.savefig(\"output-bargraph.pdf\", transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2, scatterplot\n", "\n", "Here I've passed a lot of arguments in to the `.scatter` method. It's still `plt.savefig` to save, though.\n", "\n", "Use `linewidth=0` to remove the outline of the circles (although it will still be there, invisible, for you to remove in Illustrator)." ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', linewidth=0, xlim=(0, 70000), ylim=(30, 85), figsize=(10, 6))\n", "plt.savefig(\"output-scatter.pdf\", transparent=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3, grouped scatterplot\n", "\n", "Setting `markeredgewidth=0` allows you to easily remove the invisible borders around the circles when in Illustrator. It has the same effect as `linewidth=0` in the previous example, but the code is different because we're directly using `matplotlib` instead of going through `pandas` (`ax.plot` instead of `df.plot`).\n", "\n", "But despite it being crazy complicated: `plt.savefig` once again." ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4sYiIiIhEJ6qOBcYYFzAJyGl+zlr7cpTnFgB3A1OBRuAK4EPgIaAIqADmWmtr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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10,6))\n", "for category, selection in df.groupby('Continent'):\n", " ax.plot(selection['GDP_per_capita'], selection['life_expectancy'], marker='o', markersize=5, alpha=0.5, linestyle='', label=category, markeredgewidth=0)\n", "\n", "ax.set_ylabel(\"Life expectancy\")\n", "ax.set_xlabel(\"Per capita GDP\")\n", "ax.set_ylim((30,85))\n", "ax.legend(loc='lower right')\n", "\n", "# Remember: transparent=True\n", "plt.savefig(\"output-scatter-grouped.pdf\", transparent=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }