{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "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": 2, "metadata": {}, "outputs": [], "source": [ "plt.rcParams.update({\n", " 'pdf.fonttype': 42,\n", " 'ps.fonttype': 42\n", "})" ] }, { "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. **You can only do this on OS X.**\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": 3, "metadata": {}, "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
\n", "
" ], "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": 3, "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 `color='slategray'` allows you to make every bar be the same color. `plt.savefig` is the bit that saves your graph." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.groupby(\"Continent\")['life_expectancy'].median().sort_values().plot(kind='barh', color='slategray')\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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='GDP_per_capita', y='life_expectancy', 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.\n", "\n", "But despite it being crazy complicated: `plt.savefig` once again." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10,6))\n", "for name, group in df.groupby('Continent'):\n", " group.plot(x='GDP_per_capita', y='life_expectancy', marker='o', markersize=5, alpha=0.5, linestyle='', label=name, markeredgewidth=0, ax=ax)\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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }