{ "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": [ "## Import your data" ] }, { "cell_type": "code", "execution_count": 2, "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
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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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"country-gdp-2014.csv\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Change the font just for the title or axis labels\n", "\n", "The default font is **BitstreamVeraSans Roman**, but we want to try out something else. You can pass `fontname` to `.set_xlabel`, `.set_ylabel`, `.set_title`, or `.annotate` to specify a particular font. This does **not** change the font for the numbers on the axes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Oceania has small countries')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the median life expectancy by continent\n", "ax = df.groupby('Continent')['Population'].median().sort_values().plot(kind='barh')\n", "ax.set_xlabel(\"\")\n", "\n", "# Change the y axis label to Arial\n", "ax.set_ylabel(\"Median Population\", fontname=\"Arial\", fontsize=12)\n", "\n", "# Set the title to Comic Sans\n", "ax.set_title(\"Oceania has small countries\", fontname='Comic Sans MS', fontsize=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Change the font for the tick marks/numbers on the axes\n", "\n", "Changing the fonts for the labels on each axis (the numbers) is a little bit more complicated, but you can use it in combination with the content above to specify fonts for every part of your graph." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the median life expectancy by continent\n", "ax = df.groupby('Continent')['Population'].median().sort_values().plot(kind='barh')\n", "\n", "# You can also ax.set_xlabel(\"\") but the spacing gets weird\n", "ax.xaxis.label.set_visible(\"False\")\n", "\n", "# Change the y axis label to Arial\n", "ax.set_ylabel(\"Median Population\", fontname=\"Arial\", fontsize=12)\n", "\n", "# Set the title to Comic Sans\n", "ax.set_title(\"Oceania has small countries\", fontname='Comic Sans MS', fontsize=18)\n", "\n", "# Set the font name for axis tick labels to be Comic Sans\n", "for tick in ax.get_xticklabels():\n", " tick.set_fontname(\"Comic Sans MS\")\n", "for tick in ax.get_yticklabels():\n", " tick.set_fontname(\"Comic Sans MS\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Specify a default font for everything on your graphs\n", "\n", "You can also specify a default font for *everything* in `matplotlib`. This will affect every single plot you make.\n", "\n", "> **Note:** Although you *can* do this, unless you're practicing to make a house style I recommend specifying single-use fonts (the above section) instead of defaults. \n", "\n", "I don't know why, but you can only set it **once**. If you change your mind about what you want your default font to be you'll have to **restart your kernel**.\n", "\n", "It also **knows whether your font is serif or sans-serif**. If you try it with `serif` and it doesn't work, change both of them to `sans-serif` instead. For example, if I changed my font to **Georgia** down below I'd need it to be `serif` instead, since it's a font with serifs." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Oceania has small countries')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Say, \"the default sans-serif font is COMIC SANS\"\n", "# Then, \"ALWAYS use sans-serif fonts\"\n", "plt.rcParams.update({\n", " 'font.sans-serif': 'Comic Sans MS',\n", " 'font.family': 'sans-serif'\n", "})\n", "\n", "ax = df.groupby('Continent')['Population'].median().sort_values().plot(kind='barh')\n", "ax.set_xlabel(\"\")\n", "ax.set_ylabel(\"Median Population\")\n", "ax.set_title(\"Oceania has small countries\", fontsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Listing all of the fonts matplotlib knows about" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you'd like to know what fonts are available for use, check out [list all fonts available in matplotlib plus samples](../list-all-fonts-available-in-matplotlib-plus-samples)." ] }, { "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 }