{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n", "\n", "*No changes were made to the contents of this notebook from the original.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Text and Annotation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a good visualization involves guiding the reader so that the figure tells a story.\n", "In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.\n", "Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this.\n", "Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and importing the functions we will use:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "plt.style.use('seaborn-whitegrid')\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Effect of Holidays on US Births\n", "\n", "Let's return to some data we worked with earler, in [\"Example: Birthrate Data\"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv.\n", "\n", "We'll start with the same cleaning procedure we used there, and plot the results:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "births = pd.read_csv('data/births.csv')\n", "\n", "quartiles = np.percentile(births['births'], [25, 50, 75])\n", "mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])\n", "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n", "\n", "births['day'] = births['day'].astype(int)\n", "\n", "births.index = pd.to_datetime(10000 * births.year +\n", " 100 * births.month +\n", " births.day, format='%Y%m%d')\n", "births_by_date = births.pivot_table('births',\n", " [births.index.month, births.index.day])\n", "births_by_date.index = [pd.datetime(2012, month, day)\n", " for (month, day) in births_by_date.index]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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TI9hZ040kDbZLCyU91juYJNwyijOxpCAJh9vDRxUdvH2wlZWzUlHIR2+8G6o4\nLYa5WXG4PRJX5CSgN9pDTvkbqsfsIFKlGPWFZbKYl+Xt/nGoqY9tVV18XNHJI9cW8sh1BWyt6mJ7\ntf4Cn6Ew1fRZncRFqgKbYuVyGVFqBZYQA46U5+PkBEEQhInz3U1H+OBYO9DC3Yuz+fkts0cd02d1\nIkmM2fd2fnYC7x9tw+ORhm1G6w1Sl+x3bXEKH1V0crLTSHFaDHV6ExEqOdN8wehICVo1v73zclbO\nTOXB1w7y+y3VuD3SuIHxUDNTItjeYEalkDF3yJS88eQna/n5V2bx5RGbASfSFbkJqBQy/uuvx7E6\n3Ty8YuwNe0M9ccscavUmekx29jf00OsrWwlHbxhT7y5m2YlRJGjVfHqyiyPN/RSkRHP/0lwkJF7d\n08j/bK0N2nFEEM5Uv8VJbNTw97IotSJku7awAuNbb72V6GjvpbLMzEz+7d/+jR/+8IfIZDIKCwv5\n6U9/ilwuZ9OmTWzcuBGlUsmDDz7IihUrsNlsPProoxgMBrRaLU8//TQJCefum7wgCMJUNmBz8nFF\nJ1dlRdFplQUuTY802MUgeDC1MCeeDfubqO7yBrmB+1mcgeEeI60o9vYp3lrVFQiM85KihwXWwdw4\nK43cJC0v7zoFBN94F8zMFG9ZRGlmXNiZUplMxt1X5oR17JmKUiuZlxXPvlM93DwnjcJUXVj3mzkt\nhpnTYvjwWDswupxiwObk5+9VsPeUgQ//fVlgMAn4MvnRkzcwlslkzJ0exz9OdKKUy3jxnvmBmvGv\nL87mqb9XUdtlDExnFISz1Wd1EBc5PDCOVCvOfvKd3W5HkiTKysooKyvjySef5Mknn+Q73/kO69ev\nR5IkPvnkE/R6PWVlZWzcuJGXX36ZtWvX4nA42LBhA0VFRaxfv55bbrmF559//uxWKgiCcAn7+EQn\nDreH22fFUZSqo9sUPOtoMI3fxWBBtjdBMbKcYrzMZGpMBLMzYgL9jOv0psCktfHI5TLuW5qLR4KY\nCCWZ8cEzzCPlxqtJj43gWl9AfjFZ7stuPhRmtngo/yCQoYFxc4+FL/xuJ2+Ut9DcY+Wgb2y2X6/F\nMeYXlsnCPwL8kWsLh/Wxvn1+JiqFjPX7Tq9ThyCMp8/iHNWTPEqlPPt2bVVVVVitVu677z7uuece\nDh8+zIkTJ7jiiisAWL58Obt37+bo0aPMnTsXtVqNTqcjKyuLqqoqysvLWbZsWeDYPXv2nOkaBUEQ\nLnnvH20rGYt3AAAgAElEQVQjIy6S4mQNSdEauk3BN3H5L9MnjJFlnJ4QSYpOQ/mIKXhjbdjzu644\nlfLGXmq7TLT0WskP0d7M7/Z5mcRHqZidERt0EEYwCrmMTx+9hgevzg/r+PPp/qW5fPDvS8POfg+V\novNmwrsGbIC37d2P/3qcPouDsvuvQCGXcWDE78VgckzKVm1DrVo4nR/dXMy3Vgz/fSZFa1g5K423\nDrYMawcoCGfDGxiPKKXQKEJuvgtZShEREcH999/PHXfcQUNDAw888ACSJAXe2LRaLUajEZPJhE43\neAlEq9ViMpmG3e4/NpjKyqm9K9Vms025NU7FNY00ldc4ldfmN9XWaLS72VGt59aZsd6reTYrfRYn\nR49XoFIMDzZP1HrbiBnaGqnsDf5WX5SgZHdNV+DfyOGWMNlduM39Y/67zY93IQO+t2E/kgQRzr6w\n/43/+9oUIpSysI+32WzU11SHdeyFIAcq+4MPShmP3eUB4ER9MwWFkfzp7wfYUa3n365IJMnVTV68\niu0VLdw83XucweKirc9KtGSZVK/nYH9/y5KhtvrkqGOXpsEHR53830cHWZEX3peti8FUe48ZarKv\nrcdsw2M1DluD5LRjsFqBsfcshAyMc3Nzyc7ORiaTkZubS1xcHCdOnAj83Gw2ExMTQ3R0NGazedjt\nOp1u2O3+Y4MpKSkJucjJrLKycsqtcSquaaSpvMapvDa/qbbGTQeacUtw74o5KI1tlOQmwOFekqfn\nBrox+H3cVgN0c8Vls8ZsW3atIYJd71cQm57DtLhIOgdswCmKcjIoKckOep8S4Mu1Lv7qm5y3/PIZ\nlIzTw3fkfU/HVPv9DaWLaIGIWOQqePmQgdLMWB69ZREKuYyltR427G+ioGgGKoWc/9t1Cgm459rS\nsEpXLhan8/ubMUPi93t7OdIj41tfnDy/86n8Gp3Ma3O6PVid9eRlplFSMtifPGm/mdY+67j3DVlK\n8eabb/LUU08B0NnZiclkYsmSJezbtw+AHTt2sGDBAkpLSykvL8dut2M0Gqmrq6OoqIh58+axffv2\nwLHz588/44UKgiBcyo629BEToWR2hjcQTY721qp2G0fXGZvtLiJU8nF7+V5V4B2Z7G+V1RPmdDX/\naGaZjJD9e4XgUnQaOgds1Pc40BvtfOua/EDLtwXZCdicnsDwkPePtlGcpptUQfHpkstlXD8zhR3V\n3umKH1d0csUTWxiwOS/0qQmTUJ/F+7oZVUqhVmA92xrj22+/HaPRyOrVq1mzZg2//OUv+fGPf8wf\n/vAH7rzzTpxOJzfeeCPJycncfffd3HXXXdx7772sWbMGjUbD6tWrqampYfXq1bz++us8/PDDZ7FU\nQRCES1ejwUJOkjZQypbk28QVrM7YZHehVY9/UXBGqo6MuEg+qfQO7egJc7ra7IxYlhYkkZekJUI1\nOfvqXmgpugi6jHbqe7z/5kNrlRfkeC/zHmjoobXPuxHvXLafu1jcMDMNq9PNZ7Xd/G5LtfffR28O\nfUdhUvu4opObf7+T5h7LsNubDJaQG+XG0m/1/l3FRgZp13a2NcZqtZpnnnlm1O3r1q0bdduqVatY\ntWrVsNsiIyN59tlnQz2NIAiCEEJTj4U5Q8Yc+zPG+iCBscXhRqsZ/y1eJpNxfUkKrx9oxupwh2zx\nNtRzd83FFKJRvjC2lBgNB5t6qY9QoRvRqSM1JoKshCi2V+sDXUe+VJp+oU71vFmcl0C0Rsmv/3GS\nqg7vfqT2PiuXT48LcU9hMnttXyMV7QN845XPeevBq4iNVNHeb+WG327n/qW5fP+m4tN+zMGM8Yiu\nFGqlmHwnCIIwFTjdHlp7rWQnDo4tTooeO2NstruICqP373UlqdicHj6r7Q50sginLVhclJrM+PBG\nKAujpeg0dA3YqeuxMzM9ZlSnjityE9hZ080ft9dxWWYs2YlTv2RFo1Rw9YxkqjqM6Hxf6tr6bRf4\nrIRzyepws6fOwBW5CTQazPxbWTkOl4c/bK3F7vJQ0T5wRo8bCIyDZozF5DtBEIRJr63PissjkZ0w\nGCBFqhVo1YrgNcYOV8iMMcCivAS0agWfVHWSGuNtIxYfFXzynTBxUnQR2F0eag12vl44Ohv8o5tL\nuL4kBZVCzuyM028JN1mtnJnKB0fbuX9ZLi98Wkd7iI1SwuS2t96A3eXh4RUFGMx21rx+hG+9dpBP\nT3Yhk0Ftl+mMHrfPGrzGOFKtINQkdhEYC4IgTAKNBm/9XVbi8Cxtki54L2Oz3Y0uIvRbvEapYHlR\nMn8/3kF2opbYSBXKcTbsCRMjJcab7Xd5vBPxRkrQqrlp9tQvnxjpptlpPPaFYv55cTbvHm6jfSB4\nxnjbyS5KM2JJ9F01ESanrVVdRKoULMpLQKNU0Giw8LstNWiUcu6cP531+5uwOtxhT7706/Nd/Rpd\nShH6cURgLAiCMAk0+jamZI8MjMcY8mFxuEiPjQjrsR+8Jp/qTiNHmvsoThMjec+HZN1gQDczzHZ3\nlwKNUsH/8w10SY+NCJox7jLa+MafP6coNZrXv3llyM2iwsVJkiS2nexiSUESGqU3YP32dYV4JJgW\nG4EuQsVr+5qo7zad9iCdfqsTuYxASY6fCIwFQRCmiCaDGbVSTqpueLCbFK3mVPfonftmu5uoEF0p\n/Eoz4/jku9dQpzeJLhPniX/6nUIGhalTtw3b2UiPjWR3Xfeo24+19ANQ3WniX/68n43fvPK0M4rC\nhVen907PfPCawUmIMpmM/7ihCICTvg2YtV2nHxj3WZzERqqQy4fX7ofzniiulwmCIEwCjQYLWQlR\no97ovRnjsWqMTy9YyE+OJiMuMvSBwlnzl1JMj1MHsmXCcNPiIugcsOFye4bdfqy1H5kMfvXVUo60\n9LPtZNcFOkPhbPj7dC/MSQj685ykKOQyqDuDOuM+q3NUGQWElzEWgbEgCMIk0NRjISdxdBeIZJ2G\nHrMD54jgwWIP3a5NuHB0GiVRagX58aIMYCxpsRF4JOgyDi8VOtbST35yNF+6zFuDfSaBk3Dhdfg6\njoxV8qVRKshKiKJWfwaBscUxqocxENaVBREYC4IgXEDNPRb+srshsFkkGEmSfBnj0S27/C3b/D2I\nARwuDw63B624vHzRkslk/GH1XFZfFn+hT+WiNc035ry9f3id8bHWfkozYolSK5kWG0F9kFIi4eLX\n3m8jWqNEFzF2F5yClOgz6kzRZ3GO6kgBopRCEAThovfbLdX89G8nWPLUVl7d0xD0GL3RjtXpHrXx\nDgYDY/2QrJq/T2e4NcbChXFdSSoZMaI13ljS47yZxLY+Gz999zgPriunc8BGl9EeaGGXm6wVgfEk\n1TlgIzVm/K4i+SnRNHRbRpXThNJndYzqYQxi850gCMJFzeOR2H5Sz5KCRFxuiSc+qOSWuRnEjMig\n+DtSjGzVBpCs816KH9qZwuyb7HS6NcaCcDFJ92WMa7pMbPy8GbvLw/QE799AaaY3MM5Liuavh1uR\nJGnUkBTh4tYxYCMtROecguRoHG4Pzb1WcpPCH3LjzRiLGmPhApIkCU+ortmCIAxztLUfg9nBHfOn\n86ObS7C7PHxwtH3YMZIk8dr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T6VCPeb/Kykqy8goxOeqZmZtOSYl3MteXql387XAbOflF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\nAIBKGAMAQFV/AM7EO25GPoQAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 4))\n", "births_by_date.plot(ax=ax);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n", "This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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nu1OxAEdvwXZlv57Opdsb20gvUje8cz2MJjMPfLCPZbtTaj2WnHuli1R1UF2f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e0XFEKI1u7XNo9f81izpbq7ocV7ph955BHef/99Bg0axNChQ+nXr98199+8eTOBgYEMHjyYVatW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\nEEI0jFbjg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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 4))\n", "births_by_date.plot(ax=ax)\n", "\n", "# Add labels to the plot\n", "style = dict(size=10, color='gray')\n", "\n", "ax.text('2012-1-1', 3950, \"New Year's Day\", **style)\n", "ax.text('2012-7-4', 4250, \"Independence Day\", ha='center', **style)\n", "ax.text('2012-9-4', 4850, \"Labor Day\", ha='center', **style)\n", "ax.text('2012-10-31', 4600, \"Halloween\", ha='right', **style)\n", "ax.text('2012-11-25', 4450, \"Thanksgiving\", ha='center', **style)\n", "ax.text('2012-12-25', 3850, \"Christmas \", ha='right', **style)\n", "\n", "# Label the axes\n", "ax.set(title='USA births by day of year (1969-1988)',\n", " ylabel='average daily births')\n", "\n", "# Format the x axis with centered month labels\n", "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n", "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n", "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n", "Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*.\n", "See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transforms and Text Position\n", "\n", "In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n", "\n", "Any graphics display framework needs some scheme for translating between coordinate systems.\n", "For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen.\n", "Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule).\n", "\n", "The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation:\n", "\n", "- ``ax.transData``: Transform associated with data coordinates\n", "- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions)\n", "- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions)\n", "\n", "Here let's look at an example of drawing text at various locations using these transforms:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(facecolor='lightgray')\n", "ax.axis([0, 10, 0, 10])\n", "\n", "# transform=ax.transData is the default, but we'll specify it anyway\n", "ax.text(1, 5, \". Data: (1, 5)\", transform=ax.transData)\n", "ax.text(0.5, 0.1, \". Axes: (0.5, 0.1)\", transform=ax.transAxes)\n", "ax.text(0.2, 0.2, \". Figure: (0.2, 0.2)\", transform=fig.transFigure);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that by default, the text is aligned above and to the left of the specified coordinates: here the \".\" at the beginning of each string will approximately mark the given coordinate location.\n", "\n", "The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n", "The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size.\n", "The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size.\n", "\n", "Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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C\n", "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax.set_xlim(0, 2)\n", "ax.set_ylim(-6, 6)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arrows and Annotation\n", "\n", "Along with tick marks and text, another useful annotation mark is the simple arrow.\n", "\n", "Drawing arrows in Matplotlib is often much harder than you'd bargain for.\n", "While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended.\n", "Instead, I'd suggest using the ``plt.annotate()`` function.\n", "This function creates some text and an arrow, and the arrows can be very flexibly specified.\n", "\n", "Here we'll use ``annotate`` with several of its options:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Hvx2nR5Oa/K2jL41quur9X7E6mqax6WgiX/4Zx+HzN3CwtSHEpxoDWtaiUU0XPF0rknjh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\nwWDQ+78ihMlIYVuYgQMHPnD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "fig, ax = plt.subplots()\n", "\n", "x = np.linspace(0, 20, 1000)\n", "ax.plot(x, np.cos(x))\n", "ax.axis('equal')\n", "\n", "ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),\n", " arrowprops=dict(facecolor='black', shrink=0.05))\n", "\n", "ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),\n", " arrowprops=dict(arrowstyle=\"->\",\n", " connectionstyle=\"angle3,angleA=0,angleB=-90\"));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available.\n", "These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities.\n", "Let's demonstrate several of the possible options using the birthrate plot from before:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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8tKd8xCdjKkogCms7sDlczJsUTXiw1mNaZaYVRVGUgHmDaU9mWs9aytrBA1an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\nz5+ZhF6j5u19lV6fy+Yo4ATlAlHbfuqidKy27wY2u0qauOf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MlhPkJ4Xz4MUziArW8U4/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l2bd999l6eeesqBkQohhHMJDQ1l1KhRpKWlOdWIBSL/JCcnk5SUROnSpXO0/qlTpxgzZgxxcXE0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\n8tUE0tNdw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 4))\n", "births_by_date.plot(ax=ax)\n", "\n", "# Add labels to the plot\n", "ax.annotate(\"New Year's Day\", xy=('2012-1-1', 4100), xycoords='data',\n", " xytext=(50, -30), textcoords='offset points',\n", " arrowprops=dict(arrowstyle=\"->\",\n", " connectionstyle=\"arc3,rad=-0.2\"))\n", "\n", "ax.annotate(\"Independence Day\", xy=('2012-7-4', 4250), xycoords='data',\n", " bbox=dict(boxstyle=\"round\", fc=\"none\", ec=\"gray\"),\n", " xytext=(10, -40), textcoords='offset points', ha='center',\n", " arrowprops=dict(arrowstyle=\"->\"))\n", "\n", "ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center',\n", " xytext=(0, -20), textcoords='offset points')\n", "ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\n", " xycoords='data', textcoords='data',\n", " arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\n", "\n", "ax.annotate('Halloween', xy=('2012-10-31', 4600), xycoords='data',\n", " xytext=(-80, -40), textcoords='offset points',\n", " arrowprops=dict(arrowstyle=\"fancy\",\n", " fc=\"0.6\", ec=\"none\",\n", " connectionstyle=\"angle3,angleA=0,angleB=-90\"))\n", "\n", "ax.annotate('Thanksgiving', xy=('2012-11-25', 4500), xycoords='data',\n", " xytext=(-120, -60), textcoords='offset points',\n", " bbox=dict(boxstyle=\"round4,pad=.5\", fc=\"0.9\"),\n", " arrowprops=dict(arrowstyle=\"->\",\n", " connectionstyle=\"angle,angleA=0,angleB=80,rad=20\"))\n", "\n", "\n", "ax.annotate('Christmas', xy=('2012-12-25', 3850), xycoords='data',\n", " xytext=(-30, 0), textcoords='offset points',\n", " size=13, ha='right', va=\"center\",\n", " bbox=dict(boxstyle=\"round\", alpha=0.1),\n", " arrowprops=dict(arrowstyle=\"wedge,tail_width=0.5\", alpha=0.1));\n", "\n", "# Label the axes\n", "ax.set(title='USA births by day of year (1969-1988)',\n", " ylabel='average daily births')\n", "\n", "# Format the x axis with centered month labels\n", "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n", "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n", "ax.xaxis.set_major_formatter(plt.NullFormatter())\n", "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\n", "\n", "ax.set_ylim(3600, 5400);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish.\n", "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics!\n", "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options.\n", "\n", "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }