data-science-ipython-notebooks/matplotlib/04.06-Customizing-Legends.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\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": [
    "<!--NAVIGATION-->\n",
    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing Plot Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot legends give meaning to a visualization, assigning meaning to the various plot elements.\n",
    "We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.\n",
    "\n",
    "The simplest legend can be created with the ``plt.legend()`` command, which automatically creates a legend for any labeled plot elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nefZcv+oqoGVLS+ShyMy
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 1000)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, np.sin(x), '-b', label='Sine')\n",
    "ax.plot(x, np.cos(x), '--r', label='Cosine')\n",
    "ax.axis('equal')\n",
    "leg = ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there are many ways we might want to customize such a legend.\n",
    "For example, we can specify the location and turn off the frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n96uWLq36t7+pLlyo6vU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(loc='upper left', frameon=False)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the ``ncol`` command to specify the number of columns in the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4lFX2B/DvCSALEgxVpINUlaaiUh2UFSysuKyuICqu\nyqqo8ANdQVSCIioKK64iuiIqGlHRpSkoAhGwUSQgRZpIaAKhSQ1J5vz+OKGIIWTqO3Pz/TzPPCTh\nzfuemcycuXPvufeKqoKIiNyR4HUAREQUXkzsRESOYWInInIMEzsRkWOY2ImIHMPETkTkmKLRupCI\nsK6SiCgIqiqBHB/VFruq8qaKQYMGeR5DrNz4WPCx4GOR/y0Y7IohInIMEzsRkWOY2D3g8/m8DiFm\n8LE4jo/FcXwsQiPB9uEEfCERjda1iIhcISLQAAdPQ66KEZHiAOYAOCP3fBNUdXCo5yUiouCEpcUu\nIiVV9aCIFAHwNYAHVXX+ScewxU5EFKBgWuxh6WNX1YO5XxaHtdqZwYmIPBKWxC4iCSKyGMCvAGao\n6oJwnJeIiAIXrha7X1WbAagK4FIROS8c5yUiosCFdUkBVf1NRGYD6Ahgxcn/n5ycfOxrn8/HkiYi\nopOkpqYiNTU1pHOEPHgqIuUBZKnqXhEpAeBzAM+q6mcnHcfBUyKiAHlS7gjgHABvi0gCrGvng5OT\nOhERRQ8nKBERxTDPyh2JiCh2MLETETmGiZ2IyDFM7EREjmFiJyJyDBM7EZFjmNiJiBzDxE5E5Bgm\ndiIixzCxExE5homdiMgxTOxERI5hYicicgwTOxGRY5jYiYgcw8ROROQYJnYiIscwsRMROYaJnYjI\nMUzsRESOYWInInIMEzsRkWOY2ImIHMPETkTkGCZ2IiLHhJzYRaSqiMwSkeUi8qOIPBiOwIiIKDii\nqqGdQKQSgEqqmiYipQAsAnC9qv500nEa6rWIiAobEYGqSiC/E3KLXVV/VdW03K/3A1gJoEqo5yUi\nouCEtY9dRGoCaArg+3Cel4iICq5ouE6U2w0zAUDv3Jb7HyQnJx/72ufzwefzhevyREROSE1NRWpq\nakjnCLmPHQBEpCiAqQCmqerIUxzDPnYiogAF08cersT+DoAMVe2bzzFM7EREAfIksYtIKwBzAPwI\nQHNvj6rq9JOOY2InIgqQZy32Al2IiZ2IKGCelDsSEVFsYWInInIMEzsRkWOY2ImIHMPETkTkGCZ2\nIiLHMLETETmGiZ2IyDFM7EREjmFiJyJyDBM7EZFjmNiJiBzDxE5E5BgmdiIixzCxExE5homdiMgx\nTOxERI5hYicicgwTOxGRY5jYiYgcw8ROROQYJnYiIscwsRMROYaJnYjIMWFJ7CIyRkS2icjScJyP\niIiCF64W+1gAHcJ0LiIiCkFYEruqzgOwOxznIiKi0LCPnYjIMUWjerWOHYEzzwQqVwZq1ADOPRfo\n3BkQiWoYBfXbb8CyZcDKlcDq1cD27UBGhv38qDPPBCpUACpWBOrWBRo2BM4/Hyhb1ru4ySGffw6M\nHQvs22e3nBzgT38CunUD7rzT6+gKJDsb+Omn47dNm+x1tGuX/Z8IUKwYUL68vZaqVwfOO89uNWvG\nbHqIaVFN7MnlywNHjgBr1sD366/wrVgB3HBDNEPIV2YmMHMmMH06MHcusGaNPbkaNgTq17db+fJA\n6dL2ZFMF9u8HduwAtm0DvvsOeOstezOoVg1o0wa46iqgQwd7AyDK065dwNat1iI42TnnAH/5C3DW\nWUCpUkBCgj1Rq1bN+1xvvw2sWgX8+c9A69aWMaNM1V4DkycDX31lr4tKlezuNWgANGlir6OyZYGi\nRe34I0eAnTut8bRhg70Oly2zn7dtC1x+OXD99Zb0XZeamorU1NSQziGqGpZgRKQmgCmq2ugU/68F\nvtaKFcCnnwJdu576CRwmOTmWyN99F5g2DWjcGLjuOnsyXXghcMYZgZ8zOxtYsgSYM8fuxvz5QPv2\n1si6/npPXmsUaw4cAD75xFoCCxYA99wDDBsW+nkXLwY+/tha+r/8Avztb/bEa9XK3hQiaPVqYMwY\nu3x2tj3Xr7wSaNnSEnkw0tOtkTVzpr1R1KoF3HgjcNtt9mZRGIgIVDWwzy2qGvINQAqALQAyAaQD\nuCOPY7TAVq1Svesu1TJlVP/yF9VZs1T9/oL/fgFs26b65JOq1aurNm+u+uqrqr/+GtZLHJORoTp2\nrGrbtqqVKqk++qjqxo2RuRbFuAMHVO+5RzUpSfXaa1U//FD14MHIXOvnn1WHDlU97zzVpUsjcoms\nLNXx41V9PtWKFVUfflj1hx/C/nI9dq0vv1S98057+G68UXX27MhcK5bk5s7AcnKgvxDsLaDEftSB\nA6qjR6s2aKDapIk9Y0K0caPqgw/ae0bPnmE5ZUBWrLDrly2revfdquvWRff65DG/X3XkSNUtW6J7\nzTA7fFj19ddVzz1XtVUr1Q8+UM3MDPtlTmnPHtWXX1atX1/10ktVp051N8G7l9iPysmxv9y2bUGf\nIiPjeELv1y+6r6tTxfP446rlylkLxOt4qBDavl11376AfiUnR3XcOPuk26GD6pw5EYqtgLKz7U2l\nUSPViy6yD/euCSaxx0e5Y0ICcO21VnoSoCNHgBdftAHQo6PzL7xgY1JeKlcOePJJYO1aG0Rq1Ah4\n+mng0CFv46Iw2bXLBm9i2f/+Zy+M8eNtBPM05s4FLr0U+M9/gJQUu3tt2kQhznwUKQLcdBOQlgb8\n61/AP/5h9Rhr13obl+cCfScI9oZQWuz5WbfulK2OuXOtF6djR9XlyyNz+XBZu1a1SxfVGjVUp03z\nOhoKmt+v+vbbqmefrdq/v9fRnN68eapNm6pefrnqjz/meciOHardu1srPSXFWu2x6tAh1WeesU/C\nAwfa9/EOznbF5Oepp1Rr1vzdZ7C9e1XvvVe1cmXVjz+OzGUj5fPP7e50724vKIoja9faKOKFF6ou\nWOB1NAWXna06apRqhQqqTz997Md+v+p779l7VN++qvv3exhjgDZvVu3c2Rp28+Z5HU1oCmdiV1X9\n9FPVKlVU779fZ0/dr9WqWVHN7t2Ru2Qk7dun2qePvaAmTvQ6GiqQyZNVy5dXHTHCEmU82rjRyrfU\nut87dbK+6++/9zasYPn9qh99pHrOOaq9e8dv6z2YxB62OvbTCaiOPQhZ23fjxyt6o8xP3+LXF8ej\nxf0XRexa0fL110D37sA119i4QIkSXkdEp7RuHXD4cN6TjOLMF18Ad9wB3HqrjQMFM5cjluzaBfzz\nn1Zn//77NukwngRTx+5EYl+3zuYyVagApHT+EGeVyrEfOGDPHpu7smyZPSkb5Tn9iyh0R44Ajz5q\nY6lvv22Ti1yhapOnBgywIoW7746fpQoKZWKfMsWWzBg4EHjwwfj5YwVC1V5oDz8M/Pvf1oonCqct\nW4AuXaxx9OabJ80UnTvX5v63aOFZfOGycqW1+c47D3jjDaBkSa8jOr1gEnt8lDvmwe8HkpOBe+8F\nJk0Cevd2M6kDdr969ABmzbL73KcPkJXldVSF1N69wPDhBSoPjBfz5gHNm9tSGhMn5jH9f/9+Wx/g\njTc8iS+cGjYEvv3WyiRbtgTWr/c6osiIy8S+d689z2bOBBYuLGBDYtu2iMcVaY0a2bIiq1fbGk/b\nt3sdUSGzapVlwJ9/tkkRcU4VePlla6m/8YZ96s1zOZmrr7ZW+/PPAw88EPetihIlgHfesZr3Fi2A\nGTO8jij84i6xr1tnkyRq1LDEXqCFgDIz7e152LC4b2mVKWPdT61bW45Zys0Io2P2bFsZ7pFHgFde\nifuV3LKybEDxtdeAb76x3J2v+vWB77+3F2CHDrYUYxwTsa7b8eNtQbGRI72OKMwCLaMJ9oYwlDt+\n840tojVqVBC/nJ5uc45vvTW6i1pEUEqKlR5//rnXkThuzBhb4cqR+ep79qhedZXq1Ver/vZbgL+c\nna36yCOqM2dGJDYv/PK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(frameon=False, loc='lower center', ncol=2)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a rounded box (``fancybox``) or add a shadow, change the transparency (alpha value) of the frame, or change the padding around the text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ne7KpAgcPAjt3Atu3A/P
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c21e9e8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on available legend options, see the ``plt.legend`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing Elements for the Legend\n",
    "\n",
    "As we have already seen, the legend includes all labeled elements by default.\n",
    "If this is not what is desired, we can fine-tune which elements and labels appear in the legend by using the objects returned by plot commands.\n",
    "The ``plt.plot()`` command is able to create multiple lines at once, and returns a list of created line instances.\n",
    "Passing any of these to ``plt.legend()`` will tell it which to identify, along with the labels we'd like to specify:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neQFffmnf+I9v3kScjw8
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d779518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5))\n",
    "lines = plt.plot(x, y)\n",
    "\n",
    "# lines is a list of plt.Line2D instances\n",
    "plt.legend(lines[:2], ['first', 'second']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I generally find in practice that it is clearer to use the first method, applying labels to the plot elements you'd like to show on the legend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neQFffmnf+I9v3kScjw8
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11025d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y[:, 0], label='first')\n",
    "plt.plot(x, y[:, 1], label='second')\n",
    "plt.plot(x, y[:, 2:])\n",
    "plt.legend(framealpha=1, frameon=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that by default, the legend ignores all elements without a ``label`` attribute set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legend for Size of Points\n",
    "\n",
    "Sometimes the legend defaults are not sufficient for the given visualization.\n",
    "For example, perhaps you're be using the size of points to mark certain features of the data, and want to create a legend reflecting this.\n",
    "Here is an example where we'll use the size of points to indicate populations of California cities.\n",
    "We'd like a legend that specifies the scale of the sizes of the points, and we'll accomplish this by plotting some labeled data with no entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEZCAYAAACU3p4jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nggsu4KqrriIvL4/i4mK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d74cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cities = pd.read_csv('data/california_cities.csv')\n",
    "\n",
    "# Extract the data we're interested in\n",
    "lat, lon = cities['latd'], cities['longd']\n",
    "population, area = cities['population_total'], cities['area_total_km2']\n",
    "\n",
    "# Scatter the points, using size and color but no label\n",
    "plt.scatter(lon, lat, label=None,\n",
    "            c=np.log10(population), cmap='viridis',\n",
    "            s=area, linewidth=0, alpha=0.5)\n",
    "plt.axis(aspect='equal')\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')\n",
    "plt.colorbar(label='log$_{10}$(population)')\n",
    "plt.clim(3, 7)\n",
    "\n",
    "# Here we create a legend:\n",
    "# we'll plot empty lists with the desired size and label\n",
    "for area in [100, 300, 500]:\n",
    "    plt.scatter([], [], c='k', alpha=0.3, s=area,\n",
    "                label=str(area) + ' km$^2$')\n",
    "plt.legend(scatterpoints=1, frameon=False, labelspacing=1, title='City Area')\n",
    "\n",
    "plt.title('California Cities: Area and Population');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The legend will always reference some object that is on the plot, so if we'd like to display a particular shape we need to plot it.\n",
    "In this case, the objects we want (gray circles) are not on the plot, so we fake them by plotting empty lists.\n",
    "Notice too that the legend only lists plot elements that have a label specified.\n",
    "\n",
    "By plotting empty lists, we create labeled plot objects which are picked up by the legend, and now our legend tells us some useful information.\n",
    "This strategy can be useful for creating more sophisticated visualizations.\n",
    "\n",
    "Finally, note that for geographic data like this, it would be clearer if we could show state boundaries or other map-specific elements.\n",
    "For this, an excellent choice of tool is Matplotlib's Basemap addon toolkit, which we'll explore in [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Legends\n",
    "\n",
    "Sometimes when designing a plot you'd like to add multiple legends to the same axes.\n",
    "Unfortunately, Matplotlib does not make this easy: via the standard ``legend`` interface, it is only possible to create a single legend for the entire plot.\n",
    "If you try to create a second legend using ``plt.legend()`` or ``ax.legend()``, it will simply override the first one.\n",
    "We can work around this by creating a new legend artist from scratch, and then using the lower-level ``ax.add_artist()`` method to manually add the second artist to the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD7CAYAAAB+B7/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnq+/zJ365WN935BnIYA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f1e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "lines = []\n",
    "styles = ['-', '--', '-.', ':']\n",
    "x = np.linspace(0, 10, 1000)\n",
    "\n",
    "for i in range(4):\n",
    "    lines += ax.plot(x, np.sin(x - i * np.pi / 2),\n",
    "                     styles[i], color='black')\n",
    "ax.axis('equal')\n",
    "\n",
    "# specify the lines and labels of the first legend\n",
    "ax.legend(lines[:2], ['line A', 'line B'],\n",
    "          loc='upper right', frameon=False)\n",
    "\n",
    "# Create the second legend and add the artist manually.\n",
    "from matplotlib.legend import Legend\n",
    "leg = Legend(ax, lines[2:], ['line C', 'line D'],\n",
    "             loc='lower right', frameon=False)\n",
    "ax.add_artist(leg);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a peek into the low-level artist objects that comprise any Matplotlib plot.\n",
    "If you examine the source code of ``ax.legend()`` (recall that you can do this with within the IPython notebook using ``ax.legend??``) you'll see that the function simply consists of some logic to create a suitable ``Legend`` artist, which is then saved in the ``legend_`` attribute and added to the figure when the plot is drawn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Histograms, Binnings, and Density](04.05-Histograms-and-Binnings.ipynb) | [Contents](Index.ipynb) | [Customizing Colorbars](04.07-Customizing-Colorbars.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
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