tufte-in-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tufte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Jupyter notebook with examples of how to use `tufte`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, there are four supported plot types:\n",
    "* bar\n",
    "* boxplot\n",
    "* line\n",
    "* scatter\n",
    "\n",
    "The designs are based on Edward R. Tufte's designs in _The Visual Display of Quantitative Information_.\n",
    "\n",
    "This module is built on top of `matplotlib`, which means that it's possible to use those functions or methods in conjunction with `tufte` plots. In addition, an effort has been made to keep most changes to `matplotlibrc` properties contained within the module. That is, we try not to make global changes that will affect other plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by importing several libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import string\n",
    "import random\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tufte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`tufte` plots can take inputs of several types: `list`, `np.ndarray`, `pd.Series`, and, in some cases, `pd.DataFrame`.\n",
    "\n",
    "To create a line plot, do the following. (_Note: if you'd like higher resolution plots, use_ `mpl.rc('savefig', dpi=200)`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x1108aced0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1108c0310>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAE4CAYAAADVQw/kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQOSIWhAMv4fQBpBCT95kRoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1108aced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tufte.line(range(3), range(3), figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that the default Tufte line style includes circle markers with gaps between line segments. You are also able to specify the figure size directly to the `line` function.\n",
    "\n",
    "There are several other differences. We'll create another plot below as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x1109a9d50>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x110917090>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ro0ZpmtZP1PVVyX7q4MGDTJkyBYvFwuzZs+ncubPZkVxeWloaw4YNY+3atbRv397sOJfR\nZ+QiTsowDHbt2sX777/P999/z/jx41mwYEGdLKfrYRiGSz83u6KKioqYO3cuK1asYOrUqQQHB1dq\nvXz5ZbfddhuTJk0iMjKSpKQk6tevb3akcvRTFnECP/9st6ysjBkzZjBlyhSGDh1KcnIyoaGhKvFf\n8GurkqWkpBAdHU1ubq7JKavPli1b6NOnD7m5uWzfvp1Ro0apxKvY008/TfPmzXn33XfNjnI5wwVN\nmTLF7AjiYi5cuGBcuHDB7BhXFB0dbXh4eBgeHh5GdHS04/Xc3FzDZrOZmMz1/PznbLfbjbS0NONP\nf/qTcdtttxlPP/20sXnzZsNqtZqYsur88MMPxtNPP2307t3b2Llzp9lxar28vDzjrrvuMpKTk82O\nUo4+I5daLyYmhqioKADmz59PRETENe3HMAxKS0sdo+fS0lIuXLjg+Pqn/1zp9UuvlZaW8sorr+Dh\n4aHPdmtQYWEh69atIz4+nszMTIYNG8bIkSPp0KGD2dEqzWazERsbywcffEBYWBiRkZH6f6aGbN++\nnZdeeoktW7Y4zUJFKnKp1X6tKK1WKxMnTvzV0v35Px4eHtSvX58GDRrg6enpWM7yp/9c6XVPT0/H\n+xo0aEB4eDju7u4qcpOkp6ezfPlyEhMT6dChg2Od90vrjTuzr776iqlTp3LjjTcya9Ysp7z5qrZ7\n9dVXOXnyJB9//LFT3H+hIpda7deKsqysjHXr1lWqiKvjc8equmIglVdaWsq2bduIj4/nq6++YsiQ\nIQQHB3PnnXc6xS/onzp79iyzZ8/m008/Zfr06Tz22GNOl7GuuHDhAo888gijR48mODjY7Dgqcql9\nDMMgPz+fG264AXCNoqzrT5xyBllZWaxcuZLly5fj7e3tNOu8G4bBP//5T1577TXH87LryroBzuzw\n4cMMHTqUpKQk0z+eUZFLrWEYBjt37uTtt9/m9ttvZ9asWbi7X5xhqaKUirLb7ezevZtly5bx2Wef\n8cADDxAcHMx9991X43eCZ2Rk8Morr3D69Glmz55N9+7da/T7y69buHAhCQkJrF271tQpaSpyqRX2\n7NnD22+/TV5eHpMnT2bIkCGafiPX7dI678uXL+fMmTOMHDmSESNGVPvDaEpKSoiOjiYuLo7x48c7\n7qkQ52IYBk8//TS33norf/7zn03Lod904tL+/e9/88c//pHnn3+eESNGsH37dh599FGVuFSJS+u8\nb968mYULF3LmzBkGDBjAyJEjSUpKqpZ13ZOTk3n44Yf59ttv2bx5M2PHjlWJOymLxcLcuXNJTEwk\nOTnZvBwakYsrOnjwIO+88w6pqak8//zzjBw50ulWW5La6cKFC2zatIn4+HgOHjzIE088QXBwMP7+\n/te131OnTjFz5ky+/PJL3nzzTa2N7kJ27NjB5MmT2b59Oz4+PjX+/TVsEZdy5MgRxo0bR3BwML16\n9SI5OZknn3xSJS41xtPTk8cff5wVK1awYcMGGjVqREhICIMHD2bJkiWcO3euUvuz2+188sknPPjg\ng9x00016wIkLeuCBB5g3bx4eHh6mfP+rjsgNw2DmzJn4+flhs9nIzs5m4sSJHD9+nMTERDp27MiR\nI0cICgpy3Lm3atUqiouLKSwspGvXrvTs2fOy/ebk5JCQkECrVq3IyckhPDwcT0/PCoXWiLzu+f77\n75k3bx7btm1j7NixPPPMMzRs2NDsWCLAxSV1d+zYQXx8PMnJyQwYMIDg4GB69uxZborYz2+6tNvt\nzJgxg5SUFN5++2094MSFGT9Z07+mb66t0Ii8c+fOBAcHExoaSklJCbt27WLJkiU88MADDBs2jD59\n+rB48WLg4i35qamphISEMGbMGGJjYykqKrpsn9HR0QwaNIigoCDatm1LYmJi1R6Z1ApZWVlMmTKF\nwYMH06ZNG3bt2sX48eNV4uJU3Nzc6Nu3LwsWLCA5ORl/f3+mTJlCSkqKY5srPWa1Xr16REREkJSU\npBJ3cWY+TveqRW6xWBg+fDhw8a/OU6dO0bp1a5o2bcrZs2cBKCgocMzZ3bt3r+OzIjc3N9q0aUNq\namq5fdpsNlJTU+nUqRNw8Q+Fffv2Vd1RicvLy8vj1VdfpV+/fjRu3Jjk5GQmTZpE48aNzY4m8qua\nNWvG2LFj2bFjB3fddRdwcYQWFRWF1WrFarUSFRXlGLW1bNlSN2fWEr/2c65OFb4V8sCBAyQlJdGj\nRw86duxISEgIc+bMISsri/T0dCZNmgRcLPU2bdo43uft7U1BQUG5fZ07d67cZ5peXl6XbSN1U35+\nPn/9619ZunQpTzzxBNu3b+emm24yO5ZIpVksFq28JjWiwn8GduvWjZkzZ5Kdnc2GDRt4/fXXGT16\nNGFhYYwdO5Zp06YBF6drFBcXO95XVFR02SpEjRs3prS0tNw2TZo0ud5jERd2/vx55s6dy3333cfZ\ns2fZunUrr7/+ukpcaoVfe8yq1B5m/ZyvWuSZmZnlLnu3aNGCnJwcTp8+7Vi6sGnTppw+fRqAwMBA\n0tLSgIuX0DMzMwkICAAuXi612+24u7vTpUsX0tPTATh06BA9evSo2iMTl1BUVER0dDS9evXi+PHj\nrF+/njlz5nDzzTebHU2kSkVERHD+/HnOnz/vlMsES9Uw4+d81UvrHh4ebN26lYyMDGw2GydOnOC5\n557D39+fRYsW0bZtW44fP86ECRMA8PPzo0uXLixevJjCwkLCw8MdTxSaM2cOYWFh+Pv7ExkZSXx8\nPCkpKeTl5REeHl69RypO5cKFCyxdupQPPviAe+65h9WrVzvumRCprTQKrxtq+uesBWGkRlmtVhIS\nEnj//fcJCAhg8uTJ3H777WbHEhFxWVr3T2pEWVkZq1ev5r333uO3v/0tH3/8MV27djU7loiIy1OR\nS7Wy2+2sX7+ed999lxtvvJF58+Zxzz33mB1LRKTWUJFLtTAMg61btzJnzhzq16/P66+/zv3336/p\nOCIiVUxFLtft58sRlpWV8eqrr7Jnzx5eeukl+vXrpwIXEakmWk5IrsuVliN0c3PjhRdeYMuWLfTv\n318lLiJSjVTkcs1+bTnCZs2aadlJEZEaoN+0IiIiLkxFLtdMy06KiJhPN7vJdYmIiGD06NGAVq0S\nETGDilyumWEYWCwWFbiIiIl0aV2u2eHDhzlw4AAuuMqviEitoSKXa/bee+/x9ddfa3qZiIiJVORy\nTX744Qe++OILgoKCzI4iIlKnqcjlmixatIhhw4bRqFEjs6OIiNRputlNKq24uJhly5axbt06s6OI\niNR5GpFLpa1Zs4bu3bvTrl07s6OIiNR5KnKpFMMwiI2NdcwdFxERc6nIpVJ2796NzWbjvvvuMzuK\niIigIpdKWrBgAaNHj9aUMxERJ6Eilwo7fvw4e/bsYdiwYWZHERGR/6MilwpbtGgRI0aMwNvb2+wo\nIiLyfzT9TCqkqKiIhIQENm3aZHYUERH5CY3IpUISExPp0aMHbdq0MTuKiIj8hIpcrkpTzkREnJeK\nXK4qOTkZNzc3evXqZXYUERH5GRW5XFVsbCzh4eGaciYi4oRU5PKrvvvuO/bv38/jjz9udhQREbkC\nFbn8qoULFxIcHIyXl5fZUURE5Ao0/Ux+UWFhIYmJiWzZssXsKCIi8gs0IpdftHLlSu69915uueUW\ns6OIiMgvUJHLFdntduLi4jTlTETEyanI5Yo+//xzvLy86NGjh9lRRETkV6jI5YpiY2MJCwvTlDMR\nESd31ZvdDMNg5syZ+Pn5YbPZyM7OZuLEiVgsFhITE2nYsCE//PADjRo1IjQ0FIBVq1ZRXFxMYWEh\nXbt2pWfPnpftNycnh4SEBFq1akVOTg7h4eF4enpW/RFKpR09epRvvvmGBQsWmB1FRESuokJ3rXfu\n3Jnhw4cD8MYbb7Br1y5OnjxJu3btuOeee4CL840BDh8+TGpqKjNmzKCsrIxx48Zxxx13XPbErOjo\naEJDQ+nUqRPr1q0jMTGRkJCQKjw0uVYLFy5k1KhR+sNKRMQFXPXSusVicZR4WVkZp06donXr1nz+\n+eecPn2atWvX8sknn+Dr6wvA3r178ff3B8DNzY02bdqQmppabp82m43U1FQ6deoEXPxDYd++fVV6\nYHJtzp07x+rVq3nyySfNjiIiIhVQ4c/IDxw4wGuvvUaPHj3o2LEjeXl52Gw2/vCHP9ClSxdmz54N\nQEFBQbmRnLe3NwUFBeX2de7cOerXr+/42svL67JtxBwrVqygT58+tGrVyuwoIiJSARUu8m7dujFz\n5kyys7PZsGEDXl5e+Pn5AeDv78/Bgwex2+34+vpSXFzseF9RUZFjtH5J48aNKS0tLbdNkyZNrvdY\n5DqVlZVpypmIiIu5apFnZmaWu+zdokULcnJyuOuuu8jOzgYgNzeXm2++mXr16hEYGEhaWhpw8RJ6\nZmYmAQEBAOTl5WG323F3d6dLly6kp6cDcOjQIU1zcgL/+te/aNKkCd27dzc7ioiIVNBVb3bz8PBg\n69atZGRkYLPZOHHiBM899xxlZWUsWrSI3NxcsrKymDRpEgB+fn506dKFxYsXU1hYSHh4uONGtzlz\n5hAWFoa/vz+RkZHEx8eTkpJCXl4e4eHh1XukclWXnjmuKWciIq7DYhiGYXaIypo6darjM3mpGunp\n6QwfPpw9e/bQoEEDs+OIiEgFaUEYASAuLo7Q0FCVuIiIi9HTz4SzZ8+SlJTEjh07zI4iIiKVpBG5\nsHz5cvr27UuLFi3MjiIiIpWkIq/jysrKWLhwoaaciYi4KBV5Hbd161ZuuukmunbtanYUERG5Biry\nOu7SlDMREXFNKvI67NChQxw5coTBgwebHUVERK6RirwOi4uL48knnyy37r2IiLgWTT+ro/Lz81m/\nfj07d+40O4qIiFwHjcjrqGXLljFgwACaNWtmdhQREbkOKvI6yGaz8Y9//IOwsDCzo4iIyHVSkddB\nmzdv5pZbbqFLly5mRxERkeukIq+DFixYoClnIiK1hIq8jvn222/JzMxk4MCBZkcREZEqoCKvY+Li\n4njqqafw8PAwO4qIiFQBTT+rQ06fPs2mTZv44osvzI4iIiJVRCPyOuSTTz5h8ODB3HDDDWZHERGR\nKqIReR1htVpZtGgRS5YsMTuKiIhUIY3I64iNGzfSrl07br/9drOjiIhIFVKR1xFxcXGaciYiUgup\nyOuAr7/+mpMnT9K/f3+zo4iISBVTkdcBsbGxPP3007i765YIEZHaRkVey+Xm5rJ161aCg4PNjiIi\nItVARV7LffLJJwwZMoSmTZuaHUVERKqBrrXWYqWlpSxevJiEhASzo4iISDXRiLwWW79+PX5+fvj5\n+ZkdRUREqomKvBaLjY3VlDMRkVpORV5LHThwgNOnT9O3b1+zo4iISDVSkddSCxYsICwsDDc3N7Oj\niIhINVKR10LZ2dls376dESNGmB1FRESqmYq8FlqyZAmPP/44TZo0MTuKiIhUM00/q2VKSkr45JNP\nWLVqldlRRESkBmhEXsskJSVx++2307FjR7OjiIhIDVCR1yKGYWjKmYhIHXPVS+uGYTBz5kz8/Pyw\n2WxkZ2czceJE6tevD0BCQgJJSUksW7bM8Z5Vq1ZRXFxMYWEhXbt2pWfPnpftNycnh4SEBFq1akVO\nTg7h4eF4enpW4aHVPfv376ewsJAHH3zQ7CgiIlJDKjQi79y5M8HBwYSGhlJSUsKuXbsA+Oabbygs\nLMRisTi2PXz4MKmpqYSEhDBmzBhiY2MpKiq6bJ/R0dEMGjSIoKAg2rZtS2JiYhUdUt11acpZvXq6\n0CIiUldc9Te+xWJh+PDhAJSVlXHq1Clat25Nfn4+ycnJDBkyBMMwHNvv3bsXf39/ANzc3GjTpg2p\nqanl9mmz2UhNTaVTp07AxT8U9u3bV2UHVRdlZWWxc+dOx89KRETqhgoP3Q4cOMBrr71Gjx496NCh\nA0uWLOGpp54qV+IABQUF5S6Re3t7U1BQUG6bc+fOOS7NA3h5eV22jVTOokWLGDp0KD4+PmZHERGR\nGlTh6WfdunWjW7duvPfeeyxfvhw3Nzc2bdpEYWEhpaWlJCYm0qtXL3x9fSkuLna8r6ioCF9f33L7\naty4MaW+cU1QAAARGklEQVSlpeW20Zzna3fhwgWWLVtGUlKS2VFERKSGXXVEnpmZWe6yd4sWLSgu\nLiYyMpJhw4YxaNAg6tevz7Bhw7j55psJDAwkLS0NuHgJPTMzk4CAAADy8vKw2+24u7vTpUsX0tPT\nATh06BA9evSojuOrE9asWcNdd91F+/btzY4iIiI17Kojcg8PD7Zu3UpGRgY2m40TJ07w3HPPAXDy\n5Ek2btyI1WplxYoV/OEPf8DPz48uXbqwePFiCgsLCQ8Px9vbG4A5c+YQFhaGv78/kZGRxMfHk5KS\nQl5eHuHh4dV7pLXUpSln06ZNMzuKiIiYwGL8/ENuFzB16lRmz55tdgynsHv3bqZMmcLnn39ebvaA\niIjUDZqn5OJiY2MJCwtTiYuI1FEqcheWmZnJ7t27CQoKMjuKiIiYREXuwhYtWsTw4cNp2LCh2VFE\nRMQkevqZiyoqKmL58uVs2LDB7CgiImIijchd1OrVqwkMDKRt27ZmRxEREROpyF3QpSlnYWFhZkcR\nERGTqchdUHJyMgC9e/c2OYmIiJhNRe6C4uLiGD16tKaciYiIitzVfP/99+zbt48nnnjC7CgiIuIE\nVOQuZuHChYwcOdKx7K2IiNRtmn7mQn788UdWrlzJ5s2bzY4iIiJOQiNyF7Jy5Up+97vf0bp1a7Oj\niIiIk9CI3EUYhsGdd96px72KiEg5KnIXYbFY6Nq1Ky74sDoREalGurTuAkpKSigpKQHQlDMRESlH\nRe7kYmJi8PHxwcfHh5iYGLPjiIiIk1GRO7GSkhKioqKwWq1YrVaioqIcI3MRERFQkYuIiLg0FbkT\na9CgAfPnz8fDwwMPDw/mz59PgwYNzI4lIiJORHetOymbzYa7uzsRERGMHj0aQCUuIiKX0YjcCX32\n2WcsXLiQsrIy4GKBq8RFRORKVORO5vPPPycqKopu3brh5uZmdhwREXFyurTuRJKTk4mMjCQuLo7u\n3bubHUdERFyARuRO4ssvv2Ts2LF8/PHHWoZVREQqTEXuBPbt28eYMWP461//Sq9evcyOIyIiLkRF\nbrKUlBTCwsL4n//5H+677z6z44iIiItRkZsoNTWVp556ivfee48HH3zQ7DgiIuKCVOQm+c9//kNI\nSAhz5syhX79+ZscREREXpSI3QVpaGqNGjeKNN95g4MCBZscREREXpiKvYf/9738JDg5mxowZDBky\nxOw4IiLi4lTkNejo0aOMHDmSV155hccee8zsOCIiUguoyGvId999x4gRI3jxxRcJCgoyO46IiNQS\nKvIakJmZyfDhw5k4cSIjR440O46IiNQiKvJqlpWVRVBQEOPGjSM0NNTsOCIiUstcda11wzCYOXMm\nfn5+2Gw2srOzmThxIosWLcLT0xNPT0+OHTvGmDFjaNq0KQCrVq2iuLiYwsJCunbtSs+ePS/bb05O\nDgkJCbRq1YqcnBzCw8Px9PSs+iM00cmTJxk2bBhhYWE888wzZscREZFaqEIj8s6dOxMcHExoaCgl\nJSXs2rULLy8vQkNDCQoKon379qxYsQKAw4cPk5qaSkhICGPGjCE2NpaioqLL9hkdHc2gQYMICgqi\nbdu2JCYmVu2RmSw3N5fhw4czatQonn32WbPjiIhILXXVIrdYLAwfPhyAsrIyTp06RevWrQkJCXFs\nY7fb8fLyAmDv3r34+/sD4ObmRps2bUhNTS23T5vNRmpqKp06dQIu/qGwb9++qjkiJ3Dq1CmCgoIY\nOnQokZGRZscREZFarMKfkR84cIDXXnuNHj160LFjR8frhYWF/Pvf/+aJJ54AoKCgoNwlcm9vbwoK\nCsrt69y5c9SvX9/xtZeX12XbuKozZ84wYsQIHnnkEaKiosyOIyIitVyFi7xbt27MnDmT7OxsNm7c\nCMCPP/7IRx99RFRUFI0aNQLA19eX4uJix/uKiorw9fUtt6/GjRtTWlpabpsmTZpc14E4g/z8fEaO\nHEnfvn2ZPHmy2XFERKQOuGqRZ2Zmlrvs3aJFC7KzsykoKOCvf/0rzzzzDDfddBNffPEFAIGBgaSl\npQEXL6FnZmYSEBAAQF5eHna7HXd3d7p06UJ6ejoAhw4dcvlncBcUFBAcHEzv3r15+eWXsVgsZkcS\nEZE64Kp3rXt4eLB161YyMjKw2WycOHGCZ599lldffRW73c67774LXLyEfu+99+Ln50eXLl1YvHgx\nhYWFhIeH4+3tDcCcOXMICwvD39+fyMhI4uPjSUlJIS8vj/Dw8Oo90mp0/vx5Ro0aRWBgINOmTVOJ\ni4hIjbEYhmGYHaKypk6dyuzZs82OAVz8eGHUqFH4+/sza9YslbiIiNQoLQhzHYqKinjyySfp2LEj\nb775pkpcRERqnIr8Gl24cIGnn36aNm3aMGfOHOrV06kUEZGap/a5BhcuXOCZZ56hefPmzJ07VyUu\nIiKmUQNVUklJCc8++yyNGzfm/fffx83NzexIIiJSh6nIK8FqtTJ27Fg8PDz48MMPcXe/6k3/IiIi\n1UpNVEE2m43IyEjsdjt/+9vf8PDwMDuSiIiIirwiysrKeP755/nxxx+Ji4srt7ysiIiImVTkV1FW\nVsYLL7zAmTNn+Mc//kGDBg3MjiQiIuKgIv8Vdrudl156iaysLJYsWVLrnpcuIiKuT0X+CwzD4OWX\nX+bo0aMsXbrU8ZhWERERZ6IivwLDMJg2bRqHDh1i2bJlNGzY0OxIIiIiV6Qi/xnDMJg5cyYHDhxg\n+fLljsezioiIOCMV+U8YhsGsWbPYvXs3CQkJNG7c2OxIIiIiv+qqRX5phOrn54fNZiM7O5uJEyfi\n4eHBp59+ytKlS5k1axa/+c1vHO9ZtWoVxcXFFBYW0rVrV3r27HnZfufOncstt9yCYRicPHmSiIgI\n028ms1qttGvXjoiICHx9fU3NIiIiUhEVWtmtc+fOBAcHExoaSklJCbt27eLYsWP4+fldNh3r8OHD\npKamEhISwpgxY4iNjaWoqKjcNna7nZYtWzJy5EiCg4Px9PRk06ZNVXdUlVBSUkJJSQkA9evXJzg4\nmKZNm5qSRUREpLKuWuQWi4Xhw4cDF+dUnzp1itatW9O+fXvat29/2fZ79+7F398fADc3N9q0aUNq\namr5b1qvHqNGjXJ8bbfbTbkrPCYmBh8fH3x8fIiJiQHQo0hFRMSlVHit9QMHDvDaa6/Ro0cPOnbs\n+IvbFRQUlLtE7u3tTUFBwS9un5OTQ05ODg8//HBFo1SJkpISoqKisFqtWK1WoqKiHCNzERERV1Hh\nIu/WrRszZ84kOzubjRs3/uJ2vr6+FBcXO74uKir6xc+bT506xeLFi5kyZUqlHkCiS98iIiIXXbXI\nMzMz2bdvn+PrFi1akJ2dXW4bwzAc/x4YGEhaWhpw8UEjmZmZBAQEAJCXl4fdbgfg5MmTLFq0iMjI\nSBo1asSuXbsqHHrKlCkV3vaXNGjQgPnz5+Ph4YGHhwfz58/X8qsiIuJyLMZPW/gKsrOziYuLo0OH\nDthsNk6cOMFzzz2Hu7s769evJykpiQcffJA+ffrg5+cHwOrVqyksLKSwsJC7776bHj16APDiiy8S\nFhZGhw4dGDNmDM2aNXM8gOSWW25h/Pjx1Xy4l7t0OV0lLiIiruiqRS4iIiLOq8KfkYuIiIjzUZGL\niIi4MBW5iIiIC1ORi4iIuDAVuYiIiAtTkYuIiLgwPcYUePvtt8nPzzc7hoiI1BJNmzatksXLKkLz\nyEVERFyYLq2LiIi4MBW5iIiIC1ORi4iIuDAVuYiIiAtTkYuIiLgwFbmIiIgLU5GLiIi4MBW5iIiI\nC6uVK7vl5+ezZMkSjh07xrx58wA4evQo69ato02bNhw/fpyQkBCaN28OwN69e8nIyMAwDL755hve\neOMN3NzcePLJJ7nlllsAsNlsuLu789ZbbzlVxn/961+kpqbSqlUrjh07RmRkJI0aNaqSjM6uMudw\n27ZtrF+/Hi8vLwDy8vIIDg6mb9++ZGRksGHDBlq0aEFBQQFhYWG4ubmZeWg1pirOYc+ePYmJiaF9\n+/YUFRVRVFTEs88+S716dWOcUFX/HwLY7XamT5+Ot7c3L7/8smnHZIaqOo9nzpxh3bp1NG7cmLS0\nNB566CF69uzpNPmqpVeMWig5OdnYs2ePERUV5Xht/PjxxtGjRw3DMIw9e/YYr7/+umEYhnHy5Enj\nrbfecmz3/fffG3a73TAMw9i5c6fj9W3bthnbt293qoxlZWXG0KFDjcLCQsMwDONvf/ubsXLlyirL\n6Owqcw4PHTpk5OXlObZ74403jOLiYsNutxsRERFGfn6+YRiGsWDBAmPz5s01eBTmqopzmJeXZ2za\ntMnx+quvvmp88cUXNXQE5quKc3jJypUrjZiYGGPWrFk1lN55VNV5nD59unH27FnDMAzjxx9/NHJz\nc50qX3X0Sq38k/nee+/F09Oz3GsnT57kpptuAqBFixZ8/fXXAOzcuZOGDRvyz3/+k6VLl3LmzBks\nFgsAvXv3drw/OTmZ++67z6ky1qtXD19fX86ePQtAQUEBN9xwQ5VldHaVOYe33XYbzZo1AyAjI4NW\nrVrh6elJdnY2paWl+Pr6AuDv78/+/ftr8CjMVRXnsFmzZgwcONDxfsMwHCORuqAqziHAN998Q4MG\nDbj11ltrML3zqIrzmJ+fzw8//MCuXbtYs2YNmzZtomnTpk6TD6qnV2plkV9J586dOXToEADp6emU\nlJRQVlZGbm4uR44c4dFHH2XEiBF8/PHH/PDDD+Xe+8033+Dv71/tl1uvJWNkZCQffvghH330ETab\njT59+lRrRmd3pXNot9vLbbN+/XqGDBkCwNmzZ8uVjre3t+MPo7qqsufwp9LT0/H29qZr1641ktVZ\nVfYc5ufns3PnToYMGYKhx184VPY85ubmkp2djZ+fH48//jgXLlwgISHBafL9VFX2Sp0p8kmTJpGW\nlsbatWuxWCzceOONuLm50bBhQzp16oTFYsHd3Z127dqRlpZW7r2bN29m0KBBTpcxPz+fDz74gOnT\npzN27FgCAgL46KOPqj2nM/v5ObzhhhvKfVabn5+P1Wp13Hvg6+tLcXGx478XFRU5Rud1VWXP4SXf\nffcdGzZsYNKkSTUd2elU9hzu27ePRo0akZiYyP79+/nhhx9ITEzEarWadQhOobLn0dvbGzc3N9q3\nbw9cvML27bffOk2+n6rKXqmVN7tdyZkzZwgNDQXgwIEDPPjggwDceeedrF692rFdXl6e40YEgOzs\nbLy8vPDx8XG6jOfOncNisTgu2TRt2pSvvvqq2nM6s5+fw4ceeqjcf9+4cSODBw92fN2yZUvq169P\nfn4+TZs25eDBgwQGBtZoZmdT2XMIF0cjW7ZsYcKECRiGwb59++r0eazsOezfv7/j37dt24ZhGAwb\nNqxmwjqxyp7Hm2++maZNm3L69GluvPFGcnNzy/0+NzvfJVXdK24zZsyYUSV7ciLffvst27dv59ix\nY5SWltKpUyc+++wzPvvsM7KyssjKymLkyJG4ubnRqlUrTp48SUpKCikpKbRv357777/fsa/4+Hh+\n//vf06RJE6fL6OvrS1FRETt37uTYsWOkpKQQEhJSZZ8JObvKnEMAq9VKUlJSuV+QFosFf39/EhMT\nycjIoLS0lKFDhzruk6jtquIcnjp1ismTJ+Pm5sb27dv57LPPqF+/PnfccYdZh1WjquIcXvL111+z\nY8cOsrKysFgsdOrUqaYPxzRVcR7r1avHrbfeypo1azh+/DjHjh3jqaeeuuyzbbPyXVLVvaLnkYuI\niLiwOvMZuYiISG2kIhcREXFhKnIREREXpiIXERFxYSpyERERF6YiFxERcWEqchERERemIhcREXFh\n/w8hp8bEQeAEOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109a9d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1967, 1977 + 1)\n",
    "y = [310.2, 330, 375, 385, 385.6, 395, 387.5, 380, 392, 407.1, 380]\n",
    "\n",
    "tufte.line(x, y, figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we use Tufte's _range-frame_ concept, which aims to make the frame (axis) lines \"effective data-communicating element[s]\" by showing the minimum and maximum values in each axis. This way, the tick labels are more informative. In this example, the range of the outcome variable is 96.9 units (407.1 - 310.2). Similarly, this data covers the years 1967 through 1977, inclusive.\n",
    "\n",
    "The range-frame is applied to both axes for line and scatter plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11099ee90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HEKZbt2544403cOjQIdSqVUtntyF06dixY4VWQNyzZw8LN+nd06dPYWlpKT1urC9fffUV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a62490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8675309)\n",
    "\n",
    "fig, ax = tufte.scatter(np.random.randint(5, 95, 100), np.random.randint(1000, 1234, 100), figsize=(8, 4))\n",
    "\n",
    "plt.title('Title')\n",
    "ax.set_xlabel('x-axis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll also notice that `tufte.scatter()` returns figure and axis objects. This is true for all `tufte` plots. With this, we can add a title to the figure and a label to the x-axis, for example. `tufte` plots are meant to be able to interact with `matplotlib` functions and methods.\n",
    "\n",
    "When you need to create a bar plot, do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x110a629d0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x110af0250>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a629d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8675309)\n",
    "\n",
    "tufte.bar(range(10),\n",
    "          np.random.randint(1, 25, 10),\n",
    "          label=['First', 'Second', 'Third', 'Fourth', 'Fifth',\n",
    "                 'Sixth', 'Seventh', 'Eight', 'Ninth', 'Tenth'],\n",
    "          figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A feature of the `bar()` function is the ability for x-axis labels to auto-rotate. We can see this when we change the one of the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x110ce54d0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x110cda510>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UcRvLzc3VsmXLtGHDBqWkpNj6doQjR45o27ZtDRZanLvgzE5efvllJSUlacqU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ce54d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8675309)\n",
    "\n",
    "tufte.bar(range(10),\n",
    "          np.random.randint(1, 25, 10),\n",
    "          label=['First', 'Second', 'Third', 'Fourth', 'Fifth',\n",
    "                 'Sixth', 'Lucky 7th', 'Eight', 'Ninth', 'Tenth'],\n",
    "          figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tufte's boxplot is, perhaps, the most radical redesign of an existing plot. His approach is to maximize data-ink, the \"non-erasable core of a graphic,\" by removing unnecessary elements. The boxplot removes boxes (which is why we refer to it as `bplot()`) and caps and simply shows a dot between two lines. This plot currently only takes a `list`, `np.ndarray`, or `pd.DataFrame`.\n",
    "\n",
    "Let's create a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x110ce5c10>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x110d2c0d0>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hUEjFxcUqLy9XSUmJGhoaUhYQAAA3S1rEp06dUmFhoZYtWzb+Wmtrq/Lz8yVJBQUFCofD\nqUsIAICL3baIr1y5oq6uLm3evFmJRGL89f7+flmWJUmyLEsDAwMaGxtLbVIAAFzotveIz549q+zs\nbDU3N+v999/XyMiIXnvtNfl8Ptm2LcuyZNu2vF6vPB7n674ikYii0ej4cVFRkQKBwNS/C+A2WOUN\nIJ3dtogfffTR8a+HhoY0NDSk7du36+rVq2pvb1dZWZna2toUDAYnddJAIEDxYsawyhtAOnO0avqt\nt97Se++9p9HRUZ0+fVp79+5VY2Ojuru7FY/HVV1dneqcAAC4kqMi3rx5szZv3vyh12pqalIS6E4w\nBQnAzTLtMy7T8priqg09mIIE4GaZ9hmXaXlNcVURAwCQaShiAAAMoogBADCIIgYAwCCKGAAAgyhi\nAAAMoogBADCIIgYAwCCKGMAdYwclYOooYgB3jB2UgKmjiAEAMIgiBgDAIIoYAACDKGIAAAyiiA1j\ntSkAzG4UsWGsNgWA2Y0iBgDAIIoYSEPcsgBmD4oYSEPcsgBmj6xkb0gkEvrWt76lvLw8jYyMqKen\nR88884yys7N17NgxvfTSS/rud7+rVatWzUReAABcxdGIuLCwUDt37tSePXs0NDSkt956S5cvX1Ze\nXp7mzZuX6owAALhW0hHxnDlz9Oijj0qSRkdHde3aNX30ox/V/fffn/JwAAC4XdIivuWdd97Ra6+9\npocfflgPPPBAKjMBADBrOC7ijRs3auPGjfrhD3+o3/zmN9q2bduUTxqJRBSNRsePi4qKFAgEpvz/\nAcBksTId6SJpEXd1damnp0fBYFCStGzZMvX09HzoPYlEYlInDQQCFC8Ao1iZjnSRtIizs7P1xhtv\nqKOjQyMjI7p69aqefPJJDQwM6PXXX5dt2zp+/Li2bNmivLy8mcgMAIBrzElMdjgLAACmDRt6AABg\nEEUMAIBBFDEAAAZRxAAAGEQRAwBgEEUMAIBBFDEAAAZRxAAAGEQRAwBgEEUMAIBBFDEAAAZRxAAA\nGEQRAwBgEEUMAIBBFDEAAAZRxAAAGEQRG3Dq1Cnt2LHDdIxZo62tTc8884yi0ajpKK705ptv6siR\nI/rJT36iSCRiOo7rtLS0qLq6Wr29vaajTNqzzz6rRCJhOsak1NbWanR0dEbPSREbsHXrVs2fP990\njFmjsLBQa9asMR3DtU6cOKHPfe5z+spXvqINGzaYjuM6wWBQS5cuNR1jSg4fPqw5c+aYjjEphw4d\n0ty5c2f0nFkzerYUGRwc1A9+8AOtX79e3d3d2rJli4qLi03HSurYsWO6fv26Ll26pCeffFLLli0z\nHem2fvnLX2pkZETZ2dm6fPmynnvuOdORbuvIkSMaHR2V3+/XtWvXTMdx5Be/+IXGxsbk8Xh0zz33\n6Atf+ILpSLf19ttvKxaL6de//rU+/vGPa9OmTaYjJXXruvjIRz6icDisFStW6IknnpBlWaaj3dbv\nfvc79fb2qqenRwcOHEj7vG+//baOHj2q733vexnzi8S5c+d05MiRGc/sihGxx+PR9u3bVV5erqqq\nKoVCIdORHCkuLtbu3bu1detWNTY2mo5zW62trfrjH/+oPXv2aMeOHWn/gdvS0qJYLKannnpKn//8\n5/XPf/4z7X8zv/Uzrqio0GOPPaZwOKyOjg7TsW7rE5/4hJYuXart27en/TUhffi6+NKXvqQ5c+bo\nkUceSftSkz6Y2fnqV7+q1atX6w9/+IPpOEndujYyycMPP2wksytGxIlEQtFoVO3t7crKytKNGzdM\nR3LE7/dLknJzc3XlyhXDaW7vz3/+s3Jzc8ePP/3pTxtMk9yVK1e0fPny8eNbP+t01tnZqaGhITU3\nN0uSlixZkjHXcqaY6LrIlHuYt3IvXLhQg4ODhtNgOrliRHz8+HH19fVpx44d2r59u+k4jsXjcUlS\nd3e3Vq1aZTjN7a1Zs0Y9PT3jx2+88YZGRkYMJrq9VatWKRaLjR/39PSk/Qfu/fffL5/Pp/LycpWX\nl+uRRx7RihUrTMdylYmui3SfKflP6X4dY/LmPv/888+bDnGnvF6vTpw4oa6uLl2+fFmRSETLly/X\nypUrTUeb0KlTp3T27Fl5vV61tLTo3Xff1eOPPy6v12s62v+Um5ureDyud955R++//76ysrKUn59v\nOtb/tGLFCrW3t+vMmTPq6OhQT0+P+vr6FAgElJ2dbTrehHJzcxWLxdTa2qo//elP6uzs1ObNm9O6\nKFpaWnT69Gn94x//UG5ublpfw9L/Xxdnz55VV1eXent7VVRUlNZTqO+++65OnTqlRCKhpUuX6vXX\nX9e1a9cUCAR09913m473P926NoaHhzNizc4tJ06cUGlp6YwuqJ2T4NcrALPIhQsX9OCDD0qS9u/f\nr2effVaLFy82nAqmdXd3a8WKFfr617+u/fv36957752xc7viHjEAOPXSSy9pw4YNGhwcVElJCSUM\nSVJDQ4MefPBBrVmzZkZLWGJEDACAUa5YrAUAQKaiiAEAMIgiBgDAIIoYAACDKGIAAAyiiAEAMOj/\nAGCaNhpW3BGxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ce5c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 10 # Must be less than or equal to 26\n",
    "size = 100\n",
    "\n",
    "letters = string.ascii_lowercase\n",
    "\n",
    "df_dict = defaultdict(list)\n",
    "for c in letters[:n_cols]:\n",
    "    df_dict[c] = np.random.randint(random.randint(25, 50), random.randint(75, 100), size)\n",
    "\n",
    "df = pd.DataFrame(df_dict)\n",
    "\n",
    "tufte.bplot(df, figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dot represents the median and the lines correspond to the top and bottom 25% of the data. The empty space between the lines is the interquartile range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range-Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed&mdash;if you cloned this repo and ran the notebook&mdash;that the range-frame feature isn;t perfect. It is possible, for example, for a minimum or maximum value to be _too_ close to an existing tick label, causing overlap.\n",
    "\n",
    "Additionally, in cases where the data in a given dimension (`x` or `y`) contains `float` values, the tick labels are converted to float. (This isn't the issue.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x110ce5cd0>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x110dee750>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MmjRJ+O554sSJWLBgQY1kri1KSkrg5uaGhw8fCseGDh2KH374QcRUROqPC5WQ2jhy5AgO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\n8PPzg7e3t6h5Tp48ieTkZGhpacHKygp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ce5cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(8675309)\n",
    "\n",
    "tufte.scatter(np.random.randn(100), np.random.randn(100), figsize=(8, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This becomes problematic based on our decision to round to the nearest tenth. In this example, the maximum value on the y-axis might be 2.56, which gets rounded to 2.6. A reader might incorrectly conclude that the maximum value in `y` is 2.6.\n",
    "\n",
    "(The above plot also shows what can happen with the minimum or maximum value is too close to an existing tick label. See -2.2 and -2.0 in `y`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tufte's book provides many useful and functional plots, many of which we plan to add to this module."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}