seaborn error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x1 = np.random.randn(10)\n",
    "x2 = np.random.randn(20)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x7fc6d0ef58d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0ea1590>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fc6d0f06890>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0f06ed0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0eaa4d0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0eaab10>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fc6d0f15b90>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0eb77d0>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fc6d0f15550>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0eb7190>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x7fc6d0ef5b50>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0f06250>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0ea1810>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc6d0ea1e50>]}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2oRuXIQMdMMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6d123ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.boxplot([x1,x2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7.dev\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (10,) (20,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-c321b5b9722e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/local/home/jkiggins/Code/seaborn/seaborn/categorical.pyc\u001b[0m in \u001b[0;36mpointplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, markers, linestyles, dodge, join, scale, orient, color, palette, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2602\u001b[0m                             \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2603\u001b[0m                             \u001b[0mmarkers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyles\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2604\u001b[1;33m                             orient, color, palette)\n\u001b[0m\u001b[0;32m   2605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2606\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/home/jkiggins/Code/seaborn/seaborn/categorical.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, markers, linestyles, dodge, join, scale, orient, color, palette)\u001b[0m\n\u001b[0;32m   1318\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m   1319\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1320\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1322\u001b[0m         \u001b[1;31m# Override the default palette for single-color plots\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/home/jkiggins/Code/seaborn/seaborn/categorical.pyc\u001b[0m in \u001b[0;36mestimate_statistic\u001b[1;34m(self, estimator, ci, n_boot)\u001b[0m\n\u001b[0;32m   1177\u001b[0m                     \u001b[0mstatistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1178\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1179\u001b[1;33m                     \u001b[0mstatistic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstat_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1181\u001b[0m                 \u001b[1;31m# Get a confidence interval for this estimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jkiggins/envs/jkiggins/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mmean\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m   2872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2873\u001b[0m     return _methods._mean(a, axis=axis, dtype=dtype,\n\u001b[1;32m-> 2874\u001b[1;33m                           out=out, keepdims=keepdims)\n\u001b[0m\u001b[0;32m   2875\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jkiggins/envs/jkiggins/lib/python2.7/site-packages/numpy/core/_methods.pyc\u001b[0m in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'f8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m         ret = um.true_divide(\n",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (10,) (20,) "
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "print sns.__version__\n",
    "sns.pointplot([x1,x2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
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