bxp_with_ylabels.ipynb

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      "[{'whishi': 12.576334012545718, 'cilo': 1.6164348064249057, 'mean': 4.9486856766955922, 'whislo': 0.24252084924003742, 'med': 2.9472801284780168, 'iqr': 5.1561885723613567, 'q1': 1.7655440553898782, 'q3': 6.9217326277512345, 'fliers': array([ 19.69118642,  19.01171604]), 'cihi': 4.2781254505311281}, {'whishi': 12.205320946547864, 'cilo': 1.9963715983778565, 'mean': 5.006451255923583, 'whislo': 0.31764761044962103, 'med': 3.1575002584016336, 'iqr': 4.49864325225988, 'q1': 2.2907895145889481, 'q3': 6.7894327668488277, 'fliers': array([ 13.65016238,  13.70021861,  13.73090639]), 'cihi': 4.3186289184254107}, {'whishi': 14.56216455548525, 'cilo': 1.9921051541831216, 'mean': 7.1031658510815543, 'whislo': 0.68665283499698182, 'med': 3.4988701980901733, 'iqr': 5.837766847796793, 'q1': 2.0404638489800195, 'q3': 7.8782306967768125, 'fliers': array([ 17.55427227,  23.19442181,  23.68874295,  43.1575316 ]), 'cihi': 5.0056352419972248}, {'whishi': 14.984540828467003, 'cilo': 1.7913425734573791, 'mean': 7.1757668534171923, 'whislo': 0.2310542626316737, 'med': 3.424413187205503, 'iqr': 6.3271214895784738, 'q1': 1.9373851418431323, 'q3': 8.2645066314216056, 'fliers': array([ 56.38077402,  19.85442724,  19.88824326,  20.18376492]), 'cihi': 5.0574838009536265}]\n"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc854702198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def test_bxp_with_ylabels():\n",
    "    np.random.seed(937)\n",
    "    logstats = matplotlib.cbook.boxplot_stats(\n",
    "        np.random.lognormal(mean=1.25, sigma=1., size=(37,4))\n",
    "    )\n",
    "    print(logstats)\n",
    "    for stats, label in zip(logstats, list('ABCD')):\n",
    "        stats['label'] = label\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_xscale('log')\n",
    "    ax.bxp(logstats, vert=False)\n",
    "\n",
    "test_bxp_with_ylabels()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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