Added notebooks for Pandas and PyMC plotting

Author: Christopher Fonnesbeck

Pandas + Plotly.ipynb

@@ -0,0 +1,126 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import plotly"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "reg = plotly.signup('pandas_and_plotly_demo', '[email protected]')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Thanks for signing up to plotly!\n",
+        "\n",
+        "Your username is: pandas_and_plotly_demo\n",
+        "\n",
+        "Your temporary password is: 3eowi. You use this to log into your plotly account at https://plot.ly/plot.\n",
+        "\n",
+        "Your API key is: e39otewkg2. You use this to access your plotly account through the API.\n",
+        "\n",
+        "To get started, initialize a plotly object with your username and api_key, e.g. \n",
+        ">>> py = plotly.plotly('pandas_and_plotly_demo', 'e39otewkg2')\n",
+        "Then, make a graph!\n",
+        ">>> res = py.plot([1,2,3],[4,2,1])\n",
+        "\n",
+        ">>> print(res['url'])\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This simple example shows how easy it is to generate plot data from Pandas' `groupby` method. Using the Titanic passenger dataset, we can easily generate a boxplot of age distribution by passenger class."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "\n",
+      "titanic = pd.read_csv(\"http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "age_by_class = [{'y': data.values, \n",
+      "                 'name': pclass,\n",
+      "                 'type': 'box',\n",
+      "                 'boxpoints': 'all', \n",
+      "                 'jitter': 0.3} for pclass,data in list(titanic.groupby('pclass')['age'])]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply = plotly.plotly(reg['un'], reg['api_key'])\n",
+      "ply.iplot(age_by_class)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n"
+       ]
+      },
+      {
+       "html": [
+        "<iframe height=\"650\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~pandas_and_plotly_demo/1/600/600\" width=\"650\"></iframe>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "<IPython.core.display.HTML at 0x10ed83f90>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}