From 8d712a57f8d02c8204866b5a5e282410a062d1de Mon Sep 17 00:00:00 2001
From: Joel Thompson <chameleonsrock@live.com>
Date: Wed, 4 Feb 2015 21:56:31 -0500
Subject: [PATCH 1/2] Finished as much as I could, I give up

---
 .~lock.cohort_3_rails.xls# |    1 +
 Untitled0.ipynb            | 1304 ++++++++++++++++++++++++++++++++++++
 cohort_3_rails.xls         |  Bin 0 -> 159232 bytes
 3 files changed, 1305 insertions(+)
 create mode 100644 .~lock.cohort_3_rails.xls#
 create mode 100644 Untitled0.ipynb
 create mode 100644 cohort_3_rails.xls

diff --git a/.~lock.cohort_3_rails.xls# b/.~lock.cohort_3_rails.xls#
new file mode 100644
index 0000000..ecac8db
--- /dev/null
+++ b/.~lock.cohort_3_rails.xls#
@@ -0,0 +1 @@
+Joel Thompson,chameleonsrock,joel-thompsons-macbook-pro.local,04.02.2015 14:06,file:///Users/chameleonsrock/Library/Application%20Support/OpenOffice/4;
\ No newline at end of file
diff --git a/Untitled0.ipynb b/Untitled0.ipynb
new file mode 100644
index 0000000..0c76955
--- /dev/null
+++ b/Untitled0.ipynb
@@ -0,0 +1,1304 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:a29bc752abfe1470ec4df1bfc91db2978b63209cf2cbefa7dd0b878c739aaca7"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import xlrd\n",
+      "import numpy as np\n",
+      "import seaborn\n",
+      "import datetime"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_data =  pd.read_csv(\"cohort_3_python.csv\", skip_footer=4, engine='python')\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_class = python_data.set_index('Name')\n",
+      "lecture = python_class[python_class.columns[[0, 2, 4, 6, 8, 10, 12, 13, 15, 17, 19, 21, 23]]]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lecture = python_class[python_class.columns[[0, 2, 4, 6, 8, 10, 12, 13, 15, 17, 19, 21, 23]]]\n",
+      "lecture_averages = lecture[::1].mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "homework_data = python_class[python_class.columns[[1, 3, 5, 7, 9, 11, 14, 16, 18, 20, 22]]]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Python Lecture Average Difficulty"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.title(\"Mean Difficulty for Lectures Per Day\")\n",
+      "lecture_averages.plot(kind='bar', color='#6ced50', figsize=(15,7), ylim=(0, 6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10d6667b8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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5cEQ8EHhtZj5+ne5dkiRJkgSs28zX54BHR8R5/ecHD7EeSZIkSRpLM858SZIk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\nJEmSJBVg+JIkSZKkAv5/iAY7zXvDZSgAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10d55b7b8>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Python Homework Average Difficulty"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "homework_averages = homework_data[::1].mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "homework_averages.plot(kind='bar', color='#6ced50', figsize=(15,7), ylim=(0, 6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10d72ac88>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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wBQAAMAHhCwAAYALCFwAAwASELwAAgAkIXwAAABMQvgAAACYgfAEAAExA+AIAAJiA8AUA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\n3T4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10d692898>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Mean Difficulty for Rails Lectures per Day"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data = pd.read_excel(\"cohort_3_rails.xls\", skip_footer=3, header=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_mean_day = ruby_data[::1].mean()\n",
+      "ruby_mean = ruby_mean_day.dropna()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.title('Mean Difficulty for Rails Lectures per Day')\n",
+      "ruby_mean.plot(kind='bar', color='red', figsize=(17,7), ylim=(0, 6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10dbb7550>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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d7Zehn6v3u2XZ31bcO5/9uyJZjt8Xq/e7ZdrfVrY/+ndFshz724qrsnz728r2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\nIDwDAADAwP8HwQ2QyiRC75kAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10dbaf2b0>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Mean Difficulty for Rails Homework per Day"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data_hw = pd.read_excel(\"cohort_3_rails.xls\", sheetname=1, skip_footer=3, header=1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_mean_hw_day = ruby_data_hw[::1].mean()\n",
+      "ruby_mean_hw = ruby_mean_hw_day.dropna()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.title('Mean Difficulty for Rails Homework per Day')\n",
+      "ruby_mean_hw.plot(kind='bar', color='red', figsize=(17,7), ylim=(0, 6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10dbaf748>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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4BgAAgAHhGQAAAAaEZwAAABgQngEAAGBAeAYAAIAB4RkAAAAGhGcAAAAYEJ4BAABgQHgG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\nAQAAYEB4BgAAgIH/D5HuQGBtsDf9AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10de12278>"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plecture_week_1 = lecture.icol([0,1,2,3])\n",
+      "plecture_week_1_mean = plecture_week_1.mean()\n",
+      "w1_lectures = plecture_week_1_mean.mean()\n",
+      "\n",
+      "plecture_week_2 = lecture.icol([4, 5, 6, 7])\n",
+      "plecture_week_2_mean = plecture_week_2.mean()\n",
+      "w2_lectures = plecture_week_2_mean.mean()\n",
+      "\n",
+      "plecture_week_3 = lecture.icol([8, 9, 10, 11])\n",
+      "plecture_week_3_mean = plecture_week_3.mean()\n",
+      "w3_lectures = plecture_week_3_mean.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_weeks = [w1_lectures, w2_lectures, w3_lectures]\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pw = np.array(python_weeks)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python = pd.DataFrame({\"Week 1\":pw[0:1], \"Week 2\":pw[1:2], \"Week 3\":pw[2:3]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python.plot(kind='bar', ylim=(0, 6))\n",
+      "plt.title(\"Average Python Lecture Difficulty Per Week\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "<matplotlib.text.Text at 0x10e14a908>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Mo6GhfotBlg45pD99+tjm+xs3bqK4eOdiViEtIh1WrkfBKykpoaSkhA0bNnD99T/i5JO/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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x108413710>"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "homework_data_week_1 = homework_data.icol([0, 1, 2, 3])\n",
+      "homework_week_1_mean = homework_data_week_1.mean()\n",
+      "week1_hw = homework_week_1_mean.mean()\n",
+      "\n",
+      "homework_data_week_2 = homework_data.icol([4, 5, 6])\n",
+      "homework_week_2_mean = homework_data_week_2.mean()\n",
+      "week2_hw = homework_week_2_mean.mean()\n",
+      "\n",
+      "homework_data_week_3 = homework_data.icol([7, 8, 9, 10])\n",
+      "homework_week_3_mean = homework_data_week_3.mean()\n",
+      "week3_hw = homework_week_3_mean.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_hw_weeks = [week1_hw, week2_hw, week3_hw]\n",
+      "phw = np.array(python_hw_weeks)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_hw = pd.DataFrame({\"Week 1\":phw[0:1], \"Week 2\":phw[1:2], \"Week 3\":phw[2:3]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_hw.plot(kind='bar', ylim=(0, 6))\n",
+      "plt.title(\"Average Python Homework Difficulty Per Week\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 24,
+       "text": [
+        "<matplotlib.text.Text at 0x10e36f668>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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xYcMGRo8+iaOOOppu3brt8DYopEWkwxo0aCizZ9/BySefumUUvNtuu5n6+jrcX282Ct49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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10de0ea20>"
+       ]
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lec_week1 = ruby_data.icol([0, 1, 2, 3])\n",
+      "ruby_lec_week1_mean = ruby_lec_week1.mean()\n",
+      "rweek_mean_1 = ruby_lec_week1_mean.mean()\n",
+      "\n",
+      "ruby_lec_week2 = ruby_data.icol([5, 6, 7])\n",
+      "ruby_lec_week2_mean = ruby_lec_week2.mean()\n",
+      "rweek_mean_2 = ruby_lec_week2_mean.mean()\n",
+      "\n",
+      "ruby_lec_week3 = ruby_data.icol([8, 9, 10, 11])\n",
+      "ruby_lec_week3_mean = ruby_lec_week3.mean()\n",
+      "rweek_mean_3 = ruby_lec_week3_mean.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lec_weeks = [rweek_mean_1, rweek_mean_2, rweek_mean_3]\n",
+      "rlec = np.array(ruby_lec_weeks)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lec = pd.DataFrame({\"Week 1\":rlec[0:1], \"Week 2\":rlec[1:2], \"Week 3\":rlec[2:3]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lec.plot(kind='bar', ylim=(0, 6))\n",
+      "plt.title(\"Average Ruby Lecture Difficulty Per Week\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 28,
+       "text": [
+        "<matplotlib.text.Text at 0x10e4468d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SEhoaGrjqqu9w0klfYffdd9+hfVBIi0iXlY9Z8NauXcuVV07jiCOO5Mwzx+7wPiikRaRL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\nRK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e35e550>"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hw_week1 = ruby_data_hw.icol([0, 1, 2])\n",
+      "ruby_hw_week1_mean = ruby_hw_week1.mean()\n",
+      "rhw_mean_1 = ruby_hw_week1_mean.mean()\n",
+      "\n",
+      "ruby_hw_week2 = ruby_data_hw.icol([5, 6, 7])\n",
+      "ruby_hw_week2_mean = ruby_hw_week2.mean()\n",
+      "rhw_mean_2 = ruby_hw_week2_mean.mean()\n",
+      "\n",
+      "ruby_hw_week3 = ruby_data_hw.icol([8, 9, 10, 11])\n",
+      "ruby_hw_week3_mean = ruby_hw_week3.mean()\n",
+      "rhw_mean_3 = ruby_hw_week3_mean.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hw_weeks = [rhw_mean_1, rhw_mean_2, rhw_mean_3]\n",
+      "rhw = np.array(ruby_hw_weeks)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hw = pd.DataFrame({\"Week 1\":rhw[0:1], \"Week 2\":rhw[1:2], \"Week 3\":rhw[2:3]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hw.plot(kind='bar', ylim=(0, 6))\n",
+      "plt.title(\"Average Ruby Homework Difficulty Per Week\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 32,
+       "text": [
+        "<matplotlib.text.Text at 0x10e517128>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x10e4e7080>"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "time = ['01/12/2015', '01/13/2015', '01/13/2015', '01/14/2015', '01/14/2015',       \n",
+      "        '01/15/2015', '01/15/2015', '01/20/2015', '01/20/2015', '01/21/2015', '01/21/2015',\n",
+      "        '01/22/2015', '01/22/2015', '01/23/2015', '01/26/2015', '01/26/2015',\n",
+      "        '01/27/2015', '01/27/2015', '01/28/2015', '01/28/2015', '01/29/2015', '01/29/2015', \n",
+      "        '01/30/2015', '02/02/2015']\n",
+      "\n",
+      "index_time = pd.to_datetime(time)\n",
+      "index_time"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 33,
+       "text": [
+        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
+        "[2015-01-12, ..., 2015-02-02]\n",
+        "Length: 24, Freq: None, Timezone: None"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_data = python_data.set_index(['Name'])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pdata = python_data.transpose()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pdata.index = index_time"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pdata"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th>Name</th>\n",
+        "      <th>P01</th>\n",
+        "      <th>P02</th>\n",
+        "      <th>P03</th>\n",
+        "      <th>P04</th>\n",
+        "      <th>P05</th>\n",
+        "      <th>P06</th>\n",
+        "      <th>P07</th>\n",
+        "      <th>P08</th>\n",
+        "      <th>P09</th>\n",
+        "      <th>P10</th>\n",
+        "      <th>P11</th>\n",
+        "      <th>P12</th>\n",
+        "      <th>P13</th>\n",
+        "      <th>P14</th>\n",
+        "      <th>P15</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-15</th>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-15</th>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-23</th>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  6</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  2</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.9</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.9</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-30</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.9</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 37,
+       "text": [
+        "Name        P01  P02  P03  P04  P05  P06  P07  P08  P09  P10  P11  P12  P13  \\\n",
+        "2015-01-12  3.0  4.0  NaN    3  NaN  3.0  3.5  2.0  NaN    2    2  3.5  2.5   \n",
+        "2015-01-13  4.0  3.5    5    3    3  3.5  4.0  3.0    1    2    5  4.0  3.0   \n",
+        "2015-01-13  3.0  3.0    3    2    3  3.0  3.0  2.0    1    2    4  4.0  3.0   \n",
+        "2015-01-14  4.0  5.0    4    3    3  3.0  4.0  3.0    1    3    3  4.0  3.0   \n",
+        "2015-01-14  4.0  4.0    5    4    3  3.0  5.0  4.0    2  NaN    5  4.5  3.0   \n",
+        "2015-01-15  5.0  4.5    5    4    4  3.0  4.0  4.0    2    3    4  5.0  3.0   \n",
+        "2015-01-15  5.0  4.5    5    4    4  4.0  4.5  3.0    2    3    4  5.0  4.0   \n",
+        "2015-01-20  5.0  5.0    5    4    4  4.0  4.0  4.0    3    3    4  5.0  NaN   \n",
+        "2015-01-20  4.0  5.0    5    5    5  3.0  5.0  3.0    3    3    4  5.0  3.0   \n",
+        "2015-01-21  4.0  5.0    5  NaN    4  3.0  5.0  3.0    2    4  NaN  4.0  3.0   \n",
+        "2015-01-21  4.0  5.0    5    4    4  5.0  5.0  5.0    3    4  NaN  4.0  3.0   \n",
+        "2015-01-22  5.5  5.0    5  NaN    5  NaN  5.0  5.5    2    4    4  4.0  4.0   \n",
+        "2015-01-22  4.0  NaN  NaN    4    4  5.0  4.0  4.0    3    4    4  NaN  4.0   \n",
+        "2015-01-23  5.5  NaN    5    4    4  5.0  5.0  5.0    3    5    4  5.5  4.0   \n",
+        "2015-01-26  NaN  5.0    6  NaN    4  5.0  NaN  5.0    3    5    4  5.0  4.0   \n",
+        "2015-01-26  4.0  5.0  NaN    1    3  4.0  5.0  5.0    3    4    4  4.0  NaN   \n",
+        "2015-01-27  5.0  5.0  NaN    3    6  5.0  4.0  5.0    2    5    4  4.0  5.0   \n",
+        "2015-01-27  NaN  NaN    5    1  NaN  3.0  4.9  5.0  NaN    4    4  4.0  3.0   \n",
+        "2015-01-28  NaN  NaN    5    3  NaN  4.0  5.0  5.0  NaN    5    5  6.0  NaN   \n",
+        "2015-01-28  NaN  5.0    5    5  NaN  4.0  4.0  4.0  NaN    4    5  5.0  NaN   \n",
+        "2015-01-29  NaN  5.0  NaN    5  NaN  4.0  4.0  4.0  NaN    5    4  5.0  NaN   \n",
+        "2015-01-29  NaN  5.0  NaN    5  NaN  NaN  4.9  4.0  NaN    4    5  6.0  NaN   \n",
+        "2015-01-30  NaN  NaN  NaN    5  NaN  NaN  4.9  5.0  NaN  NaN    5  NaN  NaN   \n",
+        "2015-02-02  NaN  NaN  NaN  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  NaN   \n",
+        "\n",
+        "Name        P14  P15  \n",
+        "2015-01-12    3    2  \n",
+        "2015-01-13    3    2  \n",
+        "2015-01-13    3    2  \n",
+        "2015-01-14    3    2  \n",
+        "2015-01-14    4    3  \n",
+        "2015-01-15    3    3  \n",
+        "2015-01-15    4    3  \n",
+        "2015-01-20    4    3  \n",
+        "2015-01-20    4    3  \n",
+        "2015-01-21    4    3  \n",
+        "2015-01-21    4    3  \n",
+        "2015-01-22    4    3  \n",
+        "2015-01-22  NaN    3  \n",
+        "2015-01-23  NaN    3  \n",
+        "2015-01-26    4    3  \n",
+        "2015-01-26    4    3  \n",
+        "2015-01-27    3    4  \n",
+        "2015-01-27  NaN    3  \n",
+        "2015-01-28  NaN    3  \n",
+        "2015-01-28  NaN    3  \n",
+        "2015-01-29  NaN    3  \n",
+        "2015-01-29  NaN    3  \n",
+        "2015-01-30  NaN    5  \n",
+        "2015-02-02  NaN  NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "time_listr = ['01/12/2015', '01/13/2015', '01/14/2015',       \n",
+      "              '01/15/2015', '01/20/2015', '01/21/2015', \n",
+      "              '01/22/2015', '01/26/2015', '01/27/2015', \n",
+      "              '01/28/2015', '01/29/2015', '02/02/2015']\n",
+      "\n",
+      "ruby_time = pd.to_datetime(time_listr)\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data = ruby_data.transpose()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data = pd.concat([ruby_data[:4], ruby_data[5:13]])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data = ruby_data.transpose()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 41
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data.index = ruby_time"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ValueError",
+       "evalue": "Length mismatch: Expected axis has 16 elements, new values have 12 elements",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-42-aee3d8d9d82e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mruby_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mruby_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   1956\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1957\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1959\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas/lib.c:41295)\u001b[0;34m()\u001b[0m\n",
+        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m   2215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnew_len\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mold_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2216\u001b[0m             raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[0;32m-> 2217\u001b[0;31m                              'new values have %d elements' % (old_len, new_len))\n\u001b[0m\u001b[1;32m   2218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 16 elements, new values have 12 elements"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data_hw"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "time_listr2 = ['01/12/2015', '01/13/2015', '01/14/2015',       \n",
+      "               '01/20/2015', '01/21/2015', '01/22/2015', \n",
+      "               '01/26/2015', '01/27/2015', '01/28/2015', \n",
+      "               '01/29/2015', '02/02/2015']\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
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From dcaf7505d4459eaea587ffce1c3866f5e020c341 Mon Sep 17 00:00:00 2001
From: Joel Thompson <chameleonsrock@live.com>
Date: Thu, 5 Feb 2015 18:19:46 -0500
Subject: [PATCH 2/2] Updated python notebook, added student story almost got
 the weekday

---
 .~lock.cohort_3_rails.xls# |    1 -
 Untitled0.ipynb            | 1739 +++++++++++++++++++++++++++++++++++-
 2 files changed, 1690 insertions(+), 50 deletions(-)
 delete mode 100644 .~lock.cohort_3_rails.xls#

diff --git a/.~lock.cohort_3_rails.xls# b/.~lock.cohort_3_rails.xls#
deleted file mode 100644
index ecac8db..0000000
--- a/.~lock.cohort_3_rails.xls#
+++ /dev/null
@@ -1 +0,0 @@
-Joel Thompson,chameleonsrock,joel-thompsons-macbook-pro.local,04.02.2015 14:06,file:///Users/chameleonsrock/Library/Application%20Support/OpenOffice/4;
\ No newline at end of file
diff --git a/Untitled0.ipynb b/Untitled0.ipynb
index 0c76955..035c020 100644
--- a/Untitled0.ipynb
+++ b/Untitled0.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a29bc752abfe1470ec4df1bfc91db2978b63209cf2cbefa7dd0b878c739aaca7"
+  "signature": "sha256:74dee07e6f6c481d971a17b2cb48061a75e4e5fe75e3acb45771f1c9537af261"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -94,7 +94,7 @@
      "collapsed": false,
      "input": [
       "plt.title(\"Mean Difficulty for Lectures Per Day\")\n",
-      "lecture_averages.plot(kind='bar', color='#6ced50', figsize=(15,7), ylim=(0, 6))"
+      "lecture_averages.plot(color='#6ced50', figsize=(15,7), ylim=(0, 6))"
      ],
      "language": "python",
      "metadata": {},
@@ -104,15 +104,15 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "<matplotlib.axes._subplots.AxesSubplot at 0x10d6667b8>"
+        "<matplotlib.axes._subplots.AxesSubplot at 0x11207c710>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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5cEQ8EHhtZj5+ne5dkiRJkgSs28zX54BHR8R5/ecHD7EeSZIkSRpLM858SZIk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\nXe89gC2BP2fmryuXVEVEzM3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X+YatripyrKzflXx30Z3udCUrQbKF4T3UldzaVtHGa7xYmXJbDaDLw5J11yex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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10d55b7b8>"
+        "<matplotlib.figure.Figure at 0x112062940>"
        ]
       }
      ],
@@ -141,7 +141,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "homework_averages.plot(kind='bar', color='#6ced50', figsize=(15,7), ylim=(0, 6))"
+      "homework_averages.plot(color='#6ced50', figsize=(15,7), ylim=(0, 6))"
      ],
      "language": "python",
      "metadata": {},
@@ -151,15 +151,15 @@
        "output_type": "pyout",
        "prompt_number": 9,
        "text": [
-        "<matplotlib.axes._subplots.AxesSubplot at 0x10d72ac88>"
+        "<matplotlib.axes._subplots.AxesSubplot at 0x11210aef0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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wBQAAMAHhCwAAYALCFwAAwASELwAAgAkIXwAAABMQvgAAACYgfAEAAExA+AIAAJiA8AUA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\nLxZ//21V/XNVPW7Wwpbfyd3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lTlXiOEfZzsbGYkIfnMs7gbksoXPUOF44+k1OhGO05xFcnVeyLK9mFpc1sXqp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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10d692898>"
+        "<matplotlib.figure.Figure at 0x1120629e8>"
        ]
       }
      ],
@@ -201,7 +201,7 @@
      "collapsed": false,
      "input": [
       "plt.title('Mean Difficulty for Rails Lectures per Day')\n",
-      "ruby_mean.plot(kind='bar', color='red', figsize=(17,7), ylim=(0, 6))"
+      "ruby_mean.plot(color='red', figsize=(17,7), ylim=(0, 6))"
      ],
      "language": "python",
      "metadata": {},
@@ -211,15 +211,15 @@
        "output_type": "pyout",
        "prompt_number": 12,
        "text": [
-        "<matplotlib.axes._subplots.AxesSubplot at 0x10dbb7550>"
+        "<matplotlib.axes._subplots.AxesSubplot at 0x112371f60>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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vVuDdeQq+M88JyzVGD8Xbd1Cki9OrHO3SjSWM6pttSxUVyRBeKm/5bwO4ImHl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\nTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10dbaf2b0>"
+        "<matplotlib.figure.Figure at 0x11209e630>"
        ]
       }
      ],
@@ -237,7 +237,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "ruby_data_hw = pd.read_excel(\"cohort_3_rails.xls\", sheetname=1, skip_footer=3, header=1)"
+      "ruby_data_hw = pd.read_excel(\"cohort_3_rails.xls\", sheetname=1, skip_footer=3, header=0)"
      ],
      "language": "python",
      "metadata": {},
@@ -261,7 +261,7 @@
      "collapsed": false,
      "input": [
       "plt.title('Mean Difficulty for Rails Homework per Day')\n",
-      "ruby_mean_hw.plot(kind='bar', color='red', figsize=(17,7), ylim=(0, 6))"
+      "ruby_mean_hw.plot(color='red', figsize=(17,7), ylim=(0, 6))"
      ],
      "language": "python",
      "metadata": {},
@@ -271,15 +271,15 @@
        "output_type": "pyout",
        "prompt_number": 15,
        "text": [
-        "<matplotlib.axes._subplots.AxesSubplot at 0x10dbaf748>"
+        "<matplotlib.axes._subplots.AxesSubplot at 0x11257ddd8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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4BgAAgAHhGQAAAAaEZwAAABgQngEAAGBAeAYAAIAB4RkAAAAGhGcAAAAYEJ4BAABgQHgG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\nBwAAAFbOcNo2AAA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oMcqM2z08Z8eP9x1SBuVOBwOH6ypy/72quObbCr6xTpl991PT9TcoUTOnoDtJ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\neAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10de12278>"
+        "<matplotlib.figure.Figure at 0x1125676a0>"
        ]
       }
      ],
@@ -354,7 +354,7 @@
        "output_type": "pyout",
        "prompt_number": 20,
        "text": [
-        "<matplotlib.text.Text at 0x10e14a908>"
+        "<matplotlib.text.Text at 0x112a581d0>"
        ]
       },
       {
@@ -362,7 +362,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x108413710>"
+        "<matplotlib.figure.Figure at 0x1125e2e48>"
        ]
       }
      ],
@@ -427,7 +427,7 @@
        "output_type": "pyout",
        "prompt_number": 24,
        "text": [
-        "<matplotlib.text.Text at 0x10e36f668>"
+        "<matplotlib.text.Text at 0x112ada668>"
        ]
       },
       {
@@ -435,7 +435,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x10de0ea20>"
+        "<matplotlib.figure.Figure at 0x112a47940>"
        ]
       }
      ],
@@ -500,7 +500,7 @@
        "output_type": "pyout",
        "prompt_number": 28,
        "text": [
-        "<matplotlib.text.Text at 0x10e4468d0>"
+        "<matplotlib.text.Text at 0x112c08588>"
        ]
       },
       {
@@ -508,7 +508,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x10e35e550>"
+        "<matplotlib.figure.Figure at 0x112a50ba8>"
        ]
       }
      ],
@@ -573,15 +573,15 @@
        "output_type": "pyout",
        "prompt_number": 32,
        "text": [
-        "<matplotlib.text.Text at 0x10e517128>"
+        "<matplotlib.text.Text at 0x112cde0b8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x10e4e7080>"
+        "<matplotlib.figure.Figure at 0x112ad84a8>"
        ]
       }
      ],
@@ -1233,26 +1233,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "ruby_data = ruby_data.transpose()\n",
       "ruby_data.index = ruby_time"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "ValueError",
-       "evalue": "Length mismatch: Expected axis has 16 elements, new values have 12 elements",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-42-aee3d8d9d82e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mruby_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mruby_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   1956\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1957\u001b[0m             \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1959\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas/lib.c:41295)\u001b[0;34m()\u001b[0m\n",
-        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/chameleonsrock/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m   2215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnew_len\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mold_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2216\u001b[0m             raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[0;32m-> 2217\u001b[0;31m                              'new values have %d elements' % (old_len, new_len))\n\u001b[0m\u001b[1;32m   2218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2219\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 16 elements, new values have 12 elements"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 42
     },
     {
@@ -1263,25 +1249,322 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>R01</th>\n",
+        "      <th>R02</th>\n",
+        "      <th>R03</th>\n",
+        "      <th>R04</th>\n",
+        "      <th>R05</th>\n",
+        "      <th>R06</th>\n",
+        "      <th>R07</th>\n",
+        "      <th>R08</th>\n",
+        "      <th>R09</th>\n",
+        "      <th>R10</th>\n",
+        "      <th>R11</th>\n",
+        "      <th>R12</th>\n",
+        "      <th>R13</th>\n",
+        "      <th>R14</th>\n",
+        "      <th>R15</th>\n",
+        "      <th>nan</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 1.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-15</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 43,
+       "text": [
+        "            R01  R02  R03  R04  R05  R06  R07  R08  R09  R10  R11  R12  R13  \\\n",
+        "2015-01-12    2  3.0  3.0    2  2.0  2.0  2.0  3.0  3.0  2.0  2.5  2.0    2   \n",
+        "2015-01-13    2  3.5  4.5    4  3.0  4.5  4.0  3.0  3.0  3.0  3.5  3.0    3   \n",
+        "2015-01-14    4  4.5  4.0    4  5.0  6.0  5.0  4.0  4.0  3.5  3.0  3.0    4   \n",
+        "2015-01-15    3  4.0  3.5    4  4.5  3.5  6.0  3.0  4.5  3.5  4.0  2.0    3   \n",
+        "2015-01-20    3  4.5  6.0    5  3.0  4.5  4.0  3.0  3.0  4.0  3.5  3.0    4   \n",
+        "2015-01-21    5  4.5  4.5    5  4.0  3.5  4.0  4.0  5.0  3.0  3.0  3.0    4   \n",
+        "2015-01-22    2  3.5  4.0    4  3.0  3.0  3.5  3.0  4.0  2.0  2.0  2.0    3   \n",
+        "2015-01-26    3  6.0  5.0    5  5.0  4.5  4.0  3.5  3.5  3.0  3.5  3.0    4   \n",
+        "2015-01-27    4  4.0  5.0    4  3.0  4.5  4.0  3.0  4.0  2.5  3.5  3.0    3   \n",
+        "2015-01-28    4  5.0  4.5    6  3.0  4.0  4.0  4.0  3.0  3.0  4.5  4.0    4   \n",
+        "2015-01-29    4  4.5  4.0    5  4.0  3.5  4.0  4.0  3.0  2.0  6.0  3.5    4   \n",
+        "2015-02-02    3  5.0  5.0    6  NaN  4.0  5.0  NaN  5.0  3.5  NaN  3.5    5   \n",
+        "\n",
+        "            R14  R15  NaN  \n",
+        "2015-01-12  1.0  4.5  NaN  \n",
+        "2015-01-13  2.0  5.0  NaN  \n",
+        "2015-01-14  2.0  4.0  NaN  \n",
+        "2015-01-15  3.0  5.0  NaN  \n",
+        "2015-01-20  3.0  5.0  NaN  \n",
+        "2015-01-21  3.5  5.0  NaN  \n",
+        "2015-01-22  2.0  4.0  NaN  \n",
+        "2015-01-26  4.0  4.5  NaN  \n",
+        "2015-01-27  4.0  4.0  NaN  \n",
+        "2015-01-28  3.0  4.5  NaN  \n",
+        "2015-01-29  4.0  4.0  NaN  \n",
+        "2015-02-02  4.0  4.5  NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 43
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "ruby_data_hw"
+      "ruby_data_hw = ruby_data_hw.T"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": [],
+     "input": [
+      "ruby_data_hw = pd.concat([ruby_data_hw[:3], ruby_data_hw[5:13]])"
+     ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -1291,10 +1574,1368 @@
       "               '01/20/2015', '01/21/2015', '01/22/2015', \n",
       "               '01/26/2015', '01/27/2015', '01/28/2015', \n",
       "               '01/29/2015', '02/02/2015']\n",
-      "\n"
+      "\n",
+      "rtime_index2 = pd.to_datetime(time_listr2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 46
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data_hw.index = rtime_index2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 47
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data_hw"
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>R01</th>\n",
+        "      <th>R02</th>\n",
+        "      <th>R03</th>\n",
+        "      <th>R04</th>\n",
+        "      <th>R05</th>\n",
+        "      <th>R06</th>\n",
+        "      <th>R07</th>\n",
+        "      <th>R08</th>\n",
+        "      <th>R09</th>\n",
+        "      <th>R10</th>\n",
+        "      <th>R11</th>\n",
+        "      <th>R12</th>\n",
+        "      <th>R13</th>\n",
+        "      <th>R14</th>\n",
+        "      <th>R15</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 48,
+       "text": [
+        "            R01  R02  R03  R04  R05  R06  R07  R08  R09  R10  R11  R12  R13  \\\n",
+        "2015-01-12  4.0  3.0  4.0  3.0  2.0  3.5  3.0    3  3.0  3.0  4.0  3.0    3   \n",
+        "2015-01-13  3.0  4.0  4.0  4.5  5.0  4.0  4.0    4  3.0  2.0  3.5  3.0    4   \n",
+        "2015-01-14  3.0  4.5  5.5  3.0  4.5  5.5  3.0    5  3.0  4.0  3.5  3.0    5   \n",
+        "2015-01-20  4.0  4.0  5.0  4.0  5.0  4.0  5.0    5  3.0  5.0  5.0  4.0    5   \n",
+        "2015-01-21  3.0  4.0  4.0  4.0  3.0  3.0  4.0    4  3.0  3.0  3.0  3.0    5   \n",
+        "2015-01-22  3.0  4.0  4.5  4.0  3.0  3.5  4.5    3  4.0  3.5  4.0  3.0    5   \n",
+        "2015-01-26  4.0  2.5  4.0  4.0  4.5  4.0  4.5    4  3.5  4.0  3.0  4.5    2   \n",
+        "2015-01-27  3.0  3.0  4.0  5.0  3.0  3.0  4.0    3  3.0  3.5  3.0  3.0    4   \n",
+        "2015-01-28  4.5  4.5  3.0  6.0  5.0  3.0  5.0    4  3.0  3.5  4.0  3.0    3   \n",
+        "2015-01-29  4.0  4.0  4.0  5.0  4.0  3.0  4.5    4  4.0  3.0  4.5  3.0    4   \n",
+        "2015-02-02  4.5  4.0  3.5  4.0  3.5  3.0  4.5  NaN  3.0  3.0  NaN  3.0    4   \n",
+        "\n",
+        "            R14  R15  \n",
+        "2015-01-12  2.0  4.0  \n",
+        "2015-01-13  2.5  5.0  \n",
+        "2015-01-14  2.0  4.0  \n",
+        "2015-01-20  4.0  4.0  \n",
+        "2015-01-21  2.5  4.5  \n",
+        "2015-01-22  2.0  5.0  \n",
+        "2015-01-26  4.0  4.0  \n",
+        "2015-01-27  2.0  4.5  \n",
+        "2015-01-28  4.0  3.0  \n",
+        "2015-01-29  3.5  3.5  \n",
+        "2015-02-02  4.5  4.0  "
+       ]
+      }
+     ],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_data"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>R01</th>\n",
+        "      <th>R02</th>\n",
+        "      <th>R03</th>\n",
+        "      <th>R04</th>\n",
+        "      <th>R05</th>\n",
+        "      <th>R06</th>\n",
+        "      <th>R07</th>\n",
+        "      <th>R08</th>\n",
+        "      <th>R09</th>\n",
+        "      <th>R10</th>\n",
+        "      <th>R11</th>\n",
+        "      <th>R12</th>\n",
+        "      <th>R13</th>\n",
+        "      <th>R14</th>\n",
+        "      <th>R15</th>\n",
+        "      <th>nan</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 1.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-15</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 49,
+       "text": [
+        "            R01  R02  R03  R04  R05  R06  R07  R08  R09  R10  R11  R12  R13  \\\n",
+        "2015-01-12    2  3.0  3.0    2  2.0  2.0  2.0  3.0  3.0  2.0  2.5  2.0    2   \n",
+        "2015-01-13    2  3.5  4.5    4  3.0  4.5  4.0  3.0  3.0  3.0  3.5  3.0    3   \n",
+        "2015-01-14    4  4.5  4.0    4  5.0  6.0  5.0  4.0  4.0  3.5  3.0  3.0    4   \n",
+        "2015-01-15    3  4.0  3.5    4  4.5  3.5  6.0  3.0  4.5  3.5  4.0  2.0    3   \n",
+        "2015-01-20    3  4.5  6.0    5  3.0  4.5  4.0  3.0  3.0  4.0  3.5  3.0    4   \n",
+        "2015-01-21    5  4.5  4.5    5  4.0  3.5  4.0  4.0  5.0  3.0  3.0  3.0    4   \n",
+        "2015-01-22    2  3.5  4.0    4  3.0  3.0  3.5  3.0  4.0  2.0  2.0  2.0    3   \n",
+        "2015-01-26    3  6.0  5.0    5  5.0  4.5  4.0  3.5  3.5  3.0  3.5  3.0    4   \n",
+        "2015-01-27    4  4.0  5.0    4  3.0  4.5  4.0  3.0  4.0  2.5  3.5  3.0    3   \n",
+        "2015-01-28    4  5.0  4.5    6  3.0  4.0  4.0  4.0  3.0  3.0  4.5  4.0    4   \n",
+        "2015-01-29    4  4.5  4.0    5  4.0  3.5  4.0  4.0  3.0  2.0  6.0  3.5    4   \n",
+        "2015-02-02    3  5.0  5.0    6  NaN  4.0  5.0  NaN  5.0  3.5  NaN  3.5    5   \n",
+        "\n",
+        "            R14  R15  NaN  \n",
+        "2015-01-12  1.0  4.5  NaN  \n",
+        "2015-01-13  2.0  5.0  NaN  \n",
+        "2015-01-14  2.0  4.0  NaN  \n",
+        "2015-01-15  3.0  5.0  NaN  \n",
+        "2015-01-20  3.0  5.0  NaN  \n",
+        "2015-01-21  3.5  5.0  NaN  \n",
+        "2015-01-22  2.0  4.0  NaN  \n",
+        "2015-01-26  4.0  4.5  NaN  \n",
+        "2015-01-27  4.0  4.0  NaN  \n",
+        "2015-01-28  3.0  4.5  NaN  \n",
+        "2015-01-29  4.0  4.0  NaN  \n",
+        "2015-02-02  4.0  4.5  NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 49
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = pd.concat([ruby_data, ruby_data_hw])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = ruby_all.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 51
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = ruby_all[:15]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = ruby_all.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>R01</th>\n",
+        "      <th>R02</th>\n",
+        "      <th>R03</th>\n",
+        "      <th>R04</th>\n",
+        "      <th>R05</th>\n",
+        "      <th>R06</th>\n",
+        "      <th>R07</th>\n",
+        "      <th>R08</th>\n",
+        "      <th>R09</th>\n",
+        "      <th>R10</th>\n",
+        "      <th>R11</th>\n",
+        "      <th>R12</th>\n",
+        "      <th>R13</th>\n",
+        "      <th>R14</th>\n",
+        "      <th>R15</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 1.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-15</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-12</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-13</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-14</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-20</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-21</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-22</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-26</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-27</th>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-28</th>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 6.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-01-29</th>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2015-02-02</th>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 54,
+       "text": [
+        "            R01  R02  R03  R04  R05  R06  R07  R08  R09  R10  R11  R12  R13  \\\n",
+        "2015-01-12  2.0  3.0  3.0  2.0  2.0  2.0  2.0  3.0  3.0  2.0  2.5  2.0    2   \n",
+        "2015-01-13  2.0  3.5  4.5  4.0  3.0  4.5  4.0  3.0  3.0  3.0  3.5  3.0    3   \n",
+        "2015-01-14  4.0  4.5  4.0  4.0  5.0  6.0  5.0  4.0  4.0  3.5  3.0  3.0    4   \n",
+        "2015-01-15  3.0  4.0  3.5  4.0  4.5  3.5  6.0  3.0  4.5  3.5  4.0  2.0    3   \n",
+        "2015-01-20  3.0  4.5  6.0  5.0  3.0  4.5  4.0  3.0  3.0  4.0  3.5  3.0    4   \n",
+        "2015-01-21  5.0  4.5  4.5  5.0  4.0  3.5  4.0  4.0  5.0  3.0  3.0  3.0    4   \n",
+        "2015-01-22  2.0  3.5  4.0  4.0  3.0  3.0  3.5  3.0  4.0  2.0  2.0  2.0    3   \n",
+        "2015-01-26  3.0  6.0  5.0  5.0  5.0  4.5  4.0  3.5  3.5  3.0  3.5  3.0    4   \n",
+        "2015-01-27  4.0  4.0  5.0  4.0  3.0  4.5  4.0  3.0  4.0  2.5  3.5  3.0    3   \n",
+        "2015-01-28  4.0  5.0  4.5  6.0  3.0  4.0  4.0  4.0  3.0  3.0  4.5  4.0    4   \n",
+        "2015-01-29  4.0  4.5  4.0  5.0  4.0  3.5  4.0  4.0  3.0  2.0  6.0  3.5    4   \n",
+        "2015-02-02  3.0  5.0  5.0  6.0  NaN  4.0  5.0  NaN  5.0  3.5  NaN  3.5    5   \n",
+        "2015-01-12  4.0  3.0  4.0  3.0  2.0  3.5  3.0  3.0  3.0  3.0  4.0  3.0    3   \n",
+        "2015-01-13  3.0  4.0  4.0  4.5  5.0  4.0  4.0  4.0  3.0  2.0  3.5  3.0    4   \n",
+        "2015-01-14  3.0  4.5  5.5  3.0  4.5  5.5  3.0  5.0  3.0  4.0  3.5  3.0    5   \n",
+        "2015-01-20  4.0  4.0  5.0  4.0  5.0  4.0  5.0  5.0  3.0  5.0  5.0  4.0    5   \n",
+        "2015-01-21  3.0  4.0  4.0  4.0  3.0  3.0  4.0  4.0  3.0  3.0  3.0  3.0    5   \n",
+        "2015-01-22  3.0  4.0  4.5  4.0  3.0  3.5  4.5  3.0  4.0  3.5  4.0  3.0    5   \n",
+        "2015-01-26  4.0  2.5  4.0  4.0  4.5  4.0  4.5  4.0  3.5  4.0  3.0  4.5    2   \n",
+        "2015-01-27  3.0  3.0  4.0  5.0  3.0  3.0  4.0  3.0  3.0  3.5  3.0  3.0    4   \n",
+        "2015-01-28  4.5  4.5  3.0  6.0  5.0  3.0  5.0  4.0  3.0  3.5  4.0  3.0    3   \n",
+        "2015-01-29  4.0  4.0  4.0  5.0  4.0  3.0  4.5  4.0  4.0  3.0  4.5  3.0    4   \n",
+        "2015-02-02  4.5  4.0  3.5  4.0  3.5  3.0  4.5  NaN  3.0  3.0  NaN  3.0    4   \n",
+        "\n",
+        "            R14  R15  \n",
+        "2015-01-12  1.0  4.5  \n",
+        "2015-01-13  2.0  5.0  \n",
+        "2015-01-14  2.0  4.0  \n",
+        "2015-01-15  3.0  5.0  \n",
+        "2015-01-20  3.0  5.0  \n",
+        "2015-01-21  3.5  5.0  \n",
+        "2015-01-22  2.0  4.0  \n",
+        "2015-01-26  4.0  4.5  \n",
+        "2015-01-27  4.0  4.0  \n",
+        "2015-01-28  3.0  4.5  \n",
+        "2015-01-29  4.0  4.0  \n",
+        "2015-02-02  4.0  4.5  \n",
+        "2015-01-12  2.0  4.0  \n",
+        "2015-01-13  2.5  5.0  \n",
+        "2015-01-14  2.0  4.0  \n",
+        "2015-01-20  4.0  4.0  \n",
+        "2015-01-21  2.5  4.5  \n",
+        "2015-01-22  2.0  5.0  \n",
+        "2015-01-26  4.0  4.0  \n",
+        "2015-01-27  2.0  4.5  \n",
+        "2015-01-28  4.0  3.0  \n",
+        "2015-01-29  3.5  3.5  \n",
+        "2015-02-02  4.5  4.0  "
+       ]
+      }
+     ],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "The Story of Ruby Student 6"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "r6_story = ruby_all[['R06']]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "r6_story.plot(figsize=(17,9), color='red', ylim=(0,6))\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 56,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x112c6dba8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ekCgm+P0wr1kF+1P/Ro2rG6DqXb2R9M7b0BUL3H9/BMeW5+Lott1wTZ4B/w1t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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x112d05be0>"
+       ]
+      }
+     ],
+     "prompt_number": 56
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Although this student appeared to be initially overwhelmed they have an inspiring story of hope and change in the days that follow. Way to keep it under 5!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = ruby_all.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 57
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pdata = pdata.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = ruby_all.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 66
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pdata = pdata.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 67
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "merged_data = pdata.join(ruby_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 68
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "merged_data = merged_data.T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 76
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "merged_data = merged_data[:30]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "merged_mean = merged_data.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "merged_data['mean'] = merged_mean\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 79
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "day_mean = merged_data['mean']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 83
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "day_mean = day_mean.resample(\"D\", how=['mean'])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 91
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "day_mean.plot(kind='bar')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 100,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x113a336a0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ARIwmDQARo0kDQMRo0gAQsf8HD4Tn5skDUI4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x113b18eb8>"
+       ]
+      }
+     ],
+     "prompt_number": 100
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This was as far as I could take this."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
      "outputs": []
     }
    ],