From 0b82923b8f2fd9c5fa5715355f824fe6a38b0e11 Mon Sep 17 00:00:00 2001
From: Ana Maria Echeverri <ame@anamariaecheverri.com>
Date: Wed, 4 Feb 2015 23:34:59 -0500
Subject: [PATCH] Final Version

---
 cohorts.ipynb | 1832 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1832 insertions(+)
 create mode 100644 cohorts.ipynb

diff --git a/cohorts.ipynb b/cohorts.ipynb
new file mode 100644
index 0000000..e27473d
--- /dev/null
+++ b/cohorts.ipynb
@@ -0,0 +1,1832 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:a34070d692272350d7bb90552eec30f2de24ba96891ed9b84d9d846f1efe5c68"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import numpy as md\n",
+      "import datetime\n",
+      "import seaborn as sb\n",
+      "import matplotlib.pyplot as plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 427
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "plt.rc('figure', figsize=(16, 6))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 428
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python='cohort_3_python.csv'\n",
+      "pythondf = pd.read_csv(python, skipfooter=4, engine='python')\n",
+      "pythondf.head(2)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Lecture 1, Jan12</th>\n",
+        "      <th>Homework 1, Jan13</th>\n",
+        "      <th>Lecture 2, Jan 13</th>\n",
+        "      <th>Homework 2, Jan14</th>\n",
+        "      <th>Lecture 3, Jan 14</th>\n",
+        "      <th>Homework 3, Jan15</th>\n",
+        "      <th>Lecture 4, Jan 15</th>\n",
+        "      <th>Mystery Word, Jan 20</th>\n",
+        "      <th>Lecture 5, Jan 20</th>\n",
+        "      <th>...</th>\n",
+        "      <th>Blackjack2, Jan26</th>\n",
+        "      <th>Lecture 9, Jan26</th>\n",
+        "      <th>Random Art, Jan 27</th>\n",
+        "      <th>Lecture10, Jan27</th>\n",
+        "      <th>Charting</th>\n",
+        "      <th>Lecture11, Jan28</th>\n",
+        "      <th>PigSim</th>\n",
+        "      <th>Lecture12, Jan29</th>\n",
+        "      <th>Traffic Sim I</th>\n",
+        "      <th>Lecture13,Feb2</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> P01</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>...</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</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",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> P02</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>2 rows \u00d7 25 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 429,
+       "text": [
+        "  Name  Lecture 1, Jan12  Homework 1, Jan13  Lecture 2, Jan 13  \\\n",
+        "0  P01                 3                4.0                  3   \n",
+        "1  P02                 4                3.5                  3   \n",
+        "\n",
+        "   Homework 2, Jan14  Lecture 3, Jan 14  Homework 3, Jan15  Lecture 4, Jan 15  \\\n",
+        "0                  4                  4                5.0                5.0   \n",
+        "1                  5                  4                4.5                4.5   \n",
+        "\n",
+        "   Mystery Word, Jan 20  Lecture 5, Jan 20       ...        Blackjack2, Jan26  \\\n",
+        "0                     5                  4       ...                      NaN   \n",
+        "1                     5                  5       ...                        5   \n",
+        "\n",
+        "   Lecture 9, Jan26  Random Art, Jan 27  Lecture10, Jan27  Charting  \\\n",
+        "0                 4                   5               NaN       NaN   \n",
+        "1                 5                   5               NaN       NaN   \n",
+        "\n",
+        "   Lecture11, Jan28  PigSim  Lecture12, Jan29  Traffic Sim I  Lecture13,Feb2  \n",
+        "0               NaN     NaN               NaN            NaN             NaN  \n",
+        "1                 5       5                 5            NaN             NaN  \n",
+        "\n",
+        "[2 rows x 25 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 429
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pythondf = pd.melt(pythondf, id_vars=['Name'], var_name='Title_Date', value_name='Score')\n",
+      "pythondf.head(2)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Title_Date</th>\n",
+        "      <th>Score</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> P01</td>\n",
+        "      <td> Lecture 1, Jan12</td>\n",
+        "      <td> 3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> P02</td>\n",
+        "      <td> Lecture 1, Jan12</td>\n",
+        "      <td> 4</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 430,
+       "text": [
+        "  Name        Title_Date  Score\n",
+        "0  P01  Lecture 1, Jan12      3\n",
+        "1  P02  Lecture 1, Jan12      4"
+       ]
+      }
+     ],
+     "prompt_number": 430
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pythondf['Course'] = \"Python\"\n",
+      "pythondf.head(2)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Title_Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Course</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> P01</td>\n",
+        "      <td> Lecture 1, Jan12</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> Python</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> P02</td>\n",
+        "      <td> Lecture 1, Jan12</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> Python</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 431,
+       "text": [
+        "  Name        Title_Date  Score  Course\n",
+        "0  P01  Lecture 1, Jan12      3  Python\n",
+        "1  P02  Lecture 1, Jan12      4  Python"
+       ]
+      }
+     ],
+     "prompt_number": 431
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def reformat_date(string_date):\n",
+      "    return datetime.datetime.strptime(string_date+\"2015 07\", '%b%d%Y %H')\n",
+      "\n",
+      "def reformat_python_date(pythondf):\n",
+      "    for i, myfield in pythondf.iterrows(): \n",
+      "        date = myfield['Title_Date'].split(',')\n",
+      "        if len(date) == 2:\n",
+      "            if date[1].find(\"21\") != -1:\n",
+      "                date[1] = 'Jan21'   \n",
+      "            pythondf.loc[i,'Date'] = reformat_date(date[1].replace(\" \",\"\"))\n",
+      "        else:\n",
+      "            if date[0].find(\"Charting\") != -1:\n",
+      "                pythondf.loc[i,'Date'] = reformat_date(\"Jan28\")\n",
+      "            elif date[0].find(\"PigSim\") != -1:\n",
+      "                pythondf.loc[i,'Date'] = reformat_date(\"Jan29\")\n",
+      "            elif date[0].find(\"Traffic\") != -1:\n",
+      "                pythondf.loc[i,'Date'] = reformat_date(\"Feb2\") \n",
+      "        if myfield['Title_Date'].find(\"Lecture\") == -1:\n",
+      "           pythondf.loc[i, 'Type'] = \"HW\"\n",
+      "        else:\n",
+      "            pythondf.loc[i,'Type'] = \"Lecture\"\n",
+      "\n",
+      "reformat_python_date(pythondf)   "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 432
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del(pythondf['Title_Date'])\n",
+      "pythondf.head(20)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Course</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Type</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0 </th>\n",
+        "      <td> P01</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1 </th>\n",
+        "      <td> P02</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2 </th>\n",
+        "      <td> P03</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3 </th>\n",
+        "      <td> P04</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4 </th>\n",
+        "      <td> P05</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5 </th>\n",
+        "      <td> P06</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td> P07</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td> P08</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8 </th>\n",
+        "      <td> P09</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> P10</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td> P11</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11</th>\n",
+        "      <td> P12</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td> P13</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td> P14</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td> P15</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td> P01</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td>      HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>16</th>\n",
+        "      <td> P02</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td>      HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>17</th>\n",
+        "      <td> P03</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td>      HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>18</th>\n",
+        "      <td> P04</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td>      HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>19</th>\n",
+        "      <td> P05</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> Python</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td>      HW</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 433,
+       "text": [
+        "   Name  Score  Course                Date     Type\n",
+        "0   P01    3.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "1   P02    4.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "2   P03    NaN  Python 2015-01-12 07:00:00  Lecture\n",
+        "3   P04    3.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "4   P05    NaN  Python 2015-01-12 07:00:00  Lecture\n",
+        "5   P06    3.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "6   P07    3.5  Python 2015-01-12 07:00:00  Lecture\n",
+        "7   P08    2.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "8   P09    NaN  Python 2015-01-12 07:00:00  Lecture\n",
+        "9   P10    2.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "10  P11    2.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "11  P12    3.5  Python 2015-01-12 07:00:00  Lecture\n",
+        "12  P13    2.5  Python 2015-01-12 07:00:00  Lecture\n",
+        "13  P14    3.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "14  P15    2.0  Python 2015-01-12 07:00:00  Lecture\n",
+        "15  P01    4.0  Python 2015-01-13 07:00:00       HW\n",
+        "16  P02    3.5  Python 2015-01-13 07:00:00       HW\n",
+        "17  P03    5.0  Python 2015-01-13 07:00:00       HW\n",
+        "18  P04    3.0  Python 2015-01-13 07:00:00       HW\n",
+        "19  P05    3.0  Python 2015-01-13 07:00:00       HW"
+       ]
+      }
+     ],
+     "prompt_number": 433
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we work with the Ruby Excel worksheets"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby='cohort_3_rails.xlsx'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 434
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lecture = pd.read_excel(ruby,\n",
+      "                             'Lecture Score', \n",
+      "                             index_col=None,\n",
+      "                             na_values=['NA'],\n",
+      "                             skiprows=1,\n",
+      "                             skip_footer=3,\n",
+      "                             header=0,\n",
+      "                             parse_cols='A:N')\n",
+      "\n",
+      "ruby_lecture.head(2)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>M</th>\n",
+        "      <th>T</th>\n",
+        "      <th>W</th>\n",
+        "      <th>Th</th>\n",
+        "      <th>M.1</th>\n",
+        "      <th>T.1</th>\n",
+        "      <th>W.1</th>\n",
+        "      <th>Th.1</th>\n",
+        "      <th>M.2</th>\n",
+        "      <th>T.2</th>\n",
+        "      <th>W.2</th>\n",
+        "      <th>Th.2</th>\n",
+        "      <th>M.3</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 6</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 435,
+       "text": [
+        "  Unnamed: 0  M    T    W  Th  M.1  T.1  W.1  Th.1  M.2  T.2  W.2  Th.2  M.3\n",
+        "0        R01  2  2.0  4.0   3  NaN  3.0  5.0   2.0    3    4    4   4.0    3\n",
+        "1        R02  3  3.5  4.5   4  NaN  4.5  4.5   3.5    6    4    5   4.5    5"
+       ]
+      }
+     ],
+     "prompt_number": 435
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hw = pd.read_excel(ruby,\n",
+      "                        'HW Score',\n",
+      "                        index_col=None,\n",
+      "                        na_values=['NA'],\n",
+      "                        skiprows=1,\n",
+      "                        skip_footer=2,\n",
+      "                        header=0,\n",
+      "                        parse_cols='A:N')\n",
+      "ruby_hw.head(2)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>M</th>\n",
+        "      <th>T</th>\n",
+        "      <th>W</th>\n",
+        "      <th>Th</th>\n",
+        "      <th>M.1</th>\n",
+        "      <th>T.1</th>\n",
+        "      <th>W.1</th>\n",
+        "      <th>Th.1</th>\n",
+        "      <th>M.2</th>\n",
+        "      <th>T.2</th>\n",
+        "      <th>W.2</th>\n",
+        "      <th>Th.2</th>\n",
+        "      <th>M.3</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 2.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 436,
+       "text": [
+        "  Unnamed: 0  M  T    W  Th  M.1  T.1  W.1  Th.1  M.2  T.2  W.2  Th.2  M.3\n",
+        "0        R01  4  3  3.0 NaN  NaN    4    3     3  4.0    3  4.5     4  4.5\n",
+        "1        R02  3  4  4.5 NaN  NaN    4    4     4  2.5    3  4.5     4  4.0"
+       ]
+      }
+     ],
+     "prompt_number": 436
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_lecturedf = pd.melt(ruby_lecture, id_vars='Unnamed: 0', var_name='Date', value_name='Score')\n",
+      "ruby_lecturedf['Type'] = \"Lecture\"\n",
+      "ruby_lecturedf.head(2)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 437,
+       "text": [
+        "  Unnamed: 0 Date  Score     Type\n",
+        "0        R01    M      2  Lecture\n",
+        "1        R02    M      3  Lecture"
+       ]
+      }
+     ],
+     "prompt_number": 437
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_hwdf = pd.melt(ruby_hw, id_vars='Unnamed: 0', var_name='Date', value_name='Score')\n",
+      "ruby_hwdf['Type'] = \"HW\"\n",
+      "ruby_hwdf.head(2)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 438,
+       "text": [
+        "  Unnamed: 0 Date  Score Type\n",
+        "0        R01    M      4   HW\n",
+        "1        R02    M      3   HW"
+       ]
+      }
+     ],
+     "prompt_number": 438
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all = pd.concat([ruby_hwdf,ruby_lecturedf])\n",
+      "ruby_all.tail(10)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>185</th>\n",
+        "      <td> R06</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>186</th>\n",
+        "      <td> R07</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>187</th>\n",
+        "      <td> R08</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>188</th>\n",
+        "      <td> R09</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>189</th>\n",
+        "      <td> R10</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>190</th>\n",
+        "      <td> R11</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> NaN</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>191</th>\n",
+        "      <td> R12</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>192</th>\n",
+        "      <td> R13</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>193</th>\n",
+        "      <td> R14</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>194</th>\n",
+        "      <td> R15</td>\n",
+        "      <td> M.3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> Lecture</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 439,
+       "text": [
+        "    Unnamed: 0 Date  Score     Type\n",
+        "185        R06  M.3    4.0  Lecture\n",
+        "186        R07  M.3    5.0  Lecture\n",
+        "187        R08  M.3    NaN  Lecture\n",
+        "188        R09  M.3    5.0  Lecture\n",
+        "189        R10  M.3    3.5  Lecture\n",
+        "190        R11  M.3    NaN  Lecture\n",
+        "191        R12  M.3    3.5  Lecture\n",
+        "192        R13  M.3    5.0  Lecture\n",
+        "193        R14  M.3    4.0  Lecture\n",
+        "194        R15  M.3    4.5  Lecture"
+       ]
+      }
+     ],
+     "prompt_number": 439
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all.head(10)"
+     ],
+     "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>Unnamed: 0</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> R03</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> R04</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> R05</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> R06</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> R07</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td> R08</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td> R09</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td> R10</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 440,
+       "text": [
+        "  Unnamed: 0 Date  Score Type\n",
+        "0        R01    M    4.0   HW\n",
+        "1        R02    M    3.0   HW\n",
+        "2        R03    M    4.0   HW\n",
+        "3        R04    M    3.0   HW\n",
+        "4        R05    M    2.0   HW\n",
+        "5        R06    M    3.5   HW\n",
+        "6        R07    M    3.0   HW\n",
+        "7        R08    M    3.0   HW\n",
+        "8        R09    M    3.0   HW\n",
+        "9        R10    M    3.0   HW"
+       ]
+      }
+     ],
+     "prompt_number": 440
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_all.columns.values[0]='Name'\n",
+      "ruby_all['Course'] = \"Ruby\"\n",
+      "ruby_all.head(2)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "      <th>Course</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td> R01</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> R02</td>\n",
+        "      <td> M</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 441,
+       "text": [
+        "  Name Date  Score Type Course\n",
+        "0  R01    M      4   HW   Ruby\n",
+        "1  R02    M      3   HW   Ruby"
+       ]
+      }
+     ],
+     "prompt_number": 441
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def process_ruby_date(rubydf):\n",
+      "    for i, myfield in rubydf.iterrows(): \n",
+      "        date1 = myfield['Date'].replace(\" \",\"\")\n",
+      "        if date1 == \"M\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(12))\n",
+      "        elif date1 ==\"T\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(13))\n",
+      "        elif date1 ==\"W\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(14))\n",
+      "        elif date1 ==\"Th\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(15))\n",
+      "        elif date1 == \"M.1\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(19))\n",
+      "        elif date1 ==\"T.1\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(20))\n",
+      "        elif date1 ==\"W.1\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(21))\n",
+      "        elif date1 ==\"Th.1\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(22))\n",
+      "        elif date1 == \"M.2\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(26))\n",
+      "        elif date1 ==\"T.2\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(27))\n",
+      "        elif date1 ==\"W.2\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(28))\n",
+      "        elif date1 ==\"Th.2\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Jan\"+str(29))  \n",
+      "        if date1 == \"M.3\":\n",
+      "            rubydf.loc[i,'Date'] = reformat_date(\"Feb\"+str(2))\n",
+      "        \n",
+      "    \n",
+      "process_ruby_date(ruby_all)\n",
+      "ruby_all.head(20)"
+     ],
+     "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>Name</th>\n",
+        "      <th>Date</th>\n",
+        "      <th>Score</th>\n",
+        "      <th>Type</th>\n",
+        "      <th>Course</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0 </th>\n",
+        "      <td> R01</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1 </th>\n",
+        "      <td> R02</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2 </th>\n",
+        "      <td> R03</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3 </th>\n",
+        "      <td> R04</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4 </th>\n",
+        "      <td> R05</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5 </th>\n",
+        "      <td> R06</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td> R07</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td> R08</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8 </th>\n",
+        "      <td> R09</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> R10</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td> R11</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11</th>\n",
+        "      <td> R12</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>12</th>\n",
+        "      <td> R13</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td> R14</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td> R15</td>\n",
+        "      <td>2015-01-12 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td> R01</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>16</th>\n",
+        "      <td> R02</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>17</th>\n",
+        "      <td> R03</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>18</th>\n",
+        "      <td> R04</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>19</th>\n",
+        "      <td> R05</td>\n",
+        "      <td>2015-01-13 07:00:00</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> HW</td>\n",
+        "      <td> Ruby</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 442,
+       "text": [
+        "   Name                Date  Score Type Course\n",
+        "0   R01 2015-01-12 07:00:00    4.0   HW   Ruby\n",
+        "1   R02 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "2   R03 2015-01-12 07:00:00    4.0   HW   Ruby\n",
+        "3   R04 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "4   R05 2015-01-12 07:00:00    2.0   HW   Ruby\n",
+        "5   R06 2015-01-12 07:00:00    3.5   HW   Ruby\n",
+        "6   R07 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "7   R08 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "8   R09 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "9   R10 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "10  R11 2015-01-12 07:00:00    4.0   HW   Ruby\n",
+        "11  R12 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "12  R13 2015-01-12 07:00:00    3.0   HW   Ruby\n",
+        "13  R14 2015-01-12 07:00:00    2.0   HW   Ruby\n",
+        "14  R15 2015-01-12 07:00:00    4.0   HW   Ruby\n",
+        "15  R01 2015-01-13 07:00:00    3.0   HW   Ruby\n",
+        "16  R02 2015-01-13 07:00:00    4.0   HW   Ruby\n",
+        "17  R03 2015-01-13 07:00:00    4.0   HW   Ruby\n",
+        "18  R04 2015-01-13 07:00:00    4.5   HW   Ruby\n",
+        "19  R05 2015-01-13 07:00:00    5.0   HW   Ruby"
+       ]
+      }
+     ],
+     "prompt_number": 442
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohortdf = pd.concat([ruby_all,pythondf])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 443
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lecturedf = cohortdf[cohortdf['Type'] == \"Lecture\"].dropna()\n",
+      "lecturedf_ruby = lecturedf[lecturedf['Course'] == \"Ruby\"]\n",
+      "del lecturedf_ruby['Course']\n",
+      "lecturedf_python = lecturedf[lecturedf['Course'] == \"Python\"]\n",
+      "del lecturedf_python['Course']\n",
+      "\n",
+      "hwdf = cohortdf[cohortdf['Type'] == \"HW\"].dropna()\n",
+      "hwdf_ruby = hwdf[hwdf['Course'] == \"Ruby\"]\n",
+      "del hwdf_ruby['Course']\n",
+      "hwdf_python = hwdf[hwdf['Course'] == \"Python\"]\n",
+      "del hwdf_python['Course']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 444
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "lecturedf_ruby.set_index(['Date'])\n",
+      "lecturedf_python.set_index(['Date'])\n",
+      "lecturedf_ruby.index = pd.to_datetime(lecturedf_ruby['Date'], format=\"%Y%m%d\")\n",
+      "lecturedf_python.index = pd.to_datetime(lecturedf_python['Date'], format=\"%Y%m%d\")\n",
+      "lecturedf_ruby['day'] = lecturedf_ruby.index.day\n",
+      "lecturedf_python['day'] = lecturedf_python.index.day\n",
+      "\n",
+      "lec_ruby_mean = lecturedf_ruby.groupby(['day']).mean()\n",
+      "lec_python_mean = lecturedf_python.groupby(['day']).mean()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 445
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(lec_ruby_mean)), lec_ruby_mean.index)\n",
+      "plt.plot(lec_ruby_mean,label = \"Ruby\")\n",
+      "plt.plot(lec_python_mean, label = \"Python\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Mean Daily Scores for Lectures by Cohort\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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2k2VNB6CssXLUNQkhhBBCjKfmnhb+tOFRim3lzI2YzX8ULCUhJN7t/eXHZnNj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\nM7Ox8+DTzMzMzMzMxu5v0ZmGuYqaNgMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111f2ff28>"
+       ]
+      }
+     ],
+     "prompt_number": 446
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "hwdf_ruby.set_index(['Date'])\n",
+      "hwdf_python.set_index(['Date'])\n",
+      "hwdf_ruby.index = pd.to_datetime(hwdf_ruby['Date'], format=\"%Y%m%d\")\n",
+      "hwdf_python.index = pd.to_datetime(hwdf_python['Date'], format=\"%Y%m%d\")\n",
+      "hwdf_ruby['day'] = hwdf_ruby.index.day\n",
+      "hwdf_python['day'] = hwdf_python.index.day\n",
+      "\n",
+      "hw_ruby_mean = hwdf_ruby.groupby(['day']).mean()\n",
+      "hw_python_mean = hwdf_python.groupby(['day']).mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 447
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(hw_ruby_mean)), hw_ruby_mean.index)\n",
+      "plt.plot(hw_ruby_mean,label = \"Ruby\")\n",
+      "plt.plot(hw_python_mean, label = \"Python\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Mean Daily Scores for Homework by Cohort\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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br/DqgRWcmzyPC1MWkBgSb3epI84nA+nsuGxWFq8hr65AgVRERERExEZNR5pZ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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111e4f1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 448
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del lecturedf_ruby['day']\n",
+      "del lecturedf_python['day']\n",
+      "lecturedf_ruby.set_index(['Date'])\n",
+      "lecturedf_python.set_index(['Date'])\n",
+      "\n",
+      "lecturedf_ruby['week'] = lecturedf_ruby.index.weekofyear\n",
+      "lecturedf_python['week'] = lecturedf_python.index.weekofyear\n",
+      "\n",
+      "lec_ruby_mean = lecturedf_ruby.groupby(['week']).mean()\n",
+      "lec_python_mean = lecturedf_python.groupby(['week']).mean()\n",
+      "lec_python_mean"
+     ],
+     "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>Score</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>week</th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 3.339286</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 4.132075</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> 4.068293</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> 5.000000</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 449,
+       "text": [
+        "         Score\n",
+        "week          \n",
+        "3     3.339286\n",
+        "4     4.132075\n",
+        "5     4.068293\n",
+        "6     5.000000"
+       ]
+      }
+     ],
+     "prompt_number": 449
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(lec_ruby_mean)), lec_ruby_mean.index)\n",
+      "plt.plot(lec_ruby_mean,label = \"Ruby\")\n",
+      "plt.plot(lec_python_mean, label = \"Python\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Mean Weekly Scores for Lectures by Cohort\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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rbSVwG/AvV1JVMNhyJVcTERl0fn+mPqNEZEjT55QMNYebqlkf2MCRllqSEpJY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\nAABAxfz/uHXGVnr+gsEAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111f16898>"
+       ]
+      }
+     ],
+     "prompt_number": 450
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "del hwdf_ruby['day']\n",
+      "del hwdf_python['day']\n",
+      "hwdf_ruby.set_index(['Date'])\n",
+      "hwdf_python.set_index(['Date'])\n",
+      "hwdf_ruby.index = pd.to_datetime(hwdf_ruby.pop('Date'), format=\"%Y%m%d\")\n",
+      "hwdf_python.index = pd.to_datetime(hwdf_python.pop('Date'), format=\"%Y%m%d\")\n",
+      "hwdf_ruby['week'] = hwdf_ruby.index.weekofyear\n",
+      "hwdf_python['week'] = hwdf_python.index.weekofyear\n",
+      "\n",
+      "hw_ruby_mean = hwdf_ruby.groupby(['week']).mean()\n",
+      "hw_python_mean = hwdf_python.groupby(['week']).mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 451
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(hw_ruby_mean)), hw_ruby_mean.index)\n",
+      "plt.plot(hw_ruby_mean,label = \"Ruby\")\n",
+      "plt.plot(hw_python_mean, label = \"Python\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Mean Weekly Scores for Homework by Cohort\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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yCLQNtrMv0sjRrmeIxqcIBVOpW3kL4Yo6qnIq/C5PJEEhVERERERkgYrGohzr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\n57sAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111ecf470>"
+       ]
+      }
+     ],
+     "prompt_number": 452
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohortdf.set_index(['Date'])\n",
+      "cohortdf.index = pd.to_datetime(cohortdf.pop('Date'), format=\"%Y%m%d\")\n",
+      "cohortdf['weekday'] = cohortdf.index.weekday\n",
+      "cohort_weekdaydf = cohortdf.groupby(['weekday']).mean()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 453
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(cohort_weekdaydf)), cohort_weekdaydf.index)\n",
+      "plt.plot(cohort_weekdaydf,label = \"Both Cohorts\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Mean Scores for Homework and Lectures by Day of the Week\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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k5mTzuYsmM2Fk/0yX1KUYRCVJkiTpADyzdAM/eugF8vKyufriKYwf3i/TJXU5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\nJEmSJClV/w+fr0hYKekZcgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111e2b358>"
+       ]
+      }
+     ],
+     "prompt_number": 454
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_students = cohortdf[cohortdf['Course'] == \"Ruby\"]\n",
+      "ruby_students = ruby_students.groupby(['Name']).mean()\n",
+      "ruby_st = ruby_students[ruby_students['Score'] == ruby_students['Score'].max()]\n",
+      "ruby_st.index"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 455,
+       "text": [
+        "Index(['R04'], dtype='object')"
+       ]
+      }
+     ],
+     "prompt_number": 455
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_students = cohortdf[cohortdf['Course'] == \"Python\"]\n",
+      "python_students = python_students.groupby(['Name']).mean()\n",
+      "python_st = python_students[python_students['Score'] == python_students['Score'].max()]\n",
+      "python_st.index"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 456,
+       "text": [
+        "Index(['P03'], dtype='object')"
+       ]
+      }
+     ],
+     "prompt_number": 456
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Student with Most Difficulty in the Ruby class (Highest Score) is R04"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_s = cohortdf[cohortdf['Name'] == 'R04']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 461
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_s['day'] = ruby_s.index.day\n",
+      "ruby_final = ruby_s.groupby(['day']).mean()\n",
+      "del ruby_final['weekday']\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 459
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(ruby_final)), ruby_final.index)\n",
+      "plt.plot(ruby_final,label = \"Ruby Highest Scores\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Ruby Student with Most Difficulties in class\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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vfFgAwMzJ6TgcXWeqbnOhrhDmzBzOky9vZP6KfAYkdSe1d4zdsVol+K/6FRER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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111f467f0>"
+       ]
+      }
+     ],
+     "prompt_number": 460
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_s = cohortdf[cohortdf['Name'] == 'P03']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 462
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_s['day'] = python_s.index.day\n",
+      "python_final = python_s.groupby(['day']).mean()\n",
+      "del python_final['weekday']\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 463
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.xticks(range(len(python_final)), python_final.index)\n",
+      "plt.plot(python_final,label = \"Python Highest Scores\")\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.title(\"Python Student with Most Difficulties in class\")\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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UkiRJzcrrxaWsWLeT0wd0YlRBXtRxTrkz8rsybFBn3ly/iyWrSqOOI50UC6kk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\nQppOp1nxyjs8/NwminsVcuMVkzkzDE86liRJkiT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+       "text": [
+        "<matplotlib.figure.Figure at 0x111edc198>"
+       ]
+      }
+     ],
+     "prompt_number": 464
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In each class, the student who has the highest average score, seems to be having a harder time as time goes by."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file