From 7a7fca069f8ca133ea8f0355216c3036d1f2dd6b Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Wed, 4 Feb 2015 15:39:40 -0500
Subject: [PATCH 1/3] only one task left to do. mean difficulty by day

---
 cohort_data.ipynb | 588 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 588 insertions(+)
 create mode 100644 cohort_data.ipynb

diff --git a/cohort_data.ipynb b/cohort_data.ipynb
new file mode 100644
index 0000000..88eddc7
--- /dev/null
+++ b/cohort_data.ipynb
@@ -0,0 +1,588 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:a09cfa9b62318bb01a1529e71b9948b88a82d9a144a0c50ec7bbfe92bd045660"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as ply\n",
+      "import seaborn as sbn\n",
+      "\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 318
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_python = pd.read_csv(\"cohort_3_python.csv\")\n",
+      "cohort_python.head()"
+     ],
+     "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",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> P03</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>  6</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",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> P04</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> P05</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  6</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",
+        "<p>5 rows \u00d7 25 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 319,
+       "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",
+        "2  P03               NaN                5.0                  3   \n",
+        "3  P04                 3                3.0                  2   \n",
+        "4  P05               NaN                3.0                  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",
+        "2                  4                  5                5.0                5.0   \n",
+        "3                  3                  4                4.0                4.0   \n",
+        "4                  3                  3                4.0                4.0   \n",
+        "\n",
+        "   Mystery Word, Jan 20  Lecture 5, Jan 20       ...        Blackjack2, Jan26  \\\n",
+        "0                     5                  4       ...                      NaN   \n",
+        "1                     5                  5       ...                        5   \n",
+        "2                     5                  5       ...                        6   \n",
+        "3                     4                  5       ...                      NaN   \n",
+        "4                     4                  5       ...                        4   \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",
+        "2               NaN                 NaN                 5         5   \n",
+        "3                 1                   3                 1         3   \n",
+        "4                 3                   6               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",
+        "2                 5     NaN               NaN            NaN             NaN  \n",
+        "3                 5       5                 5              5             NaN  \n",
+        "4               NaN     NaN               NaN            NaN             NaN  \n",
+        "\n",
+        "[5 rows x 25 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 319
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_rails = pd.ExcelFile (\"cohort_3_rails.xlsx\")\n",
+      "ru_lec = cohort_rails.parse('Lecture Score')\n",
+      "ru_hw = cohort_rails.parse('HW Score')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 320
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Deliverables"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures per day, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lectures = [col for col in cohort_python.columns if 'Lecture' in col]\n",
+      "py_homework = [col for col in cohort_python.columns if 'Lecture' not in col]\n",
+      "\n",
+      "rub_lect_col = ru_lec.columns[:13]\n",
+      "rub_hw_col = ru_hw.columns[:13]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 321
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lec_mean = cohort_python[py_lectures][:16].mean(axis=0)\n",
+      "ru_lec_mean = ru_lec[rub_lect_col][1:15].mean(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 322
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_lec_mean, c='red', label='Python')\n",
+      "ply.plot(ru_lec_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('day')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per day, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9StznU4mbOQPL4UMA+Gpdi+fue0jv1BW9TMEEossO7dhREl8ZTvyUyQC4ez5I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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115951358>"
+       ]
+      }
+     ],
+     "prompt_number": 323
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures per week, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_lec_week1_mean = py_lec_mean[0:4].mean(axis=0)\n",
+      "py_lec_week2_mean = py_lec_mean[4:8].mean(axis=0)\n",
+      "py_lec_week3_mean = py_lec_mean[8:12].mean(axis=0)\n",
+      "py_lec_week4_mean = py_lec_mean[12:].mean(axis=0)\n",
+      "py_lec_weekly_mean = [py_lec_week1_mean, py_lec_week2_mean, py_lec_week3_mean, py_lec_week4_mean]\n",
+      "\n",
+      "ru_lec_week1_mean = ru_lec_mean[0:4].mean(axis=0)\n",
+      "ru_lec_week2_mean = ru_lec_mean[4:8].mean(axis=0)\n",
+      "ru_lec_week3_mean = ru_lec_mean[8:12].mean(axis=0)\n",
+      "ru_lec_week4_mean = ru_lec_mean[12:].mean(axis=0)\n",
+      "ru_lec_weekly_mean = [ru_lec_week1_mean, ru_lec_week2_mean, ru_lec_week3_mean, ru_lec_week4_mean]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 324
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_lec_weekly_mean, c='red', label='Python')\n",
+      "ply.plot(ru_lec_weekly_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('Week')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per week, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ju0IbN+40h9Rp2dkZtnx1WCKXL5HLBjVcvpISMocPJXn+q5S0Ppncl17BKfFCDI9f1QQY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\nEun/DM9ZxuYeskkAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x11520db38>"
+       ]
+      }
+     ],
+     "prompt_number": 325
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for homework per day, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_hw_mean = cohort_python[py_homework][:16].mean(axis=0)\n",
+      "ru_hw_mean = ru_hw[rub_hw_col][1:15].mean(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 326
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_hw_mean, c='red', label='Python')\n",
+      "ply.plot(ru_hw_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('day')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for homework per day, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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xA/CzEOKOcAemKOVlOHyI5Hmz8dSqjbP7TSc97q9aCvvqcIqSAIJJDv8BZqL3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\nSfF/knpB4bYkxHMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x114f176d8>"
+       ]
+      }
+     ],
+     "prompt_number": 327
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for homework per week, per class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "py_hw_week1_mean = py_hw_mean[0:4].mean(axis=0)\n",
+      "py_hw_week2_mean = py_hw_mean[4:8].mean(axis=0)\n",
+      "py_hw_week3_mean = py_hw_mean[8:12].mean(axis=0)\n",
+      "py_hw_week4_mean = py_hw_mean[12:].mean(axis=0)\n",
+      "py_hw_weekly_mean = [py_hw_week1_mean, py_hw_week2_mean, py_hw_week3_mean, py_hw_week4_mean]\n",
+      "\n",
+      "ru_hw_week1_mean = ru_hw_mean[0:4].mean(axis=0)\n",
+      "ru_hw_week2_mean = ru_hw_mean[4:8].mean(axis=0)\n",
+      "ru_hw_week3_mean = ru_hw_mean[8:12].mean(axis=0)\n",
+      "ru_hw_week4_mean = ru_hw_mean[12:].mean(axis=0)\n",
+      "ru_hw_weekly_mean = [ru_hw_week1_mean, ru_hw_week2_mean, ru_hw_week3_mean, ru_hw_week4_mean]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 328
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ply.plot(py_hw_weekly_mean, c='red', label='Python')\n",
+      "ply.plot(ru_hw_weekly_mean, c='blue', label='Ruby')\n",
+      "ply.xlabel('Week')\n",
+      "ply.ylabel('mean scoring per student')\n",
+      "ply.title('Mean difficulty for lectures per week, per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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am+YA80TkcdxZYE8AnhGRYcCyWAZn4if1tVfIHXE6lJVRNP52yk4/K94hGWPi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\nUusLhUpywFK1PTEAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115ba53c8>"
+       ]
+      }
+     ],
+     "prompt_number": 329
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean difficulty for lectures and homework per week day. Is there a trend, like Mondays are easier?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 329
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 329
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 329
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Choose a sample student from each class and graph their data. What's their story? Are they getting better over time?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ruby_student = ru_lec[6:7]\n",
+      "python_student = cohort_python[7:8]\n",
+      "\n",
+      "py_columns = [column for \n",
+      "              column in \n",
+      "              python_student[py_lectures].columns \n",
+      "              if python_student[column].dtype == 'float64']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 330
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_student[py_columns].max().plot(c='red', label='Python Student')\n",
+      "ruby_student[rub_lect_col].max().plot(c='blue', label='Ruby Student')\n",
+      "ply.xlabel('Day')\n",
+      "ply.ylabel('mean lecture scoring per student')\n",
+      "ply.title('Difficulty for 1 student per class')\n",
+      "ply.legend(loc=4)\n",
+      "ply.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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mDmHldgMij1LyKToBfxrKviKS/JxJPeSbIJ8mH4T5IPQTL7mMUpIaneRjuiuT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\nRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x115ba0fd0>"
+       ]
+      }
+     ],
+     "prompt_number": 331
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The ruby student is maintaining an average pace in regards to how they view lectures. The python student on the other hand is progressively getting more and more challenged in lectures every day. They have a steady growth into pain. As seen in week1/week2, the Ruby had one rough day, but otherwise has hovered between 4 and 4.5."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 3fb8d3aeaf7028c23e47cfb4048f6473c51395a6 Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Wed, 4 Feb 2015 17:11:34 -0500
Subject: [PATCH 2/3] got as far as I can. Cleaned up documents a LOT, but was
 not able to get the day of week view

---
 cohort_data.ipynb | 1075 ++++++++++++++++++++++++++++++++++-----------
 1 file changed, 827 insertions(+), 248 deletions(-)

diff --git a/cohort_data.ipynb b/cohort_data.ipynb
index 88eddc7..7ba346b 100644
--- a/cohort_data.ipynb
+++ b/cohort_data.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a09cfa9b62318bb01a1529e71b9948b88a82d9a144a0c50ec7bbfe92bd045660"
+  "signature": "sha256:3bfb000025f3bb71d1b8373514d0b4d71b558482f4a1bdc40f23791ca08de415"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,237 +15,52 @@
       "import pandas as pd\n",
       "import matplotlib.pyplot as ply\n",
       "import seaborn as sbn\n",
+      "import numpy as np\n",
       "\n",
       "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 318
+     "prompt_number": 40
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cohort_python = pd.read_csv(\"cohort_3_python.csv\")\n",
-      "cohort_python.head()"
+      "cohort_rails = pd.ExcelFile (\"cohort_3_rails.xlsx\")\n",
+      "ru_lec = cohort_rails.parse('Lecture Score')\n",
+      "ru_hw = cohort_rails.parse('HW Score')\n",
+      "\n",
+      "ru_lec.drop(['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Week 5', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Week 6', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Week 7', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27', 'Week 8', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Week 9', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35'],inplace=True,axis=1)\n",
+      "ru_hw.drop(['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Week 5', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Week 6', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Week 7', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27', 'Week 8', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Week 9', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35'],inplace=True,axis=1)\n",
+      "ru_lec.columns = ['lec1', 'lec2', 'lec3', 'lec4', 'lec5', 'lec6', 'lec7', 'lec8', 'lec9', 'lec10', 'lec11', 'lec12', 'lec13']\n",
+      "ru_hw.columns = ['hw1', 'hw2', 'hw3', 'hw4', 'hw5', 'hw6', 'hw7', 'hw8', 'hw9', 'hw10', 'hw11', 'hw12', 'hw13']\n",
+      "\n",
+      "ru_combined = ru_lec.join(ru_hw)\n",
+      "ru_combined = ru_combined[pd.notnull(ru_combined['hw9'])]\n",
+      "ru_combined = ru_combined.ix[1:-2]\n",
+      "ru_combined = ru_combined.dropna(axis=1,how='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>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",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> P03</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 5</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  6</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",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td> P04</td>\n",
-        "      <td>  3</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>...</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>  1</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>  1</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td> P05</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  4</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>  6</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",
-        "<p>5 rows \u00d7 25 columns</p>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 319,
-       "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",
-        "2  P03               NaN                5.0                  3   \n",
-        "3  P04                 3                3.0                  2   \n",
-        "4  P05               NaN                3.0                  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",
-        "2                  4                  5                5.0                5.0   \n",
-        "3                  3                  4                4.0                4.0   \n",
-        "4                  3                  3                4.0                4.0   \n",
-        "\n",
-        "   Mystery Word, Jan 20  Lecture 5, Jan 20       ...        Blackjack2, Jan26  \\\n",
-        "0                     5                  4       ...                      NaN   \n",
-        "1                     5                  5       ...                        5   \n",
-        "2                     5                  5       ...                        6   \n",
-        "3                     4                  5       ...                      NaN   \n",
-        "4                     4                  5       ...                        4   \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",
-        "2               NaN                 NaN                 5         5   \n",
-        "3                 1                   3                 1         3   \n",
-        "4                 3                   6               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",
-        "2                 5     NaN               NaN            NaN             NaN  \n",
-        "3                 5       5                 5              5             NaN  \n",
-        "4               NaN     NaN               NaN            NaN             NaN  \n",
-        "\n",
-        "[5 rows x 25 columns]"
-       ]
-      }
-     ],
-     "prompt_number": 319
+     "outputs": [],
+     "prompt_number": 53
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cohort_rails = pd.ExcelFile (\"cohort_3_rails.xlsx\")\n",
-      "ru_lec = cohort_rails.parse('Lecture Score')\n",
-      "ru_hw = cohort_rails.parse('HW Score')"
+      "cohort_python = pd.read_csv(\"cohort_3_python.csv\")\n",
+      "cohort_python.columns = ['Name', 'lec1', 'hw1', 'lec2', 'hw2', 'lec3', 'hw3', 'lec4', 'hw4', 'lec5', 'hw5', 'lec6', 'hw6', 'lec7', 'lec8', 'hw8', 'lec9', 'hw9', 'lec10', 'hw10', 'lec11', 'hw11', 'lec12', 'hw12', 'lec13']\n",
+      "cohort_python.drop(['lec13'],inplace=True,axis=1)\n",
+      "cohort_python = cohort_python.dropna(axis=1,how='all')\n",
+      "cohort_python = cohort_python.dropna(axis=0,how='all')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 320
+     "prompt_number": 73
     },
     {
      "cell_type": "heading",
@@ -267,28 +82,28 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "py_lectures = [col for col in cohort_python.columns if 'Lecture' in col]\n",
-      "py_homework = [col for col in cohort_python.columns if 'Lecture' not in col]\n",
+      "py_lectures = [col for col in cohort_python.columns if 'lec' in col]\n",
+      "py_homework = [col for col in cohort_python.columns if 'hw' in col]\n",
       "\n",
-      "rub_lect_col = ru_lec.columns[:13]\n",
-      "rub_hw_col = ru_hw.columns[:13]"
+      "ru_lectures = [col for col in ru_combined.columns if 'lec' in col]\n",
+      "ru_homework = [col for col in ru_combined.columns if 'hw' in col]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 321
+     "prompt_number": 86
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "py_lec_mean = cohort_python[py_lectures][:16].mean(axis=0)\n",
-      "ru_lec_mean = ru_lec[rub_lect_col][1:15].mean(axis=0)"
+      "py_lec_mean = cohort_python[py_lectures][:14].mean(axis=0)\n",
+      "ru_lec_mean = ru_combined[ru_lectures][:14].mean(axis=0)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 322
+     "prompt_number": 85
     },
     {
      "cell_type": "code",
@@ -308,13 +123,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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9StznU4mbOQPL4UMA+Gpdi+fue0jv1BW9TMEEossO7dhREl8ZTvyUyQC4ez5I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\nWJeuUF4OwOHLr2jUboiIZFsqySFhZtm7x3ACKip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FLmrU0Jk61ccjj3iZPNnDVVelMHNmDtWqlaPNRMyJpOukvPgcYCi1yuS55zys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\nrdP6AAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x115951358>"
+        "<matplotlib.figure.Figure at 0x111c5b080>"
        ]
       }
      ],
-     "prompt_number": 323
+     "prompt_number": 87
     },
     {
      "cell_type": "heading",
@@ -343,7 +158,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 324
+     "prompt_number": 88
     },
     {
      "cell_type": "code",
@@ -363,13 +178,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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CqFFFdOkSquuVor+JJkm0Bk4s5/EW1RyLZVkJyveNktWtC77fN5B31xgKL6u7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\nrkVEJJKKhIiIRFKREBGRSHl7PQkzqwTuI9il/ai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P9zrK5iOW3krLROR9bJtDAjDCGLMp7pGpBsGXm0vmVX0JfryOkp59yH9mAiQm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\n59t8ouYHhlwAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x11520db38>"
+        "<matplotlib.figure.Figure at 0x111c71eb8>"
        ]
       }
      ],
-     "prompt_number": 325
+     "prompt_number": 89
     },
     {
      "cell_type": "heading",
@@ -383,13 +198,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "py_hw_mean = cohort_python[py_homework][:16].mean(axis=0)\n",
-      "ru_hw_mean = ru_hw[rub_hw_col][1:15].mean(axis=0)"
+      "py_hw_mean = cohort_python[py_homework][:14].mean(axis=0)\n",
+      "ru_hw_mean = ru_combined[ru_homework][:14].mean(axis=0)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 326
+     "prompt_number": 91
     },
     {
      "cell_type": "code",
@@ -409,13 +224,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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xA/CzEOKOcAemKOVlOHyI5Hmz8dSqjbP7TSc97q9aCvvqcIqSAIJJDv8BZqL3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\nMplk06aNTJgwntGjL2LGjGkATJ8+ldmzZ+35WVO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ct9Xj7NbdJPTsQuRHRgHF5DkLgl6bWHFpePNo1wRjxBGe/6cxXEiKYorLBaNH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\nRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x114f176d8>"
+        "<matplotlib.figure.Figure at 0x111adc470>"
        ]
       }
      ],
-     "prompt_number": 327
+     "prompt_number": 92
     },
     {
      "cell_type": "heading",
@@ -444,7 +259,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 328
+     "prompt_number": 93
     },
     {
      "cell_type": "code",
@@ -464,13 +279,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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am+YA80TkcdxZYE8AnhGRYcCyWAZn4if1tVfIHXE6lJVRNP52yk4/K94hGWPi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\ng0DA4bjjKhk5soLeve2KaGOMiYWESxJ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vJfuRCQQiEcoPOIiiG28l2qVrskMzpsmI5WqlySIyF3c5awgYpqpLEx6ZMfUV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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x115ba53c8>"
+        "<matplotlib.figure.Figure at 0x111702c50>"
        ]
       }
      ],
-     "prompt_number": 329
+     "prompt_number": 94
     },
     {
      "cell_type": "heading",
@@ -480,6 +295,770 @@
       "Mean difficulty for lectures and homework per week day. Is there a trend, like Mondays are easier?"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ru_combined.head()"
+     ],
+     "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>lec1</th>\n",
+        "      <th>lec2</th>\n",
+        "      <th>lec3</th>\n",
+        "      <th>lec4</th>\n",
+        "      <th>lec6</th>\n",
+        "      <th>lec7</th>\n",
+        "      <th>lec8</th>\n",
+        "      <th>lec9</th>\n",
+        "      <th>lec10</th>\n",
+        "      <th>lec11</th>\n",
+        "      <th>...</th>\n",
+        "      <th>hw2</th>\n",
+        "      <th>hw3</th>\n",
+        "      <th>hw6</th>\n",
+        "      <th>hw7</th>\n",
+        "      <th>hw8</th>\n",
+        "      <th>hw9</th>\n",
+        "      <th>hw10</th>\n",
+        "      <th>hw11</th>\n",
+        "      <th>hw12</th>\n",
+        "      <th>hw13</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>R01</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   2</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   2</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   4</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>R02</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</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>...</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 4.5</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</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R03</th>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 3.5</td>\n",
+        "      <td>   6</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R04</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   5</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   6</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   4</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>   6</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>   4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>R05</th>\n",
+        "      <td> 2</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>   4</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td>...</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   3</td>\n",
+        "      <td> 4.5</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>   5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.5</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>5 rows \u00d7 23 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 101,
+       "text": [
+        "    lec1 lec2 lec3 lec4 lec6 lec7 lec8 lec9 lec10 lec11 ...   hw2  hw3 hw6  \\\n",
+        "R01    2    2    4    3    3    5    2    3     4     4 ...     3    3   4   \n",
+        "R02    3  3.5  4.5    4  4.5  4.5  3.5    6     4     5 ...     4  4.5   4   \n",
+        "R03    3  4.5    4  3.5    6  4.5    4    5     5   4.5 ...     4  5.5   5   \n",
+        "R04    2    4    4    4    5    5    4    5     4     6 ...   4.5    3   4   \n",
+        "R05    2    3    5  4.5    3    4    3    5     3     3 ...     5  4.5   5   \n",
+        "\n",
+        "    hw7  hw8  hw9 hw10 hw11 hw12 hw13  \n",
+        "R01   3    3    4    3  4.5    4  4.5  \n",
+        "R02   4    4  2.5    3  4.5    4    4  \n",
+        "R03   4  4.5    4    4    3    4  3.5  \n",
+        "R04   4    4    4    5    6    5    4  \n",
+        "R05   3    3  4.5    3    5    4  3.5  \n",
+        "\n",
+        "[5 rows x 23 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 101
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cohort_python = cohort_python.set_index('Name')\n",
+      "cohort_python"
+     ],
+     "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>lec1</th>\n",
+        "      <th>hw1</th>\n",
+        "      <th>lec2</th>\n",
+        "      <th>hw2</th>\n",
+        "      <th>lec3</th>\n",
+        "      <th>hw3</th>\n",
+        "      <th>lec4</th>\n",
+        "      <th>hw4</th>\n",
+        "      <th>lec5</th>\n",
+        "      <th>hw5</th>\n",
+        "      <th>...</th>\n",
+        "      <th>lec8</th>\n",
+        "      <th>hw8</th>\n",
+        "      <th>lec9</th>\n",
+        "      <th>hw9</th>\n",
+        "      <th>lec10</th>\n",
+        "      <th>hw10</th>\n",
+        "      <th>lec11</th>\n",
+        "      <th>hw11</th>\n",
+        "      <th>lec12</th>\n",
+        "      <th>hw12</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Name</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>P01</th>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.500000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.000000</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>P02</th>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.500000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.500000</td>\n",
+        "      <td> 4.500000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>  NaN</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.0000</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P03</th>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 6.000000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.00</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P04</th>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 1.00</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.0000</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P05</th>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 6.000000</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>P06</th>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.500000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 3.00</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P07</th>\n",
+        "      <td> 3.500000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.500000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.90</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.9000</td>\n",
+        "      <td> 4.9</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P08</th>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.00</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.0000</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P09</th>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 1.0</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 2.000000</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>P10</th>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.00</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.0000</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P11</th>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.00</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.0000</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P12</th>\n",
+        "      <td> 3.500000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td> 4.500000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.500000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.00</td>\n",
+        "      <td> 6.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 6.0000</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>P13</th>\n",
+        "      <td> 2.500000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 3.00</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>P14</th>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td>      NaN</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.000000</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>P15</th>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 3.00</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0000</td>\n",
+        "      <td> 5.0</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Average</th>\n",
+        "      <td> 2.791667</td>\n",
+        "      <td> 3.266667</td>\n",
+        "      <td> 2.733333</td>\n",
+        "      <td> 3.2</td>\n",
+        "      <td> 3.821429</td>\n",
+        "      <td> 3.766667</td>\n",
+        "      <td> 3.933333</td>\n",
+        "      <td> 4.071429</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 3.769231</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 4.461538</td>\n",
+        "      <td> 4.416667</td>\n",
+        "      <td> 3.769231</td>\n",
+        "      <td> 4.285714</td>\n",
+        "      <td> 3.69</td>\n",
+        "      <td> 4.555556</td>\n",
+        "      <td> 4.4</td>\n",
+        "      <td> 4.333333</td>\n",
+        "      <td> 4.6125</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Min</th>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 1.0</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 1.000000</td>\n",
+        "      <td> 2.000000</td>\n",
+        "      <td> 1.00</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0</td>\n",
+        "      <td> 3.000000</td>\n",
+        "      <td> 3.0000</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Max</th>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 4.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td>...</td>\n",
+        "      <td> 5.500000</td>\n",
+        "      <td> 6.000000</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 6.000000</td>\n",
+        "      <td> 5.00</td>\n",
+        "      <td> 6.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.000000</td>\n",
+        "      <td> 6.0000</td>\n",
+        "      <td> NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>18 rows \u00d7 23 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 105,
+       "text": [
+        "             lec1       hw1      lec2  hw2      lec3       hw3      lec4  \\\n",
+        "Name                                                                       \n",
+        "P01      3.000000  4.000000  3.000000  4.0  4.000000  5.000000  5.000000   \n",
+        "P02      4.000000  3.500000  3.000000  5.0  4.000000  4.500000  4.500000   \n",
+        "P03           NaN  5.000000  3.000000  4.0  5.000000  5.000000  5.000000   \n",
+        "P04      3.000000  3.000000  2.000000  3.0  4.000000  4.000000  4.000000   \n",
+        "P05           NaN  3.000000  3.000000  3.0  3.000000  4.000000  4.000000   \n",
+        "P06      3.000000  3.500000  3.000000  3.0  3.000000  3.000000  4.000000   \n",
+        "P07      3.500000  4.000000  3.000000  4.0  5.000000  4.000000  4.500000   \n",
+        "P08      2.000000  3.000000  2.000000  3.0  4.000000  4.000000  3.000000   \n",
+        "P09           NaN  1.000000  1.000000  1.0  2.000000  2.000000  2.000000   \n",
+        "P10      2.000000  2.000000  2.000000  3.0       NaN  3.000000  3.000000   \n",
+        "P11      2.000000  5.000000  4.000000  3.0  5.000000  4.000000  4.000000   \n",
+        "P12      3.500000  4.000000  4.000000  4.0  4.500000  5.000000  5.000000   \n",
+        "P13      2.500000  3.000000  3.000000  3.0  3.000000  3.000000  4.000000   \n",
+        "P14      3.000000  3.000000  3.000000  3.0  4.000000  3.000000  4.000000   \n",
+        "P15      2.000000  2.000000  2.000000  2.0  3.000000  3.000000  3.000000   \n",
+        "Average  2.791667  3.266667  2.733333  3.2  3.821429  3.766667  3.933333   \n",
+        "Min      2.000000  1.000000  1.000000  1.0  2.000000  2.000000  2.000000   \n",
+        "Max      4.000000  5.000000  4.000000  5.0  5.000000  5.000000  5.000000   \n",
+        "\n",
+        "              hw4  lec5       hw5  ...       lec8       hw8      lec9  \\\n",
+        "Name                               ...                                  \n",
+        "P01      5.000000     4  4.000000  ...   5.500000       NaN  4.000000   \n",
+        "P02      5.000000     5  5.000000  ...        NaN  5.000000  5.000000   \n",
+        "P03      5.000000     5  5.000000  ...   5.000000  6.000000       NaN   \n",
+        "P04      4.000000     5       NaN  ...   4.000000       NaN  1.000000   \n",
+        "P05      4.000000     5  4.000000  ...   4.000000  4.000000  3.000000   \n",
+        "P06      4.000000     3  3.000000  ...   5.000000  5.000000  4.000000   \n",
+        "P07      4.000000     5  5.000000  ...   5.000000       NaN  5.000000   \n",
+        "P08      4.000000     3  3.000000  ...   5.000000  5.000000  5.000000   \n",
+        "P09      3.000000     3  2.000000  ...   3.000000  3.000000  3.000000   \n",
+        "P10      3.000000     3  4.000000  ...   5.000000  5.000000  4.000000   \n",
+        "P11      4.000000     4       NaN  ...   4.000000  4.000000  4.000000   \n",
+        "P12      5.000000     5  4.000000  ...   5.500000  5.000000  4.000000   \n",
+        "P13           NaN     3  3.000000  ...   4.000000  4.000000       NaN   \n",
+        "P14      4.000000     4  4.000000  ...        NaN  4.000000  4.000000   \n",
+        "P15      3.000000     3  3.000000  ...   3.000000  3.000000  3.000000   \n",
+        "Average  4.071429     4  3.769231  ...   4.461538  4.416667  3.769231   \n",
+        "Min      3.000000     3  2.000000  ...   3.000000  3.000000  1.000000   \n",
+        "Max      5.000000     5  5.000000  ...   5.500000  6.000000  5.000000   \n",
+        "\n",
+        "              hw9  lec10      hw10  lec11      hw11   lec12  hw12  \n",
+        "Name                                                               \n",
+        "P01      5.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
+        "P02      5.000000    NaN       NaN    5.0  5.000000  5.0000   NaN  \n",
+        "P03           NaN   5.00  5.000000    5.0       NaN     NaN   NaN  \n",
+        "P04      3.000000   1.00  3.000000    5.0  5.000000  5.0000   5.0  \n",
+        "P05      6.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
+        "P06      5.000000   3.00  4.000000    4.0  4.000000     NaN   NaN  \n",
+        "P07      4.000000   4.90  5.000000    4.0  4.000000  4.9000   4.9  \n",
+        "P08      5.000000   5.00  5.000000    4.0  4.000000  4.0000   5.0  \n",
+        "P09      2.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
+        "P10      5.000000   4.00  5.000000    4.0  5.000000  4.0000   NaN  \n",
+        "P11      4.000000   4.00  5.000000    5.0  4.000000  5.0000   5.0  \n",
+        "P12      4.000000   4.00  6.000000    5.0  5.000000  6.0000   NaN  \n",
+        "P13      5.000000   3.00       NaN    NaN       NaN     NaN   NaN  \n",
+        "P14      3.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
+        "P15      4.000000   3.00  3.000000    3.0  3.000000  3.0000   5.0  \n",
+        "Average  4.285714   3.69  4.555556    4.4  4.333333  4.6125   NaN  \n",
+        "Min      2.000000   1.00  3.000000    3.0  3.000000  3.0000   NaN  \n",
+        "Max      6.000000   5.00  6.000000    5.0  5.000000  6.0000   NaN  \n",
+        "\n",
+        "[18 rows x 23 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 105
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -487,7 +1066,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 329
+     "prompt_number": 94
     },
     {
      "cell_type": "code",
@@ -495,7 +1074,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 94
     },
     {
      "cell_type": "code",
@@ -503,7 +1083,8 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 94
     },
     {
      "cell_type": "code",
@@ -512,7 +1093,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 329
+     "prompt_number": 94
     },
     {
      "cell_type": "code",
@@ -521,7 +1102,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 329
+     "prompt_number": 94
     },
     {
      "cell_type": "heading",
@@ -538,21 +1119,19 @@
       "ruby_student = ru_lec[6:7]\n",
       "python_student = cohort_python[7:8]\n",
       "\n",
-      "py_columns = [column for \n",
-      "              column in \n",
-      "              python_student[py_lectures].columns \n",
-      "              if python_student[column].dtype == 'float64']"
+      "py_lectures = [col for col in cohort_python.columns if 'lec' in col]\n",
+      "rub_lect_col = [col for col in ru_lec.columns if 'lec' in col]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 330
+     "prompt_number": 95
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "python_student[py_columns].max().plot(c='red', label='Python Student')\n",
+      "python_student[py_lectures].max().plot(c='red', label='Python Student')\n",
       "ruby_student[rub_lect_col].max().plot(c='blue', label='Ruby Student')\n",
       "ply.xlabel('Day')\n",
       "ply.ylabel('mean lecture scoring per student')\n",
@@ -566,13 +1145,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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mDmHldgMij1LyKToBfxrKviKS/JxJPeSbIJ8mH4T5IPQTL7mMUpIaneRjuiuT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\nffZprFy5khdffJ65cz/ib3+7hOOPP5K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RduE7TP3CSxBaBrffnrroAlpHOqo+a5wGbRnCv5h9ybOmZfjEzxKtxwKjgIk4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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x115ba0fd0>"
+        "<matplotlib.figure.Figure at 0x11191ada0>"
        ]
       }
      ],
-     "prompt_number": 331
+     "prompt_number": 96
     },
     {
      "cell_type": "markdown",

From abdd9f6598530af45bf3b5058e286e1c8d126274 Mon Sep 17 00:00:00 2001
From: Zack Cooper <zackjcooper@gmail.com>
Date: Thu, 5 Feb 2015 08:54:26 -0500
Subject: [PATCH 3/3] added to requirements.txt

---
 cohort_data.ipynb | 563 +++++++++-------------------------------------
 requirements.txt  |   2 +
 2 files changed, 104 insertions(+), 461 deletions(-)

diff --git a/cohort_data.ipynb b/cohort_data.ipynb
index 7ba346b..d5d1765 100644
--- a/cohort_data.ipynb
+++ b/cohort_data.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3bfb000025f3bb71d1b8373514d0b4d71b558482f4a1bdc40f23791ca08de415"
+  "signature": "sha256:9527bd5df2a3419c742c609dff1b891167d5e533dd44cab60cebd298de75b7e1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -55,12 +55,13 @@
       "cohort_python.columns = ['Name', 'lec1', 'hw1', 'lec2', 'hw2', 'lec3', 'hw3', 'lec4', 'hw4', 'lec5', 'hw5', 'lec6', 'hw6', 'lec7', 'lec8', 'hw8', 'lec9', 'hw9', 'lec10', 'hw10', 'lec11', 'hw11', 'lec12', 'hw12', 'lec13']\n",
       "cohort_python.drop(['lec13'],inplace=True,axis=1)\n",
       "cohort_python = cohort_python.dropna(axis=1,how='all')\n",
-      "cohort_python = cohort_python.dropna(axis=0,how='all')"
+      "cohort_python = cohort_python.dropna(axis=0,how='all')\n",
+      "cohort_python = cohort_python.set_index('Name')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 73
+     "prompt_number": 107
     },
     {
      "cell_type": "heading",
@@ -488,8 +489,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "cohort_python = cohort_python.set_index('Name')\n",
-      "cohort_python"
+      "cohort_python.head()"
      ],
      "language": "python",
      "metadata": {},
@@ -551,513 +551,154 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>P01</th>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</td>\n",
+        "      <td>  3</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
         "      <td> 4</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5.0</td>\n",
+        "      <td> 5</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>  4</td>\n",
         "      <td>...</td>\n",
-        "      <td> 5.500000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.000000</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> 5.5</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",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>P02</th>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.500000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.500000</td>\n",
-        "      <td> 4.500000</td>\n",
-        "      <td> 5.000000</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> 5.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  5</td>\n",
         "      <td>...</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td>  NaN</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.0000</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",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>NaN</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>P03</th>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 6.000000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.00</td>\n",
-        "      <td> 5.000000</td>\n",
+        "      <td>NaN</td>\n",
         "      <td> 5.0</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P04</th>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 4</td>\n",
         "      <td> 5</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 1.00</td>\n",
-        "      <td> 3.000000</td>\n",
         "      <td> 5.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.0000</td>\n",
         "      <td> 5.0</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P05</th>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
         "      <td> 5</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  5</td>\n",
         "      <td>...</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 6.000000</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>  6</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",
+        "      <td>NaN</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>P06</th>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.500000</td>\n",
-        "      <td> 3.000000</td>\n",
+        "      <th>P04</th>\n",
+        "      <td>  3</td>\n",
         "      <td> 3.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 2</td>\n",
         "      <td> 3</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 3.00</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 4</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P07</th>\n",
-        "      <td> 3.500000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.500000</td>\n",
-        "      <td> 4.000000</td>\n",
+        "      <td> 4</td>\n",
         "      <td> 5</td>\n",
-        "      <td> 5.000000</td>\n",
+        "      <td>NaN</td>\n",
         "      <td>...</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.90</td>\n",
-        "      <td> 5.000000</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.9000</td>\n",
-        "      <td> 4.9</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  5</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>P08</th>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 2.000000</td>\n",
+        "      <th>P05</th>\n",
+        "      <td>NaN</td>\n",
         "      <td> 3.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
         "      <td> 3</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.00</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.0000</td>\n",
-        "      <td> 5.0</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P09</th>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 1.0</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 3.000000</td>\n",
         "      <td> 3</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 2.000000</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>P10</th>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
         "      <td> 3</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.00</td>\n",
-        "      <td> 5.000000</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.0000</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P11</th>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.00</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.0000</td>\n",
-        "      <td> 5.0</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P12</th>\n",
-        "      <td> 3.500000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
         "      <td> 4.0</td>\n",
-        "      <td> 4.500000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 5.500000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.00</td>\n",
-        "      <td> 6.000000</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 6.0000</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>P13</th>\n",
-        "      <td> 2.500000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 3.00</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>P14</th>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
         "      <td> 4</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.000000</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>P15</th>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 3.00</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0000</td>\n",
-        "      <td> 5.0</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>Average</th>\n",
-        "      <td> 2.791667</td>\n",
-        "      <td> 3.266667</td>\n",
-        "      <td> 2.733333</td>\n",
-        "      <td> 3.2</td>\n",
-        "      <td> 3.821429</td>\n",
-        "      <td> 3.766667</td>\n",
-        "      <td> 3.933333</td>\n",
-        "      <td> 4.071429</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 3.769231</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 4.461538</td>\n",
-        "      <td> 4.416667</td>\n",
-        "      <td> 3.769231</td>\n",
-        "      <td> 4.285714</td>\n",
-        "      <td> 3.69</td>\n",
-        "      <td> 4.555556</td>\n",
-        "      <td> 4.4</td>\n",
-        "      <td> 4.333333</td>\n",
-        "      <td> 4.6125</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>Min</th>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 1.0</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 1.000000</td>\n",
-        "      <td> 2.000000</td>\n",
-        "      <td> 1.00</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0</td>\n",
-        "      <td> 3.000000</td>\n",
-        "      <td> 3.0000</td>\n",
-        "      <td> NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>Max</th>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 4.000000</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 5.000000</td>\n",
         "      <td> 5</td>\n",
-        "      <td> 5.000000</td>\n",
+        "      <td>  4</td>\n",
         "      <td>...</td>\n",
-        "      <td> 5.500000</td>\n",
-        "      <td> 6.000000</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 6.000000</td>\n",
-        "      <td> 5.00</td>\n",
-        "      <td> 6.000000</td>\n",
-        "      <td> 5.0</td>\n",
-        "      <td> 5.000000</td>\n",
-        "      <td> 6.0000</td>\n",
-        "      <td> NaN</td>\n",
+        "      <td> 4.0</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  6</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",
-        "<p>18 rows \u00d7 23 columns</p>\n",
+        "<p>5 rows \u00d7 23 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 105,
+       "prompt_number": 106,
        "text": [
-        "             lec1       hw1      lec2  hw2      lec3       hw3      lec4  \\\n",
-        "Name                                                                       \n",
-        "P01      3.000000  4.000000  3.000000  4.0  4.000000  5.000000  5.000000   \n",
-        "P02      4.000000  3.500000  3.000000  5.0  4.000000  4.500000  4.500000   \n",
-        "P03           NaN  5.000000  3.000000  4.0  5.000000  5.000000  5.000000   \n",
-        "P04      3.000000  3.000000  2.000000  3.0  4.000000  4.000000  4.000000   \n",
-        "P05           NaN  3.000000  3.000000  3.0  3.000000  4.000000  4.000000   \n",
-        "P06      3.000000  3.500000  3.000000  3.0  3.000000  3.000000  4.000000   \n",
-        "P07      3.500000  4.000000  3.000000  4.0  5.000000  4.000000  4.500000   \n",
-        "P08      2.000000  3.000000  2.000000  3.0  4.000000  4.000000  3.000000   \n",
-        "P09           NaN  1.000000  1.000000  1.0  2.000000  2.000000  2.000000   \n",
-        "P10      2.000000  2.000000  2.000000  3.0       NaN  3.000000  3.000000   \n",
-        "P11      2.000000  5.000000  4.000000  3.0  5.000000  4.000000  4.000000   \n",
-        "P12      3.500000  4.000000  4.000000  4.0  4.500000  5.000000  5.000000   \n",
-        "P13      2.500000  3.000000  3.000000  3.0  3.000000  3.000000  4.000000   \n",
-        "P14      3.000000  3.000000  3.000000  3.0  4.000000  3.000000  4.000000   \n",
-        "P15      2.000000  2.000000  2.000000  2.0  3.000000  3.000000  3.000000   \n",
-        "Average  2.791667  3.266667  2.733333  3.2  3.821429  3.766667  3.933333   \n",
-        "Min      2.000000  1.000000  1.000000  1.0  2.000000  2.000000  2.000000   \n",
-        "Max      4.000000  5.000000  4.000000  5.0  5.000000  5.000000  5.000000   \n",
-        "\n",
-        "              hw4  lec5       hw5  ...       lec8       hw8      lec9  \\\n",
-        "Name                               ...                                  \n",
-        "P01      5.000000     4  4.000000  ...   5.500000       NaN  4.000000   \n",
-        "P02      5.000000     5  5.000000  ...        NaN  5.000000  5.000000   \n",
-        "P03      5.000000     5  5.000000  ...   5.000000  6.000000       NaN   \n",
-        "P04      4.000000     5       NaN  ...   4.000000       NaN  1.000000   \n",
-        "P05      4.000000     5  4.000000  ...   4.000000  4.000000  3.000000   \n",
-        "P06      4.000000     3  3.000000  ...   5.000000  5.000000  4.000000   \n",
-        "P07      4.000000     5  5.000000  ...   5.000000       NaN  5.000000   \n",
-        "P08      4.000000     3  3.000000  ...   5.000000  5.000000  5.000000   \n",
-        "P09      3.000000     3  2.000000  ...   3.000000  3.000000  3.000000   \n",
-        "P10      3.000000     3  4.000000  ...   5.000000  5.000000  4.000000   \n",
-        "P11      4.000000     4       NaN  ...   4.000000  4.000000  4.000000   \n",
-        "P12      5.000000     5  4.000000  ...   5.500000  5.000000  4.000000   \n",
-        "P13           NaN     3  3.000000  ...   4.000000  4.000000       NaN   \n",
-        "P14      4.000000     4  4.000000  ...        NaN  4.000000  4.000000   \n",
-        "P15      3.000000     3  3.000000  ...   3.000000  3.000000  3.000000   \n",
-        "Average  4.071429     4  3.769231  ...   4.461538  4.416667  3.769231   \n",
-        "Min      3.000000     3  2.000000  ...   3.000000  3.000000  1.000000   \n",
-        "Max      5.000000     5  5.000000  ...   5.500000  6.000000  5.000000   \n",
+        "      lec1  hw1  lec2  hw2  lec3  hw3  lec4  hw4  lec5  hw5  ...   lec8  hw8  \\\n",
+        "Name                                                         ...               \n",
+        "P01      3  4.0     3    4     4  5.0   5.0    5     4    4  ...    5.5  NaN   \n",
+        "P02      4  3.5     3    5     4  4.5   4.5    5     5    5  ...    NaN    5   \n",
+        "P03    NaN  5.0     3    4     5  5.0   5.0    5     5    5  ...    5.0    6   \n",
+        "P04      3  3.0     2    3     4  4.0   4.0    4     5  NaN  ...    4.0  NaN   \n",
+        "P05    NaN  3.0     3    3     3  4.0   4.0    4     5    4  ...    4.0    4   \n",
         "\n",
-        "              hw9  lec10      hw10  lec11      hw11   lec12  hw12  \n",
-        "Name                                                               \n",
-        "P01      5.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
-        "P02      5.000000    NaN       NaN    5.0  5.000000  5.0000   NaN  \n",
-        "P03           NaN   5.00  5.000000    5.0       NaN     NaN   NaN  \n",
-        "P04      3.000000   1.00  3.000000    5.0  5.000000  5.0000   5.0  \n",
-        "P05      6.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
-        "P06      5.000000   3.00  4.000000    4.0  4.000000     NaN   NaN  \n",
-        "P07      4.000000   4.90  5.000000    4.0  4.000000  4.9000   4.9  \n",
-        "P08      5.000000   5.00  5.000000    4.0  4.000000  4.0000   5.0  \n",
-        "P09      2.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
-        "P10      5.000000   4.00  5.000000    4.0  5.000000  4.0000   NaN  \n",
-        "P11      4.000000   4.00  5.000000    5.0  4.000000  5.0000   5.0  \n",
-        "P12      4.000000   4.00  6.000000    5.0  5.000000  6.0000   NaN  \n",
-        "P13      5.000000   3.00       NaN    NaN       NaN     NaN   NaN  \n",
-        "P14      3.000000    NaN       NaN    NaN       NaN     NaN   NaN  \n",
-        "P15      4.000000   3.00  3.000000    3.0  3.000000  3.0000   5.0  \n",
-        "Average  4.285714   3.69  4.555556    4.4  4.333333  4.6125   NaN  \n",
-        "Min      2.000000   1.00  3.000000    3.0  3.000000  3.0000   NaN  \n",
-        "Max      6.000000   5.00  6.000000    5.0  5.000000  6.0000   NaN  \n",
+        "      lec9  hw9  lec10  hw10  lec11  hw11  lec12  hw12  \n",
+        "Name                                                    \n",
+        "P01      4    5    NaN   NaN    NaN   NaN    NaN   NaN  \n",
+        "P02      5    5    NaN   NaN      5     5      5   NaN  \n",
+        "P03    NaN  NaN      5     5      5   NaN    NaN   NaN  \n",
+        "P04      1    3      1     3      5     5      5     5  \n",
+        "P05      3    6    NaN   NaN    NaN   NaN    NaN   NaN  \n",
         "\n",
-        "[18 rows x 23 columns]"
+        "[5 rows x 23 columns]"
        ]
       }
      ],
-     "prompt_number": 105
+     "prompt_number": 106
     },
     {
      "cell_type": "code",
diff --git a/requirements.txt b/requirements.txt
index 1ac83fd..98261ae 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,4 @@
 pandas
 xlrd
+matplotlib
+ipython[all]