diff --git a/cohort_data.ipynb b/cohort_data.ipynb new file mode 100644 index 0000000..5da51ef --- /dev/null +++ b/cohort_data.ipynb @@ -0,0 +1,2174 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:130ee50289a3a06e9d15406bb89e264abaaa5077143857544625e6c92838380b" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python = pd.read_csv(\"cohort_3_python.csv\")\n", + "ruby = pd.ExcelFile(\"cohort_3_rails.xlsx\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby.sheet_names" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "['Lecture Score', 'HW Score']" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture = ruby.parse('Lecture Score')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_hw = ruby.parse('HW Score')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Ruby Lecture" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ordered_ruby_lecture = ruby_lecture.transpose()\n", + "ordered_ruby_lecture = ordered_ruby_lecture[:13]\n", + "\n", + "ordered_ruby_lecture" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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nanR01R02R03R04R05R06R07R08R09R10R11R12R13R14R15nanAverageStDev
Week 1 M 2 3 3 2 2 2 2 3 3 2 2.5 2 2 1 4.5 NaN 2.4 0.8
Unnamed: 1 T 2 3.5 4.5 4 3 4.5 4 3 3 3 3.5 3 3 2 5 NaN 3.4 0.9
Unnamed: 2 W 4 4.5 4 4 5 6 5 4 4 3.5 3 3 4 2 4 NaN 4.0 0.9
Unnamed: 3 Th 3 4 3.5 4 4.5 3.5 6 3 4.5 3.5 4 2 3 3 5 NaN 3.8 1.0
Week 2 M NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Unnamed: 5 T 3 4.5 6 5 3 4.5 4 3 3 4 3.5 3 4 3 5 NaN 3.9 0.9
Unnamed: 6 W 5 4.5 4.5 5 4 3.5 4 4 5 3 3 3 4 3.5 5 NaN 4.1 0.8
Unnamed: 7 Th 2 3.5 4 4 3 3 3.5 3 4 2 2 2 3 2 4 NaN 3.0 0.8
Week 3 M 3 6 5 5 5 4.5 4 3.5 3.5 3 3.5 3 4 4 4.5 NaN 4.1 0.9
Unnamed: 9 T 4 4 5 4 3 4.5 4 3 4 2.5 3.5 3 3 4 4 NaN 3.7 0.7
Unnamed: 10 W 4 5 4.5 6 3 4 4 4 3 3 4.5 4 4 3 4.5 NaN 4.0 0.8
Unnamed: 11 Th 4 4.5 4 5 4 3.5 4 4 3 2 6 3.5 4 4 4 NaN 4.0 0.9
Week 4 M 3 5 5 6 NaN 4 5 NaN 5 3.5 NaN 3.5 5 4 4.5 NaN 4.5 0.9
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + " NaN R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 \\\n", + "Week 1 M 2 3 3 2 2 2 2 3 3 2 2.5 2 \n", + "Unnamed: 1 T 2 3.5 4.5 4 3 4.5 4 3 3 3 3.5 3 \n", + "Unnamed: 2 W 4 4.5 4 4 5 6 5 4 4 3.5 3 3 \n", + "Unnamed: 3 Th 3 4 3.5 4 4.5 3.5 6 3 4.5 3.5 4 2 \n", + "Week 2 M NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "Unnamed: 5 T 3 4.5 6 5 3 4.5 4 3 3 4 3.5 3 \n", + "Unnamed: 6 W 5 4.5 4.5 5 4 3.5 4 4 5 3 3 3 \n", + "Unnamed: 7 Th 2 3.5 4 4 3 3 3.5 3 4 2 2 2 \n", + "Week 3 M 3 6 5 5 5 4.5 4 3.5 3.5 3 3.5 3 \n", + "Unnamed: 9 T 4 4 5 4 3 4.5 4 3 4 2.5 3.5 3 \n", + "Unnamed: 10 W 4 5 4.5 6 3 4 4 4 3 3 4.5 4 \n", + "Unnamed: 11 Th 4 4.5 4 5 4 3.5 4 4 3 2 6 3.5 \n", + "Week 4 M 3 5 5 6 NaN 4 5 NaN 5 3.5 NaN 3.5 \n", + "\n", + " R13 R14 R15 NaN Average StDev \n", + "Week 1 2 1 4.5 NaN 2.4 0.8 \n", + "Unnamed: 1 3 2 5 NaN 3.4 0.9 \n", + "Unnamed: 2 4 2 4 NaN 4.0 0.9 \n", + "Unnamed: 3 3 3 5 NaN 3.8 1.0 \n", + "Week 2 NaN NaN NaN NaN NaN NaN \n", + "Unnamed: 5 4 3 5 NaN 3.9 0.9 \n", + "Unnamed: 6 4 3.5 5 NaN 4.1 0.8 \n", + "Unnamed: 7 3 2 4 NaN 3.0 0.8 \n", + "Week 3 4 4 4.5 NaN 4.1 0.9 \n", + "Unnamed: 9 3 4 4 NaN 3.7 0.7 \n", + "Unnamed: 10 4 3 4.5 NaN 4.0 0.8 \n", + "Unnamed: 11 4 4 4 NaN 4.0 0.9 \n", + "Week 4 5 4 4.5 NaN 4.5 0.9 " + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_lecture = ordered_ruby_lecture['Average'].astype(float)\n", + "mean_ruby_lecture = mean_ruby_lecture.dropna()\n", + "mean_ruby_lecture.plot(kind='bar', title = \"Ruby Lecture Average Difficulty per Day\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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74nh5x2qV3NsmcUlPAG9usXwhqV/czMwK5ot9zMxKbFLeAMu33jSzqpiUSdy33jSzqpiU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\nNzMrMSdxM7MS+189bxBtIdPM2AAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 217 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_lecture_week1 = mean_ruby_lecture[:4]\n", + "ruby_week_1_lecture_mean = mean_ruby_lecture_week1.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 67 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_lecture_week2 = mean_ruby_lecture[4:7]\n", + "ruby_week_2_lecture_mean = mean_ruby_lecture_week2.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 68 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_lecture_week3 = mean_ruby_lecture[7:11]\n", + "ruby_week_3_lecture_mean = mean_ruby_lecture_week3.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 69 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_lecture_week4 = mean_ruby_lecture[11]\n", + "ruby_week_4_lecture_mean = mean_ruby_lecture_week4.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 70 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot([ruby_week_1_lecture_mean, ruby_week_2_lecture_mean, ruby_week_3_lecture_mean, ruby_week_4_lecture_mean])\n", + "plt.ylim(0, 5)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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nanR01R02R03R04R05R06R07R08R09R10R11R12R13R14R15nanAverage
Week 1 M 4 3 4 3 2 3.5 3 3 3 3 4 3 3 2 4 NaN 3.2
Unnamed: 1 T 3 4 4 4.5 5 4 4 4 3 2 3.5 3 4 2.5 5 NaN 3.7
Unnamed: 2 W 3 4.5 5.5 3 4.5 5.5 3 5 3 4 3.5 3 5 2 4 NaN 3.9
Unnamed: 3 Th NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Week 2 M NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Unnamed: 5 T 4 4 5 4 5 4 5 5 3 5 5 4 5 4 4 NaN 4.4
Unnamed: 6 W 3 4 4 4 3 3 4 4 3 3 3 3 5 2.5 4.5 NaN 3.5
Unnamed: 7 Th 3 4 4.5 4 3 3.5 4.5 3 4 3.5 4 3 5 2 5 NaN 3.7
Week 3 M 4 2.5 4 4 4.5 4 4.5 4 3.5 4 3 4.5 2 4 4 NaN 3.8
Unnamed: 9 T 3 3 4 5 3 3 4 3 3 3.5 3 3 4 2 4.5 NaN 3.4
Unnamed: 10 W 4.5 4.5 3 6 5 3 5 4 3 3.5 4 3 3 4 3 NaN 3.9
Unnamed: 11 Th 4 4 4 5 4 3 4.5 4 4 3 4.5 3 4 3.5 3.5 NaN 3.9
Week 4 M 4.5 4 3.5 4 3.5 3 4.5 NaN 3 3 NaN 3 4 4.5 4 NaN 3.7
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + " NaN R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 \\\n", + "Week 1 M 4 3 4 3 2 3.5 3 3 3 3 4 3 \n", + "Unnamed: 1 T 3 4 4 4.5 5 4 4 4 3 2 3.5 3 \n", + "Unnamed: 2 W 3 4.5 5.5 3 4.5 5.5 3 5 3 4 3.5 3 \n", + "Unnamed: 3 Th NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "Week 2 M NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "Unnamed: 5 T 4 4 5 4 5 4 5 5 3 5 5 4 \n", + "Unnamed: 6 W 3 4 4 4 3 3 4 4 3 3 3 3 \n", + "Unnamed: 7 Th 3 4 4.5 4 3 3.5 4.5 3 4 3.5 4 3 \n", + "Week 3 M 4 2.5 4 4 4.5 4 4.5 4 3.5 4 3 4.5 \n", + "Unnamed: 9 T 3 3 4 5 3 3 4 3 3 3.5 3 3 \n", + "Unnamed: 10 W 4.5 4.5 3 6 5 3 5 4 3 3.5 4 3 \n", + "Unnamed: 11 Th 4 4 4 5 4 3 4.5 4 4 3 4.5 3 \n", + "Week 4 M 4.5 4 3.5 4 3.5 3 4.5 NaN 3 3 NaN 3 \n", + "\n", + " R13 R14 R15 NaN Average \n", + "Week 1 3 2 4 NaN 3.2 \n", + "Unnamed: 1 4 2.5 5 NaN 3.7 \n", + "Unnamed: 2 5 2 4 NaN 3.9 \n", + "Unnamed: 3 NaN NaN NaN NaN NaN \n", + "Week 2 NaN NaN NaN NaN NaN \n", + "Unnamed: 5 5 4 4 NaN 4.4 \n", + "Unnamed: 6 5 2.5 4.5 NaN 3.5 \n", + "Unnamed: 7 5 2 5 NaN 3.7 \n", + "Week 3 2 4 4 NaN 3.8 \n", + "Unnamed: 9 4 2 4.5 NaN 3.4 \n", + "Unnamed: 10 3 4 3 NaN 3.9 \n", + "Unnamed: 11 4 3.5 3.5 NaN 3.9 \n", + "Week 4 4 4.5 4 NaN 3.7 " + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_hw = ordered_ruby_hw['Average'].astype(float)\n", + "mean_ruby_hw = mean_ruby_hw.dropna()\n", + "mean_ruby_hw.plot(kind='bar', title = \"Ruby HW Average per Day\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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VzD8FOA7ozyfNkbSsoLKamdkIrV7PdiTQL+mo/GXI9wPX1czfBThK0n1FFdDMzBprlcSv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\njk7iZLcKfwD4Gtm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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_hw_week1 = mean_ruby_hw[:3]\n", + "ruby_week_1_hw_mean = mean_ruby_hw_week1.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 54 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_hw_week2 = mean_ruby_hw[3:6]\n", + "ruby_week_2_hw_mean = mean_ruby_hw_week2.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 55 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_hw_week3 = mean_ruby_hw[6:10]\n", + "ruby_week_3_hw_mean = mean_ruby_lecture_week3.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 56 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mean_ruby_hw_week4 = mean_ruby_hw[10]\n", + "ruby_week_4_hw_mean = mean_ruby_hw_week4.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 57 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot([ruby_week_1_hw_mean, ruby_week_2_hw_mean, ruby_week_3_hw_mean, ruby_week_4_hw_mean])\n", + "plt.title(\"Ruby Weekly HW Mean\")\n", + "plt.ylim(0, 5)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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5 3.000000 3.000000 3.000000 4.000000 3 5.000000 5.000000 5.000000 4.000000 3.00 4.0 NaNNaN
6 3.500000 3.000000 5.000000 4.500000 5 5.000000 4.000000 5.000000 5.000000 4.90 4.0 4.9000NaN
7 2.000000 2.000000 4.000000 3.000000 3 5.000000 4.000000 5.000000 5.000000 5.00 4.0 4.0000 5
8 NaN 1.000000 2.000000 2.000000 3 3.000000 3.000000 3.000000 3.000000 NaN NaN NaNNaN
9 2.000000 2.000000 NaN 3.000000 3 4.000000 4.000000 5.000000 4.000000 4.00 4.0 4.0000NaN
10 2.000000 4.000000 5.000000 4.000000 4 NaN 4.000000 4.000000 4.000000 4.00 5.0 5.0000NaN
11 3.500000 4.000000 4.500000 5.000000 5 4.000000 NaN 5.500000 4.000000 4.00 5.0 6.0000NaN
12 2.500000 3.000000 3.000000 4.000000 3 3.000000 4.000000 4.000000 NaN 3.00 NaN NaNNaN
13 3.000000 3.000000 4.000000 4.000000 4 4.000000 NaN NaN 4.000000 NaN NaN NaNNaN
14 2.000000 2.000000 3.000000 3.000000 3 3.000000 3.000000 3.000000 3.000000 3.00 3.0 3.0000NaN
15 NaN NaN NaN NaNNaN NaN NaN NaN NaN NaN NaN NaNNaN
16 2.791667 2.733333 3.821429 3.933333 4 4.142857 3.909091 4.461538 3.769231 3.69 4.4 4.6125NaN
17 2.000000 1.000000 2.000000 2.000000 3 3.000000 3.000000 3.000000 1.000000 1.00 3.0 3.0000NaN
18 4.000000 4.000000 5.000000 5.000000 5 5.000000 5.000000 5.500000 5.000000 5.00 5.0 6.0000NaN
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 95, + "text": [ + " Lecture 1, Jan12 Lecture 2, Jan 13 Lecture 3, Jan 14 Lecture 4, Jan 15 \\\n", + "0 3.000000 3.000000 4.000000 5.000000 \n", + "1 4.000000 3.000000 4.000000 4.500000 \n", + "2 NaN 3.000000 5.000000 5.000000 \n", + "3 3.000000 2.000000 4.000000 4.000000 \n", + "4 NaN 3.000000 3.000000 4.000000 \n", + "5 3.000000 3.000000 3.000000 4.000000 \n", + "6 3.500000 3.000000 5.000000 4.500000 \n", + "7 2.000000 2.000000 4.000000 3.000000 \n", + "8 NaN 1.000000 2.000000 2.000000 \n", + "9 2.000000 2.000000 NaN 3.000000 \n", + "10 2.000000 4.000000 5.000000 4.000000 \n", + "11 3.500000 4.000000 4.500000 5.000000 \n", + "12 2.500000 3.000000 3.000000 4.000000 \n", + "13 3.000000 3.000000 4.000000 4.000000 \n", + "14 2.000000 2.000000 3.000000 3.000000 \n", + "15 NaN NaN NaN NaN \n", + "16 2.791667 2.733333 3.821429 3.933333 \n", + "17 2.000000 1.000000 2.000000 2.000000 \n", + "18 4.000000 4.000000 5.000000 5.000000 \n", + "\n", + " Lecture 5, Jan 20 Lecture 6, 21 Lecture 7, Jan 22 Lecture 8, Jan 23 \\\n", + "0 4 4.000000 4.000000 5.500000 \n", + "1 5 5.000000 NaN NaN \n", + "2 5 5.000000 NaN 5.000000 \n", + "3 5 4.000000 4.000000 4.000000 \n", + "4 5 4.000000 4.000000 4.000000 \n", + "5 3 5.000000 5.000000 5.000000 \n", + "6 5 5.000000 4.000000 5.000000 \n", + "7 3 5.000000 4.000000 5.000000 \n", + "8 3 3.000000 3.000000 3.000000 \n", + "9 3 4.000000 4.000000 5.000000 \n", + "10 4 NaN 4.000000 4.000000 \n", + "11 5 4.000000 NaN 5.500000 \n", + "12 3 3.000000 4.000000 4.000000 \n", + "13 4 4.000000 NaN NaN \n", + "14 3 3.000000 3.000000 3.000000 \n", + "15 NaN NaN NaN NaN \n", + "16 4 4.142857 3.909091 4.461538 \n", + "17 3 3.000000 3.000000 3.000000 \n", + "18 5 5.000000 5.000000 5.500000 \n", + "\n", + " Lecture 9, Jan26 Lecture10, Jan27 Lecture11, Jan28 Lecture12, Jan29 \\\n", + "0 4.000000 NaN NaN NaN \n", + "1 5.000000 NaN 5.0 5.0000 \n", + "2 NaN 5.00 5.0 NaN \n", + "3 1.000000 1.00 5.0 5.0000 \n", + "4 3.000000 NaN NaN NaN \n", + "5 4.000000 3.00 4.0 NaN \n", + "6 5.000000 4.90 4.0 4.9000 \n", + "7 5.000000 5.00 4.0 4.0000 \n", + "8 3.000000 NaN NaN NaN \n", + "9 4.000000 4.00 4.0 4.0000 \n", + "10 4.000000 4.00 5.0 5.0000 \n", + "11 4.000000 4.00 5.0 6.0000 \n", + "12 NaN 3.00 NaN NaN \n", + "13 4.000000 NaN NaN NaN \n", + "14 3.000000 3.00 3.0 3.0000 \n", + "15 NaN NaN NaN NaN \n", + "16 3.769231 3.69 4.4 4.6125 \n", + "17 1.000000 1.00 3.0 3.0000 \n", + "18 5.000000 5.00 5.0 6.0000 \n", + "\n", + " Lecture13,Feb2 \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 5 \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN " + ] + } + ], + "prompt_number": 95 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ordered_python_lecture = python_lecture.transpose()\n", + "mean_python_lecture = ordered_python_lecture[16].astype(float) #line 16 is the average\n", + "mean_python_lecture.dropna()\n", + "mean_python_lecture.plot(kind='bar', title = \"Python Daily Lecture Average\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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MzCZktDHpQ4FDG6qLmZn18MUsZmYFc5I2MyuYk7SZWcGcpM3MCuYkbWZWMCdpM7OCOUmb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\n5I5De9qs9s5N78n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+ "text": [ + "" + ] + } + ], + "prompt_number": 130 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week1_lecture_mean = mean_python_lecture[:4]\n", + "python_week1_lecture_mean = python_week1_lecture_mean.mean()\n", + "python_week1_lecture_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 114, + "text": [ + "3.3199404760000002" + ] + } + ], + "prompt_number": 114 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week2_lecture_mean = mean_python_lecture[4:8]\n", + "python_week2_lecture_mean = python_week2_lecture_mean.mean()\n", + "python_week2_lecture_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 121, + "text": [ + "4.1283716285000001" + ] + } + ], + "prompt_number": 121 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week3_lecture_mean = mean_python_lecture[8:12]\n", + "python_week3_lecture_mean = python_week3_lecture_mean.mean()\n", + "python_week3_lecture_mean" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 120, + "text": [ + "4.1179326922500001" + ] + } + ], + "prompt_number": 120 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot([python_week1_lecture_mean, python_week2_lecture_mean, python_week3_lecture_mean])\n", + "plt.title(\"Python Lecture Weekly Average\")\n", + "plt.ylim(0, 5)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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5.000000 5.000000 5.000000 NaN \n", + "10 4.000000 4.000000 5.000000 4.000000 5.0 \n", + "11 5.000000 4.000000 6.000000 5.000000 NaN \n", + "12 4.000000 5.000000 NaN NaN NaN \n", + "13 4.000000 3.000000 NaN NaN NaN \n", + "14 3.000000 4.000000 3.000000 3.000000 5.0 \n", + "15 NaN NaN NaN NaN NaN \n", + "16 4.416667 4.285714 4.555556 4.333333 NaN \n", + "17 3.000000 2.000000 3.000000 3.000000 NaN \n", + "18 6.000000 6.000000 6.000000 5.000000 NaN " + ] + } + ], + "prompt_number": 127 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ordered_python_hw = ordered_python.transpose()\n", + "mean_python_hw = ordered_python_hw[16].astype(float) #line 16 is the average\n", + "mean_python_hw.dropna()\n", + "mean_python_hw.plot(kind='bar', title = \"Python Daily HW Average\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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PQMAKpNZ01SJNGuL5c9JM2pcD/0XqHx9VrSnSkfZie9FYya2V3YaWXVfmZNLY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\nK7pvWwAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 136 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week1_hw_mean = mean_python_hw[:3]\n", + "python_week1_hw_mean = python_week1_hw_mean.mean()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 142 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week2_hw_mean = mean_python_hw[3:6]\n", + "python_week2_hw_mean = python_week2_hw_mean.mean()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 140 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_week3_hw_mean = mean_python_hw[6:10]\n", + "python_week3_hw_mean = python_week3_hw_mean.mean()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 141 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "plt.plot([python_week1_hw_mean, python_week2_hw_mean, python_week3_hw_mean])\n", + "plt.title(\"Python Weekly HW Average\")\n", + "plt.ylim(0, 5)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 157 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Sample Student from Python" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ordered_python_hw = ordered_python.transpose()\n", + "student7lecture = ordered_python_hw[7].astype(float)\n", + "student7lecture.plot(kind='line', figsize=(8, 6))\n", + "plt.title(\"Student HW Over Time\")\n", + "plt.ylim(0, 7)\n", + "plt.show()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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LK1qV1AusXLOReYueYf89hzF25OBKl9NhI4b057xXJ159zN5cffsj3DN/OV/++d2VLqtX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\n", + "text": [ + "" + ] + } + ], + "prompt_number": 218 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ordered_python_lecture = python_lecture.transpose()\n", + "student7hw = ordered_python_lecture[7].astype(float)\n", + "student7hw.plot(kind = 'line', figsize=(10, 5))\n", + "plt.ylim(0, 7)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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G0AOzyELsPwwAqB5hEJLi6cS/RNOJB1QxndjIPTCLLOT+wwCA6hAGIWlw04nsNM6f0PsP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\njDAIAAAQMMIgAABAwP4/m/y6gZ/5txMAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 221 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Student from Ruby" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_student7_lecture = ordered_ruby_lecture.transpose()\n", + "ruby_student7_lecture = ruby_student7_lecture[7:8]\n", + "ruby_student7_lecture.transpose().plot(kind= 'bar')\n", + "plt.title(\"Ruby Student Lecture\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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PpL9E1tucT9aInhJrvWFmd5HdLr6XbOOcWUKd59z9RyUst6/S9wwS3MtJpk5q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\n/TcXpEPtKYev7AAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 192 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_student7_hw = ordered_ruby_hw\n", + "ruby_student7_hw = ruby_student7_hw[7:8]\n", + "ruby_student7_hw = ruby_student7_hw.transpose()\n", + "ruby_student7_hw = ruby_student7_hw[1:16]\n", + "ruby_student7_hw.plot(kind = 'bar')\n", + "plt.title(\"Ruby Student HW Over Time\")\n", + "plt.ylim(0, 7)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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