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"python_homework_data_transposed = python_homework_data.transpose()\n", + "python_homework_data_transposed.index = pd.to_datetime(python_homework_data_transposed.index, format=\"%m/%d/%y\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_lecture_data_transposed = rails_lecture_data.transpose()\n", + "rails_lecture_data_transposed.index = pd.to_datetime(rails_lecture_data_transposed.index, format=\"%m/%d/%y\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_homework_data_transposed = rails_homework_data.transpose()\n", + "rails_homework_data_transposed.index = pd.to_datetime(rails_homework_data_transposed.index, format=\"%m/%d/%y\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 14 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "py_lecture_by_day = python_lecture_data_transposed.mean(1)\n", + "labels = [\"Lecture {}\".format(x) for x in range(1, 14)]\n", + "py_lecture_by_day.plot(kind=\"bar\", title=\"Mean Difficulty of Python Lectures by Day\")\n", + "plt.xticks(np.arange(13), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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0p6gOt6gpoiF/ULdjIZ03d58LbAe8DpgMzFedNOqUaVtKWKeog6BFTREN+YO6\nnTpPuhDuXiFe38DMLie8DVGdBOqUaVtKVmcW4a36PsD9hA733QM+oj1FTREVtT251Sm0kzazH9b7\nB7y2yHGISENFdbhFTREN+YO6RXfSs4AK4SpetWYWPA4Rqa+QDregkwegBAd1i76e9G1514ideT2Z\nduuqk2YN1Vln1Q53U0KHe3wONYDCpoiK2p7c6pRxTrqobl110qyhOuugwA63EEVtT551hlUqlazW\nJSIlY2b3uXsuB0FVpzll7KRFJFEFT92UgkJaRIqkkwdapJAWkcI63CJOHoByHdRVSIsIlK/DLc1B\nXR04FBFJWGmv3SEiUgYKaRGRhCmkRUQSppAWEUmYQlpEJGH/D6qO2LoW72OMAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the above graph we can see that as the course has progressed the average difficulty rating by students has increased, especially lectures toward the end of the week and the most recent lectures." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "py_homework_by_day = python_homework_data_transposed.mean(1)\n", + "labels = [\"Homework {}\".format(x) for x in range(1, 12)]\n", + "py_homework_by_day.plot(kind=\"bar\", title=\"Mean Difficulty of Python Homework by Day\")\n", + "plt.xticks(np.arange(11), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difficulty of the Python homework assignements have gone steadily up since the beginning of the course." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_lecture_by_day = rails_lecture_data_transposed.mean(1)\n", + "labels = [\"Lecture {}\".format(x) for x in range(1, 14)]\n", + "rails_lecture_by_day.plot(kind=\"bar\", title=\"Mean Difficulty of Rails Lectures by Day\")\n", + "plt.xticks(np.arange(13), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rails lectures have stayed consistantly difficult, save the first two days of the course and one day on Jan 23." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_homework_by_day = rails_homework_data_transposed.mean(1)\n", + "labels = [\"Homework {}\".format(x) for x in range(1, 14)]\n", + "rails_homework_by_day.plot(kind=\"bar\", title=\"Mean Difficulty of Rails Homework by Day\")\n", + "plt.xticks(np.arange(13), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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iZmZWMr/Zx8ysxja6D8Ays/74I5yrzUXczDryRzhXm4u4mXXlZZnV5TlxM7MacxE3M6sx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\nE0UNohaFUCsAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 30 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rails homeworks seems to be consistantly difficult, with the most difficult assignment occuring on Jan 21." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture_week_1 = python_lecture_data_transposed[0:4].mean().mean()\n", + "python_lecture_week_2 = python_lecture_data_transposed[4:8].mean().mean()\n", + "python_lecture_week_3 = python_lecture_data_transposed[8:12].mean().mean()\n", + "python_lecture_week_4 = python_lecture_data_transposed[12:].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 32 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "labels = [\"Week {}\".format(x) for x in range(1,5)]\n", + "pd.DataFrame([python_lecture_week_1, python_lecture_week_2, python_lecture_week_3, python_lecture_week_4]).plot(kind=\"bar\", \n", + " title=\"Python Lecture Difficulty By Week\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python lecture difficulties have stayed fairly consistant over the past couple of weeks. Week 4 data is not really accurate because it contains only a single data point." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework_week_1 = python_homework_data_transposed[0:4].mean().mean()\n", + "python_homework_week_2 = python_homework_data_transposed[4:8].mean().mean()\n", + "python_homework_week_3 = python_homework_data_transposed[8:12].mean().mean()\n", + "python_homework_week_4 = python_homework_data_transposed[12:].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.DataFrame([python_homework_week_1, python_homework_week_2, python_homework_week_3, python_homework_week_4]).plot(kind=\"bar\", \n", + " title=\"Python homework Difficulty By Week\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 37 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python homeworks have become increasingly difficult as the course has progressed." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_lecture_week_1 = rails_lecture_data_transposed[0:4].mean().mean()\n", + "rails_lecture_week_2 = rails_lecture_data_transposed[4:8].mean().mean()\n", + "rails_lecture_week_3 = rails_lecture_data_transposed[8:12].mean().mean()\n", + "rails_lecture_week_4 = rails_lecture_data_transposed[12:].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 38 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.DataFrame([rails_lecture_week_1, rails_lecture_week_2, rails_lecture_week_3, rails_lecture_week_4]).plot(kind=\"bar\", \n", + " title=\"Rails Lecture Difficulty By Week\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 39 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rails lectures have had a near linear increase in difficulty over the first four weeks." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_homework_week_1 = rails_homework_data_transposed[0:4].mean().mean()\n", + "rails_homework_week_2 = rails_homework_data_transposed[4:8].mean().mean()\n", + "rails_homework_week_3 = rails_homework_data_transposed[8:12].mean().mean()\n", + "rails_homework_week_4 = rails_homework_data_transposed[12:].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.DataFrame([rails_homework_week_1, rails_homework_week_2, rails_homework_week_3, rails_homework_week_4]).plot(kind=\"bar\", \n", + " title=\"Rails Homework Difficulty By Week\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 41 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rails homeworks have maintained an almost equal level of difficulty for the duration of the course." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_monday_lecture = python_lecture_data[[\"1/12/15\", \"1/20/15\", \"1/26/15\", \"2/2/15\"]].mean().mean()\n", + "python_tuesday_lecture = python_lecture_data[[\"1/13/15\", \"1/21/15\", \"1/27/15\"]].mean().mean()\n", + "python_wednesday_lecture = python_lecture_data[[\"1/14/15\", \"1/22/15\", \"1/28/15\"]].mean().mean()\n", + "python_thursday_lecture = python_lecture_data[[\"1/15/15\", \"1/23/15\", \"1/29/15\"]].mean().mean()\n", + "python_monday_homework = python_homework_data[[\"1/12/15\", \"1/20/15\", \"1/26/15\"]].mean().mean()\n", + "python_tuesday_homework = python_homework_data[[\"1/13/15\", \"1/21/15\", \"1/27/15\"]].mean().mean()\n", + "python_wednesday_homework = python_homework_data[[\"1/14/15\", \"1/22/15\", \"1/28/15\"]].mean().mean()\n", + "python_thursday_homework = python_homework_data[[\"1/15/15\", \"1/29/15\"]].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 42 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "labels = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\"]\n", + "python_difficulty_by_weekday = pd.DataFrame([python_monday_lecture, python_tuesday_lecture, python_wednesday_lecture, \n", + " python_thursday_lecture]).plot(kind=\"Bar\", title=\"Python Lecture Difficulty by \"\\\n", + " \"Weekday\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 43 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this data it appears that Tuesdays are generally the easiest days for lecture, while Thursdays are the most difficult in the Python class." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_hw_difficulty_by_weekday = pd.DataFrame([python_monday_homework, python_tuesday_homework, python_wednesday_homework, \n", + " python_thursday_homework]).plot(kind=\"Bar\", title=\"Python Homework Difficulty by \"\\\n", + " \"Weekday\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 44 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difficulty of the homework assignments increases almost linearly throughout the week, with the most difficult assignments being the weekend assignments." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_monday_lecture = rails_lecture_data[[\"1/12/15\", \"1/20/15\", \"1/26/15\", \"2/2/15\"]].mean().mean()\n", + "rails_tuesday_lecture = rails_lecture_data[[\"1/13/15\", \"1/21/15\", \"1/27/15\"]].mean().mean()\n", + "rails_wednesday_lecture = rails_lecture_data[[\"1/14/15\", \"1/22/15\", \"1/28/15\"]].mean().mean()\n", + "rails_thursday_lecture = rails_lecture_data[[\"1/15/15\", \"1/23/15\", \"1/29/15\"]].mean().mean()\n", + "rails_monday_homework = rails_homework_data[[\"1/12/15\", \"1/20/15\", \"1/26/15\", \"2/2/15\"]].mean().mean()\n", + "rails_tuesday_homework = rails_homework_data[[\"1/13/15\", \"1/21/15\", \"1/27/15\"]].mean().mean()\n", + "rails_wednesday_homework = rails_homework_data[[\"1/14/15\", \"1/22/15\", \"1/28/15\"]].mean().mean()\n", + "rails_thursday_homework = rails_homework_data[[\"1/15/15\", \"1/23/15\", \"1/29/15\"]].mean().mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 45 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_difficulty_by_weekday = pd.DataFrame([rails_monday_lecture, rails_tuesday_lecture, rails_wednesday_lecture, \n", + " rails_thursday_lecture]).plot(kind=\"Bar\", title=\"Rails Lecture Difficulty by\"\\\n", + " \"Weekday\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 46 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rails lectures have an almost uniform difficulty, however it appears that Wednesdays tend to be slightly more difficult." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_hw_difficulty_by_weekday = pd.DataFrame([rails_monday_homework, rails_tuesday_homework, rails_wednesday_homework, \n", + " rails_thursday_homework]).plot(kind=\"Bar\", title=\"Rails Homework Difficulty by\"\\\n", + " \"Weekday\")\n", + "plt.xticks(np.arange(0,4), labels)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 47 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rails homework assignments difficulty ratings are all very close, but Monday assignments get rated slightly lower." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_student_lec = python_lecture_data_transposed[\"P07\"]\n", + "python_student_hw = python_homework_data_transposed[\"P07\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 48 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_student_lec.plot(title=\"Random Student Lecture Difficulty Ratings\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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737Ccs48/JGlVZI250zy/gue2dbG7K8CKxfF1OdzVOUBNZUncJpSPFBUWsLC+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\njH9PtuHM+4B8wsT8SIZpwk15HcENfTb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+ "text": [ + "" + ] + } + ], + "prompt_number": 49 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This student seems to have days that they understand the material better than others, but is staying on the edge of their panic zone." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_student_hw.plot(title=\"Random Student Homework Difficulty Ratings\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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O9l3UwHsPXhy2HCPiZJt+V9brYsFM3yhp0ukM16zfBMBH1rVRVlZc8Vmj8HT1Jsded5rp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\no6RyLi2N1QyPpBlIpMaWeem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UEGb6hmEYJYSZvmEYRglhpm8YhlFCmOkbhmGUEGb6hmEY\nJcT/A3trZVYcuLNLAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 50 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The student seems to be faring a little better on homework assignments than in lectures, however several data points are missing." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_student_lec = rails_lecture_data_transposed[\"R03\"]\n", + "rails_student_hw = rails_homework_data_transposed[\"R03\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 51 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_student_lec.plot(title=\"Random Student Lecture Difficulty Ratings\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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wVbuLl8Rc/wBuXMSYQXdohpaNi5jkFfSWiDGBdWirXGuJmORlRcSYPirKzyItNcX2WzdJ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\nXURyfYg74C2R6PF/g2ttLMENJk71Lj8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+ "text": [ + "" + ] + } + ], + "prompt_number": 52 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This student seemed to be in full panic mode after the first week, but has gotten things under a little better control since then." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rails_student_hw.plot(title=\"Random Student Homework Difficulty Ratings\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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