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15 rows \u00d7 24 columns

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Week 1Unnamed: 1Unnamed: 2Unnamed: 3Week 2Unnamed: 5Unnamed: 6Unnamed: 7Week 3Unnamed: 9Unnamed: 10Unnamed: 11Week 4
NaN M T W Th M T W Th M T W Th M
R01 2 2 4 3 NaN 3 5 2 3 4 4 4 3
R02 3 3.5 4.5 4 NaN 4.5 4.5 3.5 6 4 5 4.5 5
R03 3 4.5 4 3.5 NaN 6 4.5 4 5 5 4.5 4 5
R04 2 4 4 4 NaN 5 5 4 5 4 6 5 6
R05 2 3 5 4.5 NaN 3 4 3 5 3 3 4 NaN
R06 2 4.5 6 3.5 NaN 4.5 3.5 3 4.5 4.5 4 3.5 4
R07 2 4 5 6 NaN 4 4 3.5 4 4 4 4 5
R08 3 3 4 3 NaN 3 4 3 3.5 3 4 4 NaN
R09 3 3 4 4.5 NaN 3 5 4 3.5 4 3 3 5
R10 2 3 3.5 3.5 NaN 4 3 2 3 2.5 3 2 3.5
R11 2.5 3.5 3 4 NaN 3.5 3 2 3.5 3.5 4.5 6 NaN
R12 2 3 3 2 NaN 3 3 2 3 3 4 3.5 3.5
R13 2 3 4 3 NaN 4 4 3 4 3 4 4 5
R14 1 2 2 3 NaN 3 3.5 2 4 4 3 4 4
R15 4.5 5 4 5 NaN 5 5 4 4.5 4 4.5 4 4.5
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 349, + "text": [ + " Week 1 Unnamed: 1 Unnamed: 2 Unnamed: 3 Week 2 Unnamed: 5 Unnamed: 6 \\\n", + "NaN M T W Th M T W \n", + "R01 2 2 4 3 NaN 3 5 \n", + "R02 3 3.5 4.5 4 NaN 4.5 4.5 \n", + "R03 3 4.5 4 3.5 NaN 6 4.5 \n", + "R04 2 4 4 4 NaN 5 5 \n", + "R05 2 3 5 4.5 NaN 3 4 \n", + "R06 2 4.5 6 3.5 NaN 4.5 3.5 \n", + "R07 2 4 5 6 NaN 4 4 \n", + "R08 3 3 4 3 NaN 3 4 \n", + "R09 3 3 4 4.5 NaN 3 5 \n", + "R10 2 3 3.5 3.5 NaN 4 3 \n", + "R11 2.5 3.5 3 4 NaN 3.5 3 \n", + "R12 2 3 3 2 NaN 3 3 \n", + "R13 2 3 4 3 NaN 4 4 \n", + "R14 1 2 2 3 NaN 3 3.5 \n", + "R15 4.5 5 4 5 NaN 5 5 \n", + "\n", + " Unnamed: 7 Week 3 Unnamed: 9 Unnamed: 10 Unnamed: 11 Week 4 \n", + "NaN Th M T W Th M \n", + "R01 2 3 4 4 4 3 \n", + "R02 3.5 6 4 5 4.5 5 \n", + "R03 4 5 5 4.5 4 5 \n", + "R04 4 5 4 6 5 6 \n", + "R05 3 5 3 3 4 NaN \n", + "R06 3 4.5 4.5 4 3.5 4 \n", + "R07 3.5 4 4 4 4 5 \n", + "R08 3 3.5 3 4 4 NaN \n", + "R09 4 3.5 4 3 3 5 \n", + "R10 2 3 2.5 3 2 3.5 \n", + "R11 2 3.5 3.5 4.5 6 NaN \n", + "R12 2 3 3 4 3.5 3.5 \n", + "R13 3 4 3 4 4 5 \n", + "R14 2 4 4 3 4 4 \n", + "R15 4 4.5 4 4.5 4 4.5 " + ] + } + ], + "prompt_number": 349 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework = ruby.parse(\"HW Score\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 350 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 351, + "text": [ + "Index(['Week 1', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Week 2', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Week 3', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Week 4', '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'], dtype='object')" + ] + } + ], + "prompt_number": 351 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework = ruby_homework.drop('Average')\n", + "ruby_homework = ruby_homework.dropna(how=\"all\")\n", + "ruby_homework.drop(ruby_homework.columns[[13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]], axis=1, inplace=True)\n", + "ruby_homework" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Week 1Unnamed: 1Unnamed: 2Unnamed: 3Week 2Unnamed: 5Unnamed: 6Unnamed: 7Week 3Unnamed: 9Unnamed: 10Unnamed: 11Week 4
NaN M T W Th M T W Th M T W Th M
R01 4 3 3 NaN NaN 4 3 3 4 3 4.5 4 4.5
R02 3 4 4.5 NaN NaN 4 4 4 2.5 3 4.5 4 4
R03 4 4 5.5 NaN NaN 5 4 4.5 4 4 3 4 3.5
R04 3 4.5 3 NaN NaN 4 4 4 4 5 6 5 4
R05 2 5 4.5 NaN NaN 5 3 3 4.5 3 5 4 3.5
R06 3.5 4 5.5 NaN NaN 4 3 3.5 4 3 3 3 3
R07 3 4 3 NaN NaN 5 4 4.5 4.5 4 5 4.5 4.5
R08 3 4 5 NaN NaN 5 4 3 4 3 4 4 NaN
R09 3 3 3 NaN NaN 3 3 4 3.5 3 3 4 3
R10 3 2 4 NaN NaN 5 3 3.5 4 3.5 3.5 3 3
R11 4 3.5 3.5 NaN NaN 5 3 4 3 3 4 4.5 NaN
R12 3 3 3 NaN NaN 4 3 3 4.5 3 3 3 3
R13 3 4 5 NaN NaN 5 5 5 2 4 3 4 4
R14 2 2.5 2 NaN NaN 4 2.5 2 4 2 4 3.5 4.5
R15 4 5 4 NaN NaN 4 4.5 5 4 4.5 3 3.5 4
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 352, + "text": [ + " Week 1 Unnamed: 1 Unnamed: 2 Unnamed: 3 Week 2 Unnamed: 5 Unnamed: 6 \\\n", + "NaN M T W Th M T W \n", + "R01 4 3 3 NaN NaN 4 3 \n", + "R02 3 4 4.5 NaN NaN 4 4 \n", + "R03 4 4 5.5 NaN NaN 5 4 \n", + "R04 3 4.5 3 NaN NaN 4 4 \n", + "R05 2 5 4.5 NaN NaN 5 3 \n", + "R06 3.5 4 5.5 NaN NaN 4 3 \n", + "R07 3 4 3 NaN NaN 5 4 \n", + "R08 3 4 5 NaN NaN 5 4 \n", + "R09 3 3 3 NaN NaN 3 3 \n", + "R10 3 2 4 NaN NaN 5 3 \n", + "R11 4 3.5 3.5 NaN NaN 5 3 \n", + "R12 3 3 3 NaN NaN 4 3 \n", + "R13 3 4 5 NaN NaN 5 5 \n", + "R14 2 2.5 2 NaN NaN 4 2.5 \n", + "R15 4 5 4 NaN NaN 4 4.5 \n", + "\n", + " Unnamed: 7 Week 3 Unnamed: 9 Unnamed: 10 Unnamed: 11 Week 4 \n", + "NaN Th M T W Th M \n", + "R01 3 4 3 4.5 4 4.5 \n", + "R02 4 2.5 3 4.5 4 4 \n", + "R03 4.5 4 4 3 4 3.5 \n", + "R04 4 4 5 6 5 4 \n", + "R05 3 4.5 3 5 4 3.5 \n", + "R06 3.5 4 3 3 3 3 \n", + "R07 4.5 4.5 4 5 4.5 4.5 \n", + "R08 3 4 3 4 4 NaN \n", + "R09 4 3.5 3 3 4 3 \n", + "R10 3.5 4 3.5 3.5 3 3 \n", + "R11 4 3 3 4 4.5 NaN \n", + "R12 3 4.5 3 3 3 3 \n", + "R13 5 2 4 3 4 4 \n", + "R14 2 4 2 4 3.5 4.5 \n", + "R15 5 4 4.5 3 3.5 4 " + ] + } + ], + "prompt_number": 352 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Mean difficulty for lectures per day, per class" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture = ruby_lecture.drop(np.NAN)\n", + "ruby_lecture" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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Week 1Unnamed: 1Unnamed: 2Unnamed: 3Week 2Unnamed: 5Unnamed: 6Unnamed: 7Week 3Unnamed: 9Unnamed: 10Unnamed: 11Week 4
R01 2 2 4 3 NaN 3 5 2 3 4 4 4 3
R02 3 3.5 4.5 4 NaN 4.5 4.5 3.5 6 4 5 4.5 5
R03 3 4.5 4 3.5 NaN 6 4.5 4 5 5 4.5 4 5
R04 2 4 4 4 NaN 5 5 4 5 4 6 5 6
R05 2 3 5 4.5 NaN 3 4 3 5 3 3 4 NaN
R06 2 4.5 6 3.5 NaN 4.5 3.5 3 4.5 4.5 4 3.5 4
R07 2 4 5 6 NaN 4 4 3.5 4 4 4 4 5
R08 3 3 4 3 NaN 3 4 3 3.5 3 4 4 NaN
R09 3 3 4 4.5 NaN 3 5 4 3.5 4 3 3 5
R10 2 3 3.5 3.5 NaN 4 3 2 3 2.5 3 2 3.5
R11 2.5 3.5 3 4 NaN 3.5 3 2 3.5 3.5 4.5 6 NaN
R12 2 3 3 2 NaN 3 3 2 3 3 4 3.5 3.5
R13 2 3 4 3 NaN 4 4 3 4 3 4 4 5
R14 1 2 2 3 NaN 3 3.5 2 4 4 3 4 4
R15 4.5 5 4 5 NaN 5 5 4 4.5 4 4.5 4 4.5
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 355, + "text": [ + " Week 1 Unnamed: 1 Unnamed: 2 Unnamed: 3 Week 2 Unnamed: 5 Unnamed: 6 \\\n", + "R01 2 2 4 3 NaN 3 5 \n", + "R02 3 3.5 4.5 4 NaN 4.5 4.5 \n", + "R03 3 4.5 4 3.5 NaN 6 4.5 \n", + "R04 2 4 4 4 NaN 5 5 \n", + "R05 2 3 5 4.5 NaN 3 4 \n", + "R06 2 4.5 6 3.5 NaN 4.5 3.5 \n", + "R07 2 4 5 6 NaN 4 4 \n", + "R08 3 3 4 3 NaN 3 4 \n", + "R09 3 3 4 4.5 NaN 3 5 \n", + "R10 2 3 3.5 3.5 NaN 4 3 \n", + "R11 2.5 3.5 3 4 NaN 3.5 3 \n", + "R12 2 3 3 2 NaN 3 3 \n", + "R13 2 3 4 3 NaN 4 4 \n", + "R14 1 2 2 3 NaN 3 3.5 \n", + "R15 4.5 5 4 5 NaN 5 5 \n", + "\n", + " Unnamed: 7 Week 3 Unnamed: 9 Unnamed: 10 Unnamed: 11 Week 4 \n", + "R01 2 3 4 4 4 3 \n", + "R02 3.5 6 4 5 4.5 5 \n", + "R03 4 5 5 4.5 4 5 \n", + "R04 4 5 4 6 5 6 \n", + "R05 3 5 3 3 4 NaN \n", + "R06 3 4.5 4.5 4 3.5 4 \n", + "R07 3.5 4 4 4 4 5 \n", + "R08 3 3.5 3 4 4 NaN \n", + "R09 4 3.5 4 3 3 5 \n", + "R10 2 3 2.5 3 2 3.5 \n", + "R11 2 3.5 3.5 4.5 6 NaN \n", + "R12 2 3 3 4 3.5 3.5 \n", + "R13 3 4 3 4 4 5 \n", + "R14 2 4 4 3 4 4 \n", + "R15 4 4.5 4 4.5 4 4.5 " + ] + } + ], + "prompt_number": 355 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 358, + "text": [ + "Index(['Week 1', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Week 2', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Week 3', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Week 4'], dtype='object')" + ] + } + ], + "prompt_number": 358 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture.columns = ['M1', 'T1', 'W1', 'Th1', 'M2', 'T2', 'W2', 'Th2', 'M3', 'T3', 'W3', 'Th3', 'M4']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 361 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture = ruby_lecture.drop('M2', axis=1)\n", + "ruby_lecture" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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M1T1W1Th1T2W2Th2M3T3W3Th3M4
R01 2 2 4 3 3 5 2 3 4 4 4 3
R02 3 3.5 4.5 4 4.5 4.5 3.5 6 4 5 4.5 5
R03 3 4.5 4 3.5 6 4.5 4 5 5 4.5 4 5
R04 2 4 4 4 5 5 4 5 4 6 5 6
R05 2 3 5 4.5 3 4 3 5 3 3 4 NaN
R06 2 4.5 6 3.5 4.5 3.5 3 4.5 4.5 4 3.5 4
R07 2 4 5 6 4 4 3.5 4 4 4 4 5
R08 3 3 4 3 3 4 3 3.5 3 4 4 NaN
R09 3 3 4 4.5 3 5 4 3.5 4 3 3 5
R10 2 3 3.5 3.5 4 3 2 3 2.5 3 2 3.5
R11 2.5 3.5 3 4 3.5 3 2 3.5 3.5 4.5 6 NaN
R12 2 3 3 2 3 3 2 3 3 4 3.5 3.5
R13 2 3 4 3 4 4 3 4 3 4 4 5
R14 1 2 2 3 3 3.5 2 4 4 3 4 4
R15 4.5 5 4 5 5 5 4 4.5 4 4.5 4 4.5
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 368, + "text": [ + " M1 T1 W1 Th1 T2 W2 Th2 M3 T3 W3 Th3 M4\n", + "R01 2 2 4 3 3 5 2 3 4 4 4 3\n", + "R02 3 3.5 4.5 4 4.5 4.5 3.5 6 4 5 4.5 5\n", + "R03 3 4.5 4 3.5 6 4.5 4 5 5 4.5 4 5\n", + "R04 2 4 4 4 5 5 4 5 4 6 5 6\n", + "R05 2 3 5 4.5 3 4 3 5 3 3 4 NaN\n", + "R06 2 4.5 6 3.5 4.5 3.5 3 4.5 4.5 4 3.5 4\n", + "R07 2 4 5 6 4 4 3.5 4 4 4 4 5\n", + "R08 3 3 4 3 3 4 3 3.5 3 4 4 NaN\n", + "R09 3 3 4 4.5 3 5 4 3.5 4 3 3 5\n", + "R10 2 3 3.5 3.5 4 3 2 3 2.5 3 2 3.5\n", + "R11 2.5 3.5 3 4 3.5 3 2 3.5 3.5 4.5 6 NaN\n", + "R12 2 3 3 2 3 3 2 3 3 4 3.5 3.5\n", + "R13 2 3 4 3 4 4 3 4 3 4 4 5\n", + "R14 1 2 2 3 3 3.5 2 4 4 3 4 4\n", + "R15 4.5 5 4 5 5 5 4 4.5 4 4.5 4 4.5" + ] + } + ], + "prompt_number": 368 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture_means = ruby_lecture.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 471 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(\"Ruby Lecture Means\")\n", + "print(ruby_lecture_means)\n", + "ruby_lecture_means.plot(figsize= (10, 5))\n", + "plt.xticks(range(12), ruby_lecture_means.index)\n", + "plt.title(\"Graph of Ruby Lecture Means\")\n", + "plt.ylabel(\"Difficulty Rating\")\n", + "plt.xlabel(\"Day\")\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Ruby Lecture Means\n", + "M1 2.400000\n", + "T1 3.400000\n", + "W1 4.000000\n", + "Th1 3.766667\n", + "T2 3.900000\n", + "W2 4.066667\n", + "Th2 3.000000\n", + "M3 4.100000\n", + "T3 3.700000\n", + "W3 4.033333\n", + "Th3 3.966667\n", + "M4 4.458333\n", + "dtype: float64\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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U6xJEDhMKhfhswx7mvr2RnburSU1J4vxThnHhKcPJTO/aBamjLZLQNcnMrj14xcwWOOfu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\nrGB/TX2XbHf3vgM8MH8loRB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Lecture 1, Jan12Homework 1, Jan13Lecture 2, Jan 13Homework 2, Jan14Lecture 3, Jan 14Homework 3, Jan15Lecture 4, Jan 15Mystery Word, Jan 20Lecture 5, Jan 20Currency, Jan 21...Blackjack2, Jan26Lecture 9, Jan26Random Art, Jan 27Lecture10, Jan27ChartingLecture11, Jan28PigSimLecture12, Jan29Traffic Sim ILecture13,Feb2
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2 rows \u00d7 24 columns

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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 375, + "text": [ + " Lecture 1, Jan12 Homework 1, Jan13 Lecture 2, Jan 13 \\\n", + "Name \n", + "P01 3 4.0 3 \n", + "P02 4 3.5 3 \n", + "\n", + " Homework 2, Jan14 Lecture 3, Jan 14 Homework 3, Jan15 \\\n", + "Name \n", + "P01 4 4 5.0 \n", + "P02 5 4 4.5 \n", + "\n", + " Lecture 4, Jan 15 Mystery Word, Jan 20 Lecture 5, Jan 20 \\\n", + "Name \n", + "P01 5.0 5 4 \n", + "P02 4.5 5 5 \n", + "\n", + " Currency, Jan 21 ... Blackjack2, Jan26 Lecture 9, Jan26 \\\n", + "Name ... \n", + "P01 4 ... NaN 4 \n", + "P02 5 ... 5 5 \n", + "\n", + " Random Art, Jan 27 Lecture10, Jan27 Charting Lecture11, Jan28 \\\n", + "Name \n", + "P01 5 NaN NaN NaN \n", + "P02 5 NaN NaN 5 \n", + "\n", + " PigSim Lecture12, Jan29 Traffic Sim I Lecture13,Feb2 \n", + "Name \n", + "P01 NaN NaN NaN NaN \n", + "P02 5 5 NaN NaN \n", + "\n", + "[2 rows x 24 columns]" + ] + } + ], + "prompt_number": 375 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture = python[['Lecture 1, Jan12', 'Lecture 2, Jan 13', 'Lecture 3, Jan 14', 'Lecture 4, Jan 15', 'Lecture 5, Jan 20', 'Lecture 6, 21',\n", + " 'Lecture 7, Jan 22', 'Lecture 8, Jan 23', 'Lecture 9, Jan26', 'Lecture10, Jan27', 'Lecture11, Jan28',\n", + " 'Lecture12, Jan29', 'Lecture13,Feb2']]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 386 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture.columns = ['M1', 'T1', 'W1', 'Th1', 'T2', 'W2', 'Th2', 'F2', 'M3', 'T3', 'W3', 'Th3', 'M4']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 456 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture_means = python_lecture.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 473 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(\"Python Lecture Means\")\n", + "print(python_lecture_means)\n", + "python_lecture_means.plot(figsize = (10,5))\n", + "plt.xticks(range(13), python_lecture_means.index)\n", + "plt.title(\"Graph of Python Lecture Means\")\n", + "plt.ylabel(\"Difficulty Rating\")\n", + "plt.xlabel(\"Day\")\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python Lecture Means\n", + "M1 2.791667\n", + "T1 2.733333\n", + "W1 3.821429\n", + "Th1 3.933333\n", + "T2 4.000000\n", + "W2 4.142857\n", + "Th2 3.909091\n", + "F2 4.461538\n", + "M3 3.769231\n", + "T3 3.690000\n", + "W3 4.400000\n", + "Th3 4.612500\n", + "M4 5.000000\n", + "dtype: float64\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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2kl27aiOY1j8qXSIiInJQ6htbOpSsSraU7i1RyYnxzBg3ZE/JKhyeQXzc3hGtWNrP1sFS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\nvwcm442WXWdmyw807ECxpbS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+ "text": [ + "" + ] + } + ], + "prompt_number": 516 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Mean difficulty of lectures per week per class:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 393, + "text": [ + "M1 2.400000\n", + "T1 3.400000\n", + "W1 4.000000\n", + "Th1 3.766667\n", + "T2 3.900000\n", + "W2 4.066667\n", + "Th2 3.000000\n", + "M3 4.100000\n", + "T3 3.700000\n", + "W3 4.033333\n", + "Th3 3.966667\n", + "M4 4.458333\n", + "dtype: float64" + ] + } + ], + "prompt_number": 393 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "week1_means = ruby_lecture_means[['M1', 'T1', 'W1', 'Th1']].mean()\n", + "week2_means = ruby_lecture_means[['T2', 'W2', 'Th2']].mean()\n", + "week3_means = ruby_lecture_means[['M3', 'T3', 'W3', 'Th3']].mean()\n", + "week4_means = ruby_lecture_means['M4'].mean()\n", + "weekly_means = [week1_means, week2_means, week3_means, week4_means]\n", + "print(\"Ruby Weekly Means:\")\n", + "for _ in range(4):\n", + " print(\"Week {}: {}\".format((_+1), weekly_means[_]))\n", + "\n", + "plt.plot(weekly_means)\n", + "plt.xticks(range(4), [\"Week 1\", \"Week 2\", \"Week 3\", \"Week 4\"])\n", + "plt.title(\"Graph of Ruby Lecture Means by Week\")\n", + "plt.ylabel(\"Difficulty Rating\")\n", + "plt.xlabel(\"Week\")\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Ruby Weekly Means:\n", + "Week 1: 3.3916666666666666\n", + "Week 2: 3.6555555555555554\n", + "Week 3: 3.9499999999999997\n", + "Week 4: 4.458333333333333\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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4cpydW8hNJ2dy/MRsji9+PzGTZzzhiwd2TsEWAACATVlY6OWW287k+ImZfObE+eH1+MnZ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\nqqmT4yO68QIAsCn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+ "text": [ + "" + ] + } + ], + "prompt_number": 523 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 459, + "text": [ + "M1 2.791667\n", + "T1 2.733333\n", + "W1 3.821429\n", + "Th1 3.933333\n", + "T2 4.000000\n", + "W2 4.142857\n", + "Th2 3.909091\n", + "F2 4.461538\n", + "M3 3.769231\n", + "T3 3.690000\n", + "W3 4.400000\n", + "Th3 4.612500\n", + "M4 5.000000\n", + "dtype: float64" + ] + } + ], + "prompt_number": 459 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pweek1_means = python_lecture_means[['M1', 'T1', 'W1', 'Th1']].mean()\n", + "pweek2_means = python_lecture_means[['T2', 'W2', 'Th2', 'F2']].mean()\n", + "pweek3_means = python_lecture_means[['M3', 'T3', 'W3', 'Th3']].mean()\n", + "pweek4_means = python_lecture_means['M4'].mean()\n", + "pweekly_means = [pweek1_means, pweek2_means, pweek3_means, pweek4_means]\n", + "print(\"Python Weekly Means\")\n", + "for _ in range(4):\n", + " print(\"Week {}: {}\".format((_+1), pweekly_means[_]))\n", + "\n", + "plt.plot(pweekly_means)\n", + "plt.xticks(range(4), [\"Week 1\", \"Week 2\", \"Week 3\", \"Week 4\"])\n", + "plt.title(\"Graph of Python Lecture Means by Week\")\n", + "plt.ylabel(\"Difficulty Rating\")\n", + "plt.xlabel(\"Week\")\n", + "plt.ylim(ymin=0, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python Weekly Means\n", + "Week 1: 3.3199404761904763\n", + "Week 2: 4.128371628371628\n", + "Week 3: 4.117932692307693\n", + "Week 4: 5.0\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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pHDo+OivADm1Yk9v2bs3eXUO5oQmxe3cNZetQ/wPsfPoabGut40me1s99AAAAsDhnzo1n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\nWwAAAFpNsAUAAKDVBFsAAAB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+ "text": [ + "" + ] + } + ], + "prompt_number": 524 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Mean difficulty for homework per day/per class:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework = ruby_homework.drop(['Unnamed: 3', 'Week 2'], axis=1)\n", + "ruby_homework.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 418, + "text": [ + "Index(['Week 1', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Week 3', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Week 4'], dtype='object')" + ] + } + ], + "prompt_number": 418 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework.columns = ['M1', 'T1', 'W1', 'T2', 'W2', 'Th2', 'M3', 'T3', 'W3', 'Th3', 'M4']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 420 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework = ruby_homework.drop(np.NAN)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 425 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework_means = ruby_homework.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 428 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(\"Ruby Homework Means\")\n", + "print(ruby_homework_means)\n", + "ruby_homework_means.plot()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Ruby Homework Means\n", + "M1 3.166667\n", + "T1 3.700000\n", + "W1 3.900000\n", + "T2 4.400000\n", + "W2 3.533333\n", + "Th2 3.733333\n", + "M3 3.766667\n", + "T3 3.400000\n", + "W3 3.900000\n", + "Th3 3.866667\n", + "M4 3.730769\n", + "dtype: float64\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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P03 5.0 4 5.0 5 5 5.0 6NaN 5NaN NaN
P04 3.0 3 4.0 4NaN NaNNaN 3 3 5 5.0
P05 3.0 3 4.0 4 4 5.0 4 6NaNNaN NaN
P06 3.5 3 3.0 4 3 NaN 5 5 4 4 NaN
P07 4.0 4 4.0 4 5 5.0NaN 4 5 4 4.9
P08 3.0 3 4.0 4 3 5.5 5 5 5 4 5.0
P09 1.0 1 2.0 3 2 2.0 3 2NaNNaN NaN
P10 2.0 3 3.0 3 4 4.0 5 5 5 5 NaN
P11 5.0 3 4.0 4NaN 4.0 4 4 5 4 5.0
P12 4.0 4 5.0 5 4 4.0 5 4 6 5 NaN
P13 3.0 3 3.0NaN 3 4.0 4 5NaNNaN NaN
P14 3.0 3 3.0 4 4 4.0 4 3NaNNaN NaN
P15 2.0 2 3.0 3 3 3.0 3 4 3 3 5.0
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 444, + "text": [ + " Homework 1, Jan13 Homework 2, Jan14 Homework 3, Jan15 \\\n", + "Name \n", + "P01 4.0 4 5.0 \n", + "P02 3.5 5 4.5 \n", + "P03 5.0 4 5.0 \n", + "P04 3.0 3 4.0 \n", + "P05 3.0 3 4.0 \n", + "P06 3.5 3 3.0 \n", + "P07 4.0 4 4.0 \n", + "P08 3.0 3 4.0 \n", + "P09 1.0 1 2.0 \n", + "P10 2.0 3 3.0 \n", + "P11 5.0 3 4.0 \n", + "P12 4.0 4 5.0 \n", + "P13 3.0 3 3.0 \n", + "P14 3.0 3 3.0 \n", + "P15 2.0 2 3.0 \n", + "\n", + " Mystery Word, Jan 20 Currency, Jan 21 Blackjack1, Jan 22 \\\n", + "Name \n", + "P01 5 4 5.5 \n", + "P02 5 5 5.0 \n", + "P03 5 5 5.0 \n", + "P04 4 NaN NaN \n", + "P05 4 4 5.0 \n", + "P06 4 3 NaN \n", + "P07 4 5 5.0 \n", + "P08 4 3 5.5 \n", + "P09 3 2 2.0 \n", + "P10 3 4 4.0 \n", + "P11 4 NaN 4.0 \n", + "P12 5 4 4.0 \n", + "P13 NaN 3 4.0 \n", + "P14 4 4 4.0 \n", + "P15 3 3 3.0 \n", + "\n", + " Blackjack2, Jan26 Random Art, Jan 27 Charting PigSim Traffic Sim I \n", + "Name \n", + "P01 NaN 5 NaN NaN NaN \n", + "P02 5 5 NaN 5 NaN \n", + "P03 6 NaN 5 NaN NaN \n", + "P04 NaN 3 3 5 5.0 \n", + "P05 4 6 NaN NaN NaN \n", + "P06 5 5 4 4 NaN \n", + "P07 NaN 4 5 4 4.9 \n", + "P08 5 5 5 4 5.0 \n", + "P09 3 2 NaN NaN NaN \n", + "P10 5 5 5 5 NaN \n", + "P11 4 4 5 4 5.0 \n", + "P12 5 4 6 5 NaN \n", + "P13 4 5 NaN NaN NaN \n", + "P14 4 3 NaN NaN NaN \n", + "P15 3 4 3 3 5.0 " + ] + } + ], + "prompt_number": 444 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework.columns = ['M1', 'T1', 'W1', 'Th1', 'T2', 'W2', 'Th2', 'M3', 'T3', 'W3', 'Th3']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 445 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework_means = python_homework.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 453 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(python_homework_means)\n", + "python_homework_means.plot()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "M1 3.266667\n", + "T1 3.200000\n", + "W1 3.766667\n", + "Th1 4.071429\n", + "T2 3.769231\n", + "W2 4.307692\n", + "Th2 4.416667\n", + "M3 4.285714\n", + "T3 4.555556\n", + "W3 4.333333\n", + "Th3 4.980000\n", + "dtype: float64\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 526 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Mean difficulty for homework per week per class:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 464, + "text": [ + "M1 3.166667\n", + "T1 3.700000\n", + "W1 3.900000\n", + "T2 4.400000\n", + "W2 3.533333\n", + "Th2 3.733333\n", + "M3 3.766667\n", + "T3 3.400000\n", + "W3 3.900000\n", + "Th3 3.866667\n", + "M4 3.730769\n", + "dtype: float64" + ] + } + ], + "prompt_number": 464 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "rhweek1_means = ruby_homework_means[['M1', 'T1', 'W1']].mean()\n", + "rhweek2_means = ruby_homework_means[['T2', 'W2', 'Th2']].mean()\n", + "rhweek3_means = ruby_homework_means[['M3', 'T3', 'W3', 'Th3']].mean()\n", + "rhweek4_means = ruby_homework_means['M4'].mean()\n", + "rhweekly_means = [rhweek1_means, rhweek2_means, rhweek3_means, rhweek4_means]\n", + "print(\"Ruby Homework Weekly Means:\")\n", + "for _ in range(4):\n", + " print(\"Week {}: {}\".format((_+1), rhweekly_means[_]))\n", + "\n", + "plt.plot(rhweekly_means)\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Ruby Homework Weekly Means:\n", + "Week 1: 3.588888888888889\n", + "Week 2: 3.8888888888888893\n", + "Week 3: 3.7333333333333334\n", + "Week 4: 3.730769230769231\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 527 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 467, + "text": [ + "M1 3.266667\n", + "T1 3.200000\n", + "W1 3.766667\n", + "Th1 4.071429\n", + "T2 3.769231\n", + "W2 4.307692\n", + "Th2 4.416667\n", + "M3 4.285714\n", + "T3 4.555556\n", + "W3 4.333333\n", + "Th3 4.980000\n", + "dtype: float64" + ] + } + ], + "prompt_number": 467 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "phweek1_means = python_homework_means[['M1', 'T1', 'W1', 'Th1']].mean()\n", + "phweek2_means = python_homework_means[['T2', 'W2', 'Th2']].mean()\n", + "phweek3_means = python_homework_means[['M3', 'T3', 'W3', 'Th3']].mean()\n", + "phweekly_means = [phweek1_means, phweek2_means, phweek3_means]\n", + "print(\"Python Homework Weekly Means:\")\n", + "for _ in range(3):\n", + " print(\"Week {}: {}\".format((_+1), phweekly_means[_]))\n", + "\n", + "plt.plot(phweekly_means)\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python Homework Weekly Means:\n", + "Week 1: 3.5761904761904764\n", + "Week 2: 4.164529914529915\n", + "Week 3: 4.538650793650794\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 528 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Mean Difficulty for Lectures and Homework per weekday:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(ruby_lecture.head(3))\n", + "print(python_lecture.head(3))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " M1 T1 W1 Th1 T2 W2 Th2 M3 T3 W3 Th3 M4\n", + "R01 2 2 4 3 3 5 2 3 4 4 4 3\n", + "R02 3 3.5 4.5 4 4.5 4.5 3.5 6 4 5 4.5 5\n", + "R03 3 4.5 4 3.5 6 4.5 4 5 5 4.5 4 5\n", + " M1 T1 W1 Th1 T2 W2 Th2 F2 M3 T3 W3 Th3 M4\n", + "Name \n", + "P01 3 3 4 5.0 4 4 4 5.5 4 NaN NaN NaN NaN\n", + "P02 4 3 4 4.5 5 5 NaN NaN 5 NaN 5 5 NaN\n", + "P03 NaN 3 5 5.0 5 5 NaN 5.0 NaN 5 5 NaN NaN\n" + ] + } + ], + "prompt_number": 539 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'F2'", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mruby_lecture\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'F2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1778\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1779\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1780\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1782\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1785\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1786\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1787\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1066\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1068\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1069\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1070\u001b[0m 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"\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3807)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3687)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12310)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/bretrunestad/.pyenv/versions/charting/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12261)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'F2'" + ] + } + ], + "prompt_number": 547 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 546, + "text": [ + "Empty DataFrame\n", + "Columns: [M1, T1, W1, Th1, T2, W2, Th2, M3, T3, W3, Th3, M4, F2]\n", + "Index: []" + ] + } + ], + "prompt_number": 546 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(ruby_homework.head(3))\n", + "print(python_homework.head(3))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + " M1 T1 W1 T2 W2 Th2 M3 T3 W3 Th3 M4\n", + "R01 4 3 3 4 3 3 4 3 4.5 4 4.5\n", + "R02 3 4 4.5 4 4 4 2.5 3 4.5 4 4\n", + "R03 4 4 5.5 5 4 4.5 4 4 3 4 3.5\n", + " M1 T1 W1 Th1 T2 W2 Th2 M3 T3 W3 Th3\n", + "Name \n", + "P01 4.0 4 5.0 5 4 5.5 NaN 5 NaN NaN NaN\n", + "P02 3.5 5 4.5 5 5 5.0 5 5 NaN 5 NaN\n", + "P03 5.0 4 5.0 5 5 5.0 6 NaN 5 NaN NaN\n" + ] + } + ], + "prompt_number": 540 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Sample Students from each class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our sample students: R06 and P05" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "R06 is interesting! R06 had a crazy first week where things got very difficult very quickly in terms of understanding the lectures. After that, things calmed down and they haven't been past a difficulty level of 4.5." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "R06_lecture = ruby_lecture.loc['R06']\n", + "R06_lecture.plot()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 529, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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4AT8fLdOQG7c6J5Zgfy8OFNXTP6TOeFNh3OHgV/vP8dr7Vfh4WfiLhxeyNi9h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\nmax4tFTZmdF9W35t92md6OhV7bxCfXZVVVSPDQB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+ "text": [ + "" + ] + } + ], + "prompt_number": 529 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "R06's homework rankings are somewhat reflective of the lectures. Again, R06's first week was ROUGH. But after that, the homeworks have really leveled out and past Wednesday of week 2 R06 hasn't reported a homework difficulty greater than 4. This seems like a strong indictment of Mason's abilities to hand out significantly challenging assignments." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "R06_homework = ruby_homework.loc['R06']\n", + "R06_homework.plot()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 530, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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rF97KijVljhw7PmrrtvZ4dGiH+uxAqtxldVtGimMy7WLUhk71WYabE0N72PXu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\nf1qW5Xs1zQiDdorXF0kyqSi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+ "text": [ + "" + ] + } + ], + "prompt_number": 530 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "P06_lecture = python_lecture.loc['P06']\n", + "P06_lecture.plot()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 531, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 531 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "P06_homework = python_homework.loc['P06']\n", + "P06_homework.plot()\n", + "plt.xticks()\n", + "plt.ylim(ymin=1, ymax=6)\n", + "plt.plot()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 532, + "text": [ + "[]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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