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15 rows \u00d7 24 columns

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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, + "text": [ + " Lecture 1, Jan12 Homework 1, Jan13 Lecture 2, Jan 13 \\\n", + "Name \n", + "P01 3.0 4.0 3 \n", + "P02 4.0 3.5 3 \n", + "P03 NaN 5.0 3 \n", + "P04 3.0 3.0 2 \n", + "P05 NaN 3.0 3 \n", + "P06 3.0 3.5 3 \n", + "P07 3.5 4.0 3 \n", + "P08 2.0 3.0 2 \n", + "P09 NaN 1.0 1 \n", + "P10 2.0 2.0 2 \n", + "P11 2.0 5.0 4 \n", + "P12 3.5 4.0 4 \n", + "P13 2.5 3.0 3 \n", + "P14 3.0 3.0 3 \n", + "P15 2.0 2.0 2 \n", + "\n", + " Homework 2, Jan14 Lecture 3, Jan 14 Homework 3, Jan15 \\\n", + "Name \n", + "P01 4 4.0 5.0 \n", + "P02 5 4.0 4.5 \n", + "P03 4 5.0 5.0 \n", + "P04 3 4.0 4.0 \n", + "P05 3 3.0 4.0 \n", + "P06 3 3.0 3.0 \n", + "P07 4 5.0 4.0 \n", + "P08 3 4.0 4.0 \n", + "P09 1 2.0 2.0 \n", + "P10 3 NaN 3.0 \n", + "P11 3 5.0 4.0 \n", + "P12 4 4.5 5.0 \n", + "P13 3 3.0 3.0 \n", + "P14 3 4.0 3.0 \n", + "P15 2 3.0 3.0 \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", + "P03 5.0 5 5 \n", + "P04 4.0 4 5 \n", + "P05 4.0 4 5 \n", + "P06 4.0 4 3 \n", + "P07 4.5 4 5 \n", + "P08 3.0 4 3 \n", + "P09 2.0 3 3 \n", + "P10 3.0 3 3 \n", + "P11 4.0 4 4 \n", + "P12 5.0 5 5 \n", + "P13 4.0 NaN 3 \n", + "P14 4.0 4 4 \n", + "P15 3.0 3 3 \n", + "\n", + " Currency, Jan 21 ... Blackjack2, Jan26 Lecture 9, Jan26 \\\n", + "Name ... \n", + "P01 4 ... NaN 4 \n", + "P02 5 ... 5 5 \n", + "P03 5 ... 6 NaN \n", + "P04 NaN ... NaN 1 \n", + "P05 4 ... 4 3 \n", + "P06 3 ... 5 4 \n", + "P07 5 ... NaN 5 \n", + "P08 3 ... 5 5 \n", + "P09 2 ... 3 3 \n", + "P10 4 ... 5 4 \n", + "P11 NaN ... 4 4 \n", + "P12 4 ... 5 4 \n", + "P13 3 ... 4 NaN \n", + "P14 4 ... 4 4 \n", + "P15 3 ... 3 3 \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", + "P03 NaN 5.0 5 5 \n", + "P04 3 1.0 3 5 \n", + "P05 6 NaN NaN NaN \n", + "P06 5 3.0 4 4 \n", + "P07 4 4.9 5 4 \n", + "P08 5 5.0 5 4 \n", + "P09 2 NaN NaN NaN \n", + "P10 5 4.0 5 4 \n", + "P11 4 4.0 5 5 \n", + "P12 4 4.0 6 5 \n", + "P13 5 3.0 NaN NaN \n", + "P14 3 NaN NaN NaN \n", + "P15 4 3.0 3 3 \n", + "\n", + " PigSim Lecture12, Jan29 Traffic Sim I Lecture13,Feb2 \n", + "Name \n", + "P01 NaN NaN NaN NaN \n", + "P02 5 5.0 NaN NaN \n", + "P03 NaN NaN NaN NaN \n", + "P04 5 5.0 5.0 NaN \n", + "P05 NaN NaN NaN NaN \n", + "P06 4 NaN NaN NaN \n", + "P07 4 4.9 4.9 NaN \n", + "P08 4 4.0 5.0 5 \n", + "P09 NaN NaN NaN NaN \n", + "P10 5 4.0 NaN NaN \n", + "P11 4 5.0 5.0 NaN \n", + "P12 5 6.0 NaN NaN \n", + "P13 NaN NaN NaN NaN \n", + "P14 NaN NaN NaN NaN \n", + "P15 3 3.0 5.0 NaN \n", + "\n", + "[15 rows x 24 columns]" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python.index" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 20, + "text": [ + "Index(['P01', 'P02', 'P03', 'P04', 'P05', 'P06', 'P07', 'P08', 'P09', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15'], dtype='object')" + ] + } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby = pd.ExcelFile(\"cohort_3_rails.xlsx\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 22 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby.sheet_names" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 23, + "text": [ + "['Lecture Score', 'HW Score']" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture = ruby.parse(\"Lecture Score\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture = ruby_lecture.drop(['Average', 'StDev'])\n", + "ruby_lecture = ruby_lecture.dropna(how=\"all\")\n", + "ruby_lecture.drop(ruby_lecture.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_lecture" + ], + "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 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": 25, + "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": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework = ruby.parse(\"HW Score\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 27, + "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": 27 + }, + { + "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": 28, + "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": 28 + }, + { + "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": 29, + "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": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 30, + "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": 30 + }, + { + "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": 31 + }, + { + "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": 32, + "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": 32 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture_means = ruby_lecture.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 33 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(\"Ruby Lecture Means\")\n", + "print(ruby_lecture_means)\n", + "ruby_lecture_means.plot()\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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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 36, + "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": 36 + }, + { + "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', \n", + " 'Lecture 6, 21', 'Lecture 7, Jan 22', 'Lecture 8, Jan 23', 'Lecture 9, Jan26', 'Lecture10, Jan27', \n", + " 'Lecture11, Jan28', 'Lecture12, Jan29', 'Lecture13,Feb2']]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "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": 38 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture_means = python_lecture.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 39 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(\"Python Lecture Means\")\n", + "print(python_lecture_means)\n", + "python_lecture_means.plot()\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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+ "text": [ + "" + ] + } + ], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_lecture_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 41, + "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": 41 + }, + { + "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.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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6fzlty7h7hZmNAZ4lfCDwkLuvqXrBk7vPNbOBZvYesAMYnssa8imT4wMuB0ab\nWQWwE7gqsoKzZGaPA2cAnczsQ+Auwqqgoj93UPfxUcTnDjgVGAKsMrOvQuEOoAfE4vzVeXwU9/mr\n82LRbM+fLmISEYkh3WZPRCSGFO4iIjGkcBcRiSGFu4hIDCncRURiSOEuIhJDCncRkRhSuIuIxND/\nA5iqDwdsBy+0AAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 42 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_lecture_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 43, + "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": 43 + }, + { + "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.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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+ "text": [ + "" + ] + } + ], + "prompt_number": 45 + }, + { + "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": 46, + "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": 46 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework.columns = ['M1', 'T1', 'W1', 'T2', 'W2', 'Th2', 'M3', 'T3', 'W3', 'Th3', 'M4']\n", + "ruby_homework = ruby_homework.drop(np.NAN)\n", + "ruby_homework_means = ruby_homework.mean()\n", + "print(\"Ruby Homework Means\")\n", + "print(ruby_homework_means)\n", + "ruby_homework_means.plot()\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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+ "text": [ + "" + ] + } + ], + "prompt_number": 48 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework = python[['Homework 1, Jan13', 'Homework 2, Jan14', 'Homework 3, Jan15', 'Mystery Word, Jan 20',\n", + " 'Currency, Jan 21', 'Blackjack1, Jan 22', 'Blackjack2, Jan26', 'Random Art, Jan 27',\n", + " 'Charting', 'PigSim', 'Traffic Sim I',]]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 49 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework_means = python_homework.mean()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 50 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print(python_homework_means)\n", + "python_homework_means.plot()\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Homework 1, Jan13 3.266667\n", + "Homework 2, Jan14 3.200000\n", + "Homework 3, Jan15 3.766667\n", + "Mystery Word, Jan 20 4.071429\n", + "Currency, Jan 21 3.769231\n", + "Blackjack1, Jan 22 4.307692\n", + "Blackjack2, Jan26 4.416667\n", + "Random Art, Jan 27 4.285714\n", + "Charting 4.555556\n", + "PigSim 4.333333\n", + "Traffic Sim I 4.980000\n", + "dtype: float64\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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F9rExqojly+Gkk6BVK3jkkfz6rlRjzJ8PBx3kWo833BCOTb38spspaIxZlY1R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\nwSC28clzoIbnOwJ4R1XfDfb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+ "text": [ + "" + ] + } + ], + "prompt_number": 55 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ruby_homework_means" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 56, + "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": 56 + }, + { + "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": 57 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "python_homework_means\n", + "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=7)\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python Homework Weekly Means:\n", + "Week 1: nan\n", + "Week 2: nan\n", + "Week 3: nan\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 62 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "lectures = df.map(lambda x: True if re.search(r\"Lecture\") else False)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'df' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\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[0mlectures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr\"Lecture\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" + ] + } + ], + "prompt_number": 63 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file