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Lecture 2, Jan 13 2 3.5 4.5 4 3 4.5 4 3 3 3... 3 3.0 2 1 2 4 4.0 3.0 3 2
Lecture 3, Jan 14 4 4.5 4 4 5 6 5 4 4 3.5... 3 5.0 4 2NaN 5 4.5 3.0 4 3
Lecture 4, Jan 15 3 4 3.5 4 4.5 3.5 6 3 4.5 3.5... 4 4.5 3 2 3 4 5.0 4.0 4 3
Lecture 5, Jan 20 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN... 3 5.0 3 3 3 4 5.0 3.0 4 3
Lecture 6, 21 3 4.5 6 5 3 4.5 4 3 3 4... 5 5.0 5 3 4NaN 4.0 3.0 4 3
Lecture 7, Jan 22 5 4.5 4.5 5 4 3.5 4 4 5 3... 5 4.0 4 3 4 4 NaN 4.0NaN 3
Lecture 8, Jan 23 2 3.5 4 4 3 3 3.5 3 4 2... 5 5.0 5 3 5 4 5.5 4.0NaN 3
Lecture 9, Jan26 3 6 5 5 5 4.5 4 3.5 3.5 3... 4 5.0 5 3 4 4 4.0 NaN 4 3
Lecture10, Jan27 4 4 5 4 3 4.5 4 3 4 2.5... 3 4.9 5NaN 4 4 4.0 3.0NaN 3
Lecture11, Jan28 4 5 4.5 6 3 4 4 4 3 3... 4 4.0 4NaN 4 5 5.0 NaNNaN 3
Lecture12, Jan29 4 4.5 4 5 4 3.5 4 4 3 2...NaN 4.9 4NaN 4 5 6.0 NaNNaN 3
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13 rows \u00d7 30 columns

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NaN \n", + "\n", + " 6 7 8 9 10 11 12 13 14 \n", + "Lecture 1, Jan12 3.5 2 NaN 2 2 3.5 2.5 3 2 \n", + "Lecture 2, Jan 13 3.0 2 1 2 4 4.0 3.0 3 2 \n", + "Lecture 3, Jan 14 5.0 4 2 NaN 5 4.5 3.0 4 3 \n", + "Lecture 4, Jan 15 4.5 3 2 3 4 5.0 4.0 4 3 \n", + "Lecture 5, Jan 20 5.0 3 3 3 4 5.0 3.0 4 3 \n", + "Lecture 6, 21 5.0 5 3 4 NaN 4.0 3.0 4 3 \n", + "Lecture 7, Jan 22 4.0 4 3 4 4 NaN 4.0 NaN 3 \n", + "Lecture 8, Jan 23 5.0 5 3 5 4 5.5 4.0 NaN 3 \n", + "Lecture 9, Jan26 5.0 5 3 4 4 4.0 NaN 4 3 \n", + "Lecture10, Jan27 4.9 5 NaN 4 4 4.0 3.0 NaN 3 \n", + "Lecture11, Jan28 4.0 4 NaN 4 5 5.0 NaN NaN 3 \n", + "Lecture12, Jan29 4.9 4 NaN 4 5 6.0 NaN NaN 3 \n", + "Lecture13,Feb2 NaN 5 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "[13 rows x 30 columns]" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import datetime\n", + "date_list = pd.date_range(\"1/12/2015\", \"1/15/2015\",freq = 'D') + pd.date_range(\"1/20/2015\", \"1/23/2015\",freq = 'D') + pd.date_range(\"1/26/2015\", \"1/29/2015\",freq = 'D')\n", + " " + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "date_list = date_list.append(pd.DatetimeIndex([datetime.date(2015,2,2)]))\n", + "merged_data.index = date_list\n", + "date_list " + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, + "text": [ + "\n", + "[2015-01-12, ..., 2015-02-02]\n", + "Length: 13, Freq: None, Timezone: None" + ] + } + ], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "merged_data.mean(axis = 1).plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 99, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 100 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_mean = np.mean(merged_data.mean(axis=0)[:15])\n", + "the_sd = np.std(merged_data.mean(axis=0)[:15])\n", + "print(\"ruby mean : {} standard deviation: {}\".format(the_mean, the_sd))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python mean : 3.7169191919191915 standard deviation: 0.5510054717467954\n" + ] + } + ], + "prompt_number": 51 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_mean = np.mean(merged_data.mean(axis=0)[15:])\n", + "the_sd = np.std(merged_data.mean(axis=0)[15\n", + " :])\n", + "print(\"Python mean : {} standard deviation: {}\".format(the_mean, the_sd))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Python mean : 3.822187812187813 standard deviation: 0.616701614454918\n" + ] + } + ], + "prompt_number": 52 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python class has slightly higher difficulty ratings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting difficutly rating of one student over time" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "merged_data[\"R04\"].plot(kind = \"line\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 104, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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cl+WoWeVyu67L2aH3+b17Lno/fXyqsnn8Q+Ewt9XU0dDcztVzJlGYH72xMfUiGyv/3uZ2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\noH3+iMgNeM2SnwCIFpiIQH5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