{ "metadata": { "name": "", "signature": "sha256:ea8ccb8121c099d7558d09fc639aaec7964eb7d634834ff7191b6ae1bb24283e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Number munging: vectors, Pandas, probabilities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#this is the more or less raw version of what we did today. I've added a few comments here and there to eliminate confusion." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Render our plots inline\n", "%matplotlib inline\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n", "plt.rcParams['figure.figsize'] = (15, 5)\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#get our data--temporary home--these will disappear soon\n", "!wget http://www.columbia.edu/~mj340/ml-100k.tar.gz" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://www.columbia.edu/~mj340/HMXPC13_DI_v2_5-14-14.csv.gz" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!gunzip HMXPC13_DI_v2_5-14-14.csv.gz" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!tar -zxvf ml-100k.tar.gz" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "#Our ritual: Exploratory data analysis\n", "\n", "\n", "> Exploratory data analysis (EDA) seeks to reveal structure, or simple descriptions, in data. We look at numbers and graphs and try to find patterns. \n", " - Persi Diaconis, \"Theories of Data Analysis: From Magical Thinking Through Classical statistics\"\n", "\n", "> . . . proceeding via a \u2018dustbowl\u2019 empiricism is dangerous at worst and foolish at best . . . . The purely empirical approach is particularly dangerous in an age when computers and packaged programs are readily available, since there is temptation to substitute immediate empirical analysis for more analytic thought and theory building.\n", " - Einhorn, \u201cAlchemy in the Behavioral Sciences,\u201d 1972\n", "\n", ">. . . we can view the techniques of EDA as a ritual designed to reveal patters in a data set. Thus, we may believe that naturally occurring data sets contain structure, that EDA is a useful vehicle for revealing the structure. . . . If we make no attempt to check whether the structure could have arisen by chance, and tend to accept the findinds as gospel, then the ritual comes close to magical thinking. ... a controlled form of magical thinking--in the guise of 'working hypothesis'--is a basic ingredient of scientific progress. \n", " - Persi Diaconis, \"Theories of Data Analysis: From Magical Thinking Through Classical statistics\"\n", "\n", "#From data to databases to data mining\n", "- move from accessing and manipulating data to performing ever more complicated *queries* on our data\n", "\n", "\n", "#Pandas first-line python tool for EDA\n", "- rich data structures\n", "- powerful ways to slice, dice, reformate, fix, and eliminate data\n", " - taste of what can do\n", "- rich queries like databases\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Pandas: charismatic megafauna" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "CPI={\"2010\": 218.056, \"2011\": 224.939, \"2012\": 229.594, \"2013\": 232.957} #http://www.bls.gov/cpi/home.htm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "CPI[\"2011\"]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "224.939" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CPI provides \"a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.\" A *higher* number means it costs more to buy the same goods. It was set to 100 in 1982-4.\n", "\n", "We can thus use it to measure the effects of inflation on the value of houses in a toy example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "CPI_series=pd.Series(CPI)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "CPI_series" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "2010 218.056\n", "2011 224.939\n", "2012 229.594\n", "2013 232.957\n", "dtype: float64" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "House_sale_mean={\"2010\":100000, \"2011\":100000, \"2012\":100000, \"2013\":100000}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "house_sale_series=pd.Series(House_sale_mean)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "house_sale_series" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "2010 100000\n", "2011 100000\n", "2012 100000\n", "2013 100000\n", "dtype: int64" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "(house_sale_series/CPI_series)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "2010 458.597791\n", "2011 444.564971\n", "2012 435.551452\n", "2013 429.263770\n", "dtype: float64" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "(house_sale_series/CPI_series)*100" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "2010 45859.779139\n", "2011 44456.497095\n", "2012 43555.145169\n", "2013 42926.376971\n", "dtype: float64" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "inflation_adjusted=(house_sale_series/CPI_series)*100" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "inflation_adjusted.plot(title=\"Sorry Kids! Blame X, where X is the guy in office\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=x-large. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n", " UserWarning)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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nMeuzo1j2UwGKK+o6OrxOIT4+vqNDoE6E+ULWYq6QLZgvZC3mCrUnqyp7q1at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\nAGwLx/VBe1Tt4bg8jpKxWUCX6sZmA0OVtIUACoE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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataframes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df=pd.read_csv('./HMXPC13_DI_v2_5-14-14.csv', sep=',')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Note that df is the name of our datastructure, not the function to make something into a dataframe, which is pd.DataFrame. I failed to explain this accurately in class. So df[\"blah\"] is indexing on the particular dataframe df, not a function df on blah." ] }, { "cell_type": "code", "collapsed": false, "input": [ "df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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1 HarvardX/CS50x/2012 MHxPC130442623 1 1 0 0 United States NaN NaN NaN 0 2012-10-15 NaN NaN 9NaN 1 0NaN 1
2 HarvardX/CB22x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2013-02-08 2013-11-17 NaN 16NaNNaN 0NaN 1
3 HarvardX/CS50x/2012 MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2012-09-17 NaN NaN 16NaNNaN 0NaN 1
4 HarvardX/ER22x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2012-12-19 NaN NaN 16NaNNaN 0NaN 1
5 HarvardX/PH207x/2012_Fall MHxPC130275857 1 1 1 0 United States NaN NaN NaN 0 2012-09-17 2013-05-23 502 16 50 12 0NaNNaN
6 HarvardX/PH278x/2013_Spring MHxPC130275857 1 0 0 0 United States NaN NaN NaN 0 2013-02-08 NaN NaN 16NaNNaN 0NaN 1
7 HarvardX/CB22x/2013_Spring MHxPC130539455 1 1 0 0 France NaN NaN NaN 0 2013-01-01 2013-05-14 42 6NaN 3 0NaNNaN
8 HarvardX/CB22x/2013_Spring MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2013-02-18 2013-03-17 70 3NaN 3 0NaNNaN
9 HarvardX/CS50x/2012 MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2012-10-20 NaN NaN 12NaN 3 0NaN 1
10 HarvardX/ER22x/2013_Spring MHxPC130088379 1 1 0 0 United States NaN NaN NaN 0 2013-02-23 2013-06-14 17 2NaN 2 0NaNNaN
11 HarvardX/ER22x/2013_Spring MHxPC130198098 1 1 0 0 United States NaN NaN NaN 0 2013-06-17 2013-06-17 32 1NaN 3 0NaNNaN
12 HarvardX/CB22x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0.07 2013-01-24 2013-08-03 175 9NaN 7 0NaNNaN
13 HarvardX/CS50x/2012 MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2013-06-27 NaN NaN 2NaN 2 0NaN 1
14 HarvardX/ER22x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2012-12-19 2013-08-17 78 5NaN 4 0NaNNaN
15 HarvardX/PH207x/2012_Fall MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2012-07-26 2013-01-16 75 14 5 2 0NaNNaN
16 HarvardX/PH278x/2013_Spring MHxPC130024894 1 1 0 0 United States NaN NaN NaN 0 2013-07-30 2013-08-27 11 2 2 1 0NaNNaN
17 HarvardX/CS50x/2012 MHxPC130080986 1 1 0 0 United States NaN NaN NaN 0 2012-10-15 NaN NaN 11NaN 1 0NaN 1
18 HarvardX/PH207x/2012_Fall MHxPC130080986 1 1 0 0 United States NaN NaN NaN 0 2012-10-25 2012-12-04 56 11 1 2 1NaNNaN
19 HarvardX/CS50x/2012 MHxPC130063375 1 1 0 0 Unknown/Other NaN NaN NaN 0 2012-10-19 NaN NaNNaNNaN 1 0NaN 1
20 HarvardX/CS50x/2012 MHxPC130094371 1 1 0 0 United States NaN NaN NaN 0 2013-03-03 2013-03-03 7 1NaN 2 0NaNNaN
21 HarvardX/CS50x/2012 MHxPC130229084 1 1 0 0 Mexico NaN NaN NaN 0 2012-10-15 NaN NaNNaNNaN 1 0NaN 1
22 HarvardX/CS50x/2012 MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-10-24 NaN NaN 2NaN 1 0NaN 1
23 HarvardX/ER22x/2013_Spring MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-12-20 2013-05-18 15 2NaN 2 0NaNNaN
24 HarvardX/CS50x/2012 MHxPC130417650 1 1 0 0 Australia NaN NaN NaN 0 2012-10-29 2013-03-04 1 1NaN 2 0NaNNaN
25 HarvardX/CS50x/2012 MHxPC130506580 1 0 0 0 United States NaN NaN NaN 0 2012-09-04 NaN NaNNaNNaNNaN 0NaNNaN
26 HarvardX/CS50x/2012 MHxPC130298257 1 0 0 0 United States NaN NaN NaN 0 2012-09-05 NaN NaNNaNNaN 3 0NaN 1
27 HarvardX/CS50x/2012 MHxPC130500569 1 1 0 0 United States NaN NaN NaN 0 2012-10-22 2013-03-30 6 1NaN 5 0NaNNaN
28 HarvardX/CS50x/2012 MHxPC130466479 1 1 0 0 Unknown/Other NaN NaN NaN 0 2013-01-07 NaN NaNNaNNaN 1 0NaN 1
29 HarvardX/CB22x/2013_Spring MHxPC130340959 1 1 0 0 United States NaN NaN NaN 0.05 2013-02-11 2013-04-06 285 8NaN 4 0NaNNaN
...............................................................
641108 MITx/6.002x/2013_Spring MHxPC130140735 1 1 0 0 United States Bachelor's 1991 m NaN 2013-09-07 2013-09-07 59 1 5 3 0NaNNaN
641109 MITx/6.00x/2013_Spring MHxPC130493130 1 0 0 0 United Kingdom Master's 1977 m NaN 2013-09-07 NaN NaNNaNNaN 2 0NaN 1
641110 MITx/6.00x/2013_Spring MHxPC130400592 1 1 0 0 Other Europe Secondary 1992 m NaN 2013-09-07 2013-09-07 395 1 51 4 0NaNNaN
641111 MITx/6.00x/2013_Spring MHxPC130109892 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 49 1 14 2 0NaNNaN
641112 MITx/14.73x/2013_Spring MHxPC130183007 1 0 0 0 India Master's 1985 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641113 MITx/8.MReV/2013_Summer MHxPC130261281 1 1 0 0 India Secondary 1994 m 0 2013-09-07 2013-09-07 8 1NaN 1 0NaNNaN
641114 MITx/6.00x/2013_Spring MHxPC130481990 1 1 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 22 1 5 1 0NaNNaN
641115 MITx/6.00x/2013_Spring MHxPC130528581 1 0 0 0 United States Bachelor's 1990 f NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641116 MITx/14.73x/2013_Spring MHxPC130555418 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641117 MITx/6.002x/2013_Spring MHxPC130408810 1 0 0 0 India Secondary 1993 m NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641118 MITx/6.00x/2013_Spring MHxPC130040184 1 0 0 0 United States Secondary 1991 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641119 MITx/6.002x/2013_Spring MHxPC130566049 1 0 0 0 Other Europe Master's 1982 m NaN 2013-09-07 2013-09-07 2 1NaN 2 0NaNNaN
641120 MITx/8.MReV/2013_Summer MHxPC130374105 1 1 0 0 India Bachelor's 1992 m 0 2013-09-07 2013-09-07 49 1NaN 1 0NaNNaN
641121 MITx/6.00x/2013_Spring MHxPC130282999 1 0 0 0 Other Europe Master's 1979 m NaN 2013-09-07 NaN NaNNaNNaN 7 0NaN 1
641122 MITx/8.MReV/2013_Summer MHxPC130556398 1 0 0 0 India Bachelor's 1985 m 0 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641123 MITx/6.00x/2013_Spring MHxPC130573334 1 0 0 0 Spain Bachelor's 1989 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641124 MITx/6.00x/2013_Spring MHxPC130505931 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 59 1NaN 2 0NaNNaN
641125 MITx/6.002x/2013_Spring MHxPC130280976 1 0 0 0 United States Bachelor's NaN m NaN 2013-09-07 2013-09-07 2 1NaNNaN 0NaNNaN
641126 MITx/6.00x/2013_Spring MHxPC130137331 1 1 0 0 United States Secondary 1992 m NaN 2013-09-07 2013-09-07 251 1 77 4 0NaNNaN
641127 MITx/6.002x/2013_Spring MHxPC130271624 1 0 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641128 MITx/14.73x/2013_Spring MHxPC130256541 1 1 0 0 United States Master's 1982 m NaN 2013-09-07 2013-09-07 51 1 1 1 0NaNNaN
641129 MITx/6.00x/2013_Spring MHxPC130021638 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 NaN NaNNaNNaNNaN 0NaNNaN
641130 MITx/14.73x/2013_Spring MHxPC130591057 1 0 0 0 Canada Bachelor's NaN f NaN 2013-09-07 2013-09-07 6 1NaNNaN 0NaNNaN
641131 MITx/8.02x/2013_Spring MHxPC130226305 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 2013-09-07 11 1NaN 2 0NaNNaN
641132 MITx/6.002x/2013_Spring MHxPC130030805 1 1 0 0 Pakistan Master's 1989 m NaN 2013-09-07 2013-09-07 29 1NaN 1 0NaNNaN
641133 MITx/6.00x/2013_Spring MHxPC130184108 1 1 0 0 Canada Bachelor's 1991 m NaN 2013-09-07 2013-09-07 97 1 4 2 0NaNNaN
641134 MITx/6.00x/2013_Spring MHxPC130359782 1 0 0 0 Other Europe Bachelor's 1991 f NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641135 MITx/6.002x/2013_Spring MHxPC130098513 1 0 0 0 United States Doctorate 1979 m NaN 2013-09-07 2013-09-07 1 1NaNNaN 0NaNNaN
641136 MITx/6.00x/2013_Spring MHxPC130098513 1 1 0 0 United States Doctorate 1979 m NaN 2013-09-07 2013-09-07 74 1 14 1 0NaNNaN
641137 MITx/8.02x/2013_Spring MHxPC130098513 1 0 0 0 United States Doctorate 1979 m NaN 2013-09-07 NaN NaN 1NaNNaN 0NaN 1
\n", "

641138 rows \u00d7 20 columns

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2242 HarvardX/PH207x/2012_Fall MHxPC130024795 1 1 1 1 Other South Asia NaNNaN NaN 0.91 2012-10-07 2013-04-23 8066 32 917 15 0NaNNaN
2243 HarvardX/PH207x/2012_Fall MHxPC130524812 1 1 0 0 Other Africa NaNNaN NaN 0 2012-08-17 2012-10-21 403 3 90 2 0NaNNaN
2244 HarvardX/CS50x/2012 MHxPC130493694 1 0 0 0 United States NaNNaN NaN 0 2012-08-17 NaN NaNNaN NaNNaN 0NaNNaN
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2246 HarvardX/ER22x/2013_Spring MHxPC130527617 1 0 0 0 Germany NaNNaN NaN 0 2013-01-22 NaN NaN 2 NaNNaN 0NaN 1
2247 HarvardX/CS50x/2012 MHxPC130156189 1 0 0 0 India NaNNaN NaN 0 2012-08-17 NaN NaNNaN NaNNaN 0NaNNaN
2248 HarvardX/CS50x/2012 MHxPC130593566 1 1 0 0 India NaNNaN NaN 0 2012-07-25 2013-05-22 1 1 NaN 3 0NaNNaN
2249 HarvardX/CB22x/2013_Spring MHxPC130404169 1 0 0 0 Canada NaNNaN NaN 0 2013-02-14 2013-03-14 4 2 NaNNaN 0NaNNaN
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ " course_id userid_DI registered viewed \\\n", "2240 HarvardX/CS50x/2012 MHxPC130136599 1 0 \n", "2241 HarvardX/ER22x/2013_Spring MHxPC130024795 1 1 \n", "2242 HarvardX/PH207x/2012_Fall MHxPC130024795 1 1 \n", "2243 HarvardX/PH207x/2012_Fall MHxPC130524812 1 1 \n", "2244 HarvardX/CS50x/2012 MHxPC130493694 1 0 \n", "2245 HarvardX/CB22x/2013_Spring MHxPC130527617 1 1 \n", "2246 HarvardX/ER22x/2013_Spring MHxPC130527617 1 0 \n", "2247 HarvardX/CS50x/2012 MHxPC130156189 1 0 \n", "2248 HarvardX/CS50x/2012 MHxPC130593566 1 1 \n", "2249 HarvardX/CB22x/2013_Spring MHxPC130404169 1 0 \n", "\n", " explored certified final_cc_cname_DI LoE_DI YoB gender grade \\\n", "2240 0 0 China NaN NaN NaN 0.0 \n", "2241 0 0 Other South Asia NaN NaN NaN NaN \n", "2242 1 1 Other South Asia NaN NaN NaN 0.91 \n", "2243 0 0 Other Africa NaN NaN NaN 0 \n", "2244 0 0 United States NaN NaN NaN 0 \n", "2245 0 0 Germany NaN NaN NaN 0 \n", "2246 0 0 Germany NaN NaN NaN 0 \n", "2247 0 0 India NaN NaN NaN 0 \n", "2248 0 0 India NaN NaN NaN 0 \n", "2249 0 0 Canada NaN NaN NaN 0 \n", "\n", " start_time_DI last_event_DI nevents ndays_act nplay_video nchapters \\\n", "2240 2012-07-26 NaN NaN NaN NaN NaN \n", "2241 2013-02-06 2013-03-20 25 1 NaN 2 \n", "2242 2012-10-07 2013-04-23 8066 32 917 15 \n", "2243 2012-08-17 2012-10-21 403 3 90 2 \n", "2244 2012-08-17 NaN NaN NaN NaN NaN \n", "2245 2013-01-22 2013-03-14 6 2 NaN 1 \n", "2246 2013-01-22 NaN NaN 2 NaN NaN \n", "2247 2012-08-17 NaN NaN NaN NaN NaN \n", "2248 2012-07-25 2013-05-22 1 1 NaN 3 \n", "2249 2013-02-14 2013-03-14 4 2 NaN NaN \n", "\n", " nforum_posts roles incomplete_flag \n", "2240 0 NaN NaN \n", "2241 0 NaN NaN \n", "2242 0 NaN NaN \n", "2243 0 NaN NaN \n", "2244 0 NaN NaN \n", "2245 0 NaN NaN \n", "2246 0 NaN 1 \n", "2247 0 NaN NaN \n", "2248 0 NaN NaN \n", "2249 0 NaN NaN " ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "df[666] #error on purpose!" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "666", "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[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m666\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1682\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 1683\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\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 1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1686\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/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1689\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 1690\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-> 1691\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 1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1693\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/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1050\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 1051\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\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 1053\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 1054\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2536\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2537\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\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 2538\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2539\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1155\u001b[0m \"\"\"\n\u001b[0;32m-> 1156\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\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[0m\u001b[1;32m 1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\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/usr/local/lib/python2.7/dist-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3353)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3233)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11148)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11101)\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 666" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "df.ix[666] #evaluates to series" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "course_id HarvardX/CS50x/2012\n", "userid_DI MHxPC130297337\n", "registered 1\n", "viewed 0\n", "explored 0\n", "certified 0\n", "final_cc_cname_DI United Kingdom\n", "LoE_DI NaN\n", "YoB NaN\n", "gender NaN\n", "grade 0\n", "start_time_DI 2012-08-17\n", "last_event_DI NaN\n", "nevents NaN\n", "ndays_act NaN\n", "nplay_video NaN\n", "nchapters NaN\n", "nforum_posts 0\n", "roles NaN\n", "incomplete_flag NaN\n", "Name: 666, dtype: object" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "df.ix[[666]] #evaluates to data frame" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
666 HarvardX/CS50x/2012 MHxPC130297337 1 0 0 0 United Kingdom NaNNaN NaN 0 2012-08-17 NaNNaNNaNNaNNaN 0NaNNaN
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19 HarvardX/CS50x/2012 MHxPC130063375 1 1 0 0 Unknown/Other NaN NaN NaN 0 2012-10-19 NaN NaNNaNNaN 1 0NaN 1
20 HarvardX/CS50x/2012 MHxPC130094371 1 1 0 0 United States NaN NaN NaN 0 2013-03-03 2013-03-03 7 1NaN 2 0NaNNaN
21 HarvardX/CS50x/2012 MHxPC130229084 1 1 0 0 Mexico NaN NaN NaN 0 2012-10-15 NaN NaNNaNNaN 1 0NaN 1
22 HarvardX/CS50x/2012 MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-10-24 NaN NaN 2NaN 1 0NaN 1
23 HarvardX/ER22x/2013_Spring MHxPC130300925 1 1 0 0 United States NaN NaN NaN 0 2012-12-20 2013-05-18 15 2NaN 2 0NaNNaN
24 HarvardX/CS50x/2012 MHxPC130417650 1 1 0 0 Australia NaN NaN NaN 0 2012-10-29 2013-03-04 1 1NaN 2 0NaNNaN
26 HarvardX/CS50x/2012 MHxPC130298257 1 0 0 0 United States NaN NaN NaN 0 2012-09-05 NaN NaNNaNNaN 3 0NaN 1
27 HarvardX/CS50x/2012 MHxPC130500569 1 1 0 0 United States NaN NaN NaN 0 2012-10-22 2013-03-30 6 1NaN 5 0NaNNaN
28 HarvardX/CS50x/2012 MHxPC130466479 1 1 0 0 Unknown/Other NaN NaN NaN 0 2013-01-07 NaN NaNNaNNaN 1 0NaN 1
29 HarvardX/CB22x/2013_Spring MHxPC130340959 1 1 0 0 United States NaN NaN NaN 0.05 2013-02-11 2013-04-06 285 8NaN 4 0NaNNaN
33 HarvardX/CS50x/2012 MHxPC130356280 1 1 0 0 India NaN NaN NaN 0 2012-09-27 2013-03-31 3 1NaN 1 0NaNNaN
34 HarvardX/CS50x/2012 MHxPC130328890 1 1 0 0 Australia NaN NaN NaN 0 2012-12-10 2013-02-27 2 1NaN 5 0NaNNaN
36 HarvardX/CB22x/2013_Spring MHxPC130435030 1 1 0 0 Canada NaN NaN NaN 0 2013-02-20 2013-06-29 80 5NaN 2 0NaNNaN
37 HarvardX/CS50x/2012 MHxPC130435030 1 1 0 0 Canada NaN NaN NaN 0 2012-10-13 NaN NaN 1NaN 1 0NaN 1
41 HarvardX/CB22x/2013_Spring MHxPC130542822 1 1 0 0 United States NaN NaN NaN NaN 2012-12-20 2013-03-14 21 3NaN 1 0NaNNaN
43 HarvardX/ER22x/2013_Spring MHxPC130069044 1 0 0 0 United States NaN NaN NaN 0 2013-04-22 2013-04-23 1 1NaN 2 0NaNNaN
...............................................................
641083 MITx/6.00x/2013_Spring MHxPC130238153 1 1 0 0 United States Secondary 1988 m NaN 2013-09-07 2013-09-07 116 1 20 2 0NaNNaN
641084 MITx/6.002x/2013_Spring MHxPC130544641 1 1 0 0 India Bachelor's 1992 m NaN 2013-09-07 2013-09-07 245 1 56 1 0NaNNaN
641085 MITx/8.02x/2013_Spring MHxPC130117789 1 1 0 0 India Less than Secondary 1996 m NaN 2013-09-07 2013-09-07 169 1 45 3 0NaNNaN
641086 MITx/14.73x/2013_Spring MHxPC130122763 1 1 0 0 India Master's 1987 m NaN 2013-09-07 2013-09-07 3 1NaN 1 0NaNNaN
641088 MITx/6.00x/2013_Spring MHxPC130214187 1 0 0 0 India Bachelor's 1984 m NaN 2013-09-07 2013-09-07 1 1NaN 3 0NaNNaN
641089 MITx/6.002x/2013_Spring MHxPC130145710 1 1 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 82 1 16 5 0NaNNaN
641099 MITx/6.00x/2013_Spring MHxPC130455967 1 1 0 0 United States Bachelor's 1988 m NaN 2013-09-07 2013-09-07 242 1 22 5 0NaNNaN
641100 MITx/6.002x/2013_Spring MHxPC130040345 1 1 0 0 India Secondary 1993 f NaN 2013-09-07 2013-09-07 94 1 7 2 0NaNNaN
641101 MITx/6.00x/2013_Spring MHxPC130298117 1 0 0 0 Russian Federation Less than Secondary 1997 m NaN 2013-09-07 NaN NaNNaNNaN 1 0NaN 1
641102 MITx/6.00x/2013_Spring MHxPC130024301 1 1 0 0 Morocco Secondary 1994 m NaN 2013-09-07 2013-09-07 25 1NaN 2 0NaNNaN
641103 MITx/6.00x/2013_Spring MHxPC130097716 1 1 0 0 India Bachelor's 1981 m NaN 2013-09-07 2013-09-07 11 1 2 2 0NaNNaN
641107 MITx/8.02x/2013_Spring MHxPC130347356 1 1 0 0 India Secondary 1994 m NaN 2013-09-07 2013-09-07 153 1 31 2 0NaNNaN
641108 MITx/6.002x/2013_Spring MHxPC130140735 1 1 0 0 United States Bachelor's 1991 m NaN 2013-09-07 2013-09-07 59 1 5 3 0NaNNaN
641109 MITx/6.00x/2013_Spring MHxPC130493130 1 0 0 0 United Kingdom Master's 1977 m NaN 2013-09-07 NaN NaNNaNNaN 2 0NaN 1
641110 MITx/6.00x/2013_Spring MHxPC130400592 1 1 0 0 Other Europe Secondary 1992 m NaN 2013-09-07 2013-09-07 395 1 51 4 0NaNNaN
641111 MITx/6.00x/2013_Spring MHxPC130109892 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 49 1 14 2 0NaNNaN
641113 MITx/8.MReV/2013_Summer MHxPC130261281 1 1 0 0 India Secondary 1994 m 0 2013-09-07 2013-09-07 8 1NaN 1 0NaNNaN
641114 MITx/6.00x/2013_Spring MHxPC130481990 1 1 0 0 India Bachelor's 1989 m NaN 2013-09-07 2013-09-07 22 1 5 1 0NaNNaN
641115 MITx/6.00x/2013_Spring MHxPC130528581 1 0 0 0 United States Bachelor's 1990 f NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641117 MITx/6.002x/2013_Spring MHxPC130408810 1 0 0 0 India Secondary 1993 m NaN 2013-09-07 2013-09-07 2 1NaN 3 0NaNNaN
641119 MITx/6.002x/2013_Spring MHxPC130566049 1 0 0 0 Other Europe Master's 1982 m NaN 2013-09-07 2013-09-07 2 1NaN 2 0NaNNaN
641120 MITx/8.MReV/2013_Summer MHxPC130374105 1 1 0 0 India Bachelor's 1992 m 0 2013-09-07 2013-09-07 49 1NaN 1 0NaNNaN
641121 MITx/6.00x/2013_Spring MHxPC130282999 1 0 0 0 Other Europe Master's 1979 m NaN 2013-09-07 NaN NaNNaNNaN 7 0NaN 1
641124 MITx/6.00x/2013_Spring MHxPC130505931 1 1 0 0 India Secondary 1995 m NaN 2013-09-07 2013-09-07 59 1NaN 2 0NaNNaN
641126 MITx/6.00x/2013_Spring MHxPC130137331 1 1 0 0 United States Secondary 1992 m NaN 2013-09-07 2013-09-07 251 1 77 4 0NaNNaN
641128 MITx/14.73x/2013_Spring MHxPC130256541 1 1 0 0 United States Master's 1982 m NaN 2013-09-07 2013-09-07 51 1 1 1 0NaNNaN
641131 MITx/8.02x/2013_Spring MHxPC130226305 1 0 0 0 Unknown/Other Bachelor's 1988 m NaN 2013-09-07 2013-09-07 11 1NaN 2 0NaNNaN
641132 MITx/6.002x/2013_Spring MHxPC130030805 1 1 0 0 Pakistan Master's 1989 m NaN 2013-09-07 2013-09-07 29 1NaN 1 0NaNNaN
641133 MITx/6.00x/2013_Spring MHxPC130184108 1 1 0 0 Canada Bachelor's 1991 m NaN 2013-09-07 2013-09-07 97 1 4 2 0NaNNaN
641136 MITx/6.00x/2013_Spring MHxPC130098513 1 1 0 0 United States Doctorate 1979 m NaN 2013-09-07 2013-09-07 74 1 14 1 0NaNNaN
\n", "

382385 rows \u00d7 20 columns

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" ], "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ " course_id userid_DI registered viewed \\\n", "1 HarvardX/CS50x/2012 MHxPC130442623 1 1 \n", "5 HarvardX/PH207x/2012_Fall MHxPC130275857 1 1 \n", "7 HarvardX/CB22x/2013_Spring MHxPC130539455 1 1 \n", "8 HarvardX/CB22x/2013_Spring MHxPC130088379 1 1 \n", "9 HarvardX/CS50x/2012 MHxPC130088379 1 1 \n", "10 HarvardX/ER22x/2013_Spring MHxPC130088379 1 1 \n", "11 HarvardX/ER22x/2013_Spring MHxPC130198098 1 1 \n", "12 HarvardX/CB22x/2013_Spring MHxPC130024894 1 1 \n", "13 HarvardX/CS50x/2012 MHxPC130024894 1 1 \n", "14 HarvardX/ER22x/2013_Spring MHxPC130024894 1 1 \n", "15 HarvardX/PH207x/2012_Fall MHxPC130024894 1 1 \n", "16 HarvardX/PH278x/2013_Spring MHxPC130024894 1 1 \n", "17 HarvardX/CS50x/2012 MHxPC130080986 1 1 \n", "18 HarvardX/PH207x/2012_Fall MHxPC130080986 1 1 \n", "19 HarvardX/CS50x/2012 MHxPC130063375 1 1 \n", "20 HarvardX/CS50x/2012 MHxPC130094371 1 1 \n", "21 HarvardX/CS50x/2012 MHxPC130229084 1 1 \n", "22 HarvardX/CS50x/2012 MHxPC130300925 1 1 \n", "23 HarvardX/ER22x/2013_Spring MHxPC130300925 1 1 \n", "24 HarvardX/CS50x/2012 MHxPC130417650 1 1 \n", "26 HarvardX/CS50x/2012 MHxPC130298257 1 0 \n", "27 HarvardX/CS50x/2012 MHxPC130500569 1 1 \n", "28 HarvardX/CS50x/2012 MHxPC130466479 1 1 \n", "29 HarvardX/CB22x/2013_Spring MHxPC130340959 1 1 \n", "33 HarvardX/CS50x/2012 MHxPC130356280 1 1 \n", "34 HarvardX/CS50x/2012 MHxPC130328890 1 1 \n", "36 HarvardX/CB22x/2013_Spring MHxPC130435030 1 1 \n", "37 HarvardX/CS50x/2012 MHxPC130435030 1 1 \n", "41 HarvardX/CB22x/2013_Spring MHxPC130542822 1 1 \n", "43 HarvardX/ER22x/2013_Spring MHxPC130069044 1 0 \n", "... ... ... ... ... \n", "641083 MITx/6.00x/2013_Spring MHxPC130238153 1 1 \n", "641084 MITx/6.002x/2013_Spring MHxPC130544641 1 1 \n", "641085 MITx/8.02x/2013_Spring MHxPC130117789 1 1 \n", "641086 MITx/14.73x/2013_Spring MHxPC130122763 1 1 \n", "641088 MITx/6.00x/2013_Spring MHxPC130214187 1 0 \n", "641089 MITx/6.002x/2013_Spring 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"641132 NaN 1 0 NaN NaN \n", "641133 4 2 0 NaN NaN \n", "641136 14 1 0 NaN NaN \n", "\n", "[382385 rows x 20 columns]" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Stages of boolean indexing\n", "- select column whose values you're interested in, e.g. df['nchapters']\n", "- evaluate the result according to some test, e.g. df['nchapters']>0\n", " -that produces a *boolean* series\n", "- use the boolean series to select the rows from the dataframe\n", " - df[df['nchapters]>0]\n", " results in a dataframe with only the rows that meet the test" ] }, { "cell_type": "code", "collapsed": false, "input": [ "country_not_france=(country_series!='France')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "country_not_france" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "0 True\n", "1 True\n", 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638959 MITx/14.73x/2013_Spring MHxPC130128755 1 0 0 0 France Master's 1988 f NaN 2013-08-29 2013-08-29 3 1 NaNNaN 0NaNNaN
639270 MITx/6.00x/2013_Spring MHxPC130520698 1 1 0 0 France Master's 1991 m NaN 2013-09-03 2013-09-06 26 4 5 1 0NaNNaN
639317 MITx/6.00x/2013_Spring MHxPC130301343 1 1 0 0 France Master's 1984 f NaN 2013-08-30 2013-08-30 58 1 9 5 0NaNNaN
639722 MITx/6.002x/2013_Spring MHxPC130413420 1 1 0 0 France Bachelor's 1990 m NaN 2013-09-01 2013-09-02 25 2 2 2 0NaNNaN
639723 MITx/6.00x/2013_Spring MHxPC130413420 1 1 0 0 France Bachelor's 1990 m NaN 2013-09-01 2013-09-01 23 1 2 2 0NaNNaN
639807 MITx/6.00x/2013_Spring MHxPC130045331 1 1 0 0 France Bachelor's 1990 m NaN 2013-09-01 2013-09-01 40 1 13 2 0NaNNaN
640128 MITx/8.MReV/2013_Summer MHxPC130373510 1 1 0 0 France NaN NaN NaN 0 2013-09-03 2013-09-03 4 1 NaN 1 0NaNNaN
640208 MITx/6.00x/2013_Spring MHxPC130000556 1 1 0 0 France Master's 1989 m NaN 2013-09-03 2013-09-03 19 1 NaN 3 0NaNNaN
640249 MITx/6.00x/2013_Spring MHxPC130323078 1 0 0 0 France Bachelor's 1989 m NaN 2013-09-03 NaN NaNNaN NaN 3 0NaN 1
640463 MITx/14.73x/2013_Spring MHxPC130393176 1 1 0 0 France Master's 1983 m NaN 2013-09-04 2013-09-04 7 1 1 1 0NaNNaN
640487 MITx/14.73x/2013_Spring MHxPC130280987 1 0 0 0 France Master's 1986 f NaN 2013-09-04 2013-09-04 1 1 NaNNaN 0NaNNaN
640506 MITx/14.73x/2013_Spring MHxPC130447919 1 0 0 0 France Secondary 1990 m NaN 2013-09-04 2013-09-04 1 1 NaNNaN 0NaNNaN
640579 MITx/6.00x/2013_Spring MHxPC130195185 1 1 0 0 France Master's 1972 m NaN 2013-09-04 2013-09-05 1120 2 111 3 0NaNNaN
640646 MITx/6.00x/2013_Spring MHxPC130546344 1 1 1 0 France Master's 1980 m NaN 2013-09-05 2013-09-07 930 3 57 13 0NaNNaN
640655 MITx/6.00x/2013_Spring MHxPC130068634 1 0 0 0 France Master's 1990 m NaN 2013-09-05 2013-09-05 1 1 NaNNaN 0NaNNaN
640898 MITx/14.73x/2013_Spring MHxPC130556151 1 1 0 0 France Bachelor's 1990 f NaN 2013-09-07 2013-09-07 40 1 5 1 0NaNNaN
640935 MITx/6.00x/2013_Spring MHxPC130405848 1 1 0 0 France Bachelor's 1988 m NaN 2013-09-06 2013-09-06 69 1 7 4 0NaNNaN
\n", "

4700 rows \u00d7 20 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ " course_id userid_DI registered viewed \\\n", "7 HarvardX/CB22x/2013_Spring MHxPC130539455 1 1 \n", "256 HarvardX/CS50x/2012 MHxPC130595891 1 1 \n", "423 HarvardX/CS50x/2012 MHxPC130247412 1 0 \n", "449 HarvardX/CB22x/2013_Spring MHxPC130170185 1 1 \n", "730 HarvardX/CS50x/2012 MHxPC130254688 1 1 \n", "807 HarvardX/ER22x/2013_Spring MHxPC130156847 1 1 \n", "928 HarvardX/CS50x/2012 MHxPC130058577 1 1 \n", "1078 HarvardX/CS50x/2012 MHxPC130323627 1 0 \n", "1079 HarvardX/PH207x/2012_Fall MHxPC130323627 1 0 \n", "1171 HarvardX/CS50x/2012 MHxPC130270571 1 1 \n", "1236 HarvardX/CB22x/2013_Spring MHxPC130078849 1 0 \n", "1237 HarvardX/CS50x/2012 MHxPC130078849 1 0 \n", "1238 HarvardX/ER22x/2013_Spring MHxPC130078849 1 0 \n", "1239 HarvardX/PH278x/2013_Spring MHxPC130078849 1 0 \n", "1394 HarvardX/CS50x/2012 MHxPC130217709 1 1 \n", "1535 HarvardX/CS50x/2012 MHxPC130075520 1 1 \n", "1536 HarvardX/ER22x/2013_Spring 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NaN \n", "640898 1 0 NaN NaN \n", "640935 4 0 NaN NaN \n", "\n", "[4700 rows x 20 columns]" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Part II: Movie ratings-recommender engines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Election Mining\n", "\n", "> Campaigns are moving away from the meaningless labels of pollsters and newsweeklies \u2014 \u201cNascar dads\u201d and \u201cwaitress moms\u201d \u2014 and moving toward treating each voter as a separate person. In 2012 you didn\u2019t just have to be an African-American from Akron or a suburban married female age 45 to 54. More and more, the information age allows people to be complicated, contradictory and unique. New technologies and an abundance of data may rattle the senses, but they are also bringing a fresh appreciation of the value of the individual to American politics.\n", " - Ethan Roeder, \u201cI Am Not Big Brother\u201d http://www.nytimes.com/2012/12/06/opinion/i-am-not-big-brother.html?_r=0.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ " films=pd.read_csv('./ml-100k/u.item', sep=\"|\", names=[\"movie id\", \"movie_title\", \"release_date\", \"video_release_date\", \"IMDb_URL\", \"unknown\", \"Action\",\"Adventure\", \"Animation\", \"Children's\", \"Comedy\", \"Crime\", \"Documentary\", \"Drama\", \"Fantasy\", \"Film-Noir\", \"Horror\", \"Musical\", \"Mystery\", \"Romance\", \"Sci-Fi\", \"Thriller\", \"War\", \"Western\"])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "users=pd.read_csv('./ml-100k/u.user', sep=\"|\", names=[\"user_id\", \"age\", \"gender\",\"occupation\",\"zip_code\"], index_col=\"user_id\")" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }