{
 "metadata": {
  "name": "06 Movie rations redux",
  "signature": "sha256:8c7231e100a3898e032dc36f98988860e579ed7f2b8d4e71ca93e80d9730e391"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "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": "%matplotlib inline\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We'll be using the MovieLens data set with 100K ratings from http://grouplens.org/datasets/movielens/. There you can find much bigger sets.\n\nIn the midst of writing this, I discovered a great blog post using `pandas` on the same data: http://www.gregreda.com/2013/10/26/using-pandas-on-the-movielens-dataset/. The approach and tools are slightly different. Worth checking out!\n\n\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#First, let's look at what the files are.\n!ls ml-100k",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "allbut.pl\tu1.base  u3.base  u5.base  ub.base  u.info\r\nmku.sh\t\tu1.test  u3.test  u5.test  ub.test  u.item\r\nml-100k.tar.gz\tu2.base  u4.base  ua.base  u.data   u.occupation\r\nREADME\t\tu2.test  u4.test  ua.test  u.genre  u.user\r\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!cat ml-100k/README\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "SUMMARY & USAGE LICENSE\r\n=============================================\r\n\r\nMovieLens data sets were collected by the GroupLens Research Project\r\nat the University of Minnesota.\r\n \r\nThis data set consists of:\r\n\t* 100,000 ratings (1-5) from 943 users on 1682 movies. \r\n\t* Each user has rated at least 20 movies. \r\n        * Simple demographic info for the users (age, gender, occupation, zip)\r\n\r\nThe data was collected through the MovieLens web site\r\n(movielens.umn.edu) during the seven-month period from September 19th, \r\n1997 through April 22nd, 1998. This data has been cleaned up - users\r\nwho had less than 20 ratings or did not have complete demographic\r\ninformation were removed from this data set. Detailed descriptions of\r\nthe data file can be found at the end of this file.\r\n\r\nNeither the University of Minnesota nor any of the researchers\r\ninvolved can guarantee the correctness of the data, its suitability\r\nfor any particular purpose, or the validity of results based on the\r\nuse of the data set.  The data set may be used for any research\r\npurposes under the following conditions:\r\n\r\n     * The user may not state or imply any endorsement from the\r\n       University of Minnesota or the GroupLens Research Group.\r\n\r\n     * The user must acknowledge the use of the data set in\r\n       publications resulting from the use of the data set, and must\r\n       send us an electronic or paper copy of those publications.\r\n\r\n     * The user may not redistribute the data without separate\r\n       permission.\r\n\r\n     * The user may not use this information for any commercial or\r\n       revenue-bearing purposes without first obtaining permission\r\n       from a faculty member of the GroupLens Research Project at the\r\n       University of Minnesota.\r\n\r\nIf you have any further questions or comments, please contact Jon Herlocker\r\n<herlocke@cs.umn.edu>. \r\n\r\nACKNOWLEDGEMENTS\r\n==============================================\r\n\r\nThanks to Al Borchers for cleaning up this data and writing the\r\naccompanying scripts.\r\n\r\nPUBLISHED WORK THAT HAS USED THIS DATASET\r\n==============================================\r\n\r\nHerlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic\r\nFramework for Performing Collaborative Filtering. Proceedings of the\r\n1999 Conference on Research and Development in Information\r\nRetrieval. Aug. 1999.\r\n\r\nFURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT\r\n==============================================\r\n\r\nThe GroupLens Research Project is a research group in the Department\r\nof Computer Science and Engineering at the University of Minnesota.\r\nMembers of the GroupLens Research Project are involved in many\r\nresearch projects related to the fields of information filtering,\r\ncollaborative filtering, and recommender systems. The project is lead\r\nby professors John Riedl and Joseph Konstan. The project began to\r\nexplore automated collaborative filtering in 1992, but is most well\r\nknown for its world wide trial of an automated collaborative filtering\r\nsystem for Usenet news in 1996.  The technology developed in the\r\nUsenet trial formed the base for the formation of Net Perceptions,\r\nInc., which was founded by members of GroupLens Research. Since then\r\nthe project has expanded its scope to research overall information\r\nfiltering solutions, integrating in content-based methods as well as\r\nimproving current collaborative filtering technology.\r\n\r\nFurther information on the GroupLens Research project, including\r\nresearch publications, can be found at the following web site:\r\n        \r\n        http://www.grouplens.org/\r\n\r\nGroupLens Research currently operates a movie recommender based on\r\ncollaborative filtering:\r\n\r\n        http://www.movielens.org/\r\n\r\nDETAILED DESCRIPTIONS OF DATA FILES\r\n==============================================\r\n\r\nHere are brief descriptions of the data.\r\n\r\nml-data.tar.gz   -- Compressed tar file.  To rebuild the u data files do this:\r\n                gunzip ml-data.tar.gz\r\n                tar xvf ml-data.tar\r\n                mku.sh\r\n\r\nu.data     -- The full u data set, 100000 ratings by 943 users on 1682 items.\r\n              Each user has rated at least 20 movies.  Users and items are\r\n              numbered consecutively from 1.  The data is randomly\r\n              ordered. This is a tab separated list of \r\n\t         user id | item id | rating | timestamp. \r\n              The time stamps are unix seconds since 1/1/1970 UTC   \r\n\r\nu.info     -- The number of users, items, and ratings in the u data set.\r\n\r\nu.item     -- Information about the items (movies); this is a tab separated\r\n              list of\r\n              movie id | movie title | release date | video release date |\r\n              IMDb URL | unknown | Action | Adventure | Animation |\r\n              Children's | Comedy | Crime | Documentary | Drama | Fantasy |\r\n              Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |\r\n              Thriller | War | Western |\r\n              The last 19 fields are the genres, a 1 indicates the movie\r\n              is of that genre, a 0 indicates it is not; movies can be in\r\n              several genres at once.\r\n              The movie ids are the ones used in the u.data data set.\r\n\r\nu.genre    -- A list of the genres.\r\n\r\nu.user     -- Demographic information about the users; this is a tab\r\n              separated list of\r\n              user id | age | gender | occupation | zip code\r\n              The user ids are the ones used in the u.data data set.\r\n\r\nu.occupation -- A list of the occupations.\r\n\r\nu1.base    -- The data sets u1.base and u1.test through u5.base and u5.test\r\nu1.test       are 80%/20% splits of the u data into training and test data.\r\nu2.base       Each of u1, ..., u5 have disjoint test sets; this if for\r\nu2.test       5 fold cross validation (where you repeat your experiment\r\nu3.base       with each training and test set and average the results).\r\nu3.test       These data sets can be generated from u.data by mku.sh.\r\nu4.base\r\nu4.test\r\nu5.base\r\nu5.test\r\n\r\nua.base    -- The data sets ua.base, ua.test, ub.base, and ub.test\r\nua.test       split the u data into a training set and a test set with\r\nub.base       exactly 10 ratings per user in the test set.  The sets\r\nub.test       ua.test and ub.test are disjoint.  These data sets can\r\n              be generated from u.data by mku.sh.\r\n\r\nallbut.pl  -- The script that generates training and test sets where\r\n              all but n of a users ratings are in the training data.\r\n\r\nmku.sh     -- A shell script to generate all the u data sets from u.data.\r\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Take a close look at `u.data`, `u.item`, `u.users`. How would you glance at the format in the Unix shell?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!head ml-100k/u.data",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "196\t242\t3\t881250949\r\n186\t302\t3\t891717742\r\n22\t377\t1\t878887116\r\n244\t51\t2\t880606923\r\n166\t346\t1\t886397596\r\n298\t474\t4\t884182806\r\n115\t265\t2\t881171488\r\n253\t465\t5\t891628467\r\n305\t451\t3\t886324817\r\n6\t86\t3\t883603013\r\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!head ml-100k/u.user",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1|24|M|technician|85711\r\n2|53|F|other|94043\r\n3|23|M|writer|32067\r\n4|24|M|technician|43537\r\n5|33|F|other|15213\r\n6|42|M|executive|98101\r\n7|57|M|administrator|91344\r\n8|36|M|administrator|05201\r\n9|29|M|student|01002\r\n10|53|M|lawyer|90703\r\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!head ml-100k/u.item",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)|0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0\r\n2|GoldenEye (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?GoldenEye%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0\r\n3|Four Rooms (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0\r\n4|Get Shorty (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)|0|1|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0\r\n5|Copycat (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Copycat%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0\r\n6|Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)|01-Jan-1995||http://us.imdb.com/Title?Yao+a+yao+yao+dao+waipo+qiao+(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0\r\n7|Twelve Monkeys (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Twelve%20Monkeys%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0\r\n8|Babe (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Babe%20(1995)|0|0|0|0|1|1|0|0|1|0|0|0|0|0|0|0|0|0|0\r\n9|Dead Man Walking (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dead%20Man%20Walking%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0\r\n10|Richard III (1995)|22-Jan-1996||http://us.imdb.com/M/title-exact?Richard%20III%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0\r\n"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "How do these data files relate to one another?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import Image\nImage(\"http://imgur.com/ZhpRFTj.png\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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AJDhLtizJluVJdhzbcWwn6XTcSd/b3cm676631l3r/QPvv3qd7k763kydxHESz6M8xKNk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On/zPm//vWvYaZ+/OMfA+mLL764o2anbWmpLORqI8Cfj9iaaXFxkV7ETCLH5tXGXJYvEZAIXDME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\nb7ZQoARb1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2MeaOXzxVwuPTFphn6vks8klXD83iqepxizqLrR9wKbeY2pUgzzYeAP8E7q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\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5ghg9owhQBCgCFAGKAEVgoyFACZSN1uK0vusHAVg0YeYd1x4OnOEwMM3AtqG8Z+KxQJIY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FAEDkGARqAcAsmb7QMSfeK6jq45at/RNHcwIBuC8IzwvEcQPKLkw4sq77zZmpWWlyJAEaAI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\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gxIXVXCZda3bchTKrVfz7HafdkIYxVxi9drQ8F/5Fv8Gwh0xr6UyG5KTFchWYkuiq0KjL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UkJ4cePy3vwqWWagaIJ38yvSozcJ8fvrs/jeJQkwOr4sPDG5evUJWks/BhRfeeO73AEvzj1kZ\n7z+kP4jTd+7ccGZ84vvPP11tmP52IxpLfPnz2wYHB/VgwKzVz03q5r4gS+iK1owWVIykZkS7MFlm\naHJ5SLsD4qD5STFB/tlYQM/eccdaGOMm4lTV8q+f/2mlWp80Y1o4cvuOLWimDCXo2Oqnj7xbLU2O\n53byEKJBlKiM04ffnc7lvv3dpwHGH/niHzC2jagyp2Z4udJOjOLwD+9fhqRzW//bxbpCfoQc4qPd\nDg8ki4rnhWNH6vXpg0fHKo322tEk7Ilibiw/nfvnf/j7erOd3foAEsipezZylpxpNR8idU6fPtmo\nVA8dLaNJ9pu/92A2m96wIup1tSdPvD/pRnmgaxDHg2bKOVILBbPOtj5mimn/167AR/cthb8EFEOA\nhggAn6tqVKlovlhXA83VA1H2Qe6BB5vLl/KlqsvLMNytFXNGofvLH32fFfPlt38mlko+9sCuMJJg\ntJlK6fjhD4lhJl+eVw2wKoLECXAc2Cvp9irlwtjYBGo72R33EC0Yj2g6IWJY0tkcCyxiC+CZrdt+\nVzKXSz7/BrnaPzw+Pl1tb10/AvyHR4hPuWLtJoqPxyY90gyjCnA/GI4FtOh9DyQFQGQEJURRFdZx\nf6zklYunBmPJXjIzwjH0IPwKhsJnX2MFl3h4+Xp+XL52mxzCbwCXHCNjMZqA+V+9c7BmtHc+9mXw\nGi0SwYe+WG+5iO3rFM2xgGMBxwJ27wdkEAit37KTvm7rjpa4hIiSSs8pgHVPD7I/NbgMa4EgSFeI\n16jSQ65k/wqOph+2PIrVq6TPFA9UgItztGA5ywK+LfdPv/3u+ziGGTudqoSv2HfhIILBff5Mdohf\nh0aWs8MuA5g234dXraEblv9JBsDzkSb8SQ0op/VPzpK8CByjWKxn+QpjnlmWf3jFWq68bO2mfgk5\nB/fXtNJfvPvGy+jLPvPi6/jpKzbegajCQDwcCZ/zlASZ93gTaSlbOjvKRRmerDpKtVEF5d+yNXLl\nVRtx913QIaVeVhnWrNvM/g2bIG1bluJn2VCHBWvAAlIXtyvAr5t3xvjcLlFLLKLLFSxrS443bKWF\nYyGtF44kOGbH7SLywgFsDFV8wj6RPT1o5IHVazdyDIMWeySsnieYTrFj2ep1fPJk2ANsI+dammLD\nIyH7avbxBFjlJk6RqeflF54pFssfnCz5/KEHNw+TZQJWO6kbrUct6UQgJ1m1I2CIqhGuxTjLNRb7\ntsAAikyirXaMnTDb7Eax2C23IOWzBCMbjSqZaKdLVZ8uUTCgqrwVsLY6ba/RhCpAUwY0JUiBmEA1\nkc6EdCNYJZ0VnRHgIzMhP0LM4LHQyFRCsFn2AhEE4Jy/BitvpPWPD15dXj0/UXmsbTerB2s1UswG\nQj2vBAZGdZnBS+OwTlgQmy+Sm2IGuj4GEiJOAHJNs1QsKfQ2HsXtD4SBtz2uZhN2QA0gHxSc+Em+\nEHZRQyPbGyOpLVCFixjRaFjiYeqVZsWfy+UJ1enpKnlnpiYn+bNQqri8quSfsYwuZrffy4+9mdZL\nK4ML///oC2w/2L7N+udxSXnq/U3Osv6mSj7iRRGsMj2Idhm1XG6aaU0q7PG5u6j8AKBM5QsNs5dC\nRYCAHwv+mLmLFE+YNnBtzE6PqE++S2wpa80mi8YsArQNk6wZaLA3QdqZA0k57ELIuf3inP/Cz9Yh\nF/vt/FGL6RsTPekEYLcCoBKIQzBpsVAgfKBQIESpB6sIGXZU4kmSqkZZCQnIK0WiVgQhmq2pqQmz\n285PF8wQuGURACVfLBHsw0oPXFbobHBXpGMQBoqwV9ByNlFcJ3w1GLRoSldgCXnWlq0/yaYzv/MA\nxK26+AO6ghs6hzgWmG0B+hjI26yRkUesaSBu6KKLs7sf67Bz6bRnn2J/572i7Ye0y7lJAlu6XFq4\n72RfeBlcZCtdt3yc3xiL6b1o5B7eNVcXUZREPMJYOH/j8PmyON8cCzgWcCwwBxaQeQQgsmp1diho\nfGyzekvhN5zf6GIFTDm/w/pmQRIf2zf7TznLG5Tp+qU2KA78dB79mDkOCGbm60X/K1e2z73Yz9av\nVmntXr1/DP25wf+7Xei9xKSLF+32EhUQg0cPwfqj3TqAzsXvYh120fGC422b2Kbt3/eCL3IJSfjs\ncl1ofvtggaX6xwSDF1zB2mGVhAWDj/9qXfljde8fA5jS/y5fxCCiqIcyLqR34ZjAyWELScy4mPLc\nxldZnJj5c8n89yMNef5LjSob/+z7yhIPUKKzXcICNEQmxZNnjp86eXLPgYP5Ym3EoxcKkV/8BI06\nUZGm55Ipts8fGViDKuRnH7srHIqsXrOJV3qG7sHMhHRV5emJsWeT4ZLfSA2uTGVHVg4PJmIaL/kl\n7jzHu2WKirpnCxyANwuOHOp9oc07dkWSJ5Vv/7RXqvlX3RnOjG5eMzI0kGL61kRklLgliTZCafRc\na5njMi3yy/FcXbJWSt9DH5obP41O+Xf+/jtk/L3z1k1uf+LObetJVfb6iz+FBjd+fDNiTV2jgpbq\nB/s/RJn1lod2xjOZcDTqd2l33v/Q+Nmz+//pZ2cnDv/tX7cg7A0mIjCbjh3+oGl2St5kLJYcGUon\nokFvaZJZs2ixutwoWgF6zxjJwhi6LdGptd9fdhBC0iNPssEY0jU5sn8wJ0GVYerdbvXcABsyfREY\nA2kwNF49ol8b0pbtuquSnxre826xcvaf/+EbVHP96mXAQJOnjjHLPzxRDeqxx9augicJmo5mF0qN\n/KPDAE9BsBGujKdbrtZaz/78J5FI+N1s0NU1z5yQ7DOnTo533f79b7yTG8zdvm6d30PyO/KY0pZc\nVsdzvpxSRWAFpNfBaSyK1PnfZmq+CP8LesLkkOFnZOMmf2IiuWd/sVL48Xe+Ba30jdWDitdVnBpr\nNpofnih4/aH77tyOSvzQQLrXqg+PRKem86+++BTySGeO7pf45Ha1ZTQPHT6GXs3ohp2JVGbdMgID\nI6z+8KAbMGjLlXzFBcAZjyOgnvaLW/CJJpHWgpxvu0NOJsG6LnWCHMe/jsm/TtfPw73Ukc5+xwJX\naQHoVFo06Xvyy08K9zkQx4eGUS3Lmgu68Uag2fTY53+HZdetW9YBS4ZknXZBy+Tc3LGAYwHHAo4F\nrt4COET454nUEKqUn3l0hHCbW29F34QQnKUHDVx97S92BsvjPjATfcWqzUzedkRTLLxtXL8B+dg+\ni/Nipy2ZfQsJoDDfJ1KfgDTJAaFqkirETvO0ZKw37wWViaqAIRJjJorIHpSZEfAHWgEkkdLIer7q\n0RssR7ODtWlQPWtBHe4JExIBULzudlPTUJPu+VCTjoQ1knEQOgfLaz6qQ5EAX4Vx0PWJSoPF64M7\nTWoDNJDiRNB1vRFCkuIJLehHzs+DpIPHC2oZbPUUlr2t4+ejoIvtHrwtCJfjYPPChDSXm6kuQZZ1\nli+hK6bSKaBfpXSWmSpan6bp6xpNtHJAT3xEchF/L3JN6MF6YokkcfgxXfPWIazUet12kfy+0JJQ\nxem69WQsGk+EAiTvUToQL2GsaOEWIL6k3znXPgSkg/cSCPmD6JyLPjjRmzAgVMS2Ap0Q7/R5bW0L\n1uNUH8kr5DpW3CMHow6gUA1fl7Qvkq8nFIqCqKSSMciaTdNoul2lUhGQvAqfptUOhtEOJ2sz1whC\nt2Kj12h1PeCG3CsQ0MiPjL64x9tAb7bZcFeroGxt4BzE3hEs6LnhYQDjWAQUIdiHAmZbcwUIWRXI\ncWaTUvkUIjmVHqxJ0WSZ9ePMQYvyvzQMpOmDSL3rzYTY0FtuGYarVy6VAEVh4oBe+UPhQEiP84pF\no7x1XU+HVCFGp6V4c4SoVsolk4fcw4AIvysI+SYSxN4miQUjblnsIPgshicldNjTFWn6EE/ZZodd\n2ibSZi2zkvHH23EFrAwjXOqim3Ww8Gj8AZOMTzxZHsAljr3oBZydjgUuYwFaIsBfAiSv2pN8W4ui\nccl4rfCu8aIwGtITOi3+Mo/Q+cmxgGMBxwKL1QIMKeLwBENh0tmkvDo0jSiRKuLJLDhWvzA2s1b0\n0TMhB1AMBnk4nsRfB06yRrobwblbSACFUOGdd+5eub6QXr6Vmd6uWzbE43Hc+IV51Iv/rkwuYZck\nMoNu5YtP/i5zS7JbMadh5Rivy8JHWMeyjonpzDYlvNsDI0Gwz3O/Wt+AJKLJzO7HPt8w2sFlmzXy\nbzFjngn8/jTNYHUv6HTHUw9+8fOoWkTXbQbHIZ0VWVfSI+tUfeAP//hfooHiy6yPRKNDqTgFc7UN\n+qNddz8GE6EdW0lmMtzMT7OQi/PaEsfo8WjDq7brqVVf/QrIQr1FhCJCU4RoBQLLfuuL4Cmpd/ZX\n63WYGMI4CpFl079l524trG+/9RZ0m6LkUXP1Vt2yK7um+q9CMSsrxGmYhgR6MQPfunM5FPdNO7aT\nwGzZYNLvdTV6gz2v9thjT3Rd3pWD6QgtSiAFN0hJIBJbt+OBoVozUY7yPEk5Qc6bbbc9yDMquHSe\nF1godpTYVUIeY4Mpj/bIZx8nxmb1irToXalel9f/+OOPIO+6diQLuJONZDqDo1/5V3+CmuJ7Bw8R\nfCSxsy63tmoLql2rN21GF3b7uuVMMUg57HZ17t59PwJdkeENcJdWLIt3W01E3Dn37JSI4AL5I6W1\ncs0WKFUP3DGFWHKhpwVAkSLosHs27twNN2fIQGcVFUf01e23Rg1G0gl34IEHH+j0PCtHUii625G3\ni7NBzCoVaBYvkHd4xfpkdtnve/yVSuWDD48TtWQQ6uVyxSKst6u7h1eFw/rmbZsBqhMRf7fb2v3I\n56rVSnb5uwR55Yo17EY6Y2TINt+6G4xu29aNCGGm46QYB4QxwZT0ZBbR9YcefYz2sGXTGj2CWPFl\n+2riY73+cCTl8qj37n4Q6snqVcsow0WZbnQNNCTN7V658baBejNRcUGrQfRMuREG2VnPyvm6cBYA\nNEkMDHP/lIXh2fHwC1ccQSSBhuFPrw9HKQah9wtYGOfWjgUcCzgWcCxwzRbAh0EvVg1qazfvYMLF\nOjcerKXlwS/XfNWlfSKr5fFEBmOg+SI1wRJY6QYa6S7rAX/Kzw6sLhZPsjo9XJdcTKgVohjKpP9T\nvu0SvjyNj5w1Qa09ODSEerIdR/eRl1NWtPxMIJkmzbYkJ56rtkh8elkJJm1PyOyoUdalQWFmfv3U\nbSMILZPb1ECWPAjhhE6ydLk7RC/UnEPt7NAwE2NvAtXuMPwaSA8k6aWa5DMm+XhHiwAZzK7Xp17e\nRXQDC7sIaFrHNTCY1esN0suyXC98j0AgEky2DC0zkNLqdYK4wHq9PoA1NT0wAMMIvgmgr8Bp7h5J\naggdHRhEbTuUL9dJ1WYRlXxaJE0Kn4FUMhwmNwSR+OQU48qdVDpDQAe7IANJK5L/cSUlHEt4VDNl\nqXAjYgLEHoknlKDp7YXAdfrPiFOgzfgDnfRABgAFVlEAeS9kuNROOpUCqghbPCNbDp1jYKRN5ouN\nZhOFY8Jz/GoEwkg2kwbOgPJAigrCf6DasGYLMwViCkUkVqWn+AbJjqGHzbYLRTF/iARq/mQ6Debk\nQ9qDtGp1D6rmkiBZ8ejRpBo02gZcyyA0E/sJU06oW4raQR+eUtnlPP/WLKJmcNGiWMWXEFJ3KpMJ\naKHpfBUblhsNwnBpIiiuz+irixV5XCS/Dsfi8E1Fib1Wb7t8op0O3UTxpzNpNPMhqoBsCrZG3yDq\nmejWq2qok84MwOvRw1DCRG/soqXp76SxwGJVg51kIkkclzTDwKXOomOArOKjtXoVo+0TNTXQYC71\nCffo38z54ljgkyxAA+OQeQpV/aTC8Du9Dp83kkN5BZV2DnEs4FjAscANaAHLI5rRf7kB63ctVcKr\n4zSc/ms5edGfs5AAClrHGzfvIBvThi1NFrYjpGhCumc+qBCL/rFcrIAyAVbUWHp5ON5+NDKEfIjN\nK7GnMfZ3C+GDZx/CkmGZbl64idIyurGbb9sNT8GrEvgAnnKxAy889fr3CFAS0qLqrgesuwd1CRq0\nNJyVQDisBO+4ezcr4W5/iP3wvJi8wXAGTFm/7TaJUSI6CW7cTclRwtVmtk8cfyAcu+MuDVCAYH6a\nhB5PQBGAR4F9QvE4b1OtjkgIU16ZkBIeR0tAFoS2I0CVywVfJBAMr9u8kyPR+kaVot2RVuMPBDky\nEY9jedLAyaQ5klS12F13RQAywlHZb18B/AJ8Zdn6HQTwL+/4KJeuhZhKr9mwnTKYPcn6BtQhjQUE\njHCbSErV4rffiyq4W48luIziJgJEv/uuu9nDWy93RCfcFVi2aj1aGemRZVI7A/YEDAbEsHwIcRFT\nY0XcIH4qarg7dt4hrVfm2N6IxJd2d+y6ixpt2S7sG7GUx0NIC5CPr2Owp2p0KGcyFSMuJxbaSsyS\nKdn0KKe16gtK4HWTs0oNxXbeEaVUIT1CqTj4+pv8vFwBpAKwCYQouGzVOpTPs4Mr6B+a5CzviTwb\nBoGIJH2CFsIm1nOUNQEkScKxpAQ1gMaJ3YS0poXCM21GDCnlF1lqbzydjSQ6WizD31pE3lyawWVr\nRwN065EEuQV37opQEovaevGzaIEUnnDZFWs20ooM9HJo2wFS2V32Ds6PjgUcCzgWcCzgWMCxgGMB\nxwKOBebXAgsJoODJEw6Fu8wqJRMAlkZx3204YH6NsGTuxjQVwg5r5ZEI4sZMkyXO4eMbi8WSQ5v5\n4aWmfzKFCgbDnE3mK/6YV5vLzT1BXe4OdCOTOWsdm90oUrL0LXWSdFxSLLtqfKWdSGWZelvbx6t8\n0/wtU1Ki5UMabw3YB28NieexJyurKJlAIZG3SZE0ZpadxG78yiEzthSRHCwLJcGndJnU0oostU72\nSJsBzOJ3y5zW87DURvjTksiZuQb/9XjhFXAXb0fSmNlMAYgn7PH1ZA/bzDOZuY4G84XrWMcjSeJx\ni5KLdWVpAXJTptCwU3x6N0ItUJziV9hKlMruGexyybGAa9IeBFyz/+RAqCucBU9ipu7QKYkIcXs7\n1h4VJhONXe7utcrplR2y9ctpFxRoxy7VR36cOWgx/9cqsCSrQ8olLP1DN9CSTNcAHTx09CmlnZzH\nhOglIKJ4USfhSPhKYjfrZYTSI23GslW/vlycRsb/CbNhpw16njde/7iPf7GNSm8jVoX/Yxfy40dZ\nf3MHWgXEE0ri8dkNuP90LnqGs/OqLYD97XP6X676Es4JjgUcCzgWcCxwE1ugP3z0v9zExnCqfvNa\nYGEBFDdTPmwfkkkzk0FZbXRc5ss2RpnocsAnJXwSK56fNH/0itiZf8i4Wrstm8+j0eXuXpH2lLvb\nT9y6u1Uq17n9Vis45+rTQ7vdTPP6lbiZu2yZ4krKTHlrrJdGABExpGUdTRMrgUHJX5b5AEtmvso+\na+NYpqlWK7I+raPPvX0z6IkcCNeHTzs9JzNq61z5sJ8UihV8t7XF7ScCYDHrGLtEsoPQGD7D1tVm\nriOXC4el85nZYx+psB/tFv5gFs2nfeWPHGOVhDxocsK5diL3QtCk/zlTd7sMUouAVUn7OsA+7Jld\ncv5kA6zhU7PKaQE61t6l9gF4SpHD1udsG/LU2H/+qYjlxQ52D0wUlPxqtZnZn+y0NjnPTqFHXBjf\nZ7cT+4hLfSIgxk92UkC7DJc60m7bfo8k1bvw6VzqrJttP+QgtuuvNe+C/Tpc/6UWyRWEMibJImVb\nJEW6gYthtx/78wau5hKtmvMuLNEHt+SKbbe0JVdsp8COBebEAnPgil1POWxfx/F4rsaG4h1e+QTm\nUle+/GTmUmfN1X572n/h1S5VKqeFzLbVucnwBdMEe/8VZaO24ZUrOPRSs6wLn8iFe2aX+cLrXLjH\nPv5S+2df7cJ2Yt+d4Xz2YdZ3MdPsKdXly7l0oZPZFb8SG9rHX0WbsU64tp7nwuc1u7Szv1/+6cw+\n8mb7DhzGRpwaW6vVgjd05U+5bytO7F+HIC/7anwudbNjDbsufKEu9ncbT+nX3fkyVxbAvDSe/mZb\ne64u7lzneizAQ+Ed778L/ffieq7pnOtYoG8Bu0X1xxG7sfF5Mderf5LzxbHA1VlgTlaJru6W13T0\nAgMo11Rm5yTHAo4FHAs4FnAscFNYwEY38FxN0zxz5kyV5EmVyrWhA7VaLZfLNch/bm1cjWg41IFv\nAAAFmxQKhXK5TF1Onz5NTW+Aei3O9k0LnJ6epgUBxpHR7NSpU6GQxOg524JbwO4lxsbGStYG1ZR3\ngVdjwQvmFODGsAANjNefbpbOlhrR0vhT1/VrQPNvDIM4tZhzC4CepEkBAW9ZWezSsw6AiCpBsAAA\nQABJREFUMudP37mgYwHHAo4FHAs4FphLC9hLf0yL+MJ2bQAKk15mUwAoNomAafCNBKAUi0WqBoCS\nz+cxkQOgzGX7m3UtpkzgU5iaRoidsTbfZ/3ufF0YC9jkMmBWJre85rzsvOY8HT4XpkDOXW84C/C+\n8+7TuvikcrQ0WhdNzgFQbrhHvWAVsvJskm4CXUfRwluwclzBjR0A5QqM5BziWMCxgGMBxwKOBRbC\nArYPgcMK3vGLX/wCV9WGBq7Bt4BrfeTIET7t5Z1Dhw5JUq25EFVZCMOcvyeelmEYsCE++OADzPKD\nH/wA1eQboF7na7iYvjFfOnjwIBMnjAzH4cSJE46pF8nzYX7LxrSW1wHuiT2tpbtYJMVzirHULUBP\ny+tP6zp8+DB1oaXRuugHrmEwWuqmcMr/KVkgFot97WtfSyQSDCuLvF05AMqn1AacyzoWcCzgWMCx\ngGOBubEAngTTIVwKVmZAPa7NsWByxRVwgrmIfR0uxWWv7WpzU7G5uAo1ompM6akU16OON0a95sI2\nc3+NfivCyGzYHIPP/W2cK169BXgRkEEB1eIZ8V7LS+48nas3o3PGpSxg97S0MdoVx9g9AK//Uh9B\nLlVfZ//8W4CmtVSakzPszX/zcO7oWMCxgGMBxwKOBa7IAsyFOI44c8JtvvSlL7E+w2avLV/R+bMO\nIvgCDgvcfvJYsd16662oV9wA64eYCIYO0Qrf+MY38L3+5E/+hPUrJ4Rn1pOfy68Q+H/4wx/SiuLx\nOM3yvvvuczRQ5tK+13EtZrYQBE6ePEk42/PPPw+S8vWvf53u4jou6ZzqWOC8Behpef3RQPnZz37G\n3ieeeCISiZA989rGo/PXdb45FpixAG3JVtVZ/I3KAVBmHprzX8cCjgUcCzgWcCywKC2AM8FCXzQa\nZdYKOnBtvgWYArgJEAMOCl+SyeSNAaCwLkrV+LRn8tiHqjkAyqfUkFl2tqdMfNKQ7Fb0Kd3LuexV\nWYD5LQAK81v7XQBA4V2gx7iqizgHOxa4lAVoYDbpzO5paVoMSfZ091KnOPsdC9yoFnAAlBv1yTr1\ncizgWMCxgGOBG8cCTIqIUmHDiwUEYbuGunERTmelmo3v13CFRXhK3zIY59rMsggrtZiLROOx2yFt\n6YZpRYvZ4FdVNkSO2KxXvHNVJzoHOxa4Qgvw+nOk3dKu8BTnMMcCN5gFPJ9GfSzX7lp8u0+jMEvr\nmn3nr/9laZWf0jpPf8k9MqfAS8ICvFl93sHS7R+WhKkvLGTf4P0vFx4zD3uYrF7PfLV/7o0377UM\nc4PgQfPQkK7zFra1HZtfpxk/pdOd5/IpGda5rG0BGtjHvjiWcSxwE1pg7gGUvn/Z/3ITmvXaqozF\n+kab/f3arrawZzHT60/2FrYkS+Xu8PPZHLstlec1n+W0e4N+58Ct+x7MfBbDuRcWsN9TxxSOBRwL\nOBZwLOBYwLGAYwHHAjenBeY+hAfPntUtNjJdEY15c5r1GmqN3aBcYjSocTb3EhtiwKViQ8pvl5Zi\n2+W3q0Mg+jVY4+Y5BbtBg7RNx6e8Oc67c/M8/iuoKR2CTZS1ewY+2WgqhCJfwdnOIXNjgf57aj8L\n/uS69ufc3MC5imMBxwKOBRwLOBZwLOBYwLHAUrDAHAMorJ/jU+ZyORTaDx48iMbYUjDCYikjiEOz\n2Txz5gwK6uRXbzQaH3744eTk5GIp3yeVg0kd6mVTU1MkRCATFXWhDSBV+Enn3ey/89wx3bFjx+zn\njvDboUOHMOPNbhen/pYFaB6kFyF/ysTEBLN3GBDI4PNmkTzFsdB8WsDGrU6cOGE/BW49mxM0nyVx\n7uVYwLGAYwHHAo4FHAs4FnAssFAWmGMAxXYoQU9YRcfph0+xUBVbive1ARRmR0yWQB+oAjZk55Ko\nC8AZRaXwoCdAP3yCplF+uyJLogoLVUjsZmNPWA9z2e/OUnnuC2W0m+e+tASQNfoE+lWm7jQS3rXp\n6WkHQJnnNmADKGDEfCH1AHd3GCjz/Aic2zkWcCzgWMCxgGMBxwKOBRbcAnMJoICeMGfG3WctnUkg\nzqUTvnFVDxjTMUE6deoUAMTZs2eh6D/77LPBYPCqLrKABzOvYP5/+PBh4ABWy5ng8Sd8igUs0pK4\nNc8d00E1YobM+jYUg2eeecbOErckyu8U8lO1AH0pcCRg9JEjR2gqY2Nj9KsQlJwQnk/V7B+7uA0Q\nY3+gK/q0O+6442MHOH86FnAs4FjAsYBjAccCjgUcC9wMFphLAKVvL5CU/tbf6Xz5RAvYAqL2p32w\nbcZPPHGRHGCXvP/o+18WSfEWbTFmP/e+0fiyaAvsFGw+LTC7SXzs+3wW4ya/F5YHyeKT4EQ22xr8\neZObZSlWH5E2io3SGJ+s+fDpm3mgV1cdOxXFZduAzVGa23Zyed7T3N7r6gziHO1YwLGAY4EFt4DV\nM7OIT0E83vOZUtDnZI/dbeN1L3gxb54C2GPW7NHwxhin5hJAwTpsrItu2LCB9fOHH37Y0UC58jfE\nth7ck3feeadUKr399ttwTz73uc/FYrErv8gCHkn5oVHY/HaYFOl0OhKJ7N6929FAufxDwW5MzOAX\n2BoodOvwC3ju8Xj88ic6v94kFoD1gCISkTt79uyB25XNZnVd3759+xLipt0AT4r3lAdhv6fwBG1y\nJTtvgKrdVFWgu61VSwxV0/kiHrYa1GH8pVNxQDGP58rhMHnuiOTz6fWJlrNvlps+2568sPKrpffM\nDWb/dA3fKTxNzjRb9qjBFfibT7fLboc4pW41EOTTexV1uYaCOKc4FnAs4FjgIxaw4Qm6Vtnrlr5O\n8c3lBPMjN7vkH/SEPXpmnOp6rU45tGgUp9rnpsvsNRuIS/TMtoiXxaL61XT4l7yf/QO340ur3eLT\n5xPS/Vz1wF3Lnu2uwP0+j6zctKx7+RW5y2XRe35fFBuWr9dqmKjbkXFKUf08EUrOU/BZAQrXOy4u\nXC3nuH1jKeoCegJ0kkgkcPQXrmpL78446AAo4A58If6FDRsulYk0j555BW4o8zreFHATuw04INrl\nGyJ2w1xMzAgNsJ87czOeO9vlT3R+vUksQKugprxZ9KtM9nih6FeTyaQDoMxnA+A95UHY7ymzYttN\nxAOYzzI497oeC/AEG9UKGa2m8lM8x3y+DPqgKGXF7+fZEpYViUXAOMgkfwV3kcZQKhXxdwLhCO+m\n5vFf2BZIR1cuFjgmpEc8jIyqeuExV3Cv84f0uCLQT26a5kcBcLZmAyiEUHsBg9JZegmP3+s0zfOG\nc745FnAs8ClbgM4JV7Zer9Ipub0qk2SvBozw0X7ImiGSdpKyWAQRlxXj36Nblj1SQrd6PVH/lj9t\nGHV6SITjIBgGdJ3Pbkc6zFqlzH/MrkqPHYloHtc5Jqnc9vo2MiVSeYTq6JPD4RjjyFz0wFSm1zJF\nFbHaEE3MUED0HGqGieMRj8UEhri+Ys/H2YD+nU69XuMJtFrtnsvtZznBzbNmuPLgyvIsVL9yviI9\nS/TTAuBoDPNRwuu4xxwDKDKo93q4+PAmRkdHbaW96yjeTXQqdpM3vFY7fvw43wEg2EZGRlKp1JKw\ngrzqrRaqsTx6XgleDLsNgActifIvVCGxG309YjEIhdIhMkkGOOO5Q+FZqCI5911UFqB50CToDehO\nmfiBqNpvFnsWVTlv7MLY/RsAN2wgGHbAnTd2fW+42rUatcoLP/3+5OTEU8/trdQapktCeXqtNvSQ\noZXr0pnMV7/6B8lEfCCmi9d/ec+t16qUi3/3zb9ptto77/0s7+OW1YMB1ls/Ar4Y1XLhO9/4fwyz\nvfn+L0Ti8a1rR1Q/0UJXAtBcxPygJ83ydD439V//77/MTecLLUFQzgcfUWK3L5ZI/tH/+D+lU6nl\nmbhyrTe6yL2dXY4FHAs4Fri0BWAXlAq5Wq3y5msv0eP502s0Tb931y3BgHr+pB7ckHq7ZU6OnTRb\nrbLhhb63dvmg290bO3WyhRvsCiqKumHtSpljX8MGO69ZM5qNI+/uK5Wrb3w47g8En3zyi3o4VKnk\nQU9+9dwzDaPlS68N65HH4rt8oTkBUOCItCbOnoR9/+xLrwEd3fvQY8x6lmei19cD9zpGE/Tk5OED\n5XLljX0Huz33mm3rgR3eP3rar6q//RufjejhoPIJI9U1WHEuT+l1zUYZ+smbv36VLAiFiskkt202\nWYA6PV1Vg6HPPPxQIh7bvGa5X7GwiF6nVSX9aM8biLmgUQq78/Ij8VwW9hquNccAil0C5oFsxPKw\nXUOZbtpTACBYOwJ9sA24tGzIBION8uN79ssPuuy0gStpz9jN3rAem2O3KzHaTXIMb5PdNuyegU82\nq3N1etf5awJ2/4bZ7c6Nl3T+7u3c6fosAMnPbNTqldLZs+OTk1P1ZsNot3z+ACQNk5XDTruQmwRO\nmZousCiWDGsexTv78eLzcX9bLcVaUBWabacrcsINs11tGqrZpnlYZbR+6rCS2gNLYbWzND3FzWr1\npj/Ystki9pEsKnI8a7Ry5Us0Jfu+tDfrGDmoB//ENHITU1O5aVMLE99vDRf9C9iN9Pznha3Uvvvs\n/edqZ634LYkVTezgbI4FHAssLguwAAzNo90qlwr0iqFgDTyXvgWU2u4Z6cd6yI+wq9WqVotNwyw2\nfLD/DDPpcffAmg2zVXfrqhqUs6x5BBW0+yuButmsnvCjlBb72uc6UK4PC4brlyolAJR60+h4fNIb\nunptWKMsUpZK9MaxhJTJPvNKbMg15TCrl7V74wvPEho5dzZNABTh4ZwbDs4daPf2dq9rf9p7bHTg\nwl5XfrXtKWhDo1mvST7WnmuoWmdcyRcKATUIBiE36sj1LlWq2eW0LWk/DfuOs0cB+8hz1rYKP7u0\nF17HPpKpnvWT2HJmjzyL/pXZCV4GDAQGBIDSNACbOkat3DTN06enAFDYDeOeinAJzoStYjTrXEtV\nwm6vy9MTrspi3j4VAGUxV3iRl63f8iin3SIXeYEvLB7FXqIlv7Au87mHzpBtPu/o3GupWMB+p2ge\n/TdrdkexVGqxpMvZNziPwHlPl9ajNJv1d15++uzY2W9975ftnvdLX/3jdCa9Ze0Kv8/TLE4Upqf+\n5v/9q/Lpib//h++mBgb//Ou/k4xH8Orw3DotA7e4WKnx0P1+lTCcsKbhsKI5wmQhN5WrG+16s6m1\nWqJOiLNomN1Ot1QTIreuqc1Go1iYbDSZMJhqSLxEHESz1cDJr9WaPbcbVhlgqKpKuI09ScAtxrY+\nt4tjSpUq91UDYY4Jhfxc3wXcY5j5SUjcvi989WvJdGrFUBxSywyC4gZpzWaH+GTCgGsdCBA01PdA\nuVivWRfyfCBk7xenP58vUC6/EqJ2uh786PxkaT1np7SOBRwLLIwF6PG6rW7baJXK5ToMA73S83gh\nZQBn0O2APuvhMIcYtWq1UvrgrTeYOZ+uKcFgKKUDZHffeeNV5tclJRnWY2tXLnNHpJO1ei4ZbaEw\ncH2fPwhSoGui8WQNwXDepbek42L2rfi87KyUq+VS8eDho/VmKzm0IRKNaUGiidoTlUKpWBgv1Lpu\n7641q+MJFAa7pmn4LAUru9MjxISr2WiJqorCCAdwzVpVlFN8jBZSiwuVU6icNxJN+NXQnTu3U5zB\nRISOVyresft1sijWMYISCHEF1ZJjaaAJ0sNCsh4T1sP9XhcIigoDmQgcYzQE9WlW+FcoFsEYGvUG\nAEolXzTUZqVcYphoKfA0kE3kyoTGXJJQw90bov/SbTYN6hWwShIMfnx0kFJxjCl2CIU0Ka1KaKqb\nUrGHR8B1WpZNjFYHmCQYCFjji8BkjYZcGfyLs/pXZnFi/MyZcrk0kS8ZZic7kHV1u1PVArG0tAGP\nom7ZsrVea2xcsxJTqN6uaTQOvH+AYWnVej/wihb1e8BRFvH2qQAoWJltEdd68Ratb7f+l8VbVqdk\njgUcC8yvBfrdQv/L/N7/5r1b3+D9LzevLZZYzVn1bBemJ/PTk42OG6ctlclkBweHBrMCoGi+gOrL\nJKBb16AXQwIVnIIcPbiJQBjFvMSllqo40H7Cb3y+VnJAUbx6yMOkAD+PfxxOk2Cdzei1yvk8AXel\nao0r1HS1lJ+u1uoGon+CnACvsCRqFkoomLQqVQtACTcVxZ9KxeGd+txgJsAZeT49LpkhFEplruxX\nwwQZZQfToCzS9ogol+U6TzSWSKTS2YEUfrMNkvAj7qzqw0c1p3JFvqcGRA+FanKmYdRY/Svk6xw0\noCRYzS0ViyyZTuULsuKrqNyl28soik/7iGO9xB62U1zHAo4FFsQCQgARiglSqka5VKJXmZqYqAYC\nIMVAwO12F8UOsOxGvZrLTRfLlVzdFwiGJicmAVAmp6ZqDaOiuJF/JZgdeJorSb/KaUAYNelRFVWj\nN+v1knz6PAKsAJZwR39QVKi8WoApfgvkGOCh0WKGHw8R5RyGI0GZDICIZrPTo2f1AVj7FVelXOEq\nCMoBfyjSQ3YNG2KAROKmpFy4B+ZC/1+g/7eyo6Aw1e70iNTUwiEgjz4ybVvb+tMtKAP9u2EU60V6\nXVNYIkAwAsH7LNgirAopplKWvp2ySI3QUvX5iABlT81S6coXC2AWEFpaLQOxc+gbrRYEFOqMTVCZ\nqUHqwLZmo6EqbsaOcCRBvx3VBfL4qJw5l4TT0cSM+YKMTbVaA7BJCyOdrhALTsCDKmEivWZTVGMK\n1vgF/kXlwnqUESEREwlenimjIfHLjE2YkRoRCcU4ooUEuJHHLto3dcafUEiXsxIxSsWIyS8cD7ZC\nY+C+gCLuXgdjY14WG1wdF2fV6zCWqG1HcQlqVC1X5T+dtk9oOAvSkK/ipp8KgHIV93cOdSzgWMCx\ngGMBxwKOBRwLzL0FQByIiy+/+KuXp4vVrbs/G0tl7ty+JRnTNU3YFsHAsB5L/dm/+Xe1euPg2ZpP\n1r2COHD5k4dzU1Pf+OY388VSvePBDcZ9DIb0+x//0sBA5tF7d1ipHBBLAXoQAbyJEwdr5eLf/O03\nC8WS2VNkzsDyoGkeP3gET/R+wUd6pz98i2P+9p9+wuSh02uhotfqqrF44o/+9E/TyfiyaK9cyv/n\n//JXyJvUbe8RN7LTxXmOxOJ//Od/nkklVusdhPcUj7/nc0ciSK/Eo/G4qhD0e86ZJ8anWRibmpz8\nT//HX7V6nq//+V+g7bImG3G1Gwdeey6XL37vpaOKP/Bv/4cvenvt//5f/itaKuME2gutuhYK64/8\n9u8PDAw8es8OLRiY+0fhXNGxgGOBG9cCXi9cjF6r2QA4fvPX+xEMfeEnkt2MqBaAkm277tC10MqU\nWiuXfvzTZ/OFYrXjA5I4su9NpuBjYyfABpRYll7I1TaY1RcnTqH4dmYiz9SaiTRTehXcIhxBYSSB\n5tTyZNuo/eLHPzTa3dFbPwOavPuWNYqnVy0VSoXCVKnh8ir3b1gfj0XbLQgctVOHD0HoU0IJRQ2q\nvQbwxg9eeZ6r7rz3ceQaR6P+bqv5wdt7CTM5Om56Ff9dt28CBfnBd/8xny+emioRLaP41EAotGXX\n3al0+jcffzAS1lSof/I0QQ860F6g3vz67X0gLBWBkVpvvfBUpVI+nau32kJ1AcjoClfGO5CKc1aB\nlBHdrsdPjfS7H3o8mUjcvmkZgUYvPf2zXC730mtvN5pGMCySsc1ag8EkEssiLQpERTjMobf3giid\nOPSBjCrdNjov62+5I5FMPvTg7qgeHk4BXpyLrOnAYGnU331jD7rjv3zuBVg/07Ap3Z5YYpjh48FH\nHkomYlvXLOt1zLf2vMwxL7zyqwqhro02auSpweXRWPzRhz/DQOk28vVq+bnnnwXKyZeNVrvTNA2e\nCCwVahTwK5igWCwQ5hofXMFo9dgjj6ImtmXtSLdtjk9McVbHF/IF/EMjwwAopRPHAko9HtDaPU8u\nNwUCdHp8OhZpZkLdZrV09MghFghGNmxz+dTFn4PGAVBu3P7MqZljAccCjgUcCzgWuJktAGOj26pW\nWPk00kAOiWQoRD4cpUs4TK9LKD60E3ATf9cdUFtoC7GwCGOlOE2unolcvoDb7deiuLysa/I5fnay\n23U3DYMlM1keI/6cdUZRUZmqFPPT+VKpUgvpUS5ikkuTtT80C1WmBizdtaanJiulQrlCek0jEPKS\nWLNagSfiAsVgHW8wGGoZ5sTk2alcoeePo8aiqQq3KBfyMF3yxQoxRN0wMeHsE5e9VMj7Vd9UoEMI\nD6fL5pWF1KDFdkF7xei6wYyUQLg7oGODYn4qnysUK3UuUynkPd3WVI7wnWIvqHF2u9WgfuPj41wd\nDUhV7flmQJmbue04dXcs4FjgqixA90PvRO8HgELPC/KBbhMz//x0AdLBcDxNv0l0jhWgQ4fKP2uz\n/yu73ZA+6C/BEUSyvdSgRwqHAh63x2jU+J1eC1i5mdU7ZgeyCXcBoQBkkdtaLBL4J103CX/9MOlC\nQX+3U4PJUSNQpNH0BzQ1GBQtFnr4Yr7b8yDGoXJ+1wfnpUmsTrVayDfhTtRrVaAKtKYKhWKzRZ/r\n9rTNtukrV6v+YBBajEUDnDEMsZVABW0TFJ5kPNwIKGR6GuAAWKXb7rqIraF45WpNGCLuHoA3wwfF\nNTtluIaFfIG+tlFPd1sGqD30HEYNCqUKctSrN5r07JrO4XI7gBjsAzUF0ob82ZaQzEIhD2RfqVTh\nfXR7UTnOOhIMiKCYAqbPT/OrFUkkl6F2lIQ9kCprZE1qm4VcDkwH9ITbMcBxTLVSgYlTKJRQj/G1\ny7VKiSciFzFZToBkY2BFTC3UH/IcSbhrCyuhrc5AhN5JIOCHRIPkiQHv0jRFLhh9YAJjux1GK+7O\nGExKHo5hmUFYKorX9JKNtFmvc2U3MmIQdOyKLOZPB0BZzE/HKZtjAccCjgUcCzgWcCxwTRbAnSel\nZb1eLoBldDasHM0MDgaCqNZ1Th55Dx713rcPstYHdxj0wR8bZjXSaJqVRvWf/uFb04WiN7ly+erE\n733x8ZDqO31wX25q+v/7x58qaujWzatROWShUXzBjtkoTr/8vW+zKhhIrE+tTnzlyYdZnTx5aN/E\n+Pj/9Z/HmkA0tXyxVfnxD77D2t26XY+TK+eBu9bBCnn6+39XLNd+/pNf6pHY0FcfapmN8fF8uda8\n8zd+K50ZePTOrV2z/p3//peVuvHGvgPxeGLT49vcLm8badrp6b/8P/8TQv3hMFMLie1h1TQ+tCKT\nyfzbP/taKJVdllLBSva+/lYyk900+iDZKV5++dfca3TN/VDQX/3590zDGDejgczof/jXvx/RlNMH\nXp2amvpvf/9dAJddt9wyOOgdjAf6xJZrMr1zkmMBxwI3kQWYUfOP6bfHqw6t2hzSIl958nOgwKcP\nvlOu1Pa+f7ZWqt591+2pod5vfu5B5tinaipiHI/tvsPr6u595WlCeBpqNhAK37trCwGR3/zgPTKl\nrd95L2kHP3v/rarXdWj/62jDvvbWr5VAcMXwl/Wwdud9jzDJjwytAFw2jXqtWT9+9EOBsKNpmB1D\nSV3XAlPjk8Vi6fDJCbgqq3feCo/Do7ibdYOUw8h4EBoDHNBFE9wlcAzRJoAeABbjx44DUyBy1fOE\nvvL1r0aiesAsAyW8f3Lc6zIrRttjdAJQ/+h7pdZdkAKCh8qFEsAHMAoMlHLVMFqelVtv1yPRB7av\n7rWbv/jRd6rVhqkNhiPR337iQb/PfeDNl4nxPHXivcK4OhIiZqf9xv7jnZ77t77yJ9FoJBt1kznu\np9//HoK4JdMlEUg9jOsJxNKBRPYrX/t6PBL2NqYateorv3qtWs+9904WHko2FddYIfC6QTTGThwG\nOnlz75u1prn6ltuI3Ll9x0bCZ/btfQNUo5CfqJbzrco4QrV733wXGOnWex8O6/rWtctYXXj5pZfr\nDWP/u/thoySjPp5IrUvIU+L+2+/n+gkfUuy15557iXWEtTtuj+j65hWZbqe1d++edrdXyY+529V8\nJohZQIRQAYPzommhbCwEWOMC6Perq9aMEtuq9BqdemH8zMmaFuyFeoiwnJyqA371PCrBtufxtcX6\nGjkAymJ9Mk65HAs4FnAs4FjAsYBjgeuxgO3Vd3By3US7qIrkicOJZOELini1UiZFDn6/i1RX3SIS\nfizu8dN0oQzcoA/FY4n0wEA2HFA65XGWR3tmw+yQVaeh+L1cmI3FUlzPIkulDSM8HIunCJoZCAd8\nRnmAhVHk9NpdfFlC+9tEs+OuDyM3S4w9+QvwhVnq67XJDcGqrCwpStw9DGtvLJ5ModQyMACnOaYH\nmZRUG82G2iCk3LKEzFNQGqAaLWFSy7KtlyvIJTpUBD2+WERjRbZUKuGGoucC85vqlGuNFZEwbj/K\nuQ0SObeCCrejKB4PQirAJWgK+Iw2Pxj4xfatrsfyzrmOBRwL3FwWkO4IVRFNjwAKp9Npes7GZIzO\nzkqQ0wamVlRPJILyiKfoJfpESyVTYNHRqA77z6OEAwiXhMNmkxS2Po/SFaWnJFJOAwAo+bEE/TTE\nECAPhKWCXQ/RIjAgYBR6UZDttOm4IVnQD6vBBAqkXM9LSBEsDJARZDa6bk3n2prHRQp7IaxAj5EO\n9xy1Q/bZElN2uh/ruRELAzwttD8yHyJX4vfxeZHUwXIdeIYEfwoRhk2S8Hr9vkgkDusxnUmSBy4C\nRtDreggECoVTmVTA5x6PhkUNZLrAfrAbem9UcYHyGUKQEcmGXfVqMB6LUIBqQQYfioR5lWAIYSuO\nScYivjqdNnxKb7MlQaZUHi6OjHgWA0WUXxg4YIC0O5pOQWKMZbAUBwZS9Xqz5hIJWBkFm5iHLMno\naiU5JpsdRMA2qoewTq6OZpanoQRgUILRC0wfT5AWOuELNGpKKBQgAR05oSPRaDqTQtcE6AZzo9PV\ntvIHYQ84QrByRJVdJeYJ9gpJqxn8vEEtqACgCHuySwEQxK26RGYFWhCjGYOgnaJukb89DoCyyB+Q\nUzzHAo4FHAs4FnAs4FjgmixAQDi+rLcDb9o05B9+Mk4aiAEEZlUN4LZWixPVmvGrZ97FF7x962oR\nNSwjUhL+/9l772i5rivNr+KtW1W3cnz5AXjIiUgkCBKMYpQoUhLZYktUR4/bmtW22/Yar7GX/xn/\n4eVZy+PxdHs6uYO6e9RqSqRIiqRESiTFTDEBIAiAyPmFyrlu5fJv33oAHsAEgsQjQFbx8aFe1Q3n\nnnvuPvt8+9vf/tpNt+C7R8NRp93i33h1bGZ62fCDaOwlsjM41D0SNcUx8ZpPlPAKXbfcuDUej4eC\nYadi8a6/KjaU2LD45+TVV4o5wI3JdE7HVX3tlwRe3/kNkoetfHKSPKKSo962oXVSBxbRQiGXyXr1\nVesoWBAMBhsV66KhcNbl2FdtWSkCiYtuIoDn9PqiW7/52/5QYCTuo/wEDHCWLYrLraqq3+1BrvaG\n62+cTqT/7pe7JpFpvG2tudPcP1Vi52+vW0luzsPPPQgfO1VR0in7n/6nBBqK7UYFZ7tSK2kOezJf\nsLu1oaATQvgF9Xh/p34P9HvgS9cDRiUys+rWvBZ7ILwUBY3haAjLqceDYA4BFTtstlvIrrGwDewP\ntUUeh1PzuAFQXG4XVcOaZgdcEiq5sHoODQz6OqYNmzaEgqF4bADV2PLiNc5Axr3jYLNtzhSbZsW0\nas1SlwpMYiMRJD11AhT78PEk9WFWXHMNQAByG/VqKZ+akjyUNti5unRiyKu5KtnjpmrH0C8RsEd+\nBJcwIG1ExlUnFe7j4wtAAKJhF0mOP/ibv+IUo8MjHs27dt0mGDEhqgeJNPeZHBMDxBbQBJRDACSL\nHQicN+vWrQZc8PtdzVolOjzs1uvdwCI4KZFgEMWWUDxucaiWyWy7g7h4HhTfPzjqcmurVy6iEpy9\nXatXvTded3Umk3vuxfeAe5i1AMTHFyxAU2bJovGg32tp+KlqtODgMcDxSr1lLtXK1TpYj6IpwEzZ\nfDlb0NvOqM1tGxlbCigTjQ2pii0SiQHZMJXo1erbr76Sp2hSbDFXvXDp6kDAFxsKtZu1ZYsW5nM5\nfd8hspzyRRug0vj4uNvtmVg84VYdlspU1WEfWzRBGs7I6DAMlEg4zORK54DXqHBM2u3pyRkwrWKl\nBaYPERLVGHAuKh0xi4GQDI6MgvJYFKASay45VbBa0hYTiU3OcEwgNK/HhUjZJZ9G2gdQvnRmrn/B\n/R7o90C/B/o90O+BL0UPGOEsG6GuVptymBREQI+E6pHUnUExltgZ6Eqrmq7VupUStR5IkGkRIyNx\nnZAcrjzKgeAIeMTUSQD4AH3A8ybJn67D/zMI3BK5xI8kcKYq1K8U71E8aDtohkrAlcglqiqigsIx\nyYeHIIIf2abEJSWKnTaCnorThUtKZLANOAIt3OZyqlBXOAyLClrOD8FAGiT3SyKRhF2VEGHNSDg+\n4BMNFGMFAPGEPHMa2+lakXohdajT1BvANPks0Eyb4sg2G0VA7Vwd3zTbBFPNdvT/JGBqttLOrg8n\n1+OWoOCXYmT0L7LfA/0e+Ix7AGoJ5onyt/xQ5EXMGQIYwm8zzCVQBbJR/AHJQIh4gAxYPj7oIRnG\nRwaawT6mrllRVCAVDgAyYVcUfvgSC4ghFVuqyAvjhlWEZoE0FYQISu1omosEHz7HmNdQnKrqZqqM\nIX+i2IEPqmLdDFt65tLhbIgh5IeT0ii7AlXR6gv4qQaUrxdR5ICoYbcZFWWcTir9UPVGVT4cX+b6\naDP9wHxA1R+kSWTGsdugwRhXgc22oa5rEaoFF2SS6UbsuxQ2tsnJUeWyID5it8is4zBmh16TYaDA\nPxEejEIFN0uXg9ItdltdykVLORw5VO/CmHPkR3pXCDTCH5F7AdXSrojRR4FEaj/LVdM6Ts0B7cZ8\ngzYtf/Cip+lGXnSK9DY3VdpPEpTcU85s6jCHcEgu0da1oj7L7CESthweLRiCE1wk8JOqUvJYsk0N\nGg1dbOLO0i4mHe4dSre0siq31EyxaohJXDgjpzdiztylS+9dH0C59O5Jv0X9Huj3QL8H+j3Q74F+\nD3zKHsB7U9x2l29wNJbK5F965dVAKLpl7QJXyD+0cDlu3viytXq5sP2Fx6amk89tSxDhE1fQ3LF2\nKGrcKRbzMI2bbb+wPJp6t6aXSdmude1WJ2AEzj6ts5GtbWm7LETW2qToQ9ButUIdm8la1zu1aqGi\nExKk2gS0ZE3zebzB275+bygcGgprnEUvJcgYmsrijttDPnelQh1PE6U2wUvEN5fDk51PQ1pGeJS/\niBriDwv0M7F0aWwwPhZ2Sw1O1hRszkYU+4RobjEvWrNRi85EbY8XismHH3vMYnO4w+OU7SEmbG7X\nyd6nwueK5df5AsG7bliruRREUni9s/tdi01ZOhrzeDWuWM7Vf/V7oN8D/R74JD2ALZJ0HLvoa4PN\nir6IVKQVcyYABWtlVvWsrAFVxMjwN8t0WfgLx0KMGHuR8kLao6lQKrFuh7OASUQLHKYGqqSkgVgA\nJRySjMm26KQiXpJNnKRiWtPqos7OkgWDfq8H+VSkT/fvP1LW64GBEXgfPrfqUqx5EByTpdW1ke5o\ntI0ERyRKOlXkvYsoYgE1KxaH0+1Wb7v720AAZA+RY7Jz5zaqyTz/7BN8o/g9kUh43eIxMKL3d4yY\nYj4VLgqWWH561y0AuejqoncrCZICTUipZqiRgqHYEf6gr+qVps1cr6Fu3lFaqMq286VSvoR0CrgQ\n6rfSSVW+Y3oRBd2mHTnzBiVxqs2mblZ9APdWRUCYXquYTQQur1cpmEzRolZdcBPSSQ8f3Ie1pygP\nqFBGkk+pYlywd+jIcl2lmnKAFuvNFgWEGm3iDgAlHBQ6IlMTE5D8COzCj2SvSoqT8ZLr5S8uSa9X\n6w2rXpabrnoCpFONDA9ohATqubrRF3B1nFoACmjArzQa+iuvvVKu6LkaeJNr6zXXRCNhF8CZ9QNS\npXpnunR+f8Dtn8/G9SI5vfHWA67m8+xftnPx8HDJxi+xVLyX/z+P12wEb/bB++AWgFvyhaCz/ddn\n1wPMTBysNwb6T9xn16+X2JFmn3TjATOWVef/HPWshLgmH/zq2ZDeo2vYkA/d8tz9P/rIl451Orfd\n/b8v6x4gxmezkUpfb3dPzBRKZksylcEC+n0a3jo+IJNRjdoDUn1AnEE2xzC6nTZLvZnPZWyKrVQM\n4ER3atSsydSb5pYRCgVooVcYtEaAzeSirE+nW2B7u61cIuPd1q1TjzhdqUjWDmgL5XOcDgUiNPMa\np5ACkOZOo2y2EnMjoqfg8zIlGyuCUw7prJ3mWZbQ4qzRlpNKRFAiopLfXrO0pPZOb6JkdxYgLq7C\nLvUm3B6XUq+ZEzMJi93p1IY9viBBYbOphZ5fDbVDeYjNBDidqqNloSOAaUCFJJzIz4c9/5f1WOg3\nvt8D/R6Y3x7ouQpiw3omDEsmFq7ThTBCcgfyJG0BRyQ9sd7kEyufsMgXJapmt5jPw6krFop2a7dU\nLCBZhdXCxmHGqI9cLhabdtRPWPkjP1oRa4i9xqIJTcJaKTabtYYY4FrLHZG8EDh9BrsO2ybqJ9AA\ny+US5r5kVVp6OZ+vFIuVZksBWaF6TLNpAbChrapTAz0w+DRmvVqi4o8oi9RqKEY1AVBs5HIK+4Ne\n5bfxhve9t8aHp7q7x3Dp2VXZwthBthSzD7+DSkAmSji3a5ZsPk8Dnd1KvVomhygHMARoZBR8A3ki\nS6fTFH0rQCV7LV8pF1F7BUixuukTUwM4xGq2tkFM6sxNBvABYNOsGDV3CAkA6FCxmKmjWm8iVcJK\nUCiHcChbdfnWairm3WiglChJVKmBcpnhKQohBPhGrkquxvgl/3JJvffGLZX3vAHSqlE5iGMyuVkh\nYkLCgdnCHTE3AJNkBzoBPAikx+lUzZYO3c09rNaA/uG4QFWR5J3z9iulRZ/X63MDUEhP45kpFqvi\nDMgdtCA7xO+PYEV9Xn10KZ7XGLYkmNE2IyZ1Vht5ZvibSNrcTxnWlIiSwd0E6CXX0MEIhSo2z36S\nCC11OqV8AeMF7fnU8ze3pdIini2H1Bi3kG53WTxIcy/g0nwviHEXoakq/4C7Y+A0jywh5nkAXJqd\n88VplQBkXUTDeLLwQFjf2XjGZ5/3j7lKZlNjL4IE5KaKwJjk+Z5+zTkyDo4cWWq+Uvz145/Q3pEh\n2XIwatmxFxkVcw4s8zAOE78pfsjQhEoqbXYo/cF5upf6by60B6wut+/Ou35rZnp693/8i6mT7/3p\nn9XJSt9y1VqGd/b4oUI+9+xzTxXKtZIeVBWnHR0Upw0llFQy+fiP/s7qcBbuugPi9+Hdr+PAH5ci\nxYFFoyO47+TYU1XH5PQ67bbNaxdTV/ipH/8toUs98TXVYT+87+1MOv3Kju2k+d8TjHlc6uqFMfz1\n5371C4dL+8r1myzd9ivP/YLlg2NoXSAY2LB8CEyGJ4voIm67gXPzKJNu00I/hVAfZGz5vNMmspor\nVX720wc1n3cgiCgs/Hfpm27XRv78dTfdTC2hiN/t9gW3XLny+IkTf/3jX5kV93/7v/2foyMjDqdq\naZmuvfaqqamZv3381xTvWRBzaG41N7mvWCw/88Zht9d/1fV3BKw2HsML7fD+fv0e6PfAl64HBOYl\na6ZRx/EQm0WFYaMPUBSFqoCUKBw98GaCt2C1LOD37zkKX2LB0ADUhEoF/df6vsO7+cSPumi7mZ48\ngiDsL5J5za1lD6+jEvCxA+9CbKh1HaisRJ1tZyv39MNPcx5lYBHObfHoe5BYooNLPD6/5rAopsZM\nEdnu7FSygKuybhkKIEHJ9yFjEnKMydqmTm+x8vPHn2TJvjyuUaX+uaeeh/QyuHy932I9eegQsqk/\nfvRpfKIt11/rVBXW92RiksECRN6uFEvd5nPvvojH0g0vxqVZGEPVBSSF65RJAWiDd6zS+FNSNwU8\nYsEjL6lgzEGx1nxKLmWrQ3YNWZqDQ8NtdM0zR7PV2j/8/d843a7RiLeuV595+umq3vAGF0Cgkbmg\nXtv15mucIoLuLumWrTw77TuSID3nmpFFmtNyYPtLABTljuQ9jcQDYD8+Z5uaR6++8DQYxtH9o5z2\nwHvvkaA6svwK+nbl2GC7UTtxYFeloD/7i7xNcbw7EmGyeXfHLqab0Ym1MEcCXvCmTrpQwD2T9ZuA\nIGBLXAYXR5lquUjjGvmOi6glT06xr+qOOFT3xLIxHy+36lSslYp0GPrtqKqbUZC1mIeGBloNPehD\nEayRa5Eyq4UHhsKRMGlBRoj/Un+IPgcAhbrP9LIu8GAzly+JWyBBHChJHXpN84iAEKlal3rPfa7t\nY/wxxAulMgPXQP1OtYZlMb1HGrbV6nG7WIQIRNGFelzl8YWBxvZgmbI+IjHPZkP7B5BiXhcqmLpW\nE1yZuw9JrGdhT7V+9l/YzgQN/ZIzaCdqZ+QInrNJ/8+P6QFjMpNFKduR0clIkNT9doeCnli7JiIA\nqJe7XT2s7WOO1f/6cuiB2TteR4aduArPe5syejScgIxYA48RRQGSEKPwvpcRSmjUZC+4lAwZt5+d\nmNhB2GRlxg/zonHkMuOn1juyXZJ7e0cmzPCBR+61qq6L8ATVWFmVITrBy9BtYM4V6AQvQb6tiDUj\nLs7Z8UjYxCttlnSFD2rx+y6h/wGqpAKZdSk4QB+iosEdMe7fJ+gaHDRuik6Rg56bZKH/FWNOuYzB\nLNwLXyBEfnw46CO0RbCSnslmMgAosEQIJHYtEKhNXrvmg5bC2KTGQSwCWGE5mACuyBdypPDkKQus\n171AHcEQdRSoHUANAmurA3uEKgiRaISylOa9J9v1GtsTcCsUyjxKxNMgm6B14nS5OKbDqSTSgCQ6\nFZQJu9LPzIEasomqDPgO8rBOVwfRAG4cN02eKCKT8rXaYlbncZRnR0UxpdHUq0Wikaq50XuUCOp1\nTXYcXAouGNwSopE2fGxvQUPuxGxu897v90qoymr1hyN6s01t5kanC6YDJlMoSgQXHUHF4TLS2D/V\nM2f4eB0KRzOKmOJxSniKP3AUsYZgxOo1mafoVa73EwxWw2qVyijXmJwujX3nhzeDAyM+FVAvbTae\nsl6be7YOXUZZRsh1c+ES86bqp9xI48oM9n63amxjbILqDYI8FlRvZrcwUWekLsfHITakBzgOf7IN\nypFsw1Dsne7CfnOoarnEb4K9HM3tErt96lDyMTpB/GlXjXPNgblPbXPWv2xfq8qqqCkiCWbKc8w5\nmjQbZgA7kEHG56hKnLVz/48vVg+A40oGiaK2O2iIADQQnWVoCQOQQY6JM1sV4bbZUEjBzqji2HdF\nuwRyA7XhFdAVUw6DALkDwEWeFOGW1HSLBWwa+kK5WmP57vFGScZxQYpAO6NWIZOlpeOxAG4wBC14\nI+KQSFKjIBasklD2MFntHpdTc0F2kBIwaFDRPM3tAnyhygxwT8EN7gMRxkJCpihXqbhMLPAFBGGb\nUrHcajig6CHd4fF4abnigKZnwm+R2jXk27AjZtVmNQSw2hh/nG1sNp9xKiLZPSaIKIignQsbA76h\nTM0mg3sIpq3yBsSko9h8movHploqYgTy9q6UD6IbTDz7zAxIuCjMzqD5bEO+p6nbNDVZ07VAT5hn\nePrcTrVdQZK2Q8ljjok950RerwZslE6V67UW0QL2pc4azSVMxU2htnGnaWdfeVqbeI/NXA5hLDgu\nkIFsmtfLeTU3/kC7pNeQXenNDiLWJb1ItxgaKNweuUgkbpFvwbBgs5BdsZBPKuInTtEOoyX8zxCx\nM8uANrEd75jZrCav5iES4enYnRRJoqiSesYYXuKPyHxbNJSNE8cPFAu5X7/463yhNDmdxWPmceNm\nhMKjXp//httu9Pu8iwbCn3KeuMT7/YKbx8gGPcmcOIRC8hNPPUcNRnE9GK7GxMX4RJBuydoNZDtf\nf+U6l0MB3tOr5TdfegEi2PY9B2s1caYgVjkcbgbrpmuuC/h961YsVFEDuugvgVwb1Fmn5f/yX/Bc\np8pS2RyPkLbz9J4+P2J4qtuz8ea7wIyvXTkGO/r0V/0359EDxnK3XoHifXDvHvAyLboASzcQcJEM\nuf2Vp6k8n2j64Hd/485bkdo67T2dx5H7m1yiPcDjU6syQRLV2VEs5N/e9i5l6srMvTL1+ljDrVy7\n3uf3bVy7HLq+cCnnXodAGGXQk0N7thdLpR37Emabes0NN3u93rHBoPAu61X8mON7dxUK+Zfe2Fap\nVsu1JrxTh8vjcLqWrlpLPH/jFWtcKmkDZx2ZFUWDVunVA7veKJUqOw9nKVy49aZbqGI4HEW801St\nlMF29+x4o5AvvP3OeyxQWZrgi7i0ANUMV61d7/f51q5ZSpMdH+fKz72gL+d7XMDJg7up47jn2AxO\nylVXbSFTw0NMypgazqdPWPdOHjvA2v6NN3dQ3LdSbyLntuqKq3w+73LJ90Z29bI0xeJdDI57wwP/\n9n/9X/Agn3/hVVbde9/byQqW2gxOp/8Pvv8nuJMzmQpIRyiAM+n8yrd+By/WM/gragwfT0zjPzqC\nowG39/7rv0JdytGxOKzxLVdvJSA3Hg/j9MUXfZcH0B75Zb5QnJqZ5MHzxBb44gt/Z9FquOMTCxZ4\nNG3Dkj9iwD/17DMk9h86dJAnaGTxJhYDt95xi88Lx5yaCOXrr7uJByDq93mIHFiErrVg+cZotear\nhQSF8QZwOW+47YZsrpADF+lKGPX0+hegnCUIAQrhx5P9brdPTCzE/R1ZtNTicK9ftmBocACnFYmV\ntdfdMVEqNR1aLpc/cPw4mAtsM5c7/t/9yQOBgH98MAqD5oIDgERJU8cPFguF5155nbmmQfVop2v1\nps2EIdctXTR3CLFAquZnGLGvbNuLZ33DTTcyYs93hLFzDX2A4s9+8Uyra776hjtZ28QDrosduMT9\nyiWPVav6tn0nWR/dfN01OPzycAFc1qm3VH7thRcKxeLJmTz1OAPRYVSKr79xC6qWGs+O3K8Mt+yl\n53+NAEGmwDLGPjA6Qc9ct3WT4HHUNG00pg7t47pe27GnWqvJQgR3zWZDR2Dj1VvhFo3GL9w3bjQa\nlXLp5ad/ztNddw2ydrrj5ivdLtUwDrS/SvtfevEZIstLr7gONtNwxP8RUjg0Va9W3n75edj+U2WK\nq7q//vUbeXh6AA/rUsbzG68+xwo3NL4GhGvZ+CBry/MxRP1tLrseYHGsahr1zpav2SCsEP8weB8M\nd0IwvtCATfWu2yAZgrEINBC7uvoqRnimYjekrBW8grXrtxDta5reAjckkYf3Wjimttqh6ABAcC6b\nBfn1DyxhxF559bWssmM+K5yRYDwG/qJ43WDNbfMwgMDEmuWoO4ERA31AA4E5smz1ClgVi0aCfC6D\nuWsOhCKM7du/9tVcLvfSb7aheoKRIlZ7+z1fB/0JRYdYw69aNS5MiWYb+3/gyPFsqw1AQS7PzXde\nyTO4YvEoMqt7KinGtsXvY5b0e/0dt7ZhwzrAi4mxUWyy07QJSGE4FkYQHDJ9264sW7memr4dNYwh\nJ7+S5sVGxj0hveOMgUWML1xMXz3w+/9VPp9/6a03eECLpQoXctPtXyP9JejlOOqChYMAHPfe/y1B\naanLDBGh43K41FuvXsMcfcWKhcA+R/a/h5YuOrFgMrHBUS5ZUx2k+bz91jZmvel0Djhz2RXX4N1t\nunIjUfaBkAegyOPSisXiW9u3lyrVTKGKxVm/+Tq22bBxPblL9WKSOxKJl5hJY36/PMX2tkfzrV4N\nymSODA6BgzhdHmzjxMTycrnscvpBkL3hOE/90sXjIOPgSGQgKQ4mQ8uCVSuJ7VmdQaCj6MAwq8LN\nW28hiXZNnRulLpsYBXEGp7ksHoH5NWfggc1GPpPOZtInThwDQEmkBVPEk2Aiqehmb6mcSGaAEkei\nQYm1nOXmXxb9OR+NZLFEdS7qc83MJIrlipU1MNCfAUAAGTokt5DsQQns8MLK1CrlRCKRzeXwurAI\nJAkyJVd5UKr0dop4cr05CqB6sT0PuoaVvcgPNRvpRDLNKCDEQu1xr5vm8/Fs38GVACRWmvisWIj5\n6NAv3DkEQRGgrUkSvgCUrpjqNHV8Dh6/SjGP65Ztdt1tSHj97v1C3Huec+42QmfVSiqVYAJOZ/O4\nBZSP40mv6U1H1ZVMJaHVVvSGmFoe9dOrLvH8SUWAX6Jn2LdQTCRSVsVVrTUdTsnowX/n2ybfZlJY\nkFQ6TVIxwQlcFs6g6LVkKgXloVxtEAB2KXOPLIBpswmOV0knOXIpmSjaVE2q3OGhG68mybI6bU6y\nkMM6YbgIAeGKQQE2Ps/gJLA9cwEsiDlN/kLctc/oIoxbRLoWlr6aSc4UiqWTJxMI6enNhtKBGcsQ\nOK8zsXTjENyLQj4P9RmHhvqFNqU6MzPNTRodinEgPKe5I+e8jntpbASphHEViWEJnbFoiCz5ZtbC\nKFTJX3G744NDsLttagGvU2ZCq1XzIhxrjw/E2b5Qg3PcUd1BknsG4lGf1yMeZMceCoXxXtw4w6qi\neZ3EF3vblwEkAFACIQ7lsVsIHbrdEFAcPg+bOqPRMBUWiO8BlATDUYjZ4aCf4jhWs47/GAlHebh6\n+fZ0ODFMl9dvQWiv4WN5YAR1O9GBuAPyYKGCaYfBdbqDWZ+QlSntt5ixBi2EAQjmNppuj19xajjt\nPViEXHrCjlAaBuIDOPczuTLcHJviIYd6MBbFEQdgvWDXC0MEhz+ZmCbAk85ksRViguq1yclpIN0l\nYyMSvEHYUBKKa9CdsqlEqVSenJ4mRA0B7fS1nMcbfAnxJxOJ6VbHQj6/4jSo5eex5wVswnwqba4z\nFGqpxDTkoplkigDqbJvFALdK+RwyDdOJJMlQhVIFAKXVsbHU4WnCy1KDXuCRXAYifH4mmZHi1nVi\n3jbr1AwSPDnYwd2u14WSTqvEE5jPY2kxfRLMFhYSOpomlpWc7wIaf3oXLoGgejaZKBTLJUdX8/lB\nUhgwjH1DnICUgmY2nSLowvqNZa1hW07v/b43XHWrVcxmcvniiXTXpXnFHbVZYVRhq9kXan8+m2IE\nKmG9a7bPHavvO1b/g8u7B7jjVHLBzFK0F1jZprmhmQjlALdAUWFeBEIBlu4qsK7N1vJ4ifhGIxE8\nBw3g0KkyFPFXUWY1RFxlksEUW6wdr1/ssKWuw23xhMIAKOFwEMjPYW1aurBRvA5sF6wNYZYEeEwI\nGGDoOCvHNxgSHSiD8saw6zITGvVxOHgwFAIOCAcDnJEK8LQrDr7sUH3eAMIcICw8rVE4g6ojmcmx\nnqKJGPFwOKoJS8LJg8R0QPstLnAilTN0O7ZQIIBJxtwz/IOBEPabqcEBXQUmmq3LnOLg0XJofMTl\nsGZTVbgh9kAASRdCnCpgRygaBTQPhYJqVW93IezYmbP4zu/yw+0AfsA+DA4N6lVdLxWYfRzymKr0\nG8kEoLSwGjm7kG5aoC6cW2y51x/keuk3qDMV4u0WK9sD3PsJn7qYFVSuFIYm9y4cFpi+aZJKQJFI\nBAAFJJ9DVDuI1AJvsVATWyHkBlwL1m4+HwAKLDz6F5AXIg71h6kvVArCwuty75jv+LaX5S10JCYW\nux1Mmf40KRAzYbQ4gPv9gaDqatiaXC5wEMXkEE85P6/l835o5hFA6bYb5UwunXr0wR+dnJx6/vW3\nIEP4BiaY7e1VHOXWydxbNtU9k80PDQ+H/+j38SoC7gsPg3zeHXuxzs/z02xUp6YOTE9Pb9+7r9E2\n3f6123mkye2TMUdIzeEYisd4mJBDbtTKJ/dvm5mZeehnTxX1xuiaq3jKVo8GTM3aWy//Op+tP/Jo\nygfiOjwcDYcGAujmXeRRKyt7RKP1Yzv3pDKF7sRGLeDfeh2ZeEzVZxbzwgRTHINjAyCXINgXqyu/\nsMfFPTUhKkWs9eUXflmttyY2O/3B0IDfhUhXcuZYJk1VEgEAAEAASURBVFc8XCt7/booSH1hO+HL\nc2HyRBEPP3lwTyqVevTJp4pQ8UMLnJp/zXjc2m2f2L0TzYQXn0k6PX6N4Es4dMXiEWGBGi9Isixj\nUtPH0qnE4z97LJMtzugejy+8aWuVcAGwDCzY9MxkNp188vEncoVi3uy2q86Na1ZimqePv6fr6Zd+\n/azD7VU1khjCm1eOOU/xxViQs5RKTB5OJ2ce+9njmVwp3wl7A5HNubqioNGGln5z6tieVDL186ee\nLVZrrsHFznB41WjY3G7ChdHLMy/8Oqf5g4E4xim4dJgUicsjKDGfI08WdTVIyPUThw6Af/3LP/5t\nMp3dm6j4Q9E1V23GjPqMRM6P9Ua40bnEMVQ//r+//Nt8sRodX0Wc0GerQiD6px/8BcG37r/+H+Px\n2IqFI6ztz1LGmc+r/XTnIrrgDcU9geg99w/j5rL0FXBJgg9GEqvZ3FvgGX4/jn+X7NEtN93GljcL\n3oe7KJs6ncD9pIoIafaG62+gRfipssS1mFHLu+4rd7A96wc+Z2s+F6ARdocqSRyMX6fXc+dd35Sz\nG/kbsykebiPZykQGuO/Gm29lX1Ab2ReHU3EsXLWJsyM5xJEM+kb3tm/cxxFAC4wXm596GRdDxhAu\nwuG923miH/7hj8t6c/GareFILOzFSzg1wVPFwunecM0NHGbLzYYCotEPuLsy+V7onAuKoBeSiZmp\n/+vf//tcoRRfvZkDjno7Nb3wl3/6/6hur/vf/R+Dg/GJsKPTrG1/Z1smlXz+qZ/miuUDWWsoErv9\na3dqXs+pi/m4f2V9XiuVci+99FK9Y1mz9S6z3dUOudFu+bg9P/H3OCf1Sh4q7zvS5sTTjz1ULFfz\n6nA4OvDVW29CiQB8q1TIPvEvP5hJpp95L2Gy2G+5+go4/9t+/XNSEg8nU6yC/uAbt5patX/4q/+c\nymSPtbwsFTYvGSGY9OzjP4TOnq7XQd/uum6Tqam/+cavUzzFU3U+X7tsIfw79IWJ8PaKVH/i1p/a\ngdHCkGlUq++++iJ41lvTNS0UGV80Pjw8uGpRnIeacFutVtr25m9I4Asv3hLu2MZiwQ8lBIHFmIjV\n1Q5vf+vE5ORjb+5VNF94bHhkePj6DUuZBThZvaHv3PFWpVZf4h4LhqKrFo+wXDzVnP6/X6gewPa4\nKYHu0rS1PuPCxPIxifA+GB1k7IUHR3kPTIBl82ijfDIwsojf6GvzCekefDu2cBGP0tTUVDabPXb8\nBDkzazbdGAgEVo+GxGMR0AEL3LOW7NrFneE3uIyc0ch3Y/kjRxM75xxesIJvFywViBmKxCm7IHAk\nSTTjEyuxfotXrGWpL8Zd2iB5KJyD90bLu1caFnLrTbdxHE6NjWRP3lDql0dpkz8kh0ZkVcAa+SQU\nDPEBUII0B7BY3kuinFxct7vM4+UTdjhlyU1Do4s58pgxF4iQgtk8uGAi3umMLZW2GdMTDRIIFcBF\nfhu9tHDhBBbJQHTl/DS+l2bLReH6L1/DfCG7chZwE/byOqX/48Oj7NMLmoIocRW9M3K94Bqx0bFo\ntzu0aMLYRo46d5uOX+MI48bZBCiRlaaHT0KxYeP80mPC7jF1l7i8fL5yrTCJoQ7J54ZSnpzFZEL8\ni3lnxbpeP9DG2RltyYo17GVY7tk2n5qopCWX8mv+ABTyVckggDoxM53gp1ilWpR1SPPiEyg2CVM2\nEznwPBTjeaJ0gH7Sy6RPTw37S7kX57VtdGQbcWaizU16yGIHSSTpCUNl9JSMVxho2AuGICgpdbzK\nkA4IdzS7Ajr6faFQoNvQvZoKFnMsnyPjmuhNXXp7Pi6De8pTTMX2Rr3hQNnaoYEEezVIsHwz2wLj\nqVM0FQT1cg14XrSuNHygntJBvUF/9bqsNwmRK0/XGSTuVqWMQnkxn8nBwxdNb4utQq5yowrtFvea\n6B8/8G91FRal5HL33H1I+hwAjPl0+7ld3JdGXbKyBTYWoyfmkkmI36f42L1WidHsvQgO0rRWT67Y\nmPAuOjZ3usVftjfS95Q65UkvEv8slKhE1xxwSypwMBS0dlp54iFdk54vsl7ka9aHzJGnOoklD4z7\nZqmYB26jCiDLnloH7mfdyEGWI/MDhQS2Wp7xVK7YIhGX5gkESe0xVTIaR65Xk4RFYa1jc4xJf3YI\niKBJs9E7co6oa6lSs3gcLozW7H/EPeoUKYFTXkUQvhVm8eTBGgTN7caMS4Kf04VSx2KlBqFLMntO\nja1TTe//Sw/weNbow2plZnoqlZg5OTUD+ShXaJGQIUp1523U2ZI5RWeqyENurI8BXbucAYdFsrXK\nZfT/iiVd08C8zvuIl+TtAUMxWU0eu4QbPu6FeTNLHPJDXvKtyz33y4/evrcl1pm45dy95rznACbo\nMHM+EWtLxeKzPjGZyEs/55Nz/iRWxZCowqWuNWot01AoTDiRsOFcr5STsSZnx7Mv4pwjfbI/GY1N\nEr8qpVQWVESfwAZ5vQFvp2y31csFzEG+XNHKeicI4VR00DA56XQG76Soux2ajmfwCc8nhTTBNQBQ\nGJkMTdgvzDmQO/irN1KNBZGFCJMseyDNI2BfLvMlywsMCkJsnNHAnZBU9xjbiKN/7osLEzmEegGi\nXCaXyaYxZhVP0OWpiV0yLDBfp5LpFAWeYNiAhPn8tm4LlRmyEsGJOBfzLSU2oN5m8gVbJE6VVL8/\n0KnX7BYQtzq0bKLQyNegPlmBvlKiPpMLbI6QO+F2iiOhcQDvSe7hp3CJaS3XjvdVQ5ukXEZ5IpFO\nQ6BqjUehJfa+xRUHQDF0IfHUuKxOpVxhrx4d2MDZDBiRZnDtHA1XTpcqKJAbYS8yvzSai2jqqXPV\nGhBpmGParVln5dzO7f/9BekBxgZXgpjTOdcDr4BPjF+z34BA8I7l/TlbQmQglE59GTj0xIABUHgq\neeFvCCvhfS+n7UPtsxxfgd3wgS+WSqItxXcQLj5wi96H4L+8Occkn97e6jzjJ/c+tM39BBrI3Beq\nKNRDPvsFSMEHc7c7n1bRS2cfZs5f+OSOXkbenA+N3tbe19tzthC4hD8/rMesaPKe+5J7bSBFZ31x\n6hrf14beVhJQoM/P6Qfa/GF36qyDX4J/nDsCLl4TIVvO7HvvxPHjL76xbSZTsIQXRaKx/+b73w8H\nvY5aIpdO/t//6U+T6fyet7cnTiYPT6ZbFkfQHThrzr94jbtsjowwkgmfKHX8aDaVCwysIqp87dXX\noo0H/slF8D9WDMfIyH+y4loce3fnTCJlViPBkPfu22+ORoJhNwW/9ZirOTWdePPvHi9W9EShbNe8\nwyHSjy8+XIWPRNUqCenZItHR6NDQ6jVrAz4qLzIdyyUIZGbUhrQ5JHu/h7leNvfn4jYUIU8kt+vH\nD+yG9rzn4DGmGeYYIOFgZIAibWvWXOF0OFyIbZVLrzzzzMzM1I6de+F55Swv+fxBRyNnN7dKMPvb\nXQQIcYneeP0ltp86mYAKiIYVc8mS5avhJ07M5iqLC1QvFwFa3nl3O6TlE9MZbhwqg7Ll0jVsuXRi\nCIIfLpS06tBuSqnpXeIPXaWZQs3wSKqCnM0Vm6+FJR/3kNJ/8UfXxe3/S/Ho3CMY4AhhHjmwL5HO\nlptOmyty2y23R0LBiZgGC200HISVnXzkkVpF371zZzIWX7di3G6IHhGPKOQzxUL2Ny+9kEwmJrNl\nvdZGqVDFqQd9lWQ7knZrqZMHE8nkTKVFobw7vnJnOBy+cukIwY7JgUg2k5o58RO9nj60d08xm91y\nxQSoJ1/heRfyyWI++9qLz7FvMoe8ScflNakif2YIuNcrRHWTR4/C87c7/EGv647bbwsFA6MhiU6H\nfE5WVoefeB49l6lUsW1RV411T1FbLsW78Hm1iRSoPa+/CHryt//8cDKVPnjsKBFvLTKC8ZSw13k/\ncaRwHd/7Hss/h2fIHXbe982vhkMBn9eVy2aOHZwCUpk6mW7ULSsnBoXsL5KQn9cV98/78T0gz3U2\nhcrg0NJNiuq65+s3otuChuLH7/nptgCdP7l/x9T0TN0Zcfmdv/e7D5DxxFlzqcTR3duLlfp7e/am\n0rklwRVwMQ7s3w9jrmtz2t1WzUS8RCQeP9n52YGERBFWZEXmABPJzRwltfmhnwhXrkJVL6ggbj9z\n3zfv/XYg4Bvyoh9c/Omjj6KmT+SbeMDxE5PMfaQv+QKB3/rubwcD/nhQap2e0wyZB4E0crn9Bw6k\n02mLXVMJ+bgdpOob3kq72wBnzL/13hRhwW987/vRWPTaVcM2U3P9wiiYwl/8089LJ48duXE9E+qB\nGdSjPH/8vd+Lx6ILgkpbLw04m/BNntq+PXHMV799CzgGmU9IHGy46jqvP7BhGYoAaiDoJ9FGZCln\nI73nNPB8/6R/pce0kCvQGnUEOlb74488Eo/HF4//SSTkR2yTw7OUolyKlSuztEu5BCHPRx7+GSmW\naUoWdM3kgnm8/q/ccXfQ71sw4CchC01jh8c3tmhJvdN97onHYrHY2uUTsBH9apfVkhXeYLtt57AU\n7O6/+j3wcT0AzwPRU5Jertl8Fekho8gauVx91/Hjuq3//efTA/MHoDB5EPiuA1Y320h2BzSSf4Mx\naMEhr1I32W0dl4LRFkIyYHaTCCihyj5kfc6oMKIdhF2gDpBRbDK7yEqTqlng+4gjkd0GNUu0kxF+\nns1iFgaYzS45Zh5/0OdBIA9CaMss5bhgqUlEESazhFDOOdPF/hPHA3SEWZWEOkgz6KudaQExFl5A\nJ/LPxW7I5XJ8QTM6iH3WqtUkWdalEt4nSdH0E3e42bG4y9XMSB75Q6vaQQKHPGpCfJIq2WwT46eb\nwVwcVqq9C6mAgBG3HRkaVVGyuSxKE1Z7GVjEHx5E1Xx0OE41eJEhb7eISbM7Z0SAKpMl67Kr2CsE\nRf1BcpvrI3q424GsCxrTLBG81uvFJn5U19EElGmmMg3E/Bme8NXP3N3LpcMvk3bSsRJVbLdJ5UXw\noEu2LYAEtjVAIVMVAIVMVHAQ7EKz25a4dJXkfLkdxl4tApGVUgn+CEKAaDd3zG2CnKcUEBgrokSP\nDjVKSngzSOxzNMJBmttjM3ew4CgCOB1WCDCgbISCTx8ZXosO5FYsUikMhgRpuiZrlwgwS6TZJ1oo\nSpI1TdwVYpzNqQX93oDPg1R/2wwHRtTruQO0tPe6TO7G/DfTmCTl1oicAqaf3BOJEp8ynOdpP2Vz\nM9ofDqEqoPYR8LPgRIMcWUnmDyQ1uLOzN3f+L7F/xk/YA0ybRn0DdziKRq6bed/rkdycT3iYT7y5\ngLmIalR1SOUQYaG7AtwQu2w3a163EyuFJgulSXmiQT4I80DwQe1eqTVS9brwGg3bgGk6z0Hba584\nCfJifLbymVSJih15dL4qLZ6Erqmki/xhNl8CYhnwOJiVEtOT+Xyha/NSVYNaqlAtGgWqUjQyuQLe\nSNTvFuWoc19Cu8SyOqlH4a77A74anAqBG2ZbykMosypHaXVdbkLmVBj12E1Nn9dbLYPk1ASKFsao\nmdmQ+doj3q+fEogtGxUoXIaWtg5LFPVNzk4v0TCazQtnGE6KDba98E8cnBMK1ad5SYtFkUEJBDRU\nPcGbig47/YVcQtBJz4tfxg+bgYLDH6yWCpkspJmSDifcRNsaUFZBvbHeozGDCYW5ITnOH0TOsF2D\nmVPIZtEScniiEr2XO3MaF/tE9/XTXGR/38u4B6SqC0X9sAw8L8xBDkMG6DK+oH7Tv7g9MK8AijEr\nNCPRIdXXWXnV1oGhoRVLFgV9LlPVRsokSsUOh3AGycpHhU3MdX/V9f6RR2JEs37kZHo6kUoV8tas\n+tOHmigBtahmgyTSwCh5OtdcvRl1IIfLQqWViY03Dun1gS0Kc/BoFP1kczaXrJRy+w7sQ8lMQfbR\nFfC5nV6XCC+9/2wX6RNjHUVx5XKlXMik062GVMLrnYvpGM0k1lWBEEpLH+DLXKQmXdKHBXzEAatW\n3nv9BVQeH338GTQjzIEwyFPIqwKBvf7aa5Rg2LvvBBkQt9+8gWUzMn5o3UlpylY7m0FEvIucOAEz\ngTrqjUN79jdx5Fptqh5AX8JVmjpymBXyjn3H0DX0xeLBgDdg1omqPf/EI8SlX9pxCN5KfGSIqHat\ncJJl9fadezRfoNa9LxT0Twx4G9Xirh2vplLZdw5RWK5lN5WlWF1g3B+KbNzaoDTnJd29l3XjuqYW\nciPtZrFaKdV1xeV1ewOxWDgGtc9pMbWa1WAYtYAg1TfqjXIxY1ft8JaQNmlVC8hLH3jnrUwme2gK\nnE3ZuvU6hsfuHQek/geZYKJX1CCBJ5dMFdM5VQkqbt/CkVFUMH0+t4XUnmiU/N+RkRh8/WSlaCs4\n9Hrb1YCWntfLpV2vv5HJZo8kMOja1uuvBap9d/sRBZ44ZganWnWTrrxw9UYQu8iyJsjvogFENy3l\n3HS1XDhx7BDKi/hPmIKgzw0/pR+D+sBBSkr5kg2bx+r1oVXrMpnMg//8g1Q6c2CySLEBmT3PewIl\n6WDxxq3jrfbiLWRNWQcHI9zf9In9M9MolaP0bgpHgrGBsMPmsJkvhsrEB15c/8ML7AGq3mzainpL\nd6uU9iQRieQUY018gcc7391gvuQS+WKy7FUiTqfPiXyksSueycD4uJIvTVXL5pLD4vQFvO77v/eH\nVP6aPvgWKtR/9qOnDLUEGa+f1Asx1vvQ4Jv1UvaJB/+hVCrVtKWe0PitW9Z0mvrD//hXpcLxZ198\nIRKNjt2zRa9Xnn/maZjO4eVbA8HwN+74KgmDT/7LP+SOT/305y9FYvE//s5tGJxzLlgAgsiIFhr6\n3h8srFVLB7Y/h47Jj57biY002kuTxaNRzCjftwrptNNu7zQRA25TXAzuRr5S7FptpXpTXKxmlQSr\nYjbnpn6nxw/mUMhB1CsUq6WGxVSolKnusffIwVQqvfN4lkqfT6NCbLE1rEy2od//V/91NBJaMhS6\nYBEiQh3YBdLCXO7g9dfe2jF1dj3/426h/NhTz4WjsW/fehUsfgNB6Vq6JnIDn3/hcfrzSK5pV/z3\n3HWdzdR+7ZnHKvrMM7/6Bb23ZPQ7gDloZzoU3/rN6zha5cBz1DB46GdPIE7xxw/cJXcTe3/KtTun\nV/t/9nvg/T0AzkvKjqZ1fT7RE+HRw3L1Z//3d1T/k0uhB+YPQMFpJqXT4dSi8ZjWNA1A9QuH4P9b\nzCh+67xA3xHigIwg4UcpFt5fPJ87QsS/wC1qt1EtgcnTQojaZGKRLKm/lPKz2sh9J8KTLaDobvKo\nxFKsrKZsKgVtVGpviCpFA130LDN2KoN2eoHqFg7h3BuFOc4928X9Gx5NrUItISf1Qeq1M4mIAChu\nzQs9xhvwz0PQ7OJe5Gd0dO4yRAAkOcnFyJAPUarwtFAEDE4vwoDwj1gvMQbQgJSq9ZUG7ABWpygR\nwuKxdki6AcaX/3FSq4TqJPcbgpcFAR2LzSFVGNttUkwZUASPiGVTgcVdbzct8A6qvXpJ9aYJrRzQ\nFnQQzXWSnJuU0cR1ImrHcqsVdrNKBg6rVoowtElDD1Iywmrx0gaV+AG6XJ/UK/6MOu7LchjBJPDO\nsZhwUXqyJlhTXFvhkCBGQm69INMktBvvKZ6A3ICkwlcJxiJtYgbBtjuCfj8AtoionVrGCOugLWqw\nIDRWCVzCyBYVUTaxiL2BJ8ISXlHqShvOYFuElESMAKahXkWKo1goW0gWcpiDqLW320JlwD3npoiT\nzsMN78RjVVphqq8yjBDxbDWxTpCeKFuIXAviZkjJO+w0rq+F9MFD2UCi3IDjAxSwdzgoEMNMajaX\nPnjrD/+Uu+FwkdvZtWsS4G5LvLyWSMwA12IlgE1gM4HJCzepdwc//FD9bz73HmDJQf3I+W8GK2Vs\nAD+GzO4Z35L28BzbFKru4bx0SOwgjwTeWcvp0H0+9LhOTRBiG8TJgc0BUQ5DIrm+8gHjnPkMk8Ec\n9uGBHsyVbEpRD2o/MPW0TLCdpSxqpVJ2VTXRXuoKCwYFNka724seHJLVNY+TJKB6qQgHE7kfuCV2\nKitz9tkT01p8Eo+bpZwP7qVq93k1TOcZ54TLE/tFOUQn1eRJikRPJJ2OkDCLonM6l6dLsJX8sAs1\nJghUMI9zyVE3s2Y5nS9kyY4RIrDQWHj8wL+oAoKUCvw8zk2b89k0G6AsQ8JSZyDAuvLT3Vwed6kz\nZbJ0w34NviqPeQudLL3RoTT2GesPPiR4N7M4KsiU0rB2YTG2241GuVsGcoXD2GsGh6OIKdOPKxyg\nnNuhNEUhuhVdZ3vj3slt7b/6PXCePdB7snigznP7/mb9Hvi8euDMJHexW8AMOrR6U2hx7X9asBoj\n7QnGcIsttWw6W3zu6cdPTk7uOZwibzU6PjYwPDo6GBiMSCLcxW7VZXV8IYp2mtVmvZws5ZLlfKcV\nZP2CwhKa59X8lF7NvfCrN002dabUIa/1gXtu8GnO2NgCJuY41PxmPTe1n5z2nz7xJFHKXTvfbXXM\nwQWbYgNDcZ8r7J4viQpjeibpBJGEHduft6pqMbnD0CI1fB8zRQ2U2MhEKBz67nfv83u9jIAv+/SL\n20dCRClD9cHnn302nSu2AyOUqPrOt+4MIAWMk1LX9y98i3ycx55685hVXbh0TTDo33TdVyqF9P4j\ne8HaFm28MhiOXHPVWkun9mpyVxmOjxZ0qZ6bvnp3LBJaGNWIwp3cO8wRfvTYy3q+cHIyhaZzq3W0\nkEm99Po7lXrniq/cFwyHrlw7rtrN5RzaFpmnH/mXSrX4+suvwlhZOnQL1V4alWK1mIX3Yra7b/zq\nvdSoW71sgkJpY/CegOg+3O29rJ7BS66x9Cu14FwORyzo7zZbxek9pUxm11u7U7GIdXkMgcLDh99L\nzkzvPDZVqjVikYaT2hWNRrVSKRw/Alrxm+37a63OsvVXU2l1PNhEMfRNi1UWIoYfzSqCBU+zCo+F\ntD+bldwam1mUnQ0IhEIkqA67hIhk7aTgM+Fdk6RpTh09iNDim3sPQ2hfeyUKOOqwS8RiXwcTl+UQ\nDzSLcnByS2hglOVBQDI2G7mZ47TniccfRf3kvf2HW11LaHSlzx8ajAQiIWrj9eeCDxh7LF3cmlQ9\nYDmEiG/A5yMly0h+/ICNP+IjloCIKLEBNx558m2vPp9MJB565Ak0HQYWXxWKRNavXAgrTZNpon8j\nPqIjv+xfSWmWtskMhNKZM07IO7EwcsApDAMwp5PIeuFnzgfyFvh12wtPIzK78+gMKSOYDNXpXrn+\nRlj9V64Zoxjz3O2xKPw0cSnd3q/d/R1KkiUKZM209u1+FyR2OpHnq7gU0QBdltQhLRi1auFvf/s7\nAwND6yci3VqpeuPGdKbwytFKPZ2sJE80Mt3HnvgVuYes/s1Wu+odRHv/m3fd6vW4iUDgTSGbwk8P\n2aElAp8omtsbuvn6K6emEo/8+J8gT+/6zTKrpXtw9y4yG/PVZjDsD4SH3Kpy7fol2Lcf/OWfgRmv\nXD6KkPbubW/o9VbLF6fuKxVAvZrn6/d+B7R74YoreKL9DnO5kHvwB3+OsOwzv/xlKBJdNfpdVfkY\nFeG5/fP+9yLxjtik26953b/12/eD+PyHv36Ugk9Xr5no2XYmFMFNXK6rt96GduHoiRSQ09GD+9BW\nO3osAcPUOhQxmaXUFC8MOHeAftK82tarv1Mt5v73f//nia7l1e0bKDYrLEIpw9Hbtv+73wP9Huj3\nwBenB+YPQJFZhiJ5diUWMwO0i95vt13IZpgeTkxNTc0kiVsQrgxSoppCD067Q0ru9e3uuUONqYiA\nPoUwtBp56QHVpVGL20nKS7sMOcBKpZRGB3UMYheQUpjZpJAV6xVmfel10T0gNC3RacPtIHHYJFlT\nvU/gZs9bh9MWUZo0t6ln2kA2thd5YpAQnyFs1NdsP/vGk4SBp1kvULCgrDuoSOkNhijs7tUUQcZ0\n6rfDPIAeTH5XuayrZJcPgj21CB3h5rgJn7kpceIyk/RMF8PTpbq9yx0KBhEEDZL63KrrwQC9LjyX\nJjWZyNiGnyCLXrjFOv4tyyYDzuSRJNZG/Moof1ujvpPVpvb0+bmdoqvBQ2y3w+DF1YuEg9SDUoxq\nZv1H+ewb+hn+xR2B6GrlnrtcVbsZykk9lU6ziEmHKTvRypKik8WHB6XA65cnTeruNCzos4pKToPb\nZvX6fR7N6VSKHShGxjb8YokgrRSjIUsU2ZO1gtgIwzD3/jSCr5gk4WlTBlMGqYlaQCKkT+Ucs404\nMyifw0RgUogtbIXFkTPIjjRbijWoFqyT7C/GyODI0FD+kyQi0eHBGBAflfN9hr32hTkU3ci1EN1G\nS4rYXS98dwFXh2mgt09rHiFJK6XZzNZwlOyHKEqWLFzPqD5cwAn6u3wJekC0eDAG8ozPSdvkD+Yn\niBhiQs70glgV43X6zak/uzID1YFzqSPUZB8KQyMUQiEhOI8up4K1kkHfs0azR8BWyOGwVcAbWIxe\n5RexMobF6m3FPtgcABj0Wfx+LyVRmTXdEGGcilgbsTvMo12EoiCtiGaJ1d6x1RSHFKEzjiC/xHeZ\n86e0Q8RXbfBZGvWWU7EgqYLWH9wRJl+eIKsV8p2Q6VSHGo6QmNCxHcrSPKnLA+mEFgrRRFh9tBSj\nGAiEsHbx+IDTqSIhU1Lt6BFR02yqXBKZKgz2Z/HiPgFiBwMRJCYgy+AuplNpu93SU2kxzD32QF78\nQ7ewAT/SSoMvJLeR/0/3Ch1ts5FnRHqR22GlqhpzkEOxQl4865Z/Fi3vH6PfA/0e6PfApdAD8wig\nCA0S8qHJ7fVT9+zYe+/kMumnfvErsj1/8/abKFRp8UXDocjv/M4DAwPxsXjQzUQkk2T/dboHmKaJ\n+br8wdjd9/4uteHsWhgcanQwxhTY0EuVYt5nb6Qy+Rd3vDJ1NDT91Rth5se9zGjAEh1mPX9kkLDD\njTffydpp4UAcUYzX3z3SLGXf3rlvYGBg6xWLoaeePtlFfUOiACzj0dFlWiBw6y3rPW6pWmqcUVZV\nWiBCNUcyA8TfuqjtuBwOLm4dIEmZ+1s8mapXGtbr1109MDi4cHzc61al4EmzgTJBZHpy3P9osVqe\nnD5Wb9dvWL/BqcqiWlJvqHtJnQFfoNtUFKtUKR4cGfH4w6uXT8QjQa/NhCyKTR9CL9+ndBtI0FZq\nVZstmT2ZSSZQ2ynXWuEdr3t9npnDbjhhhPhq1fLh41PU92nVD5cber7SonybpHQo6uDYhNsXvvbq\njfFoOOwl5UcWyXRz/z5epLGG1+1QNTjzC1dd4Y2llu85QAbfs794iH5/YzTMojg9M0WKZK4OjVyI\nQFZTp5RN1Mzdl194qVSsmrAQXt+65ROay5o5+q64yWAYFGDowLdvdLtOXGRWQsbDKY8jtNo5Vrn3\ngHLvbcSAobbkklNoHr7ywiulck31jgPurl6+kEGa3pdo18gbQ6lJBFvwxgVsk9KGMjaE4W6xBcID\nqsu79abbqb49NPQOqra7D02lGuWd7+wm3TN67TqrU51z6ovUnZf3YXsLngu6BpIYdAQ4f/bP/5RM\npt/cP0kw49Z7H4AJuHHVSrfL6Q94WOQxA13Qwfs7fSl6gAHi8lMD3d40V+pd2Aezy2vW/NicMjXU\nw6PUffvYQQTkQF05xemZ6KrI4QkkQ+Zp4Wg61/mrt1/GN/jD797rhw8CldKAHUTEnDI5mfqvf/KD\ncqmku8eRzl27YknI6VmwaLhBnR2b0RgAEcgsdiIAJo/LpjnxjMS4CaMEOq4hCmtzIE5iG1+wkFRo\nAgec2qHFqIJso4qqgRt/4I3EjiEdu/aqG5fo1cGla+BrQPwEd3DctBkNkZ88+izt8WiuUCh4453f\npIbwqqtPiuIsqdXt5nXrl5Yr1efeOkrikb3bVcyWWHiYaXMoEnLAtQGAaLev3HgF2UCPvHGyWjKT\nmQmC8qnSG7gtUgqtRVwkOrYyGC3cvmUlx3/4pw/R205TnpgcYAmhzZ0vP1YqFffmAEccG9es9Aac\n44vHgbRSDfyIU6bAOBpBlI7J4gkOIhx87+3XzyRTv3jsQb3RiHu6eB8LJOx3avsP7MH+h/0e6PdA\nvwcutx6YPwCFnmFxzDqZ/OqmXs2kk9TWnJ6aQSwQbxrnPhobCPF/PEpaATrM4q1dbr05P+0FYiCu\nq6IAGojZFYXZDoegpUqfhQNemAr1WtJSUSjRIjVIdUQNmPklk1ZCIF0T1AXkMArxGEwBi+lQCzHJ\nQtnlrrRlRTNvL9Z9lBiEdesPh0JEv8VDEncLqq0VlIfYi/jr89acS/tE4qK0W7BQ6m0T2iUul0b9\nAsXQCmIdSjeRH66qTgjGNgsBe4kVEdCiFJPEtOhQ/sAjs5JnI+KgPFdoc4pGigOBCmATXJ+WUQpJ\nHEr6nKEiuhktFrxQgUQvA0eP90TMZKQZxQaoqahYu+RpS1KH8aRC+uV0CK/AhKAYkNvJse39h/ji\njyxusVRgERe9VsdHx1ctzpShmlGUR1gFrGNQEOA2SRI+/CMQjDrfojNSLOk1Nag0O7CNgFZKxRLV\neEjCQR4HeWfVpdaaNiKyxijCBcaCnxV4nQ3FUs1CyEcSZQYZbwvnJVeu1LqhAUeLgukVa9tcKFLo\np4T4gMlmKpdLjA/WGDRNBg47SvC4Szkeh6MbCIUYNpAQSeVTjkyC7VFwinodQpm/+F15mZ/hdA+d\n6avTH330pXED6nqlWiomWPqks4TFgeaFoIZxBqYVOmjfGH90F/a/FfuPi2F3UN0P76Mm2nb1GrzW\npsjFoyuik7XNWDobhmOEnnY8ZkcrcxYiIOSZBIJMQgKgCNxaK5J8CFnZ6dYq1brMXNDdULkG/8AC\nCVOtg0QYgQaHc4HDqricaHvJxAcnr/fqIcGMY36Y6XruxRmUR1gz2E5kRuzUD7arTmF6INbi9BiF\nVHubz73LbD7bYLGEHeHfQeWF2okckYIcSqdl1slNbNgVp83uRO4P0EGHT9dpo/1kR/Sk1SUyUqgo\nZDExG6OvAmGw06pDDaTdkgxJI7GtzMbIqLSJgYmm2dwWXMD7XovF5gqcBF9UUbouairzd/XI/kqt\n3rU38AtErKZtyqUzAEC1ThAvAREUQCS8CKnJJcyZ2cvn5rGv/CG+BkCTGo1HQXma+juUiiyYcUoU\nOenZd/0CWt7fpd8D/R7o98Al1QPzCqAgXomXtuP5Z1OJmZ/+7PF0Nr/7ZN6iqJtvujscCd9yw9aA\n37do4ULUqtwOWcv1X2f1gExTHTwGfBOq5wCXjI6Osg52SV8xEzc9bufqVSsDk9PmF3fVqx3UIVlC\nn5jcw25KaCEr22XjUZcnsNyDV9IYHR2iSsuud14vVgvvHjqUqNRuvmq5yek464wX4w9j2pU1mV2N\nDy6KDA6uW78RhuqZU0kBY0NujeX3mU+/xO/oMbIZiEMJDxrZYAvhLFTyZhFGWT0L9QNyr0qdQwuq\nF7V2o2a4NHg2xo/h6BkdTzeSB25CD1JzyVPmJDNdEjsMj9JYzsq2sh4WDjR+m+LxdV3msfFhL+UW\nZ3VyWMt2B0dH8L4cWkTzsMJydBuo01K/w+QPeH1BKjQ6+bC/6JqfUStcbLMlFBnUPMGvfeOb1Wr1\nxHQa9KsttRS6Ro5XZc/bL5K73sFqkKaRm6T+xVvbtuXyZVsoBzzRqR+3W7rZqeMoaOzdtw/m2gs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eFPNYM4BVizaoCUQ2FsXHub73pkdaMeTgyh2hw4chSNgdinjAoja7fTQNeRMvbJ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\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u4Tp9fxjJEMWCebti9SbQmcGldYCnxdRu83m//r0/r9dr7x48TGSNcNn6lK9EkyS/wLHA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IrYa6Y1BNkCq3JURwVgYr9AT4nnVlhrSMKImY1fKWsN0sZOD/Q5yuyXSiVe8X9nsSM2HuV0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\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2LStQbduz8+1SqfzG7gMDl3LTOl6heQ9KqhAFRvhzfPnA1PjYwMpJ+bE6dAzO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\nk9l1ajRJmj2zzpFsEUEyVzLJEbezK0d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IECBAgAABAgQIECBQqYAESqW8CidAgAABAgQI\nECBAgAABAgSaICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAESqW8CidAgAABAgQIECBAgAABAgSa\nICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAESqW8CidAgAABAgQIECBAgAABAgSaICCB0oQoagMB\nAgQIECBAgAABAgQIECBQqYAESqW8CidAgAABAgQIECBAgAABAgSaICCB0oQoagMBAgQIECBAgAAB\nAgQIECBQqYAESqW8CidAgAABAgQIECBAgAABAgSaICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAE\nSqW8CidAgAABAgQIECBAgAABAgSaICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAESqW8CidAgAAB\nAgQIECBAgAABAgSaICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAESqW8CidAgAABAgQIECBAgAAB\nAgSaICCB0oQoagMBAgQIECBAgAABAgQIECBQqYAESqW8CidAgAABAgQIECBAgAABAgSaIPA/A5+I\nJ8CF50MAAAAASUVORK5CYII=\n",
       "prompt_number": 2,
       "text": "<IPython.core.display.Image at 0x7fe094771750>"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "What's missing? Duh, the labels!\n\n`Pandas` is clever, but not so clever as to infer labels not included in the file. It's not the google(tm). \n\n`README` gives us them. Let's copy them over by putting them into list of strings."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "    Exercise for the reader: Could you use `re.sub` to make this list of strings? Oh, you will, you will. (On the homework)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "labels=[\"user_id\",\"item_id\",\"rating\",\"timestamp\"]\n#arduously hand entered when should have spent 2 hours coding a way to do it automatically",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings=pd.read_csv( './ml-100k/u.data', sep=\"\\t\", names=labels) #\\t because TAB separated",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0    </th>\n      <td> 196</td>\n      <td>  242</td>\n      <td> 3</td>\n      <td> 881250949</td>\n    </tr>\n    <tr>\n      <th>1    </th>\n      <td> 186</td>\n      <td>  302</td>\n      <td> 3</td>\n      <td> 891717742</td>\n    </tr>\n    <tr>\n      <th>2    </th>\n      <td>  22</td>\n      <td>  377</td>\n      <td> 1</td>\n      <td> 878887116</td>\n    </tr>\n    <tr>\n      <th>3    </th>\n      <td> 244</td>\n      <td>   51</td>\n      <td> 2</td>\n      <td> 880606923</td>\n    </tr>\n    <tr>\n      <th>4    </th>\n      <td> 166</td>\n      <td>  346</td>\n      <td> 1</td>\n      <td> 886397596</td>\n    </tr>\n    <tr>\n      <th>5    </th>\n      <td> 298</td>\n      <td>  474</td>\n      <td> 4</td>\n      <td> 884182806</td>\n    </tr>\n    <tr>\n      <th>6    </th>\n      <td> 115</td>\n      <td>  265</td>\n      <td> 2</td>\n      <td> 881171488</td>\n    </tr>\n    <tr>\n      <th>7    </th>\n      <td> 253</td>\n      <td>  465</td>\n      <td> 5</td>\n      <td> 891628467</td>\n    </tr>\n    <tr>\n      <th>8    </th>\n      <td> 305</td>\n      <td>  451</td>\n      <td> 3</td>\n      <td> 886324817</td>\n    </tr>\n    <tr>\n      <th>9    </th>\n      <td>   6</td>\n      <td>   86</td>\n      <td> 3</td>\n      <td> 883603013</td>\n    </tr>\n    <tr>\n      <th>10   </th>\n      <td>  62</td>\n      <td>  257</td>\n      <td> 2</td>\n      <td> 879372434</td>\n    </tr>\n    <tr>\n      <th>11   </th>\n      <td> 286</td>\n      <td> 1014</td>\n      <td> 5</td>\n      <td> 879781125</td>\n    </tr>\n    <tr>\n      <th>12   </th>\n      <td> 200</td>\n      <td>  222</td>\n      <td> 5</td>\n      <td> 876042340</td>\n    </tr>\n    <tr>\n      <th>13   </th>\n      <td> 210</td>\n      <td>   40</td>\n      <td> 3</td>\n      <td> 891035994</td>\n    </tr>\n    <tr>\n      <th>14   </th>\n      <td> 224</td>\n      <td>   29</td>\n      <td> 3</td>\n      <td> 888104457</td>\n    </tr>\n    <tr>\n      <th>15   </th>\n      <td> 303</td>\n      <td>  785</td>\n      <td> 3</td>\n      <td> 879485318</td>\n    </tr>\n    <tr>\n      <th>16   </th>\n      <td> 122</td>\n      <td>  387</td>\n      <td> 5</td>\n      <td> 879270459</td>\n    </tr>\n    <tr>\n      <th>17   </th>\n      <td> 194</td>\n      <td>  274</td>\n      <td> 2</td>\n      <td> 879539794</td>\n    </tr>\n    <tr>\n      <th>18   </th>\n      <td> 291</td>\n      <td> 1042</td>\n      <td> 4</td>\n      <td> 874834944</td>\n    </tr>\n    <tr>\n      <th>19   </th>\n      <td> 234</td>\n      <td> 1184</td>\n      <td> 2</td>\n      <td> 892079237</td>\n    </tr>\n    <tr>\n      <th>20   </th>\n      <td> 119</td>\n      <td>  392</td>\n      <td> 4</td>\n      <td> 886176814</td>\n    </tr>\n    <tr>\n      <th>21   </th>\n      <td> 167</td>\n      <td>  486</td>\n      <td> 4</td>\n      <td> 892738452</td>\n    </tr>\n    <tr>\n      <th>22   </th>\n      <td> 299</td>\n      <td>  144</td>\n      <td> 4</td>\n      <td> 877881320</td>\n    </tr>\n    <tr>\n      <th>23   </th>\n      <td> 291</td>\n      <td>  118</td>\n      <td> 2</td>\n      <td> 874833878</td>\n    </tr>\n    <tr>\n      <th>24   </th>\n      <td> 308</td>\n      <td>    1</td>\n      <td> 4</td>\n      <td> 887736532</td>\n    </tr>\n    <tr>\n      <th>25   </th>\n      <td>  95</td>\n      <td>  546</td>\n      <td> 2</td>\n      <td> 879196566</td>\n    </tr>\n    <tr>\n      <th>26   </th>\n      <td>  38</td>\n      <td>   95</td>\n      <td> 5</td>\n      <td> 892430094</td>\n    </tr>\n    <tr>\n      <th>27   </th>\n      <td> 102</td>\n      <td>  768</td>\n      <td> 2</td>\n      <td> 883748450</td>\n    </tr>\n    <tr>\n      <th>28   </th>\n      <td>  63</td>\n      <td>  277</td>\n      <td> 4</td>\n      <td> 875747401</td>\n    </tr>\n    <tr>\n      <th>29   </th>\n      <td> 160</td>\n      <td>  234</td>\n      <td> 5</td>\n      <td> 876861185</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99970</th>\n      <td> 449</td>\n      <td>  120</td>\n      <td> 1</td>\n      <td> 879959573</td>\n    </tr>\n    <tr>\n      <th>99971</th>\n      <td> 661</td>\n      <td>  762</td>\n      <td> 2</td>\n      <td> 876037121</td>\n    </tr>\n    <tr>\n      <th>99972</th>\n      <td> 721</td>\n      <td>  874</td>\n      <td> 3</td>\n      <td> 877137447</td>\n    </tr>\n    <tr>\n      <th>99973</th>\n      <td> 821</td>\n      <td>  151</td>\n      <td> 4</td>\n      <td> 874792889</td>\n    </tr>\n    <tr>\n      <th>99974</th>\n      <td> 764</td>\n      <td>  596</td>\n      <td> 3</td>\n      <td> 876243046</td>\n    </tr>\n    <tr>\n      <th>99975</th>\n      <td> 537</td>\n      <td>  443</td>\n      <td> 3</td>\n      <td> 886031752</td>\n    </tr>\n    <tr>\n      <th>99976</th>\n      <td> 618</td>\n      <td>  628</td>\n      <td> 2</td>\n      <td> 891308019</td>\n    </tr>\n    <tr>\n      <th>99977</th>\n      <td> 487</td>\n      <td>  291</td>\n      <td> 3</td>\n      <td> 883445079</td>\n    </tr>\n    <tr>\n      <th>99978</th>\n      <td> 113</td>\n      <td>  975</td>\n      <td> 5</td>\n      <td> 875936424</td>\n    </tr>\n    <tr>\n      <th>99979</th>\n      <td> 943</td>\n      <td>  391</td>\n      <td> 2</td>\n      <td> 888640291</td>\n    </tr>\n    <tr>\n      <th>99980</th>\n      <td> 864</td>\n      <td>  685</td>\n      <td> 4</td>\n      <td> 888891900</td>\n    </tr>\n    <tr>\n      <th>99981</th>\n      <td> 750</td>\n      <td>  323</td>\n      <td> 3</td>\n      <td> 879445877</td>\n    </tr>\n    <tr>\n      <th>99982</th>\n      <td> 279</td>\n      <td>   64</td>\n      <td> 1</td>\n      <td> 875308510</td>\n    </tr>\n    <tr>\n      <th>99983</th>\n      <td> 646</td>\n      <td>  750</td>\n      <td> 3</td>\n      <td> 888528902</td>\n    </tr>\n    <tr>\n      <th>99984</th>\n      <td> 654</td>\n      <td>  370</td>\n      <td> 2</td>\n      <td> 887863914</td>\n    </tr>\n    <tr>\n      <th>99985</th>\n      <td> 617</td>\n      <td>  582</td>\n      <td> 4</td>\n      <td> 883789294</td>\n    </tr>\n    <tr>\n      <th>99986</th>\n      <td> 913</td>\n      <td>  690</td>\n      <td> 3</td>\n      <td> 880824288</td>\n    </tr>\n    <tr>\n      <th>99987</th>\n      <td> 660</td>\n      <td>  229</td>\n      <td> 2</td>\n      <td> 891406212</td>\n    </tr>\n    <tr>\n      <th>99988</th>\n      <td> 421</td>\n      <td>  498</td>\n      <td> 4</td>\n      <td> 892241344</td>\n    </tr>\n    <tr>\n      <th>99989</th>\n      <td> 495</td>\n      <td> 1091</td>\n      <td> 4</td>\n      <td> 888637503</td>\n    </tr>\n    <tr>\n      <th>99990</th>\n      <td> 806</td>\n      <td>  421</td>\n      <td> 4</td>\n      <td> 882388897</td>\n    </tr>\n    <tr>\n      <th>99991</th>\n      <td> 676</td>\n      <td>  538</td>\n      <td> 4</td>\n      <td> 892685437</td>\n    </tr>\n    <tr>\n      <th>99992</th>\n      <td> 721</td>\n      <td>  262</td>\n      <td> 3</td>\n      <td> 877137285</td>\n    </tr>\n    <tr>\n      <th>99993</th>\n      <td> 913</td>\n      <td>  209</td>\n      <td> 2</td>\n      <td> 881367150</td>\n    </tr>\n    <tr>\n      <th>99994</th>\n      <td> 378</td>\n      <td>   78</td>\n      <td> 3</td>\n      <td> 880056976</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td> 880</td>\n      <td>  476</td>\n      <td> 3</td>\n      <td> 880175444</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td> 716</td>\n      <td>  204</td>\n      <td> 5</td>\n      <td> 879795543</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td> 276</td>\n      <td> 1090</td>\n      <td> 1</td>\n      <td> 874795795</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>  13</td>\n      <td>  225</td>\n      <td> 2</td>\n      <td> 882399156</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>  12</td>\n      <td>  203</td>\n      <td> 3</td>\n      <td> 879959583</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "       user_id  item_id  rating  timestamp\n0          196      242       3  881250949\n1          186      302       3  891717742\n2           22      377       1  878887116\n3          244       51       2  880606923\n4          166      346       1  886397596\n5          298      474       4  884182806\n6          115      265       2  881171488\n7          253      465       5  891628467\n8          305      451       3  886324817\n9            6       86       3  883603013\n10          62      257       2  879372434\n11         286     1014       5  879781125\n12         200      222       5  876042340\n13         210       40       3  891035994\n14         224       29       3  888104457\n15         303      785       3  879485318\n16         122      387       5  879270459\n17         194      274       2  879539794\n18         291     1042       4  874834944\n19         234     1184       2  892079237\n20         119      392       4  886176814\n21         167      486       4  892738452\n22         299      144       4  877881320\n23         291      118       2  874833878\n24         308        1       4  887736532\n25          95      546       2  879196566\n26          38       95       5  892430094\n27         102      768       2  883748450\n28          63      277       4  875747401\n29         160      234       5  876861185\n...        ...      ...     ...        ...\n99970      449      120       1  879959573\n99971      661      762       2  876037121\n99972      721      874       3  877137447\n99973      821      151       4  874792889\n99974      764      596       3  876243046\n99975      537      443       3  886031752\n99976      618      628       2  891308019\n99977      487      291       3  883445079\n99978      113      975       5  875936424\n99979      943      391       2  888640291\n99980      864      685       4  888891900\n99981      750      323       3  879445877\n99982      279       64       1  875308510\n99983      646      750       3  888528902\n99984      654      370       2  887863914\n99985      617      582       4  883789294\n99986      913      690       3  880824288\n99987      660      229       2  891406212\n99988      421      498       4  892241344\n99989      495     1091       4  888637503\n99990      806      421       4  882388897\n99991      676      538       4  892685437\n99992      721      262       3  877137285\n99993      913      209       2  881367150\n99994      378       78       3  880056976\n99995      880      476       3  880175444\n99996      716      204       5  879795543\n99997      276     1090       1  874795795\n99998       13      225       2  882399156\n99999       12      203       3  879959583\n\n[100000 rows x 4 columns]"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "What's with that crazy number? According to the `README`, it's \"unix seconds.\" A quick google search explains how to convert using the `pd.to.datetime` command. I will remember this just long enough to type the next few lines."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pd.to_datetime(individual_ratings[\"timestamp\"], unit='s')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "0    1997-12-04 15:55:49\n1    1998-04-04 19:22:22\n2    1997-11-07 07:18:36\n3    1997-11-27 05:02:03\n4    1998-02-02 05:33:16\n5    1998-01-07 14:20:06\n6    1997-12-03 17:51:28\n7    1998-04-03 18:34:27\n8    1998-02-01 09:20:17\n9    1997-12-31 21:16:53\n10   1997-11-12 22:07:14\n11   1997-11-17 15:38:45\n12   1997-10-05 09:05:40\n13   1998-03-27 21:59:54\n14   1998-02-21 23:40:57\n...\n99985   1998-01-03 01:01:34\n99986   1997-11-29 17:24:48\n99987   1998-04-01 04:50:12\n99988   1998-04-10 20:49:04\n99989   1998-02-28 03:45:03\n99990   1997-12-17 20:01:37\n99991   1998-04-16 00:10:37\n99992   1997-10-18 01:14:45\n99993   1997-12-06 00:12:30\n99994   1997-11-20 20:16:16\n99995   1997-11-22 05:10:44\n99996   1997-11-17 19:39:03\n99997   1997-09-20 22:49:55\n99998   1997-12-17 22:52:36\n99999   1997-11-19 17:13:03\nName: timestamp, Length: 100000, dtype: datetime64[ns]"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "replacement_timestamps=pd.to_datetime(individual_ratings[\"timestamp\"], unit='s')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Note that `pd.to_datetime` converted *each* of the timestamps individual without us telling it manually to loop through them. Hooray vectorized functions!"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[\"timestamp\"]=replacement_timestamps  ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "One = sign not two ( ==). Why? "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0    </th>\n      <td> 196</td>\n      <td>  242</td>\n      <td> 3</td>\n      <td>1997-12-04 15:55:49</td>\n    </tr>\n    <tr>\n      <th>1    </th>\n      <td> 186</td>\n      <td>  302</td>\n      <td> 3</td>\n      <td>1998-04-04 19:22:22</td>\n    </tr>\n    <tr>\n      <th>2    </th>\n      <td>  22</td>\n      <td>  377</td>\n      <td> 1</td>\n      <td>1997-11-07 07:18:36</td>\n    </tr>\n    <tr>\n      <th>3    </th>\n      <td> 244</td>\n      <td>   51</td>\n      <td> 2</td>\n      <td>1997-11-27 05:02:03</td>\n    </tr>\n    <tr>\n      <th>4    </th>\n      <td> 166</td>\n      <td>  346</td>\n      <td> 1</td>\n      <td>1998-02-02 05:33:16</td>\n    </tr>\n    <tr>\n      <th>5    </th>\n      <td> 298</td>\n      <td>  474</td>\n      <td> 4</td>\n      <td>1998-01-07 14:20:06</td>\n    </tr>\n    <tr>\n      <th>6    </th>\n      <td> 115</td>\n      <td>  265</td>\n      <td> 2</td>\n      <td>1997-12-03 17:51:28</td>\n    </tr>\n    <tr>\n      <th>7    </th>\n      <td> 253</td>\n      <td>  465</td>\n      <td> 5</td>\n      <td>1998-04-03 18:34:27</td>\n    </tr>\n    <tr>\n      <th>8    </th>\n      <td> 305</td>\n      <td>  451</td>\n      <td> 3</td>\n      <td>1998-02-01 09:20:17</td>\n    </tr>\n    <tr>\n      <th>9    </th>\n      <td>   6</td>\n      <td>   86</td>\n      <td> 3</td>\n      <td>1997-12-31 21:16:53</td>\n    </tr>\n    <tr>\n      <th>10   </th>\n      <td>  62</td>\n      <td>  257</td>\n      <td> 2</td>\n      <td>1997-11-12 22:07:14</td>\n    </tr>\n    <tr>\n      <th>11   </th>\n      <td> 286</td>\n      <td> 1014</td>\n      <td> 5</td>\n      <td>1997-11-17 15:38:45</td>\n    </tr>\n    <tr>\n      <th>12   </th>\n      <td> 200</td>\n      <td>  222</td>\n      <td> 5</td>\n      <td>1997-10-05 09:05:40</td>\n    </tr>\n    <tr>\n      <th>13   </th>\n      <td> 210</td>\n      <td>   40</td>\n      <td> 3</td>\n      <td>1998-03-27 21:59:54</td>\n    </tr>\n    <tr>\n      <th>14   </th>\n      <td> 224</td>\n      <td>   29</td>\n      <td> 3</td>\n      <td>1998-02-21 23:40:57</td>\n    </tr>\n    <tr>\n      <th>15   </th>\n      <td> 303</td>\n      <td>  785</td>\n      <td> 3</td>\n      <td>1997-11-14 05:28:38</td>\n    </tr>\n    <tr>\n      <th>16   </th>\n      <td> 122</td>\n      <td>  387</td>\n      <td> 5</td>\n      <td>1997-11-11 17:47:39</td>\n    </tr>\n    <tr>\n      <th>17   </th>\n      <td> 194</td>\n      <td>  274</td>\n      <td> 2</td>\n      <td>1997-11-14 20:36:34</td>\n    </tr>\n    <tr>\n      <th>18   </th>\n      <td> 291</td>\n      <td> 1042</td>\n      <td> 4</td>\n      <td>1997-09-21 09:42:24</td>\n    </tr>\n    <tr>\n      <th>19   </th>\n      <td> 234</td>\n      <td> 1184</td>\n      <td> 2</td>\n      <td>1998-04-08 23:47:17</td>\n    </tr>\n    <tr>\n      <th>20   </th>\n      <td> 119</td>\n      <td>  392</td>\n      <td> 4</td>\n      <td>1998-01-30 16:13:34</td>\n    </tr>\n    <tr>\n      <th>21   </th>\n      <td> 167</td>\n      <td>  486</td>\n      <td> 4</td>\n      <td>1998-04-16 14:54:12</td>\n    </tr>\n    <tr>\n      <th>22   </th>\n      <td> 299</td>\n      <td>  144</td>\n      <td> 4</td>\n      <td>1997-10-26 15:55:20</td>\n    </tr>\n    <tr>\n      <th>23   </th>\n      <td> 291</td>\n      <td>  118</td>\n      <td> 2</td>\n      <td>1997-09-21 09:24:38</td>\n    </tr>\n    <tr>\n      <th>24   </th>\n      <td> 308</td>\n      <td>    1</td>\n      <td> 4</td>\n      <td>1998-02-17 17:28:52</td>\n    </tr>\n    <tr>\n      <th>25   </th>\n      <td>  95</td>\n      <td>  546</td>\n      <td> 2</td>\n      <td>1997-11-10 21:16:06</td>\n    </tr>\n    <tr>\n      <th>26   </th>\n      <td>  38</td>\n      <td>   95</td>\n      <td> 5</td>\n      <td>1998-04-13 01:14:54</td>\n    </tr>\n    <tr>\n      <th>27   </th>\n      <td> 102</td>\n      <td>  768</td>\n      <td> 2</td>\n      <td>1998-01-02 13:40:50</td>\n    </tr>\n    <tr>\n      <th>28   </th>\n      <td>  63</td>\n      <td>  277</td>\n      <td> 4</td>\n      <td>1997-10-01 23:10:01</td>\n    </tr>\n    <tr>\n      <th>29   </th>\n      <td> 160</td>\n      <td>  234</td>\n      <td> 5</td>\n      <td>1997-10-14 20:33:05</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99970</th>\n      <td> 449</td>\n      <td>  120</td>\n      <td> 1</td>\n      <td>1997-11-19 17:12:53</td>\n    </tr>\n    <tr>\n      <th>99971</th>\n      <td> 661</td>\n      <td>  762</td>\n      <td> 2</td>\n      <td>1997-10-05 07:38:41</td>\n    </tr>\n    <tr>\n      <th>99972</th>\n      <td> 721</td>\n      <td>  874</td>\n      <td> 3</td>\n      <td>1997-10-18 01:17:27</td>\n    </tr>\n    <tr>\n      <th>99973</th>\n      <td> 821</td>\n      <td>  151</td>\n      <td> 4</td>\n      <td>1997-09-20 22:01:29</td>\n    </tr>\n    <tr>\n      <th>99974</th>\n      <td> 764</td>\n      <td>  596</td>\n      <td> 3</td>\n      <td>1997-10-07 16:50:46</td>\n    </tr>\n    <tr>\n      <th>99975</th>\n      <td> 537</td>\n      <td>  443</td>\n      <td> 3</td>\n      <td>1998-01-28 23:55:52</td>\n    </tr>\n    <tr>\n      <th>99976</th>\n      <td> 618</td>\n      <td>  628</td>\n      <td> 2</td>\n      <td>1998-03-31 01:33:39</td>\n    </tr>\n    <tr>\n      <th>99977</th>\n      <td> 487</td>\n      <td>  291</td>\n      <td> 3</td>\n      <td>1997-12-30 01:24:39</td>\n    </tr>\n    <tr>\n      <th>99978</th>\n      <td> 113</td>\n      <td>  975</td>\n      <td> 5</td>\n      <td>1997-10-04 03:40:24</td>\n    </tr>\n    <tr>\n      <th>99979</th>\n      <td> 943</td>\n      <td>  391</td>\n      <td> 2</td>\n      <td>1998-02-28 04:31:31</td>\n    </tr>\n    <tr>\n      <th>99980</th>\n      <td> 864</td>\n      <td>  685</td>\n      <td> 4</td>\n      <td>1998-03-03 02:25:00</td>\n    </tr>\n    <tr>\n      <th>99981</th>\n      <td> 750</td>\n      <td>  323</td>\n      <td> 3</td>\n      <td>1997-11-13 18:31:17</td>\n    </tr>\n    <tr>\n      <th>99982</th>\n      <td> 279</td>\n      <td>   64</td>\n      <td> 1</td>\n      <td>1997-09-26 21:15:10</td>\n    </tr>\n    <tr>\n      <th>99983</th>\n      <td> 646</td>\n      <td>  750</td>\n      <td> 3</td>\n      <td>1998-02-26 21:35:02</td>\n    </tr>\n    <tr>\n      <th>99984</th>\n      <td> 654</td>\n      <td>  370</td>\n      <td> 2</td>\n      <td>1998-02-19 04:51:54</td>\n    </tr>\n    <tr>\n      <th>99985</th>\n      <td> 617</td>\n      <td>  582</td>\n      <td> 4</td>\n      <td>1998-01-03 01:01:34</td>\n    </tr>\n    <tr>\n      <th>99986</th>\n      <td> 913</td>\n      <td>  690</td>\n      <td> 3</td>\n      <td>1997-11-29 17:24:48</td>\n    </tr>\n    <tr>\n      <th>99987</th>\n      <td> 660</td>\n      <td>  229</td>\n      <td> 2</td>\n      <td>1998-04-01 04:50:12</td>\n    </tr>\n    <tr>\n      <th>99988</th>\n      <td> 421</td>\n      <td>  498</td>\n      <td> 4</td>\n      <td>1998-04-10 20:49:04</td>\n    </tr>\n    <tr>\n      <th>99989</th>\n      <td> 495</td>\n      <td> 1091</td>\n      <td> 4</td>\n      <td>1998-02-28 03:45:03</td>\n    </tr>\n    <tr>\n      <th>99990</th>\n      <td> 806</td>\n      <td>  421</td>\n      <td> 4</td>\n      <td>1997-12-17 20:01:37</td>\n    </tr>\n    <tr>\n      <th>99991</th>\n      <td> 676</td>\n      <td>  538</td>\n      <td> 4</td>\n      <td>1998-04-16 00:10:37</td>\n    </tr>\n    <tr>\n      <th>99992</th>\n      <td> 721</td>\n      <td>  262</td>\n      <td> 3</td>\n      <td>1997-10-18 01:14:45</td>\n    </tr>\n    <tr>\n      <th>99993</th>\n      <td> 913</td>\n      <td>  209</td>\n      <td> 2</td>\n      <td>1997-12-06 00:12:30</td>\n    </tr>\n    <tr>\n      <th>99994</th>\n      <td> 378</td>\n      <td>   78</td>\n      <td> 3</td>\n      <td>1997-11-20 20:16:16</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td> 880</td>\n      <td>  476</td>\n      <td> 3</td>\n      <td>1997-11-22 05:10:44</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td> 716</td>\n      <td>  204</td>\n      <td> 5</td>\n      <td>1997-11-17 19:39:03</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td> 276</td>\n      <td> 1090</td>\n      <td> 1</td>\n      <td>1997-09-20 22:49:55</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>  13</td>\n      <td>  225</td>\n      <td> 2</td>\n      <td>1997-12-17 22:52:36</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>  12</td>\n      <td>  203</td>\n      <td> 3</td>\n      <td>1997-11-19 17:13:03</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": "       user_id  item_id  rating           timestamp\n0          196      242       3 1997-12-04 15:55:49\n1          186      302       3 1998-04-04 19:22:22\n2           22      377       1 1997-11-07 07:18:36\n3          244       51       2 1997-11-27 05:02:03\n4          166      346       1 1998-02-02 05:33:16\n5          298      474       4 1998-01-07 14:20:06\n6          115      265       2 1997-12-03 17:51:28\n7          253      465       5 1998-04-03 18:34:27\n8          305      451       3 1998-02-01 09:20:17\n9            6       86       3 1997-12-31 21:16:53\n10          62      257       2 1997-11-12 22:07:14\n11         286     1014       5 1997-11-17 15:38:45\n12         200      222       5 1997-10-05 09:05:40\n13         210       40       3 1998-03-27 21:59:54\n14         224       29       3 1998-02-21 23:40:57\n15         303      785       3 1997-11-14 05:28:38\n16         122      387       5 1997-11-11 17:47:39\n17         194      274       2 1997-11-14 20:36:34\n18         291     1042       4 1997-09-21 09:42:24\n19         234     1184       2 1998-04-08 23:47:17\n20         119      392       4 1998-01-30 16:13:34\n21         167      486       4 1998-04-16 14:54:12\n22         299      144       4 1997-10-26 15:55:20\n23         291      118       2 1997-09-21 09:24:38\n24         308        1       4 1998-02-17 17:28:52\n25          95      546       2 1997-11-10 21:16:06\n26          38       95       5 1998-04-13 01:14:54\n27         102      768       2 1998-01-02 13:40:50\n28          63      277       4 1997-10-01 23:10:01\n29         160      234       5 1997-10-14 20:33:05\n...        ...      ...     ...                 ...\n99970      449      120       1 1997-11-19 17:12:53\n99971      661      762       2 1997-10-05 07:38:41\n99972      721      874       3 1997-10-18 01:17:27\n99973      821      151       4 1997-09-20 22:01:29\n99974      764      596       3 1997-10-07 16:50:46\n99975      537      443       3 1998-01-28 23:55:52\n99976      618      628       2 1998-03-31 01:33:39\n99977      487      291       3 1997-12-30 01:24:39\n99978      113      975       5 1997-10-04 03:40:24\n99979      943      391       2 1998-02-28 04:31:31\n99980      864      685       4 1998-03-03 02:25:00\n99981      750      323       3 1997-11-13 18:31:17\n99982      279       64       1 1997-09-26 21:15:10\n99983      646      750       3 1998-02-26 21:35:02\n99984      654      370       2 1998-02-19 04:51:54\n99985      617      582       4 1998-01-03 01:01:34\n99986      913      690       3 1997-11-29 17:24:48\n99987      660      229       2 1998-04-01 04:50:12\n99988      421      498       4 1998-04-10 20:49:04\n99989      495     1091       4 1998-02-28 03:45:03\n99990      806      421       4 1997-12-17 20:01:37\n99991      676      538       4 1998-04-16 00:10:37\n99992      721      262       3 1997-10-18 01:14:45\n99993      913      209       2 1997-12-06 00:12:30\n99994      378       78       3 1997-11-20 20:16:16\n99995      880      476       3 1997-11-22 05:10:44\n99996      716      204       5 1997-11-17 19:39:03\n99997      276     1090       1 1997-09-20 22:49:55\n99998       13      225       2 1997-12-17 22:52:36\n99999       12      203       3 1997-11-19 17:13:03\n\n[100000 rows x 4 columns]"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now, this is nice. \n\nWhat if we wanted to get all the items that one user ranked?\n\nOur new BFF boolean indexing comes to the rescue.\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[\"user_id\"]==42",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "0     False\n1     False\n2     False\n3     False\n4     False\n5     False\n6     False\n7     False\n8     False\n9     False\n10    False\n11    False\n12    False\n13    False\n14    False\n...\n99985    False\n99986    False\n99987    False\n99988    False\n99989    False\n99990    False\n99991    False\n99992    False\n99993    False\n99994    False\n99995    False\n99996    False\n99997    False\n99998    False\n99999    False\nName: user_id, Length: 100000, dtype: bool"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "user_42_ratings_boolean=individual_ratings[\"user_id\"]==42",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[user_42_ratings_boolean]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>58   </th>\n      <td> 42</td>\n      <td>  423</td>\n      <td> 5</td>\n      <td>1997-12-03 00:08:07</td>\n    </tr>\n    <tr>\n      <th>87   </th>\n      <td> 42</td>\n      <td>  403</td>\n      <td> 3</td>\n      <td>1997-12-03 00:24:44</td>\n    </tr>\n    <tr>\n      <th>111  </th>\n      <td> 42</td>\n      <td>   96</td>\n      <td> 5</td>\n      <td>1997-12-02 23:59:38</td>\n    </tr>\n    <tr>\n      <th>166  </th>\n      <td> 42</td>\n      <td>  794</td>\n      <td> 3</td>\n      <td>1997-12-03 00:20:25</td>\n    </tr>\n    <tr>\n      <th>310  </th>\n      <td> 42</td>\n      <td>  546</td>\n      <td> 3</td>\n      <td>1997-12-02 23:36:57</td>\n    </tr>\n    <tr>\n      <th>638  </th>\n      <td> 42</td>\n      <td>  274</td>\n      <td> 5</td>\n      <td>1997-12-02 23:36:57</td>\n    </tr>\n    <tr>\n      <th>659  </th>\n      <td> 42</td>\n      <td>  588</td>\n      <td> 5</td>\n      <td>1997-12-03 00:15:47</td>\n    </tr>\n    <tr>\n      <th>754  </th>\n      <td> 42</td>\n      <td>   44</td>\n      <td> 3</td>\n      <td>1997-12-03 00:22:28</td>\n    </tr>\n    <tr>\n      <th>778  </th>\n      <td> 42</td>\n      <td> 1028</td>\n      <td> 4</td>\n      <td>1997-12-02 23:41:12</td>\n    </tr>\n    <tr>\n      <th>871  </th>\n      <td> 42</td>\n      <td>  625</td>\n      <td> 3</td>\n      <td>1997-12-03 00:27:53</td>\n    </tr>\n    <tr>\n      <th>1119 </th>\n      <td> 42</td>\n      <td>   98</td>\n      <td> 4</td>\n      <td>1997-12-02 23:51:51</td>\n    </tr>\n    <tr>\n      <th>1245 </th>\n      <td> 42</td>\n      <td>  953</td>\n      <td> 2</td>\n      <td>1997-12-03 00:26:55</td>\n    </tr>\n    <tr>\n      <th>1656 </th>\n      <td> 42</td>\n      <td>  685</td>\n      <td> 4</td>\n      <td>1997-12-02 23:39:32</td>\n    </tr>\n    <tr>\n      <th>1761 </th>\n      <td> 42</td>\n      <td>  176</td>\n      <td> 3</td>\n      <td>1997-12-02 23:59:38</td>\n    </tr>\n    <tr>\n      <th>1864 </th>\n      <td> 42</td>\n      <td>  195</td>\n      <td> 5</td>\n      <td>1997-12-03 00:12:29</td>\n    </tr>\n    <tr>\n      <th>2179 </th>\n      <td> 42</td>\n      <td>  185</td>\n      <td> 4</td>\n      <td>1997-12-03 00:04:09</td>\n    </tr>\n    <tr>\n      <th>2290 </th>\n      <td> 42</td>\n      <td>   69</td>\n      <td> 4</td>\n      <td>1997-12-03 00:02:55</td>\n    </tr>\n    <tr>\n      <th>2394 </th>\n      <td> 42</td>\n      <td>  684</td>\n      <td> 4</td>\n      <td>1997-12-03 00:14:53</td>\n    </tr>\n    <tr>\n      <th>2431 </th>\n      <td> 42</td>\n      <td>  603</td>\n      <td> 4</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>2613 </th>\n      <td> 42</td>\n      <td> 1041</td>\n      <td> 4</td>\n      <td>1997-12-03 00:31:00</td>\n    </tr>\n    <tr>\n      <th>2809 </th>\n      <td> 42</td>\n      <td>  121</td>\n      <td> 4</td>\n      <td>1997-12-03 00:56:18</td>\n    </tr>\n    <tr>\n      <th>2833 </th>\n      <td> 42</td>\n      <td>  606</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:38</td>\n    </tr>\n    <tr>\n      <th>2863 </th>\n      <td> 42</td>\n      <td>  456</td>\n      <td> 3</td>\n      <td>1997-12-02 23:41:53</td>\n    </tr>\n    <tr>\n      <th>2979 </th>\n      <td> 42</td>\n      <td>  506</td>\n      <td> 3</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>3060 </th>\n      <td> 42</td>\n      <td>  143</td>\n      <td> 4</td>\n      <td>1997-12-03 00:17:09</td>\n    </tr>\n    <tr>\n      <th>3162 </th>\n      <td> 42</td>\n      <td>  746</td>\n      <td> 3</td>\n      <td>1997-12-03 00:17:59</td>\n    </tr>\n    <tr>\n      <th>3480 </th>\n      <td> 42</td>\n      <td>   48</td>\n      <td> 5</td>\n      <td>1997-12-03 00:10:21</td>\n    </tr>\n    <tr>\n      <th>3542 </th>\n      <td> 42</td>\n      <td>  999</td>\n      <td> 4</td>\n      <td>1997-12-03 00:29:42</td>\n    </tr>\n    <tr>\n      <th>3601 </th>\n      <td> 42</td>\n      <td>  102</td>\n      <td> 5</td>\n      <td>1997-12-03 00:27:53</td>\n    </tr>\n    <tr>\n      <th>3824 </th>\n      <td> 42</td>\n      <td>  210</td>\n      <td> 5</td>\n      <td>1997-12-03 00:23:53</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>59045</th>\n      <td> 42</td>\n      <td> 1046</td>\n      <td> 3</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>60647</th>\n      <td> 42</td>\n      <td>   97</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>61040</th>\n      <td> 42</td>\n      <td>  559</td>\n      <td> 2</td>\n      <td>1997-12-03 00:34:31</td>\n    </tr>\n    <tr>\n      <th>61695</th>\n      <td> 42</td>\n      <td>   86</td>\n      <td> 3</td>\n      <td>1997-12-03 00:11:20</td>\n    </tr>\n    <tr>\n      <th>62662</th>\n      <td> 42</td>\n      <td>   71</td>\n      <td> 4</td>\n      <td>1997-12-03 00:17:09</td>\n    </tr>\n    <tr>\n      <th>63521</th>\n      <td> 42</td>\n      <td>  941</td>\n      <td> 4</td>\n      <td>1997-12-03 00:31:00</td>\n    </tr>\n    <tr>\n      <th>67740</th>\n      <td> 42</td>\n      <td>  655</td>\n      <td> 3</td>\n      <td>1997-12-03 00:07:22</td>\n    </tr>\n    <tr>\n      <th>68778</th>\n      <td> 42</td>\n      <td>  969</td>\n      <td> 5</td>\n      <td>1997-12-03 00:08:07</td>\n    </tr>\n    <tr>\n      <th>69604</th>\n      <td> 42</td>\n      <td>  132</td>\n      <td> 5</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>70257</th>\n      <td> 42</td>\n      <td>  595</td>\n      <td> 1</td>\n      <td>1997-12-02 23:49:42</td>\n    </tr>\n    <tr>\n      <th>70653</th>\n      <td> 42</td>\n      <td> 1048</td>\n      <td> 1</td>\n      <td>1997-12-02 23:43:40</td>\n    </tr>\n    <tr>\n      <th>71380</th>\n      <td> 42</td>\n      <td>  826</td>\n      <td> 3</td>\n      <td>1997-12-02 23:46:59</td>\n    </tr>\n    <tr>\n      <th>73843</th>\n      <td> 42</td>\n      <td>  111</td>\n      <td> 1</td>\n      <td>1997-12-02 23:38:51</td>\n    </tr>\n    <tr>\n      <th>74673</th>\n      <td> 42</td>\n      <td> 1050</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:38</td>\n    </tr>\n    <tr>\n      <th>76940</th>\n      <td> 42</td>\n      <td>  141</td>\n      <td> 3</td>\n      <td>1997-12-03 00:30:59</td>\n    </tr>\n    <tr>\n      <th>77201</th>\n      <td> 42</td>\n      <td>  181</td>\n      <td> 5</td>\n      <td>1997-12-03 00:01:31</td>\n    </tr>\n    <tr>\n      <th>79562</th>\n      <td> 42</td>\n      <td>  174</td>\n      <td> 5</td>\n      <td>1997-12-02 23:51:51</td>\n    </tr>\n    <tr>\n      <th>79923</th>\n      <td> 42</td>\n      <td>   73</td>\n      <td> 4</td>\n      <td>1997-12-03 00:21:24</td>\n    </tr>\n    <tr>\n      <th>82241</th>\n      <td> 42</td>\n      <td>  168</td>\n      <td> 3</td>\n      <td>1997-12-03 00:09:33</td>\n    </tr>\n    <tr>\n      <th>84817</th>\n      <td> 42</td>\n      <td>    2</td>\n      <td> 5</td>\n      <td>1997-12-03 00:34:31</td>\n    </tr>\n    <tr>\n      <th>85776</th>\n      <td> 42</td>\n      <td>  658</td>\n      <td> 2</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>86538</th>\n      <td> 42</td>\n      <td>  755</td>\n      <td> 4</td>\n      <td>1997-12-03 00:20:25</td>\n    </tr>\n    <tr>\n      <th>86768</th>\n      <td> 42</td>\n      <td>  282</td>\n      <td> 4</td>\n      <td>1997-12-02 23:34:37</td>\n    </tr>\n    <tr>\n      <th>87394</th>\n      <td> 42</td>\n      <td>  136</td>\n      <td> 4</td>\n      <td>1997-12-03 00:02:09</td>\n    </tr>\n    <tr>\n      <th>88397</th>\n      <td> 42</td>\n      <td>  735</td>\n      <td> 4</td>\n      <td>1997-12-03 00:22:28</td>\n    </tr>\n    <tr>\n      <th>89527</th>\n      <td> 42</td>\n      <td>  411</td>\n      <td> 4</td>\n      <td>1997-12-02 23:45:17</td>\n    </tr>\n    <tr>\n      <th>93817</th>\n      <td> 42</td>\n      <td>  433</td>\n      <td> 2</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>94791</th>\n      <td> 42</td>\n      <td>   87</td>\n      <td> 4</td>\n      <td>1997-12-03 00:06:16</td>\n    </tr>\n    <tr>\n      <th>97557</th>\n      <td> 42</td>\n      <td>  591</td>\n      <td> 4</td>\n      <td>1997-12-03 00:48:58</td>\n    </tr>\n    <tr>\n      <th>98433</th>\n      <td> 42</td>\n      <td>  103</td>\n      <td> 3</td>\n      <td>1997-12-02 23:42:42</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": "       user_id  item_id  rating           timestamp\n58          42      423       5 1997-12-03 00:08:07\n87          42      403       3 1997-12-03 00:24:44\n111         42       96       5 1997-12-02 23:59:38\n166         42      794       3 1997-12-03 00:20:25\n310         42      546       3 1997-12-02 23:36:57\n638         42      274       5 1997-12-02 23:36:57\n659         42      588       5 1997-12-03 00:15:47\n754         42       44       3 1997-12-03 00:22:28\n778         42     1028       4 1997-12-02 23:41:12\n871         42      625       3 1997-12-03 00:27:53\n1119        42       98       4 1997-12-02 23:51:51\n1245        42      953       2 1997-12-03 00:26:55\n1656        42      685       4 1997-12-02 23:39:32\n1761        42      176       3 1997-12-02 23:59:38\n1864        42      195       5 1997-12-03 00:12:29\n2179        42      185       4 1997-12-03 00:04:09\n2290        42       69       4 1997-12-03 00:02:55\n2394        42      684       4 1997-12-03 00:14:53\n2431        42      603       4 1997-12-03 00:05:02\n2613        42     1041       4 1997-12-03 00:31:00\n2809        42      121       4 1997-12-03 00:56:18\n2833        42      606       3 1997-12-03 00:05:38\n2863        42      456       3 1997-12-02 23:41:53\n2979        42      506       3 1997-12-03 00:26:00\n3060        42      143       4 1997-12-03 00:17:09\n3162        42      746       3 1997-12-03 00:17:59\n3480        42       48       5 1997-12-03 00:10:21\n3542        42      999       4 1997-12-03 00:29:42\n3601        42      102       5 1997-12-03 00:27:53\n3824        42      210       5 1997-12-03 00:23:53\n...        ...      ...     ...                 ...\n59045       42     1046       3 1997-12-03 00:26:00\n60647       42       97       3 1997-12-03 00:05:02\n61040       42      559       2 1997-12-03 00:34:31\n61695       42       86       3 1997-12-03 00:11:20\n62662       42       71       4 1997-12-03 00:17:09\n63521       42      941       4 1997-12-03 00:31:00\n67740       42      655       3 1997-12-03 00:07:22\n68778       42      969       5 1997-12-03 00:08:07\n69604       42      132       5 1997-12-03 00:05:02\n70257       42      595       1 1997-12-02 23:49:42\n70653       42     1048       1 1997-12-02 23:43:40\n71380       42      826       3 1997-12-02 23:46:59\n73843       42      111       1 1997-12-02 23:38:51\n74673       42     1050       3 1997-12-03 00:05:38\n76940       42      141       3 1997-12-03 00:30:59\n77201       42      181       5 1997-12-03 00:01:31\n79562       42      174       5 1997-12-02 23:51:51\n79923       42       73       4 1997-12-03 00:21:24\n82241       42      168       3 1997-12-03 00:09:33\n84817       42        2       5 1997-12-03 00:34:31\n85776       42      658       2 1997-12-03 00:05:02\n86538       42      755       4 1997-12-03 00:20:25\n86768       42      282       4 1997-12-02 23:34:37\n87394       42      136       4 1997-12-03 00:02:09\n88397       42      735       4 1997-12-03 00:22:28\n89527       42      411       4 1997-12-02 23:45:17\n93817       42      433       2 1997-12-03 00:26:00\n94791       42       87       4 1997-12-03 00:06:16\n97557       42      591       4 1997-12-03 00:48:58\n98433       42      103       3 1997-12-02 23:42:42\n\n[183 rows x 4 columns]"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Once we're in the confort zone with boolean indexing, we'd probably condense all that into one line:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[individual_ratings[\"user_id\"]==42]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>58   </th>\n      <td> 42</td>\n      <td>  423</td>\n      <td> 5</td>\n      <td>1997-12-03 00:08:07</td>\n    </tr>\n    <tr>\n      <th>87   </th>\n      <td> 42</td>\n      <td>  403</td>\n      <td> 3</td>\n      <td>1997-12-03 00:24:44</td>\n    </tr>\n    <tr>\n      <th>111  </th>\n      <td> 42</td>\n      <td>   96</td>\n      <td> 5</td>\n      <td>1997-12-02 23:59:38</td>\n    </tr>\n    <tr>\n      <th>166  </th>\n      <td> 42</td>\n      <td>  794</td>\n      <td> 3</td>\n      <td>1997-12-03 00:20:25</td>\n    </tr>\n    <tr>\n      <th>310  </th>\n      <td> 42</td>\n      <td>  546</td>\n      <td> 3</td>\n      <td>1997-12-02 23:36:57</td>\n    </tr>\n    <tr>\n      <th>638  </th>\n      <td> 42</td>\n      <td>  274</td>\n      <td> 5</td>\n      <td>1997-12-02 23:36:57</td>\n    </tr>\n    <tr>\n      <th>659  </th>\n      <td> 42</td>\n      <td>  588</td>\n      <td> 5</td>\n      <td>1997-12-03 00:15:47</td>\n    </tr>\n    <tr>\n      <th>754  </th>\n      <td> 42</td>\n      <td>   44</td>\n      <td> 3</td>\n      <td>1997-12-03 00:22:28</td>\n    </tr>\n    <tr>\n      <th>778  </th>\n      <td> 42</td>\n      <td> 1028</td>\n      <td> 4</td>\n      <td>1997-12-02 23:41:12</td>\n    </tr>\n    <tr>\n      <th>871  </th>\n      <td> 42</td>\n      <td>  625</td>\n      <td> 3</td>\n      <td>1997-12-03 00:27:53</td>\n    </tr>\n    <tr>\n      <th>1119 </th>\n      <td> 42</td>\n      <td>   98</td>\n      <td> 4</td>\n      <td>1997-12-02 23:51:51</td>\n    </tr>\n    <tr>\n      <th>1245 </th>\n      <td> 42</td>\n      <td>  953</td>\n      <td> 2</td>\n      <td>1997-12-03 00:26:55</td>\n    </tr>\n    <tr>\n      <th>1656 </th>\n      <td> 42</td>\n      <td>  685</td>\n      <td> 4</td>\n      <td>1997-12-02 23:39:32</td>\n    </tr>\n    <tr>\n      <th>1761 </th>\n      <td> 42</td>\n      <td>  176</td>\n      <td> 3</td>\n      <td>1997-12-02 23:59:38</td>\n    </tr>\n    <tr>\n      <th>1864 </th>\n      <td> 42</td>\n      <td>  195</td>\n      <td> 5</td>\n      <td>1997-12-03 00:12:29</td>\n    </tr>\n    <tr>\n      <th>2179 </th>\n      <td> 42</td>\n      <td>  185</td>\n      <td> 4</td>\n      <td>1997-12-03 00:04:09</td>\n    </tr>\n    <tr>\n      <th>2290 </th>\n      <td> 42</td>\n      <td>   69</td>\n      <td> 4</td>\n      <td>1997-12-03 00:02:55</td>\n    </tr>\n    <tr>\n      <th>2394 </th>\n      <td> 42</td>\n      <td>  684</td>\n      <td> 4</td>\n      <td>1997-12-03 00:14:53</td>\n    </tr>\n    <tr>\n      <th>2431 </th>\n      <td> 42</td>\n      <td>  603</td>\n      <td> 4</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>2613 </th>\n      <td> 42</td>\n      <td> 1041</td>\n      <td> 4</td>\n      <td>1997-12-03 00:31:00</td>\n    </tr>\n    <tr>\n      <th>2809 </th>\n      <td> 42</td>\n      <td>  121</td>\n      <td> 4</td>\n      <td>1997-12-03 00:56:18</td>\n    </tr>\n    <tr>\n      <th>2833 </th>\n      <td> 42</td>\n      <td>  606</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:38</td>\n    </tr>\n    <tr>\n      <th>2863 </th>\n      <td> 42</td>\n      <td>  456</td>\n      <td> 3</td>\n      <td>1997-12-02 23:41:53</td>\n    </tr>\n    <tr>\n      <th>2979 </th>\n      <td> 42</td>\n      <td>  506</td>\n      <td> 3</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>3060 </th>\n      <td> 42</td>\n      <td>  143</td>\n      <td> 4</td>\n      <td>1997-12-03 00:17:09</td>\n    </tr>\n    <tr>\n      <th>3162 </th>\n      <td> 42</td>\n      <td>  746</td>\n      <td> 3</td>\n      <td>1997-12-03 00:17:59</td>\n    </tr>\n    <tr>\n      <th>3480 </th>\n      <td> 42</td>\n      <td>   48</td>\n      <td> 5</td>\n      <td>1997-12-03 00:10:21</td>\n    </tr>\n    <tr>\n      <th>3542 </th>\n      <td> 42</td>\n      <td>  999</td>\n      <td> 4</td>\n      <td>1997-12-03 00:29:42</td>\n    </tr>\n    <tr>\n      <th>3601 </th>\n      <td> 42</td>\n      <td>  102</td>\n      <td> 5</td>\n      <td>1997-12-03 00:27:53</td>\n    </tr>\n    <tr>\n      <th>3824 </th>\n      <td> 42</td>\n      <td>  210</td>\n      <td> 5</td>\n      <td>1997-12-03 00:23:53</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>59045</th>\n      <td> 42</td>\n      <td> 1046</td>\n      <td> 3</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>60647</th>\n      <td> 42</td>\n      <td>   97</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>61040</th>\n      <td> 42</td>\n      <td>  559</td>\n      <td> 2</td>\n      <td>1997-12-03 00:34:31</td>\n    </tr>\n    <tr>\n      <th>61695</th>\n      <td> 42</td>\n      <td>   86</td>\n      <td> 3</td>\n      <td>1997-12-03 00:11:20</td>\n    </tr>\n    <tr>\n      <th>62662</th>\n      <td> 42</td>\n      <td>   71</td>\n      <td> 4</td>\n      <td>1997-12-03 00:17:09</td>\n    </tr>\n    <tr>\n      <th>63521</th>\n      <td> 42</td>\n      <td>  941</td>\n      <td> 4</td>\n      <td>1997-12-03 00:31:00</td>\n    </tr>\n    <tr>\n      <th>67740</th>\n      <td> 42</td>\n      <td>  655</td>\n      <td> 3</td>\n      <td>1997-12-03 00:07:22</td>\n    </tr>\n    <tr>\n      <th>68778</th>\n      <td> 42</td>\n      <td>  969</td>\n      <td> 5</td>\n      <td>1997-12-03 00:08:07</td>\n    </tr>\n    <tr>\n      <th>69604</th>\n      <td> 42</td>\n      <td>  132</td>\n      <td> 5</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>70257</th>\n      <td> 42</td>\n      <td>  595</td>\n      <td> 1</td>\n      <td>1997-12-02 23:49:42</td>\n    </tr>\n    <tr>\n      <th>70653</th>\n      <td> 42</td>\n      <td> 1048</td>\n      <td> 1</td>\n      <td>1997-12-02 23:43:40</td>\n    </tr>\n    <tr>\n      <th>71380</th>\n      <td> 42</td>\n      <td>  826</td>\n      <td> 3</td>\n      <td>1997-12-02 23:46:59</td>\n    </tr>\n    <tr>\n      <th>73843</th>\n      <td> 42</td>\n      <td>  111</td>\n      <td> 1</td>\n      <td>1997-12-02 23:38:51</td>\n    </tr>\n    <tr>\n      <th>74673</th>\n      <td> 42</td>\n      <td> 1050</td>\n      <td> 3</td>\n      <td>1997-12-03 00:05:38</td>\n    </tr>\n    <tr>\n      <th>76940</th>\n      <td> 42</td>\n      <td>  141</td>\n      <td> 3</td>\n      <td>1997-12-03 00:30:59</td>\n    </tr>\n    <tr>\n      <th>77201</th>\n      <td> 42</td>\n      <td>  181</td>\n      <td> 5</td>\n      <td>1997-12-03 00:01:31</td>\n    </tr>\n    <tr>\n      <th>79562</th>\n      <td> 42</td>\n      <td>  174</td>\n      <td> 5</td>\n      <td>1997-12-02 23:51:51</td>\n    </tr>\n    <tr>\n      <th>79923</th>\n      <td> 42</td>\n      <td>   73</td>\n      <td> 4</td>\n      <td>1997-12-03 00:21:24</td>\n    </tr>\n    <tr>\n      <th>82241</th>\n      <td> 42</td>\n      <td>  168</td>\n      <td> 3</td>\n      <td>1997-12-03 00:09:33</td>\n    </tr>\n    <tr>\n      <th>84817</th>\n      <td> 42</td>\n      <td>    2</td>\n      <td> 5</td>\n      <td>1997-12-03 00:34:31</td>\n    </tr>\n    <tr>\n      <th>85776</th>\n      <td> 42</td>\n      <td>  658</td>\n      <td> 2</td>\n      <td>1997-12-03 00:05:02</td>\n    </tr>\n    <tr>\n      <th>86538</th>\n      <td> 42</td>\n      <td>  755</td>\n      <td> 4</td>\n      <td>1997-12-03 00:20:25</td>\n    </tr>\n    <tr>\n      <th>86768</th>\n      <td> 42</td>\n      <td>  282</td>\n      <td> 4</td>\n      <td>1997-12-02 23:34:37</td>\n    </tr>\n    <tr>\n      <th>87394</th>\n      <td> 42</td>\n      <td>  136</td>\n      <td> 4</td>\n      <td>1997-12-03 00:02:09</td>\n    </tr>\n    <tr>\n      <th>88397</th>\n      <td> 42</td>\n      <td>  735</td>\n      <td> 4</td>\n      <td>1997-12-03 00:22:28</td>\n    </tr>\n    <tr>\n      <th>89527</th>\n      <td> 42</td>\n      <td>  411</td>\n      <td> 4</td>\n      <td>1997-12-02 23:45:17</td>\n    </tr>\n    <tr>\n      <th>93817</th>\n      <td> 42</td>\n      <td>  433</td>\n      <td> 2</td>\n      <td>1997-12-03 00:26:00</td>\n    </tr>\n    <tr>\n      <th>94791</th>\n      <td> 42</td>\n      <td>   87</td>\n      <td> 4</td>\n      <td>1997-12-03 00:06:16</td>\n    </tr>\n    <tr>\n      <th>97557</th>\n      <td> 42</td>\n      <td>  591</td>\n      <td> 4</td>\n      <td>1997-12-03 00:48:58</td>\n    </tr>\n    <tr>\n      <th>98433</th>\n      <td> 42</td>\n      <td>  103</td>\n      <td> 3</td>\n      <td>1997-12-02 23:42:42</td>\n    </tr>\n  </tbody>\n</table>\n<p>183 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "       user_id  item_id  rating           timestamp\n58          42      423       5 1997-12-03 00:08:07\n87          42      403       3 1997-12-03 00:24:44\n111         42       96       5 1997-12-02 23:59:38\n166         42      794       3 1997-12-03 00:20:25\n310         42      546       3 1997-12-02 23:36:57\n638         42      274       5 1997-12-02 23:36:57\n659         42      588       5 1997-12-03 00:15:47\n754         42       44       3 1997-12-03 00:22:28\n778         42     1028       4 1997-12-02 23:41:12\n871         42      625       3 1997-12-03 00:27:53\n1119        42       98       4 1997-12-02 23:51:51\n1245        42      953       2 1997-12-03 00:26:55\n1656        42      685       4 1997-12-02 23:39:32\n1761        42      176       3 1997-12-02 23:59:38\n1864        42      195       5 1997-12-03 00:12:29\n2179        42      185       4 1997-12-03 00:04:09\n2290        42       69       4 1997-12-03 00:02:55\n2394        42      684       4 1997-12-03 00:14:53\n2431        42      603       4 1997-12-03 00:05:02\n2613        42     1041       4 1997-12-03 00:31:00\n2809        42      121       4 1997-12-03 00:56:18\n2833        42      606       3 1997-12-03 00:05:38\n2863        42      456       3 1997-12-02 23:41:53\n2979        42      506       3 1997-12-03 00:26:00\n3060        42      143       4 1997-12-03 00:17:09\n3162        42      746       3 1997-12-03 00:17:59\n3480        42       48       5 1997-12-03 00:10:21\n3542        42      999       4 1997-12-03 00:29:42\n3601        42      102       5 1997-12-03 00:27:53\n3824        42      210       5 1997-12-03 00:23:53\n...        ...      ...     ...                 ...\n59045       42     1046       3 1997-12-03 00:26:00\n60647       42       97       3 1997-12-03 00:05:02\n61040       42      559       2 1997-12-03 00:34:31\n61695       42       86       3 1997-12-03 00:11:20\n62662       42       71       4 1997-12-03 00:17:09\n63521       42      941       4 1997-12-03 00:31:00\n67740       42      655       3 1997-12-03 00:07:22\n68778       42      969       5 1997-12-03 00:08:07\n69604       42      132       5 1997-12-03 00:05:02\n70257       42      595       1 1997-12-02 23:49:42\n70653       42     1048       1 1997-12-02 23:43:40\n71380       42      826       3 1997-12-02 23:46:59\n73843       42      111       1 1997-12-02 23:38:51\n74673       42     1050       3 1997-12-03 00:05:38\n76940       42      141       3 1997-12-03 00:30:59\n77201       42      181       5 1997-12-03 00:01:31\n79562       42      174       5 1997-12-02 23:51:51\n79923       42       73       4 1997-12-03 00:21:24\n82241       42      168       3 1997-12-03 00:09:33\n84817       42        2       5 1997-12-03 00:34:31\n85776       42      658       2 1997-12-03 00:05:02\n86538       42      755       4 1997-12-03 00:20:25\n86768       42      282       4 1997-12-02 23:34:37\n87394       42      136       4 1997-12-03 00:02:09\n88397       42      735       4 1997-12-03 00:22:28\n89527       42      411       4 1997-12-02 23:45:17\n93817       42      433       2 1997-12-03 00:26:00\n94791       42       87       4 1997-12-03 00:06:16\n97557       42      591       4 1997-12-03 00:48:58\n98433       42      103       3 1997-12-02 23:42:42\n\n[183 rows x 4 columns]"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now, if just wanted the series of ratings of user 42, we could add an index ['rating']."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[user_42_ratings_boolean]['rating']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": "58      5\n87      3\n111     5\n166     3\n310     3\n638     5\n659     5\n754     3\n778     4\n871     3\n1119    4\n1245    2\n1656    4\n1761    3\n1864    5\n...\n77201    5\n79562    5\n79923    4\n82241    3\n84817    5\n85776    2\n86538    4\n86768    4\n87394    4\n88397    4\n89527    4\n93817    2\n94791    4\n97557    4\n98433    3\nName: rating, Length: 183, dtype: int64"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#or in a one line formulation:\nindividual_ratings[individual_ratings[\"user_id\"]==42]['rating']",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": "58      5\n87      3\n111     5\n166     3\n310     3\n638     5\n659     5\n754     3\n778     4\n871     3\n1119    4\n1245    2\n1656    4\n1761    3\n1864    5\n...\n77201    5\n79562    5\n79923    4\n82241    3\n84817    5\n85776    2\n86538    4\n86768    4\n87394    4\n88397    4\n89527    4\n93817    2\n94791    4\n97557    4\n98433    3\nName: rating, Length: 183, dtype: int64"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "`pandas` lets us do all sorts of simple statistics, like finding the mean. just tack on the method `mean`."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[individual_ratings[\"user_id\"]==42]['rating'].mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": "3.7267759562841531"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "How about all the ratings for a given film, say no. 65? How would we get that?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[individual_ratings[\"item_id\"]==65]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>247  </th>\n      <td>  62</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-12 22:44:46</td>\n    </tr>\n    <tr>\n      <th>1042 </th>\n      <td> 271</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1998-01-26 21:16:59</td>\n    </tr>\n    <tr>\n      <th>1152 </th>\n      <td> 207</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-10-29 05:56:34</td>\n    </tr>\n    <tr>\n      <th>2321 </th>\n      <td> 303</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-14 00:30:36</td>\n    </tr>\n    <tr>\n      <th>2608 </th>\n      <td> 279</td>\n      <td> 65</td>\n      <td> 1</td>\n      <td>1997-09-26 20:46:07</td>\n    </tr>\n    <tr>\n      <th>3111 </th>\n      <td> 313</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1998-03-27 16:42:42</td>\n    </tr>\n    <tr>\n      <th>7149 </th>\n      <td>  59</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-02-23 03:41:05</td>\n    </tr>\n    <tr>\n      <th>9097 </th>\n      <td> 378</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-11-20 17:15:32</td>\n    </tr>\n    <tr>\n      <th>9178 </th>\n      <td> 151</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-14 17:32:09</td>\n    </tr>\n    <tr>\n      <th>10285</th>\n      <td> 128</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-19 19:41:52</td>\n    </tr>\n    <tr>\n      <th>11112</th>\n      <td> 210</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-02-17 16:01:45</td>\n    </tr>\n    <tr>\n      <th>11591</th>\n      <td> 347</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1997-12-09 08:04:39</td>\n    </tr>\n    <tr>\n      <th>11779</th>\n      <td> 339</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-03-27 21:17:32</td>\n    </tr>\n    <tr>\n      <th>13254</th>\n      <td> 409</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-12-03 00:26:17</td>\n    </tr>\n    <tr>\n      <th>13860</th>\n      <td> 116</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1997-10-10 03:27:32</td>\n    </tr>\n    <tr>\n      <th>14614</th>\n      <td> 144</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-02-22 00:09:42</td>\n    </tr>\n    <tr>\n      <th>16207</th>\n      <td> 235</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1998-03-11 22:35:23</td>\n    </tr>\n    <tr>\n      <th>16330</th>\n      <td> 198</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1998-01-07 21:24:01</td>\n    </tr>\n    <tr>\n      <th>16707</th>\n      <td>  71</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1998-01-17 06:02:41</td>\n    </tr>\n    <tr>\n      <th>17220</th>\n      <td>   1</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-09-24 03:35:25</td>\n    </tr>\n    <tr>\n      <th>19110</th>\n      <td> 401</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-03-27 21:14:10</td>\n    </tr>\n    <tr>\n      <th>19153</th>\n      <td> 398</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-10-01 22:08:59</td>\n    </tr>\n    <tr>\n      <th>20515</th>\n      <td> 437</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-21 19:33:07</td>\n    </tr>\n    <tr>\n      <th>20831</th>\n      <td>  65</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-11-11 02:51:12</td>\n    </tr>\n    <tr>\n      <th>21607</th>\n      <td>  18</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1997-11-21 16:38:53</td>\n    </tr>\n    <tr>\n      <th>21786</th>\n      <td> 267</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-08 06:54:31</td>\n    </tr>\n    <tr>\n      <th>23247</th>\n      <td> 450</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-12-17 16:41:25</td>\n    </tr>\n    <tr>\n      <th>26808</th>\n      <td> 239</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1998-03-06 10:27:21</td>\n    </tr>\n    <tr>\n      <th>29009</th>\n      <td> 187</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1997-11-13 23:58:27</td>\n    </tr>\n    <tr>\n      <th>30379</th>\n      <td> 417</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-16 02:23:31</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>68157</th>\n      <td> 621</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1998-01-23 23:09:04</td>\n    </tr>\n    <tr>\n      <th>69155</th>\n      <td> 790</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-01-18 20:37:26</td>\n    </tr>\n    <tr>\n      <th>69366</th>\n      <td> 916</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-11-29 23:15:27</td>\n    </tr>\n    <tr>\n      <th>69838</th>\n      <td> 405</td>\n      <td> 65</td>\n      <td> 1</td>\n      <td>1998-01-23 09:06:19</td>\n    </tr>\n    <tr>\n      <th>71118</th>\n      <td> 276</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-09-20 20:31:07</td>\n    </tr>\n    <tr>\n      <th>72450</th>\n      <td> 815</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1997-11-05 01:51:04</td>\n    </tr>\n    <tr>\n      <th>73577</th>\n      <td> 848</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1998-02-09 15:35:27</td>\n    </tr>\n    <tr>\n      <th>73753</th>\n      <td> 883</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-04-04 19:15:19</td>\n    </tr>\n    <tr>\n      <th>74031</th>\n      <td> 269</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-04-01 16:27:52</td>\n    </tr>\n    <tr>\n      <th>74869</th>\n      <td> 174</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1998-02-03 13:55:23</td>\n    </tr>\n    <tr>\n      <th>77183</th>\n      <td> 354</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-03-30 00:34:06</td>\n    </tr>\n    <tr>\n      <th>79753</th>\n      <td> 727</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1998-01-02 03:39:03</td>\n    </tr>\n    <tr>\n      <th>80688</th>\n      <td> 870</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-11-16 20:58:18</td>\n    </tr>\n    <tr>\n      <th>80856</th>\n      <td> 216</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-22 21:25:39</td>\n    </tr>\n    <tr>\n      <th>81409</th>\n      <td> 468</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-09-26 17:22:29</td>\n    </tr>\n    <tr>\n      <th>81865</th>\n      <td> 864</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1998-03-03 02:04:50</td>\n    </tr>\n    <tr>\n      <th>82834</th>\n      <td> 244</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-27 04:42:46</td>\n    </tr>\n    <tr>\n      <th>83681</th>\n      <td> 934</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-03-29 17:35:14</td>\n    </tr>\n    <tr>\n      <th>84507</th>\n      <td> 601</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-10-08 22:33:37</td>\n    </tr>\n    <tr>\n      <th>86661</th>\n      <td> 846</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1998-01-04 21:27:34</td>\n    </tr>\n    <tr>\n      <th>87592</th>\n      <td> 887</td>\n      <td> 65</td>\n      <td> 5</td>\n      <td>1997-12-06 04:14:39</td>\n    </tr>\n    <tr>\n      <th>88365</th>\n      <td> 805</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-12-09 20:21:01</td>\n    </tr>\n    <tr>\n      <th>88946</th>\n      <td> 766</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1998-03-31 02:03:30</td>\n    </tr>\n    <tr>\n      <th>89960</th>\n      <td> 897</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-20 02:26:51</td>\n    </tr>\n    <tr>\n      <th>90575</th>\n      <td> 709</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1997-11-18 09:54:28</td>\n    </tr>\n    <tr>\n      <th>93337</th>\n      <td> 788</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-11-30 05:59:44</td>\n    </tr>\n    <tr>\n      <th>95905</th>\n      <td> 886</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1997-10-05 06:11:10</td>\n    </tr>\n    <tr>\n      <th>96697</th>\n      <td> 682</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td>1998-02-26 18:23:36</td>\n    </tr>\n    <tr>\n      <th>96833</th>\n      <td> 868</td>\n      <td> 65</td>\n      <td> 2</td>\n      <td>1997-10-17 16:03:32</td>\n    </tr>\n    <tr>\n      <th>99541</th>\n      <td>  92</td>\n      <td> 65</td>\n      <td> 4</td>\n      <td>1997-09-30 21:12:40</td>\n    </tr>\n  </tbody>\n</table>\n<p>115 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": "       user_id  item_id  rating           timestamp\n247         62       65       4 1997-11-12 22:44:46\n1042       271       65       3 1998-01-26 21:16:59\n1152       207       65       3 1997-10-29 05:56:34\n2321       303       65       4 1997-11-14 00:30:36\n2608       279       65       1 1997-09-26 20:46:07\n3111       313       65       2 1998-03-27 16:42:42\n7149        59       65       4 1998-02-23 03:41:05\n9097       378       65       3 1997-11-20 17:15:32\n9178       151       65       4 1997-11-14 17:32:09\n10285      128       65       4 1997-11-19 19:41:52\n11112      210       65       4 1998-02-17 16:01:45\n11591      347       65       2 1997-12-09 08:04:39\n11779      339       65       4 1998-03-27 21:17:32\n13254      409       65       4 1997-12-03 00:26:17\n13860      116       65       2 1997-10-10 03:27:32\n14614      144       65       4 1998-02-22 00:09:42\n16207      235       65       2 1998-03-11 22:35:23\n16330      198       65       2 1998-01-07 21:24:01\n16707       71       65       5 1998-01-17 06:02:41\n17220        1       65       4 1997-09-24 03:35:25\n19110      401       65       4 1998-03-27 21:14:10\n19153      398       65       3 1997-10-01 22:08:59\n20515      437       65       4 1997-11-21 19:33:07\n20831       65       65       3 1997-11-11 02:51:12\n21607       18       65       5 1997-11-21 16:38:53\n21786      267       65       4 1997-11-08 06:54:31\n23247      450       65       3 1997-12-17 16:41:25\n26808      239       65       5 1998-03-06 10:27:21\n29009      187       65       5 1997-11-13 23:58:27\n30379      417       65       4 1997-11-16 02:23:31\n...        ...      ...     ...                 ...\n68157      621       65       3 1998-01-23 23:09:04\n69155      790       65       4 1998-01-18 20:37:26\n69366      916       65       3 1997-11-29 23:15:27\n69838      405       65       1 1998-01-23 09:06:19\n71118      276       65       4 1997-09-20 20:31:07\n72450      815       65       5 1997-11-05 01:51:04\n73577      848       65       2 1998-02-09 15:35:27\n73753      883       65       4 1998-04-04 19:15:19\n74031      269       65       4 1998-04-01 16:27:52\n74869      174       65       5 1998-02-03 13:55:23\n77183      354       65       4 1998-03-30 00:34:06\n79753      727       65       2 1998-01-02 03:39:03\n80688      870       65       3 1997-11-16 20:58:18\n80856      216       65       4 1997-11-22 21:25:39\n81409      468       65       3 1997-09-26 17:22:29\n81865      864       65       3 1998-03-03 02:04:50\n82834      244       65       4 1997-11-27 04:42:46\n83681      934       65       4 1998-03-29 17:35:14\n84507      601       65       4 1997-10-08 22:33:37\n86661      846       65       3 1998-01-04 21:27:34\n87592      887       65       5 1997-12-06 04:14:39\n88365      805       65       3 1997-12-09 20:21:01\n88946      766       65       4 1998-03-31 02:03:30\n89960      897       65       4 1997-11-20 02:26:51\n90575      709       65       2 1997-11-18 09:54:28\n93337      788       65       4 1997-11-30 05:59:44\n95905      886       65       3 1997-10-05 06:11:10\n96697      682       65       3 1998-02-26 18:23:36\n96833      868       65       2 1997-10-17 16:03:32\n99541       92       65       4 1997-09-30 21:12:40\n\n[115 rows x 4 columns]"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings[individual_ratings[\"item_id\"]==65].mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": "user_id    457.852174\nitem_id     65.000000\nrating       3.539130\ndtype: float64"
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Hmmm.....why is this output different?"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now, you're probably thinking, \"there's just no way `pandas` can't automate this!\""
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "for i in range(20):\n    mean=individual_ratings[individual_ratings[\"item_id\"]==i]['rating'].mean()\n    if mean < 3.7:\n        comment = \"the people have spoken\"\n    else:\n        comment = \"a possibility, dear\"\n    print str(i)+\"\\t\"+str(mean)+\"\\t\\t\"+comment",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0\tnan\t\ta possibility, dear\n1\t3.87831858407\t\ta possibility, dear\n2\t3.20610687023\t\tthe people have spoken\n3\t3.03333333333\t\tthe people have spoken\n4\t3.55023923445\t\tthe people have spoken\n5\t3.3023255814\t\tthe people have spoken\n6\t3.57692307692\t\tthe people have spoken\n7\t3.79846938776\t\ta possibility, dear\n8\t3.99543378995\t\ta possibility, dear\n9\t3.89632107023\t\ta possibility, dear\n10\t3.83146067416\t\ta possibility, dear\n11\t3.84745762712\t\ta possibility, dear\n12\t4.38576779026\t\ta possibility, dear\n13\t3.41847826087\t\tthe people have spoken\n14\t3.96721311475\t\ta possibility, dear\n15\t3.77815699659\t\ta possibility, dear\n16\t3.20512820513\t\tthe people have spoken\n17\t3.11956521739\t\tthe people have spoken"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n18\t2.8\t\tthe people have spoken\n19\t3.95652173913\t\ta possibility, dear\n"
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "That's what I promised you you wouldn't need to do. \n\nThe hard part is to pick among them!\n"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Profoundly badass is the `groupby` method. As its name suggests, it takes a series or dataframe and groups the data by a column of your choosing. You then perform a function on each group."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "individual_ratings.groupby(by=\"item_id\").mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>rating</th>\n    </tr>\n    <tr>\n      <th>item_id</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1   </th>\n      <td> 477.011062</td>\n      <td> 3.878319</td>\n    </tr>\n    <tr>\n      <th>2   </th>\n      <td> 492.007634</td>\n      <td> 3.206107</td>\n    </tr>\n    <tr>\n      <th>3   </th>\n      <td> 459.133333</td>\n      <td> 3.033333</td>\n    </tr>\n    <tr>\n      <th>4   </th>\n      <td> 469.497608</td>\n      <td> 3.550239</td>\n    </tr>\n    <tr>\n      <th>5   </th>\n      <td> 439.372093</td>\n      <td> 3.302326</td>\n    </tr>\n    <tr>\n      <th>6   </th>\n      <td> 454.576923</td>\n      <td> 3.576923</td>\n    </tr>\n    <tr>\n      <th>7   </th>\n      <td> 445.369898</td>\n      <td> 3.798469</td>\n    </tr>\n    <tr>\n      <th>8   </th>\n      <td> 454.675799</td>\n      <td> 3.995434</td>\n    </tr>\n    <tr>\n      <th>9   </th>\n      <td> 460.321070</td>\n      <td> 3.896321</td>\n    </tr>\n    <tr>\n      <th>10  </th>\n      <td> 450.213483</td>\n      <td> 3.831461</td>\n    </tr>\n    <tr>\n      <th>11  </th>\n      <td> 440.762712</td>\n      <td> 3.847458</td>\n    </tr>\n    <tr>\n      <th>12  </th>\n      <td> 448.382022</td>\n      <td> 4.385768</td>\n    </tr>\n    <tr>\n      <th>13  </th>\n      <td> 436.668478</td>\n      <td> 3.418478</td>\n    </tr>\n    <tr>\n      <th>14  </th>\n      <td> 458.836066</td>\n      <td> 3.967213</td>\n    </tr>\n    <tr>\n      <th>15  </th>\n      <td> 459.525597</td>\n      <td> 3.778157</td>\n    </tr>\n    <tr>\n      <th>16  </th>\n      <td> 503.692308</td>\n      <td> 3.205128</td>\n    </tr>\n    <tr>\n      <th>17  </th>\n      <td> 441.489130</td>\n      <td> 3.119565</td>\n    </tr>\n    <tr>\n      <th>18  </th>\n      <td> 219.200000</td>\n      <td> 2.800000</td>\n    </tr>\n    <tr>\n      <th>19  </th>\n      <td> 489.376812</td>\n      <td> 3.956522</td>\n    </tr>\n    <tr>\n      <th>20  </th>\n      <td> 478.916667</td>\n      <td> 3.416667</td>\n    </tr>\n    <tr>\n      <th>21  </th>\n      <td> 450.821429</td>\n      <td> 2.761905</td>\n    </tr>\n    <tr>\n      <th>22  </th>\n      <td> 471.000000</td>\n      <td> 4.151515</td>\n    </tr>\n    <tr>\n      <th>23  </th>\n      <td> 466.203297</td>\n      <td> 4.120879</td>\n    </tr>\n    <tr>\n      <th>24  </th>\n      <td> 458.477011</td>\n      <td> 3.448276</td>\n    </tr>\n    <tr>\n      <th>25  </th>\n      <td> 449.901024</td>\n      <td> 3.443686</td>\n    </tr>\n    <tr>\n      <th>26  </th>\n      <td> 472.945205</td>\n      <td> 3.452055</td>\n    </tr>\n    <tr>\n      <th>27  </th>\n      <td> 467.508772</td>\n      <td> 3.105263</td>\n    </tr>\n    <tr>\n      <th>28  </th>\n      <td> 465.710145</td>\n      <td> 3.931159</td>\n    </tr>\n    <tr>\n      <th>29  </th>\n      <td> 466.859649</td>\n      <td> 2.666667</td>\n    </tr>\n    <tr>\n      <th>30  </th>\n      <td> 483.945946</td>\n      <td> 3.945946</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1653</th>\n      <td> 675.000000</td>\n      <td> 5.000000</td>\n    </tr>\n    <tr>\n      <th>1654</th>\n      <td> 676.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1655</th>\n      <td> 682.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1656</th>\n      <td> 798.000000</td>\n      <td> 3.500000</td>\n    </tr>\n    <tr>\n      <th>1657</th>\n      <td> 727.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1658</th>\n      <td> 803.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1659</th>\n      <td> 747.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1660</th>\n      <td> 747.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1661</th>\n      <td> 751.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1662</th>\n      <td> 772.000000</td>\n      <td> 2.500000</td>\n    </tr>\n    <tr>\n      <th>1663</th>\n      <td> 782.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1664</th>\n      <td> 842.750000</td>\n      <td> 3.250000</td>\n    </tr>\n    <tr>\n      <th>1665</th>\n      <td> 782.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1666</th>\n      <td> 782.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1667</th>\n      <td> 782.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1668</th>\n      <td> 782.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1669</th>\n      <td> 782.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1670</th>\n      <td> 782.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1671</th>\n      <td> 787.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1672</th>\n      <td> 862.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1673</th>\n      <td> 835.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1674</th>\n      <td> 840.000000</td>\n      <td> 4.000000</td>\n    </tr>\n    <tr>\n      <th>1675</th>\n      <td> 851.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1676</th>\n      <td> 851.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1677</th>\n      <td> 854.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1678</th>\n      <td> 863.000000</td>\n      <td> 1.000000</td>\n    </tr>\n    <tr>\n      <th>1679</th>\n      <td> 863.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1680</th>\n      <td> 863.000000</td>\n      <td> 2.000000</td>\n    </tr>\n    <tr>\n      <th>1681</th>\n      <td> 896.000000</td>\n      <td> 3.000000</td>\n    </tr>\n    <tr>\n      <th>1682</th>\n      <td> 916.000000</td>\n      <td> 3.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1682 rows \u00d7 2 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": "            user_id    rating\nitem_id                      \n1        477.011062  3.878319\n2        492.007634  3.206107\n3        459.133333  3.033333\n4        469.497608  3.550239\n5        439.372093  3.302326\n6        454.576923  3.576923\n7        445.369898  3.798469\n8        454.675799  3.995434\n9        460.321070  3.896321\n10       450.213483  3.831461\n11       440.762712  3.847458\n12       448.382022  4.385768\n13       436.668478  3.418478\n14       458.836066  3.967213\n15       459.525597  3.778157\n16       503.692308  3.205128\n17       441.489130  3.119565\n18       219.200000  2.800000\n19       489.376812  3.956522\n20       478.916667  3.416667\n21       450.821429  2.761905\n22       471.000000  4.151515\n23       466.203297  4.120879\n24       458.477011  3.448276\n25       449.901024  3.443686\n26       472.945205  3.452055\n27       467.508772  3.105263\n28       465.710145  3.931159\n29       466.859649  2.666667\n30       483.945946  3.945946\n...             ...       ...\n1653     675.000000  5.000000\n1654     676.000000  1.000000\n1655     682.000000  2.000000\n1656     798.000000  3.500000\n1657     727.000000  3.000000\n1658     803.000000  3.000000\n1659     747.000000  1.000000\n1660     747.000000  2.000000\n1661     751.000000  1.000000\n1662     772.000000  2.500000\n1663     782.000000  2.000000\n1664     842.750000  3.250000\n1665     782.000000  2.000000\n1666     782.000000  2.000000\n1667     782.000000  3.000000\n1668     782.000000  3.000000\n1669     782.000000  2.000000\n1670     782.000000  3.000000\n1671     787.000000  1.000000\n1672     862.000000  2.000000\n1673     835.000000  3.000000\n1674     840.000000  4.000000\n1675     851.000000  3.000000\n1676     851.000000  2.000000\n1677     854.000000  3.000000\n1678     863.000000  1.000000\n1679     863.000000  3.000000\n1680     863.000000  2.000000\n1681     896.000000  3.000000\n1682     916.000000  3.000000\n\n[1682 rows x 2 columns]"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#`Pivot`\n\n\n\n\nWhat if we wanted to have a big table where each row is the user followed by all her ratings?\nWe could write a few lines of code to produce this.\n\nWhat would such code look like?\n\nFortunately, Pandas will do this heavy lifting for us using the `pivot` method.\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings=individual_ratings.pivot(index=\"user_id\", columns=\"item_id\", values=\"rating\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Basically: rework our data using user_id as row names; item_id as column names and all the ratings as the values"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[100:115]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>101</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>  3</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>  2</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>15 rows \u00d7 1682 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n101         3   NaN   NaN   NaN   NaN   NaN     3   NaN   NaN   NaN  ...    \n102         3     2   NaN     2     3   NaN     2   NaN   NaN   NaN  ...    \n103       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n104       NaN   NaN     3   NaN   NaN   NaN     3   NaN     2     2  ...    \n105       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n106         4   NaN   NaN   NaN   NaN   NaN   NaN     4     4   NaN  ...    \n107       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n108         4   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN     5  ...    \n109         4   NaN   NaN     2     3   NaN     4     3     3   NaN  ...    \n110       NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n111       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n112       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n113       NaN   NaN   NaN   NaN   NaN   NaN     3   NaN     3   NaN  ...    \n114       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n115       NaN   NaN   NaN     4   NaN   NaN     5     5     5   NaN  ...    \n\nitem_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                              \n101       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n102       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n103       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n104       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n105       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n106       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n107       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n108       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n109       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n110       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n111       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n112       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n113       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n114       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n115       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[15 rows x 1682 columns]"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Question to the user: Why all the NaNs?\n\n###Another question to the user: Why not switch all the NaNs to zeros?"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\n\n##Question: What did we lose from our original dataframe?\n##Question: What questions could no longer ask?\n    - say we wanted to know whether people rate movies differently at different times of the day? or differently during different seasons?.\n\n"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "In the biz, this is called a *sparse matrix*. Sparse matrixes are the heart of all data mining of consumers--Amazon ranking, Netflix recommendations--and lots of search technologies--Google, etc.\n"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Simple statistical operations"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\nPandas lets us easily compute standard statistics across columns, rows and so forth.\n\nThe commands require us to refer to the columns and rows by their respective 'axis' number:\n\n0 for going DOWN a column--so the ratings given by ALL users to a ONE film \n\n1 for going ACROSS a row--so the ratings given by ONE user to ALL the films\n\n(There is only Axis, no Allies)."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's start looking into the ratings. We can use the `mean` method on a `dataframe` type. We need to say which direction we mean to mean. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(axis=0) #average rating per film (axis 0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": "item_id\n1          3.878319\n2          3.206107\n3          3.033333\n4          3.550239\n5          3.302326\n6          3.576923\n7          3.798469\n8          3.995434\n9          3.896321\n10         3.831461\n11         3.847458\n12         4.385768\n13         3.418478\n14         3.967213\n15         3.778157\n...\n1668       3\n1669       2\n1670       3\n1671       1\n1672       2\n1673       3\n1674       4\n1675       3\n1676       2\n1677       3\n1678       1\n1679       3\n1680       2\n1681       3\n1682       3\nLength: 1682, dtype: float64"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(1) #we can drop the axis= bit if we want",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": "user_id\n1          3.610294\n2          3.709677\n3          2.796296\n4          4.333333\n5          2.874286\n6          3.635071\n7          3.965261\n8          3.796610\n9          4.272727\n10         4.206522\n11         3.464088\n12         4.392157\n13         3.097484\n14         4.091837\n15         2.875000\n...\n929        3.693878\n930        2.968254\n931        3.721311\n932        3.966805\n933        2.646739\n934        3.701149\n935        3.923077\n936        3.746479\n937        3.375000\n938        3.268519\n939        4.265306\n940        3.457944\n941        4.045455\n942        4.265823\n943        3.410714\nLength: 943, dtype: float64"
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "What are the top ranked films? The `.order()` method will order our series by values."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(0).order()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": "item_id\n1659       0.75\n1564       1.00\n1364       1.00\n1587       1.00\n439        1.00\n1586       1.00\n858        1.00\n1366       1.00\n437        1.00\n852        1.00\n1584       1.00\n1373       1.00\n1349       1.00\n1583       1.00\n1348       1.00\n...\n64         4.282686\n169        4.290254\n408        4.354911\n119        4.500000\n1398       4.500000\n1642       4.500000\n1293       4.583333\n1189       4.583333\n1449       4.625000\n1599       5.000000\n1536       5.000000\n814        5.000000\n1500       5.000000\n1467       5.000000\n1653       5.000000\nLength: 1682, dtype: float64"
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We might want to disregard movies with a low number of ratings. We need to know how many each film got. \n\nThe `count` method will tell us how many non NaN values there are, so we can know if there are lots of ratings, or only a few."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "How many users have rated each film:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.count(0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": "item_id\n1          452\n2          131\n3           90\n4          209\n5           86\n6           26\n7          392\n8          219\n9          299\n10          89\n11         236\n12         267\n13         184\n14         183\n15         293\n...\n1668       1\n1669       1\n1670       1\n1671       1\n1672       2\n1673       1\n1674       1\n1675       1\n1676       1\n1677       1\n1678       1\n1679       1\n1680       1\n1681       1\n1682       1\nLength: 1682, dtype: int64"
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "How many films every user has rated:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.count(1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": "user_id\n1          272\n2           62\n3           54\n4           24\n5          175\n6          211\n7          403\n8           59\n9           22\n10         184\n11         181\n12          51\n13         636\n14          98\n15         104\n...\n929         49\n930         63\n931         61\n932        241\n933        184\n934        174\n935         39\n936        142\n937         40\n938        108\n939         49\n940        107\n941         22\n942         79\n943        168\nLength: 943, dtype: int64"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The method `describe` computes a variety of standard statistics for the items."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py:3549: FutureWarning: The `percentile_width` keyword is deprecated. Use percentiles=[0.25, 0.5, 0.75] instead\n  warnings.warn(msg, FutureWarning)\n"
      },
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 452.000000</td>\n      <td> 131.000000</td>\n      <td> 90.000000</td>\n      <td> 209.000000</td>\n      <td> 86.000000</td>\n      <td> 26.000000</td>\n      <td> 392.000000</td>\n      <td> 219.000000</td>\n      <td> 299.000000</td>\n      <td> 89.000000</td>\n      <td>...</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n      <td>  1</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>   3.878319</td>\n      <td>   3.206107</td>\n      <td>  3.033333</td>\n      <td>   3.550239</td>\n      <td>  3.302326</td>\n      <td>  3.576923</td>\n      <td>   3.798469</td>\n      <td>   3.995434</td>\n      <td>   3.896321</td>\n      <td>  3.831461</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>   0.927897</td>\n      <td>   0.966497</td>\n      <td>  1.212760</td>\n      <td>   0.965069</td>\n      <td>  0.946446</td>\n      <td>  1.301478</td>\n      <td>   0.982037</td>\n      <td>   1.002281</td>\n      <td>   1.042368</td>\n      <td>  1.013948</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>   1.000000</td>\n      <td>   1.000000</td>\n      <td>  1.000000</td>\n      <td>   1.000000</td>\n      <td>  1.000000</td>\n      <td>  1.000000</td>\n      <td>   1.000000</td>\n      <td>   1.000000</td>\n      <td>   1.000000</td>\n      <td>  1.000000</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>   3.000000</td>\n      <td>   3.000000</td>\n      <td>  2.000000</td>\n      <td>   3.000000</td>\n      <td>  3.000000</td>\n      <td>  3.000000</td>\n      <td>   3.000000</td>\n      <td>   3.000000</td>\n      <td>   3.000000</td>\n      <td>  3.000000</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>   4.000000</td>\n      <td>   3.000000</td>\n      <td>  3.000000</td>\n      <td>   4.000000</td>\n      <td>  3.000000</td>\n      <td>  4.000000</td>\n      <td>   4.000000</td>\n      <td>   4.000000</td>\n      <td>   4.000000</td>\n      <td>  4.000000</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>   5.000000</td>\n      <td>   4.000000</td>\n      <td>  4.000000</td>\n      <td>   4.000000</td>\n      <td>  4.000000</td>\n      <td>  5.000000</td>\n      <td>   5.000000</td>\n      <td>   5.000000</td>\n      <td>   5.000000</td>\n      <td>  5.000000</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   5.000000</td>\n      <td>   5.000000</td>\n      <td>  5.000000</td>\n      <td>   5.000000</td>\n      <td>  5.000000</td>\n      <td>  5.000000</td>\n      <td>   5.000000</td>\n      <td>   5.000000</td>\n      <td>   5.000000</td>\n      <td>  5.000000</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  1</td>\n      <td>  3</td>\n      <td>  2</td>\n      <td>  3</td>\n      <td>  3</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows \u00d7 1682 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": "item_id        1           2          3           4          5          6     \\\ncount    452.000000  131.000000  90.000000  209.000000  86.000000  26.000000   \nmean       3.878319    3.206107   3.033333    3.550239   3.302326   3.576923   \nstd        0.927897    0.966497   1.212760    0.965069   0.946446   1.301478   \nmin        1.000000    1.000000   1.000000    1.000000   1.000000   1.000000   \n25%        3.000000    3.000000   2.000000    3.000000   3.000000   3.000000   \n50%        4.000000    3.000000   3.000000    4.000000   3.000000   4.000000   \n75%        5.000000    4.000000   4.000000    4.000000   4.000000   5.000000   \nmax        5.000000    5.000000   5.000000    5.000000   5.000000   5.000000   \n\nitem_id        7           8           9          10      ...      1673  1674  \\\ncount    392.000000  219.000000  299.000000  89.000000    ...         1     1   \nmean       3.798469    3.995434    3.896321   3.831461    ...         3     4   \nstd        0.982037    1.002281    1.042368   1.013948    ...       NaN   NaN   \nmin        1.000000    1.000000    1.000000   1.000000    ...         3     4   \n25%        3.000000    3.000000    3.000000   3.000000    ...         3     4   \n50%        4.000000    4.000000    4.000000   4.000000    ...         3     4   \n75%        5.000000    5.000000    5.000000   5.000000    ...         3     4   \nmax        5.000000    5.000000    5.000000   5.000000    ...         3     4   \n\nitem_id  1675  1676  1677  1678  1679  1680  1681  1682  \ncount       1     1     1     1     1     1     1     1  \nmean        3     2     3     1     3     2     3     3  \nstd       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \nmin         3     2     3     1     3     2     3     3  \n25%         3     2     3     1     3     2     3     3  \n50%         3     2     3     1     3     2     3     3  \n75%         3     2     3     1     3     2     3     3  \nmax         3     2     3     1     3     2     3     3  \n\n[8 rows x 1682 columns]"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Back to the ratings... what doesn't suck?\n\nHow can use use boolean indexing to find what doesn't suck according to the masses?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(0)>4.25",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": "item_id\n1          False\n2          False\n3          False\n4          False\n5          False\n6          False\n7          False\n8          False\n9          False\n10         False\n11         False\n12          True\n13         False\n14         False\n15         False\n...\n1668       False\n1669       False\n1670       False\n1671       False\n1672       False\n1673       False\n1674       False\n1675       False\n1676       False\n1677       False\n1678       False\n1679       False\n1680       False\n1681       False\n1682       False\nLength: 1682, dtype: bool"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "the_good_stuff=ratings.mean(0)>4.25",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "And if we want a dataframe of just the good stuff?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[the_good_stuff]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>64 </th>\n      <td>  4</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>98 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>272</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>285</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>318</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>357</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>408</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>427</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>474</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>479</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>480</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>483</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>513</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>603</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>657</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>814</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>26 rows \u00d7 1682 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n12        NaN   NaN   NaN     5   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n50        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     4   NaN  ...    \n64          4     3   NaN     3   NaN   NaN     4     4     4     5  ...    \n98        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n114       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n119       NaN   NaN   NaN   NaN   NaN   NaN     5   NaN     4   NaN  ...    \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n134         5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n169       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n174         3   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN  ...    \n178         4     4   NaN   NaN   NaN   NaN     4     4     2   NaN  ...    \n251         4   NaN   NaN   NaN   NaN   NaN     3   NaN   NaN   NaN  ...    \n272       NaN   NaN   NaN   NaN   NaN   NaN   NaN     4   NaN   NaN  ...    \n285       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n318       NaN   NaN   NaN     2   NaN   NaN   NaN     4   NaN   NaN  ...    \n357         5   NaN   NaN   NaN   NaN   NaN     3   NaN   NaN     5  ...    \n408       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n427       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n474       NaN   NaN   NaN     5   NaN   NaN     5     5     5   NaN  ...    \n479         5   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  ...    \n480       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  ...    \n483         4   NaN   NaN   NaN   NaN   NaN   NaN   NaN     2   NaN  ...    \n513       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n603       NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n657         3   NaN   NaN   NaN   NaN   NaN     3   NaN     4   NaN  ...    \n814       NaN   NaN   NaN   NaN     3   NaN     4   NaN   NaN   NaN  ...    \n\nitem_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                              \n12        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n50        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n64        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n98        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n114       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n119       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n134       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n169       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n174       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n178       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n251       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n272       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n285       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n318       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n357       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n408       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n427       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n474       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n479       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n480       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n483       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n513       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n603       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n657       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n814       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[26 rows x 1682 columns]"
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##No, no, no, no! \n\nWe want to use boolean indexing *across the columns* but clever `pandas` has done it on the rows. Good thing we checked.\n\nWe can use a more precise way of indexing a dataframe, the `.loc` method, which takes .loc[rows,columns]."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.loc[:,the_good_stuff]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>12</th>\n      <th>50</th>\n      <th>64</th>\n      <th>98</th>\n      <th>114</th>\n      <th>119</th>\n      <th>127</th>\n      <th>134</th>\n      <th>169</th>\n      <th>174</th>\n      <th>...</th>\n      <th>1398</th>\n      <th>1449</th>\n      <th>1467</th>\n      <th>1500</th>\n      <th>1536</th>\n      <th>1594</th>\n      <th>1599</th>\n      <th>1639</th>\n      <th>1642</th>\n      <th>1653</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1  </th>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30 </th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td>  3</td>\n      <td>  3</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>943 rows \u00d7 42 columns</p>\n</div>",
       "metadata": {},
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       "text": "item_id  12    50    64    98    114   119   127   134   169   174   ...   \\\nuser_id                                                              ...    \n1           5     5     5     4     5     5     5     4     5     5  ...    \n2         NaN     5   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n4         NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n5         NaN     4   NaN     3   NaN   NaN   NaN   NaN     5     5  ...    \n6           4     4     4     5   NaN   NaN     5     5     4     4  ...    \n7           5     5     5     4   NaN   NaN     5     4   NaN     5  ...    \n8         NaN     5   NaN   NaN   NaN   NaN     5   NaN   NaN     5  ...    \n9         NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n10          5     5     4     4   NaN   NaN     5     5   NaN     4  ...    \n11          2   NaN   NaN     2   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n12        NaN     4   NaN     5   NaN   NaN     4   NaN   NaN     5  ...    \n13          5     5     5     4   NaN   NaN     5   NaN   NaN     4  ...    \n14          5     5   NaN     3   NaN   NaN     2   NaN   NaN     5  ...    \n15        NaN     5   NaN   NaN   NaN   NaN     2   NaN   NaN   NaN  ...    \n16          5   NaN     5     5   NaN   NaN     5     4   NaN     5  ...    \n17        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n18          5     4     5     5   NaN   NaN     5     5     5     4  ...    \n19        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n20        NaN     3   NaN     3   NaN   NaN   NaN   NaN   NaN     4  ...    \n21        NaN     3   NaN     5   NaN   NaN     5   NaN   NaN   NaN  ...    \n22        NaN     5   NaN   NaN   NaN   NaN     5   NaN   NaN     5  ...    \n23        NaN     4   NaN     5   NaN   NaN   NaN     4   NaN     4  ...    \n24          5   NaN     5     5   NaN   NaN     5   NaN   NaN   NaN  ...    \n25        NaN     5   NaN     5     5   NaN     3     4     5     5  ...    \n26        NaN     4   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n27        NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n28          4     4   NaN     5   NaN   NaN   NaN   NaN   NaN     5  ...    \n29          5   NaN   NaN     4   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n30        NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5  ...    \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n914       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n915       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n916         4     5     5     5   NaN   NaN   NaN     5   NaN     5  ...    \n917       NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n918       NaN   NaN     4   NaN   NaN   NaN   NaN   NaN   NaN     3  ...    \n919         3     3     5     5   NaN   NaN   NaN   NaN   NaN     4  ...    \n920       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n921       NaN     4   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5  ...    \n922       NaN     5   NaN     5   NaN   NaN     3   NaN   NaN     5  ...    \n923       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5  ...    \n924         4     5     4   NaN     3   NaN     3     4   NaN     5  ...    \n925       NaN   NaN   NaN     4   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n926       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n927       NaN   NaN     5   NaN   NaN   NaN   NaN   NaN   NaN     3  ...    \n928       NaN   NaN   NaN     5     5   NaN     5     5   NaN   NaN  ...    \n929         4     4   NaN     5   NaN   NaN     5     4   NaN     3  ...    \n930       NaN     2     4   NaN   NaN   NaN   NaN   NaN   NaN     3  ...    \n931       NaN     3   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n932       NaN   NaN     2     5     5     5   NaN     4     5     4  ...    \n933         4     4     5     5   NaN   NaN     5   NaN   NaN     4  ...    \n934       NaN     5   NaN   NaN   NaN   NaN   NaN     4   NaN     5  ...    \n935       NaN   NaN   NaN   NaN   NaN   NaN     4   NaN   NaN   NaN  ...    \n936       NaN     4   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n937       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n938       NaN     5   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n939       NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n940         4     4   NaN     4   NaN   NaN   NaN   NaN   NaN     4  ...    \n941       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n942       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5  ...    \n943         5     4     5     5   NaN   NaN     5   NaN   NaN     4  ...    \n\nitem_id  1398  1449  1467  1500  1536  1594  1599  1639  1642  1653  \nuser_id                                                              \n1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n5         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n6         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n7         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n8         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n11        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n13        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n15        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n17        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n18        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n19        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n20        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n21        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n22        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n23        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n26        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n27        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n28        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n29        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n30        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n914       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n915       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n916       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n917       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n918       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  \n919       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n920       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n921       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n922       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n924       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n925       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n926       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n927       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n929       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n930       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n931       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n932       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n933       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n934       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n935       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n936       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n937       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n938       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n940       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n943       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[943 rows x 42 columns]"
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Cool!\nWhat are the average ratings per user?\n\nWe need to use `mean` across columns."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(1) #average rating per user axis 1 is rows--user ids",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": "user_id\n1          3.610294\n2          3.709677\n3          2.796296\n4          4.333333\n5          2.874286\n6          3.635071\n7          3.965261\n8          3.796610\n9          4.272727\n10         4.206522\n11         3.464088\n12         4.392157\n13         3.097484\n14         4.091837\n15         2.875000\n...\n929        3.693878\n930        2.968254\n931        3.721311\n932        3.966805\n933        2.646739\n934        3.701149\n935        3.923077\n936        3.746479\n937        3.375000\n938        3.268519\n939        4.265306\n940        3.457944\n941        4.045455\n942        4.265823\n943        3.410714\nLength: 943, dtype: float64"
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(1)>4 # axis 1 is rows--user ids",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": "user_id\n1          False\n2          False\n3          False\n4           True\n5          False\n6          False\n7          False\n8          False\n9           True\n10          True\n11         False\n12          True\n13         False\n14          True\n15         False\n...\n929        False\n930        False\n931        False\n932        False\n933        False\n934        False\n935        False\n936        False\n937        False\n938        False\n939         True\n940        False\n941         True\n942         True\n943        False\nLength: 943, dtype: bool"
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "those_lacking_discernment=ratings.mean(1)>4",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[those_lacking_discernment]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4  </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>34 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>46 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>60 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  3</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>89 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>90 </th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>96 </th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>97 </th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>  5</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>770</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>777</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>794</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>801</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>808</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>810</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>821</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>823</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>838</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  4</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>840</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>848</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>849</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>850</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>862</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>867</th>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>875</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>876</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>882</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>891</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>907</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>909</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>147 rows \u00d7 1682 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n9         NaN   NaN   NaN   NaN   NaN     5     4   NaN   NaN   NaN  ...    \n10          4   NaN   NaN     4   NaN   NaN     4   NaN     4   NaN  ...    \n12        NaN   NaN   NaN     5   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n14        NaN   NaN   NaN   NaN   NaN   NaN     5   NaN     4   NaN  ...    \n16          5   NaN   NaN     5   NaN   NaN     5     5     5   NaN  ...    \n24        NaN   NaN   NaN   NaN   NaN   NaN     4     5     5   NaN  ...    \n25          5   NaN   NaN   NaN   NaN   NaN     4     4   NaN   NaN  ...    \n34        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n46        NaN   NaN   NaN   NaN   NaN   NaN     4   NaN   NaN   NaN  ...    \n52        NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n60        NaN   NaN   NaN   NaN   NaN   NaN     5     3     5   NaN  ...    \n89          5   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n90        NaN   NaN   NaN   NaN   NaN     4   NaN     5     4     5  ...    \n96          5   NaN   NaN   NaN   NaN   NaN     5     5   NaN   NaN  ...    \n97          4   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n118       NaN   NaN   NaN   NaN     2   NaN     5   NaN   NaN   NaN  ...    \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n131         4   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN  ...    \n136       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN  ...    \n137         3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n138         4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n147       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n152       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  ...    \n164       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     4   NaN  ...    \n173       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n185       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     4   NaN  ...    \n187       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  ...    \n200         5     4   NaN   NaN   NaN   NaN     4     4     4   NaN  ...    \n210         5   NaN   NaN     4   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n770         5   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN   NaN  ...    \n777         4   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN  ...    \n794         4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n801       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n808       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n810       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n811       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n819       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n821         5   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n823         4   NaN   NaN     5   NaN   NaN     5     5   NaN   NaN  ...    \n835         3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n838         5   NaN   NaN   NaN   NaN   NaN     5     4     4   NaN  ...    \n840       NaN   NaN   NaN   NaN   NaN   NaN     4     5   NaN   NaN  ...    \n848       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n849       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n850       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN  ...    \n862       NaN   NaN   NaN   NaN   NaN   NaN     5   NaN   NaN     5  ...    \n867         4   NaN   NaN   NaN   NaN   NaN     5   NaN     5   NaN  ...    \n875       NaN   NaN   NaN     3   NaN   NaN   NaN     3   NaN   NaN  ...    \n876       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n882         5   NaN   NaN     4   NaN   NaN     4     5   NaN   NaN  ...    \n888       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n891       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n907         5   NaN   NaN   NaN     5   NaN   NaN     3   NaN   NaN  ...    \n909       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n923         3   NaN     4   NaN   NaN   NaN   NaN   NaN     4   NaN  ...    \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN     5     5   NaN  ...    \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     5   NaN  ...    \n941         5   NaN   NaN   NaN   NaN   NaN     4   NaN   NaN   NaN  ...    \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n\nitem_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                              \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n34        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n46        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n52        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n60        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n89        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n90        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n96        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n97        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n118       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n131       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n136       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n137       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n138       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n147       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n152       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n164       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n173       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n185       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n187       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n200       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n210       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n770       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n777       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n794       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n801       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n808       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n810       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n811       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n819       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n821       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n823       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n835         3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n838       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n840       NaN     4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n848       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n849       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n850       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n862       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n867       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n875       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n876       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n882       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n888       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n891       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n907       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n909       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[147 rows x 1682 columns]"
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Question for the reader: \nCould you create a new ratings dataframe *excluding* all those lacking discernment, i.e. excluding people who have a mean rating > 4? It's on the home work!"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "What might we want to do with our new knowledge\n\nLet's discount the less discerning viewers! Let's just lower their rankings by .75. A bit arbitrary, but so are they!\n\nWe can multiply every element in a dataframe by a constant like so:\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[those_lacking_discernment]*.75",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>34 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>46 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>60 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 2.25</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>89 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>90 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>96 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>97 </th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 1.50</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td> 3.75</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>770</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>777</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>794</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>801</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>808</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>810</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>821</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>823</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>838</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>840</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>848</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>849</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>850</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>862</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>867</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>875</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 2.25</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 2.25</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>876</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>882</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>891</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>907</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 2.25</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>909</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>147 rows \u00d7 1682 columns</p>\n</div>",
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       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n9         NaN   NaN   NaN   NaN   NaN  3.75  3.00   NaN   NaN   NaN  ...    \n10       3.00   NaN   NaN  3.00   NaN   NaN  3.00   NaN  3.00   NaN  ...    \n12        NaN   NaN   NaN  3.75   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n14        NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  3.00   NaN  ...    \n16       3.75   NaN   NaN  3.75   NaN   NaN  3.75  3.75  3.75   NaN  ...    \n24        NaN   NaN   NaN   NaN   NaN   NaN  3.00  3.75  3.75   NaN  ...    \n25       3.75   NaN   NaN   NaN   NaN   NaN  3.00  3.00   NaN   NaN  ...    \n34        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n46        NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN   NaN  ...    \n52        NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN   NaN  ...    \n60        NaN   NaN   NaN   NaN   NaN   NaN  3.75  2.25  3.75   NaN  ...    \n89       3.75   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN   NaN  ...    \n90        NaN   NaN   NaN   NaN   NaN  3.00   NaN  3.75  3.00  3.75  ...    \n96       3.75   NaN   NaN   NaN   NaN   NaN  3.75  3.75   NaN   NaN  ...    \n97       3.00   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN   NaN  ...    \n118       NaN   NaN   NaN   NaN  1.50   NaN  3.75   NaN   NaN   NaN  ...    \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n131      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  ...    \n136       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  ...    \n137      2.25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n138      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n147       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n152       NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN  ...    \n164       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN  ...    \n173       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n185       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN  ...    \n187       NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN  ...    \n200      3.75     3   NaN   NaN   NaN   NaN  3.00  3.00  3.00   NaN  ...    \n210      3.75   NaN   NaN  3.00   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n770      3.75   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN   NaN  ...    \n777      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  ...    \n794      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n801       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n808       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n810       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n811       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n819       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n821      3.75   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n823      3.00   NaN   NaN  3.75   NaN   NaN  3.75  3.75   NaN   NaN  ...    \n835      2.25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n838      3.75   NaN   NaN   NaN   NaN   NaN  3.75  3.00  3.00   NaN  ...    \n840       NaN   NaN   NaN   NaN   NaN   NaN  3.00  3.75   NaN   NaN  ...    \n848       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n849       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n850       NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN  ...    \n862       NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN   NaN  3.75  ...    \n867      3.00   NaN   NaN   NaN   NaN   NaN  3.75   NaN  3.75   NaN  ...    \n875       NaN   NaN   NaN  2.25   NaN   NaN   NaN  2.25   NaN   NaN  ...    \n876       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n882      3.75   NaN   NaN  3.00   NaN   NaN  3.00  3.75   NaN   NaN  ...    \n888       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n891       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n907      3.75   NaN   NaN   NaN  3.75   NaN   NaN  2.25   NaN   NaN  ...    \n909       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n923      2.25   NaN     3   NaN   NaN   NaN   NaN   NaN  3.00   NaN  ...    \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75  3.75   NaN  ...    \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  ...    \n941      3.75   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN   NaN  ...    \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n\nitem_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                              \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n34        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n46        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n52        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n60        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n89        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n90        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n96        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n97        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n118       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n127       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n131       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n136       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n137       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n138       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n147       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n152       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n164       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n173       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n185       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n187       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n200       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n210       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n770       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n777       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n794       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n801       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n808       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n810       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n811       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n819       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n821       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n823       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n835      2.25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n838       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n840       NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n848       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n849       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n850       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n862       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n867       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n875       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n876       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n882       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n888       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n891       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n907       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n909       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[147 rows x 1682 columns]"
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "adjusted_ratings[those_lacking_discernment]=ratings[those_lacking_discernment]*.75",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "A lot of the movies lack very many ratings. Let's exclude them for now. We can tell how many ratings each column has with the `.count` method."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.count()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 146,
       "text": "item_id\n1          452\n2          131\n3           90\n4          209\n5           86\n6           26\n7          392\n8          219\n9          299\n10          89\n11         236\n12         267\n13         184\n14         183\n15         293\n...\n1668       1\n1669       1\n1670       1\n1671       1\n1672       2\n1673       1\n1674       1\n1675       1\n1676       1\n1677       1\n1678       1\n1679       1\n1680       1\n1681       1\n1682       1\nLength: 1682, dtype: int64"
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.count()>10",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": "item_id\n1          True\n2          True\n3          True\n4          True\n5          True\n6          True\n7          True\n8          True\n9          True\n10         True\n11         True\n12         True\n13         True\n14         True\n15         True\n...\n1668       False\n1669       False\n1670       False\n1671       False\n1672       False\n1673       False\n1674       False\n1675       False\n1676       False\n1677       False\n1678       False\n1679       False\n1680       False\n1681       False\n1682       False\nLength: 1682, dtype: bool"
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We can use this index to produce a new dataframe of only the often rated movies."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.loc[:,(ratings.count()>10)] ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1421</th>\n      <th>1428</th>\n      <th>1444</th>\n      <th>1446</th>\n      <th>1451</th>\n      <th>1469</th>\n      <th>1478</th>\n      <th>1480</th>\n      <th>1483</th>\n      <th>1518</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1  </th>\n      <td> 5.00</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td> 3.00</td>\n      <td>  3</td>\n      <td> 5.00</td>\n      <td> 4.00</td>\n      <td> 1.00</td>\n      <td> 5.00</td>\n      <td>  3</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  2</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 2.00</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td>  4</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td> 3.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>  1</td>\n      <td>  NaN</td>\n      <td> 2.00</td>\n      <td> 4.00</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td> 1.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 1.00</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  2</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td>  NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30 </th>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>  3</td>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  1</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 1.00</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td> 5.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td> 2.00</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 1.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 2.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 4.00</td>\n      <td> 5.00</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td>  NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>943 rows \u00d7 1119 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 157,
       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n1        5.00     3     4  3.00     3  5.00  4.00  1.00  5.00     3  ...    \n2        4.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     2  ...    \n3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n5        4.00     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n6        4.00   NaN   NaN   NaN   NaN   NaN  2.00  4.00  4.00   NaN  ...    \n7         NaN   NaN   NaN  5.00   NaN   NaN  5.00  5.00  5.00     4  ...    \n8         NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN   NaN  ...    \n9         NaN   NaN   NaN   NaN   NaN  3.75  3.00   NaN   NaN   NaN  ...    \n10       3.00   NaN   NaN  3.00   NaN   NaN  3.00   NaN  3.00   NaN  ...    \n11        NaN   NaN   NaN   NaN   NaN   NaN   NaN  4.00  5.00   NaN  ...    \n12        NaN   NaN   NaN  3.75   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n13       3.00     3   NaN  5.00     1   NaN  2.00  4.00  3.00   NaN  ...    \n14        NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  3.00   NaN  ...    \n15       1.00   NaN   NaN   NaN   NaN   NaN  1.00   NaN  4.00   NaN  ...    \n16       3.75   NaN   NaN  3.75   NaN   NaN  3.75  3.75  3.75   NaN  ...    \n17       4.00   NaN   NaN   NaN   NaN   NaN  4.00   NaN  3.00   NaN  ...    \n18       5.00   NaN   NaN  3.00   NaN  5.00   NaN  5.00  5.00   NaN  ...    \n19        NaN   NaN   NaN  4.00   NaN   NaN   NaN  5.00   NaN   NaN  ...    \n20       3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n21       5.00   NaN   NaN   NaN     2   NaN  5.00   NaN  5.00   NaN  ...    \n22        NaN     2   NaN  5.00   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n23       5.00   NaN   NaN   NaN   NaN   NaN  4.00  4.00   NaN   NaN  ...    \n24        NaN   NaN   NaN   NaN   NaN   NaN  3.00  3.75  3.75   NaN  ...    \n25       3.75   NaN   NaN   NaN   NaN   NaN  3.00  3.00   NaN   NaN  ...    \n26       3.00   NaN   NaN   NaN   NaN   NaN  3.00   NaN  4.00   NaN  ...    \n27        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  4.00   NaN  ...    \n28        NaN   NaN   NaN   NaN     3   NaN  5.00   NaN   NaN   NaN  ...    \n29        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n30        NaN     3   NaN   NaN   NaN   NaN  4.00   NaN   NaN   NaN  ...    \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n914       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n915       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n916      4.00     3     3  4.00     3   NaN  4.00   NaN  5.00   NaN  ...    \n917      3.00   NaN     1   NaN   NaN   NaN   NaN   NaN  5.00   NaN  ...    \n918      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n919      4.00   NaN   NaN  1.00     4   NaN  3.00   NaN  5.00   NaN  ...    \n920       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n921      3.00   NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN  ...    \n922      5.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n923      2.25   NaN     3   NaN   NaN   NaN   NaN   NaN  3.00   NaN  ...    \n924      5.00     3   NaN   NaN   NaN  4.00  4.00   NaN  4.00   NaN  ...    \n925       NaN   NaN   NaN   NaN     4   NaN   NaN   NaN   NaN   NaN  ...    \n926       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n927      5.00   NaN   NaN   NaN   NaN   NaN  3.00  4.00   NaN   NaN  ...    \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75  3.75   NaN  ...    \n929      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n930      3.00   NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN  ...    \n931       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n932      4.00   NaN   NaN   NaN   NaN   NaN  4.00   NaN  5.00   NaN  ...    \n933      3.00   NaN   NaN  3.00   NaN   NaN  4.00   NaN  3.00   NaN  ...    \n934      2.00     4   NaN  5.00   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n935      3.00   NaN   NaN   NaN   NaN   NaN   NaN   NaN  1.00   NaN  ...    \n936      4.00   NaN     4   NaN   NaN  5.00  4.00   NaN  4.00   NaN  ...    \n937       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  5.00   NaN  ...    \n938      4.00   NaN   NaN   NaN   NaN   NaN  4.00   NaN  3.00   NaN  ...    \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  3.75   NaN  ...    \n940       NaN   NaN   NaN  2.00   NaN   NaN  4.00  5.00  3.00   NaN  ...    \n941      3.75   NaN   NaN   NaN   NaN   NaN  3.00   NaN   NaN   NaN  ...    \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n943       NaN     5   NaN   NaN   NaN   NaN   NaN   NaN  3.00   NaN  ...    \n\nitem_id  1421  1428  1444  1446  1451  1469  1478  1480  1483  1518  \nuser_id                                                              \n1         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n2         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n3         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n4         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n5         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n6         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n7         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n8         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n11        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n13        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n15        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n17        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n18        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n19        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n20        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n21        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n22        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n23        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n26        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n27        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n28        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n29        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n30        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n914       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n915       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n916       NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n917       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n918       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n919       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n920       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n921       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n922       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n924       NaN   NaN   NaN   NaN   NaN   NaN     4   NaN   NaN   NaN  \n925       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n926       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n927       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n929       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n930       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n931       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n932       NaN   NaN   NaN   NaN     5   NaN   NaN   NaN   NaN   NaN  \n933       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n934       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n935       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n936       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n937       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n938       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n940       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n943       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[943 rows x 1119 columns]"
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots=ratings.loc[:,(ratings.count()>10)]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots.mean(0).order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 182,
       "text": "item_id\n408        4.354911\n169        4.290254\n64         4.282686\n318        4.280201\n114        4.246269\n483        4.241770\n12         4.222846\n50         4.192967\n272        4.146465\n963        4.146341\n178        4.146000\n251        4.130435\n603        4.130383\n127        4.121671\n313        4.109286\n...\n453        1.843750\n976        1.833333\n1230       1.833333\n103        1.833333\n894        1.828947\n1254       1.818182\n688        1.806818\n545        1.791667\n457        1.740741\n901        1.729167\n1274       1.727273\n440        1.642857\n669        1.615385\n758        1.607143\n424        1.276316\nLength: 1119, dtype: float64"
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "topten=ratings_lots.mean(0).order(ascending=False)[0:9].index",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "topten\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 187,
       "text": "Int64Index([408, 169, 64, 318, 114, 483, 12, 50, 272], dtype='int64')"
      }
     ],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "elcrapo=ratings_lots.mean(0).order(ascending=True)[0:9].index",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "elcrapo",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 194,
       "text": "Int64Index([424, 758, 669, 440, 1274, 901, 457, 545, 688], dtype='int64')"
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Variance\n\nThe method `var` calculates variance, which is a measure of how wide spread a set of numbers is from a mean. A value of zero indicates all the values are at the mean: higher numbers indicate more variation. \n\nSo, higher variance mean greater disagrement!"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots.var(0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 159,
       "text": "item_id\n1          0.814642\n2          0.880725\n3          1.413241\n4          0.909818\n5          0.808618\n6          1.612404\n7          0.920142\n8          0.977381\n9          1.023329\n10         0.879836\n11         1.118584\n12         0.717540\n13         1.035814\n14         0.972190\n15         0.897890\n...\n1401       1.184845\n1404       1.443910\n1407       0.644079\n1411       1.213211\n1419       0.801823\n1421       1.290909\n1428       0.628788\n1444       0.794225\n1446       1.066667\n1451       0.539881\n1469       1.356061\n1478       1.035948\n1480       1.018182\n1483       1.151515\n1518       0.774148\nLength: 1119, dtype: float64"
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots.var(0).order(ascending=True)[0:4]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 200,
       "text": "item_id\n1124       0.272727\n608        0.290876\n424        0.339547\n587        0.369849\ndtype: float64"
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "agreement=ratings_lots.var(0).order(ascending=True)[0:4].index",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots.var(0).order(ascending=False)[0:4]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 198,
       "text": "item_id\n854        2.743490\n853        2.381868\n1085       2.290909\n1001       2.034926\ndtype: float64"
      }
     ],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "disagreement=ratings_lots.var(0).order(ascending=False)[0:4].index",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 204
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "By ploting histograms, we can get a sense of the spread of their ratings."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[agreement].hist()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": "array([[<matplotlib.axes.AxesSubplot object at 0x7f3148ff2890>,\n        <matplotlib.axes.AxesSubplot object at 0x7f31498dddd0>],\n       [<matplotlib.axes.AxesSubplot object at 0x7f314a20b410>,\n        <matplotlib.axes.AxesSubplot object at 0x7f314ab32510>]], dtype=object)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LotIsBEEQhLyg2iyErFHaum2l6eWLrddSKwlFrDMnCIIg5AXnyXzNmjUYMWIERo4ciWee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\nAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7f3148ff2bd0>"
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[disagreement].hist()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 206,
       "text": "array([[<matplotlib.axes.AxesSubplot object at 0x7f314adc2550>,\n        <matplotlib.axes.AxesSubplot object at 0x7f314911a3d0>],\n       [<matplotlib.axes.AxesSubplot object at 0x7f3148dd2290>,\n        <matplotlib.axes.AxesSubplot object at 0x7f314a622d50>]], dtype=object)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7X8zzGHW6IABUV4RQhtyYl/zJfObMmSgrKwPQVyjK1dOnW1pawBhzupWXlyM8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\nOUEQhB/w/wGXHY+LWdT+cAAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7f3148b47fd0>"
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#We don't have the titles here!\n\n#Let's add in more info on the films from our other files\nback to the README...which file has the information on the files?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "labels_films=[\"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": [],
     "prompt_number": 263
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films=pd.read_csv( './ml-100k/u.item', sep=\"|\", names=labels_films, index_col='movie_id')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 265
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>movie_title</th>\n      <th>release_date</th>\n      <th>video_release_date</th>\n      <th>IMDb_URL</th>\n      <th>unknown</th>\n      <th>Action</th>\n      <th>Adventure</th>\n      <th>Animation</th>\n      <th>Children's</th>\n      <th>Comedy</th>\n      <th>...</th>\n      <th>Fantasy</th>\n      <th>Film-Noir</th>\n      <th>Horror</th>\n      <th>Musical</th>\n      <th>Mystery</th>\n      <th>Romance</th>\n      <th>Sci-Fi</th>\n      <th>Thriller</th>\n      <th>War</th>\n      <th>Western</th>\n    </tr>\n    <tr>\n      <th>movie_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>  Toy Story (1995)</td>\n      <td> 01-Jan-1995</td>\n      <td>NaN</td>\n      <td> http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>  GoldenEye (1995)</td>\n      <td> 01-Jan-1995</td>\n      <td>NaN</td>\n      <td> http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Four Rooms (1995)</td>\n      <td> 01-Jan-1995</td>\n      <td>NaN</td>\n      <td> http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> Get Shorty (1995)</td>\n      <td> 01-Jan-1995</td>\n      <td>NaN</td>\n      <td> http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>    Copycat (1995)</td>\n      <td> 01-Jan-1995</td>\n      <td>NaN</td>\n      <td> http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 23 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 266,
       "text": "                movie_title release_date  video_release_date  \\\nmovie_id                                                       \n1          Toy Story (1995)  01-Jan-1995                 NaN   \n2          GoldenEye (1995)  01-Jan-1995                 NaN   \n3         Four Rooms (1995)  01-Jan-1995                 NaN   \n4         Get Shorty (1995)  01-Jan-1995                 NaN   \n5            Copycat (1995)  01-Jan-1995                 NaN   \n\n                                                   IMDb_URL  unknown  Action  \\\nmovie_id                                                                       \n1         http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n2         http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n3         http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n4         http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n5         http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n\n          Adventure  Animation  Children's  Comedy   ...    Fantasy  \\\nmovie_id                                             ...              \n1                 0          1           1       1   ...          0   \n2                 1          0           0       0   ...          0   \n3                 0          0           0       0   ...          0   \n4                 0          0           0       1   ...          0   \n5                 0          0           0       0   ...          0   \n\n          Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  \\\nmovie_id                                                                        \n1                 0       0        0        0        0       0         0    0   \n2                 0       0        0        0        0       0         1    0   \n3                 0       0        0        0        0       0         1    0   \n4                 0       0        0        0        0       0         0    0   \n5                 0       0        0        0        0       0         1    0   \n\n          Western  \nmovie_id           \n1               0  \n2               0  \n3               0  \n4               0  \n5               0  \n\n[5 rows x 23 columns]"
      }
     ],
     "prompt_number": 266
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films['movie_title'][0:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 267,
       "text": "movie_id\n1                                            Toy Story (1995)\n2                                            GoldenEye (1995)\n3                                           Four Rooms (1995)\n4                                           Get Shorty (1995)\n5                                              Copycat (1995)\n6           Shanghai Triad (Yao a yao yao dao waipo qiao) ...\n7                                       Twelve Monkeys (1995)\n8                                                 Babe (1995)\n9                                     Dead Man Walking (1995)\n10                                         Richard III (1995)\nName: movie_title, dtype: object"
      }
     ],
     "prompt_number": 267
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now we can connect our ratings dataframe to our dataframe with title information.\n\nOur variable `topten` provides the indexes for the top movies. Since the indices are the same in `films`, we can use top ten to get the titles."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films['movie_title'][topten]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 268,
       "text": "item_id\n408                                    Close Shave, A (1995)\n169                               Wrong Trousers, The (1993)\n64                          Shawshank Redemption, The (1994)\n318                                  Schindler's List (1993)\n114        Wallace & Gromit: The Best of Aardman Animatio...\n483                                        Casablanca (1942)\n12                                Usual Suspects, The (1995)\n50                                          Star Wars (1977)\n272                                 Good Will Hunting (1997)\nName: movie_title, dtype: object"
      }
     ],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films['movie_title'][elcrapo]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 269,
       "text": "item_id\n424        Children of the Corn: The Gathering (1996)\n758         Lawnmower Man 2: Beyond Cyberspace (1996)\n669                                 Body Parts (1991)\n440              Amityville II: The Possession (1982)\n1274                                 Robocop 3 (1993)\n901                                  Mr. Magoo (1997)\n457                   Free Willy 3: The Rescue (1997)\n545                        Vampire in Brooklyn (1995)\n688                         Leave It to Beaver (1997)\nName: movie_title, dtype: object"
      }
     ],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_lots.loc[424].mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": "3.3488372093023258"
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.loc[424].mean()\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 214,
       "text": "3.3636363636363638"
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Alternatively, we could just add the titles to our ratings dataframe.\n\nHow pull them from the dataframe."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "titles=[title for title in films['movie_title']]",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "titles[0:10]",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import re",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "titles=[title[0:10] for title in titles] #let's truncate",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "titles=[re.sub(\"\\s\", \"_\", title) for title in titles] #convert spaces to _",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(titles)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Now, let's combine data from the titles dataframe with the ratings dataframe we created"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.columns=titles",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.head()",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(0)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.mean(0).order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "I know that the method `max` will return the highest rating."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.max(1)",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.max?",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Using the question mark marks short documentation appear! And it has the info I want:\n> This method returns the maximum of the values in the object. If you\nwant the *index* of the maximum, use ``idxmax``.\n\nCool!"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.idxmax(1)\n",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.idxmax(1).value_counts()",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.idxmax(1).value_counts()[1:10].plot(kind=\"bar\", title=\"Movies most often the highest rated of individual users\")",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Add in more information on users"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "user_labels=[\"user_id\", \"age\", \"gender\",\"occupation\",\"zip_code\"]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "users=pd.read_csv('./ml-100k/u.user', sep=\"|\", names=user_labels, index_col=\"user_id\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "users",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>gender</th>\n      <th>occupation</th>\n      <th>zip_code</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1  </th>\n      <td> 24</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 85711</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td> 53</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 94043</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td> 23</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 32067</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td> 24</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 43537</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td> 33</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 15213</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td> 42</td>\n      <td> M</td>\n      <td>     executive</td>\n      <td> 98101</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td> 57</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 91344</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td> 36</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 05201</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td> 29</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 01002</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 53</td>\n      <td> M</td>\n      <td>        lawyer</td>\n      <td> 90703</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td> 39</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 30329</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td> 28</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 06405</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td> 47</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 29206</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td> 45</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 55106</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td> 49</td>\n      <td> F</td>\n      <td>      educator</td>\n      <td> 97301</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 21</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 10309</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td> 30</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 06355</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td> 35</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 37212</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td> 40</td>\n      <td> M</td>\n      <td>     librarian</td>\n      <td> 02138</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td> 42</td>\n      <td> F</td>\n      <td>     homemaker</td>\n      <td> 95660</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td> 26</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 30068</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td> 25</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 40206</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td> 30</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 48197</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td> 21</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 94533</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 39</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 55107</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td> 49</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 21044</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td> 40</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 30030</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td> 32</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 55369</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td> 41</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 94043</td>\n    </tr>\n    <tr>\n      <th>30 </th>\n      <td>  7</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 55436</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td> 44</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 08105</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td> 50</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 60614</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td> 27</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> N2L5N</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td> 22</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 20006</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td> 40</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 70116</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td> 25</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 14216</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td> 30</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 90008</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td> 20</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 98801</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td> 29</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 21114</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> E2E3R</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td> 29</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 11753</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td> 18</td>\n      <td> F</td>\n      <td>      salesman</td>\n      <td> 49036</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td> 49</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 01701</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td> 23</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 55428</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 55408</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td> 44</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 53711</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td> 28</td>\n      <td> F</td>\n      <td>     scientist</td>\n      <td> 07310</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td> 60</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 33556</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td> 58</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 06437</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td> 28</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 48105</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td> 61</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 22902</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td> 42</td>\n      <td> M</td>\n      <td>        doctor</td>\n      <td> 66221</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td> 24</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 32789</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td> 48</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 98072</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td> 38</td>\n      <td> F</td>\n      <td>    technician</td>\n      <td> 55038</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td> 26</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 33319</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td> 32</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 02215</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 20</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 97229</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td> 48</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 78209</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td> 22</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 77841</td>\n    </tr>\n  </tbody>\n</table>\n<p>943 rows \u00d7 4 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "         age gender     occupation zip_code\nuser_id                                    \n1         24      M     technician    85711\n2         53      F          other    94043\n3         23      M         writer    32067\n4         24      M     technician    43537\n5         33      F          other    15213\n6         42      M      executive    98101\n7         57      M  administrator    91344\n8         36      M  administrator    05201\n9         29      M        student    01002\n10        53      M         lawyer    90703\n11        39      F          other    30329\n12        28      F          other    06405\n13        47      M       educator    29206\n14        45      M      scientist    55106\n15        49      F       educator    97301\n16        21      M  entertainment    10309\n17        30      M     programmer    06355\n18        35      F          other    37212\n19        40      M      librarian    02138\n20        42      F      homemaker    95660\n21        26      M         writer    30068\n22        25      M         writer    40206\n23        30      F         artist    48197\n24        21      F         artist    94533\n25        39      M       engineer    55107\n26        49      M       engineer    21044\n27        40      F      librarian    30030\n28        32      M         writer    55369\n29        41      M     programmer    94043\n30         7      M        student    55436\n...      ...    ...            ...      ...\n914       44      F          other    08105\n915       50      M  entertainment    60614\n916       27      M       engineer    N2L5N\n917       22      F        student    20006\n918       40      M      scientist    70116\n919       25      M          other    14216\n920       30      F         artist    90008\n921       20      F        student    98801\n922       29      F  administrator    21114\n923       21      M        student    E2E3R\n924       29      M          other    11753\n925       18      F       salesman    49036\n926       49      M  entertainment    01701\n927       23      M     programmer    55428\n928       21      M        student    55408\n929       44      M      scientist    53711\n930       28      F      scientist    07310\n931       60      M       educator    33556\n932       58      M       educator    06437\n933       28      M        student    48105\n934       61      M       engineer    22902\n935       42      M         doctor    66221\n936       24      M          other    32789\n937       48      M       educator    98072\n938       38      F     technician    55038\n939       26      F        student    33319\n940       32      M  administrator    02215\n941       20      M        student    97229\n942       48      F      librarian    78209\n943       22      M        student    77841\n\n[943 rows x 4 columns]"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now, let's put together our ratings and our demographic data using the `join` method. We were careful to keep the `user_id`s the same."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "users.join(ratings)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>gender</th>\n      <th>occupation</th>\n      <th>zip_code</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1  </th>\n      <td> 24</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 85711</td>\n      <td> 5.00</td>\n      <td>  3</td>\n      <td>  4</td>\n      <td> 3.00</td>\n      <td>  3</td>\n      <td> 5.00</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td> 53</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 94043</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td> 23</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 32067</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td> 24</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 43537</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td> 33</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 15213</td>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td> 42</td>\n      <td> M</td>\n      <td>     executive</td>\n      <td> 98101</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td> 57</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 91344</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td> 36</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 05201</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td> 29</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 01002</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 53</td>\n      <td> M</td>\n      <td>        lawyer</td>\n      <td> 90703</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td> 39</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 30329</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td> 28</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 06405</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td> 47</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 29206</td>\n      <td> 3.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>  1</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td> 45</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 55106</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td> 49</td>\n      <td> F</td>\n      <td>      educator</td>\n      <td> 97301</td>\n      <td> 1.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 21</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 10309</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td> 30</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 06355</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td> 35</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 37212</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td> 40</td>\n      <td> M</td>\n      <td>     librarian</td>\n      <td> 02138</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td> 42</td>\n      <td> F</td>\n      <td>     homemaker</td>\n      <td> 95660</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td> 26</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 30068</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  2</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td> 25</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 40206</td>\n      <td>  NaN</td>\n      <td>  2</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td> 30</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 48197</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td> 21</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 94533</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 39</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 55107</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td> 49</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 21044</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td> 40</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 30030</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td> 32</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 55369</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td> 41</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 94043</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>30 </th>\n      <td>  7</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 55436</td>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td> 44</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 08105</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td> 50</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 60614</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td> 27</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> N2L5N</td>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>  3</td>\n      <td> 4.00</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  3</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td> 22</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 20006</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  1</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td> 40</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 70116</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td> 25</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 14216</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 1.00</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td> 30</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 90008</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td> 20</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 98801</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td> 29</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 21114</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> E2E3R</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td> 29</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 11753</td>\n      <td> 5.00</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 4.00</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td> 18</td>\n      <td> F</td>\n      <td>      salesman</td>\n      <td> 49036</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td> 49</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 01701</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td> 23</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 55428</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 55408</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td> 44</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 53711</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td> 28</td>\n      <td> F</td>\n      <td>     scientist</td>\n      <td> 07310</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td> 60</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 33556</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td> 58</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 06437</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td> 28</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 48105</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td> 61</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 22902</td>\n      <td> 2.00</td>\n      <td>  4</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td> 42</td>\n      <td> M</td>\n      <td>        doctor</td>\n      <td> 66221</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td> 24</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 32789</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>  4</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td> 5.00</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td> 48</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 98072</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td> 38</td>\n      <td> F</td>\n      <td>    technician</td>\n      <td> 55038</td>\n      <td> 4.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td> 26</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 33319</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td> 32</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 02215</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 2.00</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 20</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 97229</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td> 48</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 78209</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td> 22</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 77841</td>\n      <td>  NaN</td>\n      <td>  5</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>943 rows \u00d7 1686 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 217,
       "text": "         age gender     occupation zip_code     1   2   3     4   5     6  \\\nuser_id                                                                     \n1         24      M     technician    85711  5.00   3   4  3.00   3  5.00   \n2         53      F          other    94043  4.00 NaN NaN   NaN NaN   NaN   \n3         23      M         writer    32067   NaN NaN NaN   NaN NaN   NaN   \n4         24      M     technician    43537   NaN NaN NaN   NaN NaN   NaN   \n5         33      F          other    15213  4.00   3 NaN   NaN NaN   NaN   \n6         42      M      executive    98101  4.00 NaN NaN   NaN NaN   NaN   \n7         57      M  administrator    91344   NaN NaN NaN  5.00 NaN   NaN   \n8         36      M  administrator    05201   NaN NaN NaN   NaN NaN   NaN   \n9         29      M        student    01002   NaN NaN NaN   NaN NaN  3.75   \n10        53      M         lawyer    90703  3.00 NaN NaN  3.00 NaN   NaN   \n11        39      F          other    30329   NaN NaN NaN   NaN NaN   NaN   \n12        28      F          other    06405   NaN NaN NaN  3.75 NaN   NaN   \n13        47      M       educator    29206  3.00   3 NaN  5.00   1   NaN   \n14        45      M      scientist    55106   NaN NaN NaN   NaN NaN   NaN   \n15        49      F       educator    97301  1.00 NaN NaN   NaN NaN   NaN   \n16        21      M  entertainment    10309  3.75 NaN NaN  3.75 NaN   NaN   \n17        30      M     programmer    06355  4.00 NaN NaN   NaN NaN   NaN   \n18        35      F          other    37212  5.00 NaN NaN  3.00 NaN  5.00   \n19        40      M      librarian    02138   NaN NaN NaN  4.00 NaN   NaN   \n20        42      F      homemaker    95660  3.00 NaN NaN   NaN NaN   NaN   \n21        26      M         writer    30068  5.00 NaN NaN   NaN   2   NaN   \n22        25      M         writer    40206   NaN   2 NaN  5.00 NaN   NaN   \n23        30      F         artist    48197  5.00 NaN NaN   NaN NaN   NaN   \n24        21      F         artist    94533   NaN NaN NaN   NaN NaN   NaN   \n25        39      M       engineer    55107  3.75 NaN NaN   NaN NaN   NaN   \n26        49      M       engineer    21044  3.00 NaN NaN   NaN NaN   NaN   \n27        40      F      librarian    30030   NaN NaN NaN   NaN NaN   NaN   \n28        32      M         writer    55369   NaN NaN NaN   NaN   3   NaN   \n29        41      M     programmer    94043   NaN NaN NaN   NaN NaN   NaN   \n30         7      M        student    55436   NaN   3 NaN   NaN NaN   NaN   \n...      ...    ...            ...      ...   ...  ..  ..   ...  ..   ...   \n914       44      F          other    08105   NaN NaN NaN   NaN NaN   NaN   \n915       50      M  entertainment    60614   NaN NaN NaN   NaN NaN   NaN   \n916       27      M       engineer    N2L5N  4.00   3   3  4.00   3   NaN   \n917       22      F        student    20006  3.00 NaN   1   NaN NaN   NaN   \n918       40      M      scientist    70116  3.00 NaN NaN   NaN NaN   NaN   \n919       25      M          other    14216  4.00 NaN NaN  1.00   4   NaN   \n920       30      F         artist    90008   NaN NaN NaN   NaN NaN   NaN   \n921       20      F        student    98801  3.00 NaN NaN   NaN NaN   NaN   \n922       29      F  administrator    21114  5.00 NaN NaN   NaN NaN   NaN   \n923       21      M        student    E2E3R  2.25 NaN   3   NaN NaN   NaN   \n924       29      M          other    11753  5.00   3 NaN   NaN NaN  4.00   \n925       18      F       salesman    49036   NaN NaN NaN   NaN   4   NaN   \n926       49      M  entertainment    01701   NaN NaN NaN   NaN NaN   NaN   \n927       23      M     programmer    55428  5.00 NaN NaN   NaN NaN   NaN   \n928       21      M        student    55408   NaN NaN NaN   NaN NaN   NaN   \n929       44      M      scientist    53711  3.00 NaN NaN   NaN NaN   NaN   \n930       28      F      scientist    07310  3.00 NaN NaN   NaN NaN   NaN   \n931       60      M       educator    33556   NaN NaN NaN   NaN NaN   NaN   \n932       58      M       educator    06437  4.00 NaN NaN   NaN NaN   NaN   \n933       28      M        student    48105  3.00 NaN NaN  3.00 NaN   NaN   \n934       61      M       engineer    22902  2.00   4 NaN  5.00 NaN   NaN   \n935       42      M         doctor    66221  3.00 NaN NaN   NaN NaN   NaN   \n936       24      M          other    32789  4.00 NaN   4   NaN NaN  5.00   \n937       48      M       educator    98072   NaN NaN NaN   NaN NaN   NaN   \n938       38      F     technician    55038  4.00 NaN NaN   NaN NaN   NaN   \n939       26      F        student    33319   NaN NaN NaN   NaN NaN   NaN   \n940       32      M  administrator    02215   NaN NaN NaN  2.00 NaN   NaN   \n941       20      M        student    97229  3.75 NaN NaN   NaN NaN   NaN   \n942       48      F      librarian    78209   NaN NaN NaN   NaN NaN   NaN   \n943       22      M        student    77841   NaN   5 NaN   NaN NaN   NaN   \n\n         ...   1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id  ...                                                               \n1        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n2        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n3        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n4        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n5        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n6        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n7        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n8        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n11       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n13       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n15       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n17       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n18       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n19       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n20       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n21       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n22       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n23       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n26       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n27       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n28       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n29       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n30       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...      ...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n914      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n915      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n916      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN     3  \n917      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n918      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n919      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n920      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n921      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n922      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n924      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n925      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n926      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n927      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n929      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n930      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n931      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n932      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n933      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n934      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n935      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n936      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n937      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n938      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n940      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n943      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[943 rows x 1686 columns]"
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_users=users.join(ratings)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_users[those_lacking_discernment]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>gender</th>\n      <th>occupation</th>\n      <th>zip_code</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4  </th>\n      <td> 24</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 43537</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td> 29</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 01002</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 53</td>\n      <td> M</td>\n      <td>        lawyer</td>\n      <td> 90703</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td> 28</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 06405</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td> 45</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 55106</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 21</td>\n      <td> M</td>\n      <td> entertainment</td>\n      <td> 10309</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td> 21</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 94533</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 39</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 55107</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>34 </th>\n      <td> 38</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 42141</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>46 </th>\n      <td> 27</td>\n      <td> F</td>\n      <td>     marketing</td>\n      <td> 46538</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>52 </th>\n      <td> 18</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 55105</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>60 </th>\n      <td> 50</td>\n      <td> M</td>\n      <td>    healthcare</td>\n      <td> 06472</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>89 </th>\n      <td> 43</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 68106</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>90 </th>\n      <td> 60</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 78155</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td> 3.00</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>96 </th>\n      <td> 25</td>\n      <td> F</td>\n      <td>        artist</td>\n      <td> 75206</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>97 </th>\n      <td> 43</td>\n      <td> M</td>\n      <td>        artist</td>\n      <td> 98006</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td> 21</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 90210</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 1.50</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td> 33</td>\n      <td> M</td>\n      <td>          none</td>\n      <td> 73439</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td> 59</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 15237</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td> 51</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 97365</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td> 50</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 84408</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td> 46</td>\n      <td> M</td>\n      <td>        doctor</td>\n      <td> 53211</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td> 40</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 02143</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td> 33</td>\n      <td> F</td>\n      <td>      educator</td>\n      <td> 68767</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td> 47</td>\n      <td> M</td>\n      <td>    healthcare</td>\n      <td> 80123</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td> 56</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 22306</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td> 53</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 97403</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td> 26</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 16801</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td> 40</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 93402</td>\n      <td> 3.75</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td> 39</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 03060</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>770</th>\n      <td> 28</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 14216</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>777</th>\n      <td> 63</td>\n      <td> M</td>\n      <td>    programmer</td>\n      <td> 01810</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>794</th>\n      <td> 32</td>\n      <td> M</td>\n      <td>      educator</td>\n      <td> 57197</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>801</th>\n      <td> 22</td>\n      <td> M</td>\n      <td>        writer</td>\n      <td> 92154</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>808</th>\n      <td> 45</td>\n      <td> M</td>\n      <td>      salesman</td>\n      <td> 90016</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>810</th>\n      <td> 55</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 80526</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>811</th>\n      <td> 40</td>\n      <td> F</td>\n      <td>      educator</td>\n      <td> 73013</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td> 59</td>\n      <td> M</td>\n      <td> administrator</td>\n      <td> 40205</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>821</th>\n      <td> 37</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 77845</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>823</th>\n      <td> 27</td>\n      <td> M</td>\n      <td>        artist</td>\n      <td> 50322</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>835</th>\n      <td> 44</td>\n      <td> F</td>\n      <td>     executive</td>\n      <td> 11577</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>838</th>\n      <td> 23</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 01375</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>840</th>\n      <td> 39</td>\n      <td> M</td>\n      <td>        artist</td>\n      <td> 55406</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>  3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>848</th>\n      <td> 46</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 02146</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>849</th>\n      <td> 15</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 25652</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>850</th>\n      <td> 34</td>\n      <td> M</td>\n      <td>    technician</td>\n      <td> 78390</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>862</th>\n      <td> 25</td>\n      <td> M</td>\n      <td>     executive</td>\n      <td> 13820</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>867</th>\n      <td> 24</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 92507</td>\n      <td> 3.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>875</th>\n      <td> 24</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 35802</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 2.25</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>876</th>\n      <td> 41</td>\n      <td> M</td>\n      <td>         other</td>\n      <td> 20902</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>882</th>\n      <td> 35</td>\n      <td> M</td>\n      <td>      engineer</td>\n      <td> 40503</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> 3.00</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td> 41</td>\n      <td> M</td>\n      <td>     scientist</td>\n      <td> 17036</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>891</th>\n      <td> 51</td>\n      <td> F</td>\n      <td> administrator</td>\n      <td> 03062</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>907</th>\n      <td> 25</td>\n      <td> F</td>\n      <td>         other</td>\n      <td> 80526</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td> 3.75</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>909</th>\n      <td> 50</td>\n      <td> F</td>\n      <td>      educator</td>\n      <td> 53171</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> E2E3R</td>\n      <td> 2.25</td>\n      <td>NaN</td>\n      <td>  3</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td> 21</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 55408</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td> 26</td>\n      <td> F</td>\n      <td>       student</td>\n      <td> 33319</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 20</td>\n      <td> M</td>\n      <td>       student</td>\n      <td> 97229</td>\n      <td> 3.75</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td> 48</td>\n      <td> F</td>\n      <td>     librarian</td>\n      <td> 78209</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>  NaN</td>\n      <td>...</td>\n      <td>  NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>147 rows \u00d7 1686 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": "         age gender     occupation zip_code     1   2   3     4     5     6  \\\nuser_id                                                                       \n4         24      M     technician    43537   NaN NaN NaN   NaN   NaN   NaN   \n9         29      M        student    01002   NaN NaN NaN   NaN   NaN  3.75   \n10        53      M         lawyer    90703  3.00 NaN NaN  3.00   NaN   NaN   \n12        28      F          other    06405   NaN NaN NaN  3.75   NaN   NaN   \n14        45      M      scientist    55106   NaN NaN NaN   NaN   NaN   NaN   \n16        21      M  entertainment    10309  3.75 NaN NaN  3.75   NaN   NaN   \n24        21      F         artist    94533   NaN NaN NaN   NaN   NaN   NaN   \n25        39      M       engineer    55107  3.75 NaN NaN   NaN   NaN   NaN   \n34        38      F  administrator    42141   NaN NaN NaN   NaN   NaN   NaN   \n46        27      F      marketing    46538   NaN NaN NaN   NaN   NaN   NaN   \n52        18      F        student    55105   NaN NaN NaN   NaN   NaN   NaN   \n60        50      M     healthcare    06472   NaN NaN NaN   NaN   NaN   NaN   \n89        43      F  administrator    68106  3.75 NaN NaN   NaN   NaN   NaN   \n90        60      M       educator    78155   NaN NaN NaN   NaN   NaN  3.00   \n96        25      F         artist    75206  3.75 NaN NaN   NaN   NaN   NaN   \n97        43      M         artist    98006  3.00 NaN NaN   NaN   NaN   NaN   \n118       21      M  administrator    90210   NaN NaN NaN   NaN  1.50   NaN   \n127       33      M           none    73439   NaN NaN NaN   NaN   NaN   NaN   \n131       59      F  administrator    15237  3.00 NaN NaN   NaN   NaN   NaN   \n136       51      M          other    97365   NaN NaN NaN   NaN   NaN   NaN   \n137       50      M       educator    84408  2.25 NaN NaN   NaN   NaN   NaN   \n138       46      M         doctor    53211  3.00 NaN NaN   NaN   NaN   NaN   \n147       40      F      librarian    02143   NaN NaN NaN   NaN   NaN   NaN   \n152       33      F       educator    68767   NaN NaN NaN   NaN   NaN   NaN   \n164       47      M     healthcare    80123   NaN NaN NaN   NaN   NaN   NaN   \n173       56      M          other    22306   NaN NaN NaN   NaN   NaN   NaN   \n185       53      F      librarian    97403   NaN NaN NaN   NaN   NaN   NaN   \n187       26      M       educator    16801   NaN NaN NaN   NaN   NaN   NaN   \n200       40      M     programmer    93402  3.75   3 NaN   NaN   NaN   NaN   \n210       39      M       engineer    03060  3.75 NaN NaN  3.00   NaN   NaN   \n...      ...    ...            ...      ...   ...  ..  ..   ...   ...   ...   \n770       28      M        student    14216  3.75 NaN NaN   NaN   NaN   NaN   \n777       63      M     programmer    01810  3.00 NaN NaN   NaN   NaN   NaN   \n794       32      M       educator    57197  3.00 NaN NaN   NaN   NaN   NaN   \n801       22      M         writer    92154   NaN NaN NaN   NaN   NaN   NaN   \n808       45      M       salesman    90016   NaN NaN NaN   NaN   NaN   NaN   \n810       55      F          other    80526   NaN NaN NaN   NaN   NaN   NaN   \n811       40      F       educator    73013   NaN NaN NaN   NaN   NaN   NaN   \n819       59      M  administrator    40205   NaN NaN NaN   NaN   NaN   NaN   \n821       37      M       engineer    77845  3.75 NaN NaN   NaN   NaN   NaN   \n823       27      M         artist    50322  3.00 NaN NaN  3.75   NaN   NaN   \n835       44      F      executive    11577  2.25 NaN NaN   NaN   NaN   NaN   \n838       23      M        student    01375  3.75 NaN NaN   NaN   NaN   NaN   \n840       39      M         artist    55406   NaN NaN NaN   NaN   NaN   NaN   \n848       46      M       engineer    02146   NaN NaN NaN   NaN   NaN   NaN   \n849       15      F        student    25652   NaN NaN NaN   NaN   NaN   NaN   \n850       34      M     technician    78390   NaN NaN NaN   NaN   NaN   NaN   \n862       25      M      executive    13820   NaN NaN NaN   NaN   NaN   NaN   \n867       24      M      scientist    92507  3.00 NaN NaN   NaN   NaN   NaN   \n875       24      F        student    35802   NaN NaN NaN  2.25   NaN   NaN   \n876       41      M          other    20902   NaN NaN NaN   NaN   NaN   NaN   \n882       35      M       engineer    40503  3.75 NaN NaN  3.00   NaN   NaN   \n888       41      M      scientist    17036   NaN NaN NaN   NaN   NaN   NaN   \n891       51      F  administrator    03062   NaN NaN NaN   NaN   NaN   NaN   \n907       25      F          other    80526  3.75 NaN NaN   NaN  3.75   NaN   \n909       50      F       educator    53171   NaN NaN NaN   NaN   NaN   NaN   \n923       21      M        student    E2E3R  2.25 NaN   3   NaN   NaN   NaN   \n928       21      M        student    55408   NaN NaN NaN   NaN   NaN   NaN   \n939       26      F        student    33319   NaN NaN NaN   NaN   NaN   NaN   \n941       20      M        student    97229  3.75 NaN NaN   NaN   NaN   NaN   \n942       48      F      librarian    78209   NaN NaN NaN   NaN   NaN   NaN   \n\n         ...   1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id  ...                                                               \n4        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n9        ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n10       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n12       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n14       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n16       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n24       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n25       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n34       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n46       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n52       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n60       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n89       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n90       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n96       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n97       ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n118      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n127      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n131      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n136      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n137      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n138      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n147      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n152      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n164      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n173      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n185      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n187      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n200      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n210      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...      ...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n770      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n777      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n794      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n801      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n808      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n810      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n811      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n819      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n821      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n823      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n835      ...   2.25   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n838      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n840      ...    NaN     3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n848      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n849      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n850      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n862      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n867      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n875      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n876      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n882      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n888      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n891      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n907      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n909      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n923      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n928      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n939      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942      ...    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[147 rows x 1686 columns]"
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[users['occupation']=='doctor'].mean(1) ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 222,
       "text": "user_id\n138        3.205882\n251        3.792208\n299        3.464286\n427        3.411290\n841        3.903226\n845        3.129032\n935        3.923077\ndtype: float64"
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings[users['occupation']=='doctor'].mean(1).mean(0)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 223,
       "text": "3.5470001670869111"
      }
     ],
     "prompt_number": 223
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "What genre of movie do men age 18-25 like best?"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#SPLIT-APPLY-COMBINE: Pandas as a powerful database\n\n> - Splitting the data into groups based on some criteria\n> - Applying a function to each group independently\n> - Combining the results into a data structure\n\nhttp://pandas.pydata.org/pandas-docs/version/0.13.1/groupby.html"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import Image\nImage(\"http://i.imgur.com/yjNkiwL.png\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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osVh69uzZsGFDzYwuW7YsNTVVM3PnhRDStWvXpk2bVu5uBJSCggLNROh0qiy6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tNz035OzzyW4oDGQVRU4Wyg9K0HDhyYN29eQkLCJUmnXvwAEsI5z8zMjIyMNJlM2vh3g8GQnJxs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\np0MVQZ9MAAAAAElFTkSuQmCC\n",
       "prompt_number": 224,
       "text": "<IPython.core.display.Image at 0x7f3148f39c50>"
      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped=users['age'].groupby(users['occupation']) #here age is the y in the above diagram and occupation the x",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 225
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped.mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 226,
       "text": "occupation\nadministrator    38.746835\nartist           31.392857\ndoctor           43.571429\neducator         42.010526\nengineer         36.388060\nentertainment    29.222222\nexecutive        38.718750\nhealthcare       41.562500\nhomemaker        32.571429\nlawyer           36.750000\nlibrarian        40.000000\nmarketing        37.615385\nnone             26.555556\nother            34.523810\nprogrammer       33.121212\nretired          63.071429\nsalesman         35.666667\nscientist        35.548387\nstudent          22.081633\ntechnician       33.148148\nwriter           36.311111\ndtype: float64"
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped=users['age'].groupby(users['gender'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped.mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 228,
       "text": "gender\nF         33.813187\nM         34.149254\ndtype: float64"
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped=users['age'].groupby(users['zip_code'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "grouped.mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 230,
       "text": "zip_code\n00000       54.5\n01002       29.0\n01040       41.0\n01080       48.0\n01331       37.0\n01375       23.0\n01581       44.0\n01602       55.0\n01701       49.0\n01720       45.5\n01754       36.0\n01810       63.0\n01824       45.0\n01913       55.0\n01915       39.0\n...\nK7L5J       31\nL1V3W       38\nL9G2B       37\nM4J2K       46\nM7A1A       31\nN2L5N       27\nN4T1A       21\nR3T5K       22\nT8H1N       39\nV0R2H       37\nV0R2M       47\nV1G4L       35\nV3N4P       31\nV5A2B       29\nY1A6B       47\nLength: 795, dtype: float64"
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "##Using the rest of the columns . . . .\n##Supervised and unsupervised learning\nrecommender engines:\n\nRatings = U * V, where U is a matrix connecting users and features and V a matrix connecting factors and movies\n\n"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Just as quick example, we might use a measurement of similarity called \"cosine similarity.\" It measures how close two vectors are, with 0 being orthogonal from one another, 1 being identical. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def cosine_distance(a, b):\n    if len(a) != len(b):\n    \traise ValueError, \"a and b must be same length\" #Steve\n    numerator = 0\n    denoma = 0\n    denomb = 0\n    for i in range(len(a)-1):       #Mike's optimizations:\n        ai = a[i+1]             #only calculate once\n    \tbi = b[i+1]\n    \tnumerator += ai*bi    #faster than exponent (barely)\n    \tdenoma += ai*ai       #strip abs() since it's squaring\n    \tdenomb += bi*bi\n    result = numerator / (sqrt(denoma)*sqrt(denomb))\n    return result\n    ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 260
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cosine_distance((1,2),(3,4))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0\n1\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 249,
       "text": "0.01613008990009257"
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings.ix[104][1]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 239,
       "text": "nan"
      }
     ],
     "prompt_number": 239
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_zero=ratings.fillna(0)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 241
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ratings_zero",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>item_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1  </th>\n      <td> 5.00</td>\n      <td> 3</td>\n      <td> 4</td>\n      <td> 3.00</td>\n      <td> 3</td>\n      <td> 5.00</td>\n      <td> 4.00</td>\n      <td> 1.00</td>\n      <td> 5.00</td>\n      <td> 3</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 2</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td> 4.00</td>\n      <td> 3</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 2.00</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td> 4</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 3.75</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td> 3.00</td>\n      <td> 3</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 1</td>\n      <td> 0.00</td>\n      <td> 2.00</td>\n      <td> 4.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.75</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td> 1.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 1.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 2</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td> 0.00</td>\n      <td> 2</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>30 </th>\n      <td> 0.00</td>\n      <td> 3</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>914</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>915</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>916</th>\n      <td> 4.00</td>\n      <td> 3</td>\n      <td> 3</td>\n      <td> 4.00</td>\n      <td> 3</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3</td>\n    </tr>\n    <tr>\n      <th>917</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>918</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1.00</td>\n      <td> 4</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>920</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>921</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>922</th>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>923</th>\n      <td> 2.25</td>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>924</th>\n      <td> 5.00</td>\n      <td> 3</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 4.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>925</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>926</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>927</th>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>928</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.75</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>930</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>931</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>932</th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>933</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>934</th>\n      <td> 2.00</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>935</th>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 1.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>936</th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 4</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 5.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>937</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 5.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>938</th>\n      <td> 4.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>940</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 2.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 4.00</td>\n      <td> 5.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>941</th>\n      <td> 3.75</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>942</th>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td> 0.00</td>\n      <td> 5</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 0.00</td>\n      <td> 3.00</td>\n      <td> 0</td>\n      <td>...</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n  </tbody>\n</table>\n<p>943 rows \u00d7 1682 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 245,
       "text": "item_id  1     2     3     4     5     6     7     8     9     10    ...   \\\nuser_id                                                              ...    \n1        5.00     3     4  3.00     3  5.00  4.00  1.00  5.00     3  ...    \n2        4.00     0     0  0.00     0  0.00  0.00  0.00  0.00     2  ...    \n3        0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n4        0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n5        4.00     3     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n6        4.00     0     0  0.00     0  0.00  2.00  4.00  4.00     0  ...    \n7        0.00     0     0  5.00     0  0.00  5.00  5.00  5.00     4  ...    \n8        0.00     0     0  0.00     0  0.00  3.00  0.00  0.00     0  ...    \n9        0.00     0     0  0.00     0  3.75  3.00  0.00  0.00     0  ...    \n10       3.00     0     0  3.00     0  0.00  3.00  0.00  3.00     0  ...    \n11       0.00     0     0  0.00     0  0.00  0.00  4.00  5.00     0  ...    \n12       0.00     0     0  3.75     0  0.00  0.00  0.00  0.00     0  ...    \n13       3.00     3     0  5.00     1  0.00  2.00  4.00  3.00     0  ...    \n14       0.00     0     0  0.00     0  0.00  3.75  0.00  3.00     0  ...    \n15       1.00     0     0  0.00     0  0.00  1.00  0.00  4.00     0  ...    \n16       3.75     0     0  3.75     0  0.00  3.75  3.75  3.75     0  ...    \n17       4.00     0     0  0.00     0  0.00  4.00  0.00  3.00     0  ...    \n18       5.00     0     0  3.00     0  5.00  0.00  5.00  5.00     0  ...    \n19       0.00     0     0  4.00     0  0.00  0.00  5.00  0.00     0  ...    \n20       3.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n21       5.00     0     0  0.00     2  0.00  5.00  0.00  5.00     0  ...    \n22       0.00     2     0  5.00     0  0.00  0.00  0.00  0.00     0  ...    \n23       5.00     0     0  0.00     0  0.00  4.00  4.00  0.00     0  ...    \n24       0.00     0     0  0.00     0  0.00  3.00  3.75  3.75     0  ...    \n25       3.75     0     0  0.00     0  0.00  3.00  3.00  0.00     0  ...    \n26       3.00     0     0  0.00     0  0.00  3.00  0.00  4.00     0  ...    \n27       0.00     0     0  0.00     0  0.00  0.00  0.00  4.00     0  ...    \n28       0.00     0     0  0.00     3  0.00  5.00  0.00  0.00     0  ...    \n29       0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n30       0.00     3     0  0.00     0  0.00  4.00  0.00  0.00     0  ...    \n...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n914      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n915      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n916      4.00     3     3  4.00     3  0.00  4.00  0.00  5.00     0  ...    \n917      3.00     0     1  0.00     0  0.00  0.00  0.00  5.00     0  ...    \n918      3.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n919      4.00     0     0  1.00     4  0.00  3.00  0.00  5.00     0  ...    \n920      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n921      3.00     0     0  0.00     0  0.00  0.00  3.00  0.00     0  ...    \n922      5.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n923      2.25     0     3  0.00     0  0.00  0.00  0.00  3.00     0  ...    \n924      5.00     3     0  0.00     0  4.00  4.00  0.00  4.00     0  ...    \n925      0.00     0     0  0.00     4  0.00  0.00  0.00  0.00     0  ...    \n926      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n927      5.00     0     0  0.00     0  0.00  3.00  4.00  0.00     0  ...    \n928      0.00     0     0  0.00     0  0.00  0.00  3.75  3.75     0  ...    \n929      3.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n930      3.00     0     0  0.00     0  0.00  0.00  3.00  0.00     0  ...    \n931      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n932      4.00     0     0  0.00     0  0.00  4.00  0.00  5.00     0  ...    \n933      3.00     0     0  3.00     0  0.00  4.00  0.00  3.00     0  ...    \n934      2.00     4     0  5.00     0  0.00  0.00  0.00  0.00     0  ...    \n935      3.00     0     0  0.00     0  0.00  0.00  0.00  1.00     0  ...    \n936      4.00     0     4  0.00     0  5.00  4.00  0.00  4.00     0  ...    \n937      0.00     0     0  0.00     0  0.00  0.00  0.00  5.00     0  ...    \n938      4.00     0     0  0.00     0  0.00  4.00  0.00  3.00     0  ...    \n939      0.00     0     0  0.00     0  0.00  0.00  0.00  3.75     0  ...    \n940      0.00     0     0  2.00     0  0.00  4.00  5.00  3.00     0  ...    \n941      3.75     0     0  0.00     0  0.00  3.00  0.00  0.00     0  ...    \n942      0.00     0     0  0.00     0  0.00  0.00  0.00  0.00     0  ...    \n943      0.00     5     0  0.00     0  0.00  0.00  0.00  3.00     0  ...    \n\nitem_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                              \n1           0     0     0     0     0     0     0     0     0     0  \n2           0     0     0     0     0     0     0     0     0     0  \n3           0     0     0     0     0     0     0     0     0     0  \n4           0     0     0     0     0     0     0     0     0     0  \n5           0     0     0     0     0     0     0     0     0     0  \n6           0     0     0     0     0     0     0     0     0     0  \n7           0     0     0     0     0     0     0     0     0     0  \n8           0     0     0     0     0     0     0     0     0     0  \n9           0     0     0     0     0     0     0     0     0     0  \n10          0     0     0     0     0     0     0     0     0     0  \n11          0     0     0     0     0     0     0     0     0     0  \n12          0     0     0     0     0     0     0     0     0     0  \n13          0     0     0     0     0     0     0     0     0     0  \n14  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0     0     0     0     0     0     0     0     0  \n925         0     0     0     0     0     0     0     0     0     0  \n926         0     0     0     0     0     0     0     0     0     0  \n927         0     0     0     0     0     0     0     0     0     0  \n928         0     0     0     0     0     0     0     0     0     0  \n929         0     0     0     0     0     0     0     0     0     0  \n930         0     0     0     0     0     0     0     0     0     0  \n931         0     0     0     0     0     0     0     0     0     0  \n932         0     0     0     0     0     0     0     0     0     0  \n933         0     0     0     0     0     0     0     0     0     0  \n934         0     0     0     0     0     0     0     0     0     0  \n935         0     0     0     0     0     0     0     0     0     0  \n936         0     0     0     0     0     0     0     0     0     0  \n937         0     0     0     0     0     0     0     0     0     0  \n938         0     0     0     0     0     0     0     0     0     0  \n939         0     0     0     0     0     0     0     0     0     0  \n940         0     0     0     0     0     0     0     0     0     0  \n941         0     0     0     0     0     0     0     0     0     0  \n942         0     0     0     0     0     0     0     0     0     0  \n943         0     0     0     0     0     0     0     0     0     0  \n\n[943 rows x 1682 columns]"
      }
     ],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "tempdict={}\nfor i in range(len(ratings_zero)-1):\n    tempdict[i+1]= cosine_distance(ratings_zero.ix[3], ratings_zero.ix[i+1])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 276
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "similarity=pd.Series(tempdict)\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "most_similar=similarity.order(ascending=False)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "films['movie_title'][most_similar.index]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 288,
       "text": "3                                      Four Rooms (1995)\n863    Garden of Finzi-Contini, The (Giardino dei Fin...\n489                                     Notorious (1946)\n616                      Night of the Living Dead (1968)\n587                          Hour of the Pig, The (1993)\n784                                 Beyond Bedlam (1993)\n317                     In the Name of the Father (1993)\n752                      Replacement Killers, The (1998)\n724                             Circle of Friends (1995)\n335                            How to Be a Player (1997)\n772                                          Kids (1995)\n529          My Life as a Dog (Mitt liv som hund) (1985)\n920                                      Two Bits (1995)\n284                                       Tin Cup (1996)\n783                                    Milk Money (1994)\n...\n590         Hellraiser: Bloodline (1996)\n888               One Night Stand (1997)\n321                        Mother (1996)\n175                        Brazil (1985)\n172      Empire Strikes Back, The (1980)\n565         Village of the Damned (1995)\n93       Welcome to the Dollhouse (1995)\n792         Bullets Over Broadway (1994)\n122                Cable Guy, The (1996)\n558            Heavenly Creatures (1994)\n469                    Short Cuts (1993)\n67     Ace Ventura: Pet Detective (1994)\n132             Wizard of Oz, The (1939)\n765                     Boomerang (1992)\n471            Courage Under Fire (1996)\nName: movie_title, Length: 942, dtype: object"
      }
     ],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}