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    "info": {
        "author": "Cho-Yi Chen",
        "author_email": "ntu.joey@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: End Users/Desktop",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: General"
        ],
        "description": "tablet\r\n------\r\n\r\nTablet is a module that supports spreadsheet-like operations on tiny text tables.\r\n\r\nDealing with those tiny tables or spreadsheets (usually in CSV/TSV formats) is a daily chore.\r\n\r\nThe goal of this project is to provide a light-weight and easy-to-use\r\ntool that can handle daily-routines of manipulating tabular text data.\r\n\r\nInside the module, a \"Table\" class was defined to support all kinds of spreadsheet/table-like operations:\r\n  * tsv/csv input/output\r\n  * adding/removing rows and columns\r\n  * lookup a key or keys\r\n  * iterating\r\n  * slicing\r\n  * searching\r\n  * sorting\r\n  * filtering\r\n  * grouping\r\n  * joining\r\n  * aggregating\r\n  * removing duplicates\r\n  * and more\r\n\r\nIf you are handling larger and more complex tables, spreadsheets, or panels, I suggest you turn to pandas<http://pandas.pydata.org/>.\r\n\r\n\r\nGetting Started\r\n---------------\r\n\r\nSupposing a given csv file named <demo.csv> looks like below::\r\n\r\n    Heat,Lane,LastName,FirstName,YOB,NOC,RT,Time\r\n    1,1,SILADJI,Caba,1990,SRB,0.69,27.89\r\n    1,2,SCOZZOLI,Fabio,1988,ITA,0.62,27.37\r\n    1,3,SNYDERS,Glenn,1987,NZL,0.66,27.64\r\n    1,4,MARKIC,Matjaz,1983,SLO,0.73,27.71\r\n    1,5,GANGLOFF,Mark,1982,USA,0.67,27.57\r\n    1,6,FELDWEHR,Hendrik,1986,GER,0.70,27.53\r\n    1,7,BARTUNEK,Petr,1991,CZE,0.64,27.87\r\n    1,8,POLYAKOV,Vladislav,1983,KAZ,0.77,27.81\r\n    2,1,RICKARD,Brenton,1983,AUS,0.71,27.80\r\n    2,2,AGACHE,Dragos,1984,ROU,0.76,27.71\r\n    2,3,DALE OEN,Alexander,1985,NOR,0.70,27.33\r\n    2,4,FRANCA DA SILVA,Felipe,1987,BRA,0.68,26.95\r\n    2,5,DUGONJIC,Damir,1988,SLO,0.75,27.51\r\n    2,6,VAN DER BURGH,Cameron,1988,RSA,0.63,26.90\r\n    2,7,TRIZNOV,Aleksandr,1991,RUS,0.70,27.73\r\n    2,8,STEKELENBURG,Lennart,1986,NED,0.69,27.51\r\n\r\nUsers can use the following statements to show the top 8 best-time results:\r\n\r\n>>> import tablet as T\r\n>>> t = T.read('demo.csv', delim=',').sort('Time')\r\n>>> for row in t[:8]:\r\n...     print row[2], row[3], row[-1]\r\n...\r\nVAN DER BURGH Cameron 26.90\r\nFRANCA DA SILVA Felipe 26.95\r\nDALE OEN Alexander 27.33\r\nSCOZZOLI Fabio 27.37\r\nDUGONJIC Damir 27.51\r\nSTEKELENBURG Lennart 27.51\r\nFELDWEHR Hendrik 27.53\r\nGANGLOFF Mark 27.57\r\n\r\nAnd output the top 8 results to a new tsv (tab-separated values) file:\r\n\r\n>>> t2 = t.cut_cols(['LastName','FirstName','Time']).cut_rows(range(8))\r\n>>> t2.show()\r\nH ['LastName', 'FirstName', 'Time']\r\n0 ['VAN DER BURGH', 'Cameron', '26.90']\r\n1 ['FRANCA DA SILVA', 'Felipe', '26.95']\r\n2 ['DALE OEN', 'Alexander', '27.33']\r\n3 ['SCOZZOLI', 'Fabio', '27.37']\r\n4 ['DUGONJIC', 'Damir', '27.51']\r\n5 ['STEKELENBURG', 'Lennart', '27.51']\r\n6 ['FELDWEHR', 'Hendrik', '27.53']\r\n7 ['GANGLOFF', 'Mark', '27.57']\r\n>>> t2.write('finalists.tsv')",
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