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A tiny spreadsheet-like data structure and tool

Project description

tablet

Tablet is a module that supports spreadsheet-like operations on tiny text tables.

Dealing with those tiny tables or spreadsheets (usually in CSV/TSV formats) is a daily chore.

The goal of this project is to provide a light-weight and easy-to-use tool that can handle daily-routines of manipulating tabular text data.

Inside the module, a “Table” class was defined to support all kinds of spreadsheet/table-like operations:
  • tsv/csv input/output

  • adding/removing rows and columns

  • lookup a key or keys

  • iterating

  • slicing

  • searching

  • sorting

  • filtering

  • grouping

  • joining

  • aggregating

  • removing duplicates

  • and more

If you are handling larger and more complex tables, spreadsheets, or panels, I suggest you turn to pandas<http://pandas.pydata.org/>.

Getting Started

Supposing a given csv file named <demo.csv> looks like below:

Heat,Lane,LastName,FirstName,YOB,NOC,RT,Time
1,1,SILADJI,Caba,1990,SRB,0.69,27.89
1,2,SCOZZOLI,Fabio,1988,ITA,0.62,27.37
1,3,SNYDERS,Glenn,1987,NZL,0.66,27.64
1,4,MARKIC,Matjaz,1983,SLO,0.73,27.71
1,5,GANGLOFF,Mark,1982,USA,0.67,27.57
1,6,FELDWEHR,Hendrik,1986,GER,0.70,27.53
1,7,BARTUNEK,Petr,1991,CZE,0.64,27.87
1,8,POLYAKOV,Vladislav,1983,KAZ,0.77,27.81
2,1,RICKARD,Brenton,1983,AUS,0.71,27.80
2,2,AGACHE,Dragos,1984,ROU,0.76,27.71
2,3,DALE OEN,Alexander,1985,NOR,0.70,27.33
2,4,FRANCA DA SILVA,Felipe,1987,BRA,0.68,26.95
2,5,DUGONJIC,Damir,1988,SLO,0.75,27.51
2,6,VAN DER BURGH,Cameron,1988,RSA,0.63,26.90
2,7,TRIZNOV,Aleksandr,1991,RUS,0.70,27.73
2,8,STEKELENBURG,Lennart,1986,NED,0.69,27.51

Users can use the following statements to show the top 8 best-time results:

>>> import tablet as T
>>> t = T.read('demo.csv', delim=',').sort('Time')
>>> for row in t[:8]:
...     print row[2], row[3], row[-1]
...
VAN DER BURGH Cameron 26.90
FRANCA DA SILVA Felipe 26.95
DALE OEN Alexander 27.33
SCOZZOLI Fabio 27.37
DUGONJIC Damir 27.51
STEKELENBURG Lennart 27.51
FELDWEHR Hendrik 27.53
GANGLOFF Mark 27.57

And output the top 8 results to a new tsv (tab-separated values) file:

>>> t2 = t.cut_cols(['LastName','FirstName','Time']).cut_rows(range(8))
>>> t2.show()
H ['LastName', 'FirstName', 'Time']
0 ['VAN DER BURGH', 'Cameron', '26.90']
1 ['FRANCA DA SILVA', 'Felipe', '26.95']
2 ['DALE OEN', 'Alexander', '27.33']
3 ['SCOZZOLI', 'Fabio', '27.37']
4 ['DUGONJIC', 'Damir', '27.51']
5 ['STEKELENBURG', 'Lennart', '27.51']
6 ['FELDWEHR', 'Hendrik', '27.53']
7 ['GANGLOFF', 'Mark', '27.57']
>>> t2.write('finalists.tsv')

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