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Kenneth Reitz
98 lines
2.2 KiB
ReStructuredText
98 lines
2.2 KiB
ReStructuredText
Tabbed: format-agnostic tabular dataset library
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===============================================
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::
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_____ ______ ______ _________
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__ /_______ ____ /_ ___ /_ _____ ______ /
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_ __/_ __ `/__ __ \__ __ \_ _ \_ __ /
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/ /_ / /_/ / _ /_/ /_ /_/ // __// /_/ /
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\__/ \__,_/ /_.___/ /_.___/ \___/ \__,_/
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.. *Tabbed is under active documentation-driven development.*
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Tabbed is a format-agnostic tabular dataset library, written in Python.
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It is a full python module which doubles as a CLI application for quick
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dataset conversions.
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Formats supported:
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- JSON
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- YAML
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- Excel
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- CSV
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.. - HTML
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At this time, Tabbed supports the **export** of it's powerful Dataset object instances into any of the above formats. Import is underway.
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Please note that tabbed *purposefully* excludes XML support. It always will.
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Features
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--------
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.. Convert datafile formats via API: ::
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..
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.. tablib.source(filename='data.csv').export('data.json')
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.. Convert datafile formats via CLI: ::
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..
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.. $ tabbed data.csv data.json
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.. Convert data formats via CLI pipe interface: ::
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..
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.. $ curl http://domain.dev/dataset.json | tabbed --to excel | gist -p
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Populate fresh data files: ::
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headers = ('first_name', 'last_name', 'gpa')
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data = [
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('John', 'Adams', 4.0),
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('George', 'Washington', 2.6),
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('Henry', 'Ford', 2.3)
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]
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data = tablib.Dataset(*data, headers=headers)
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# Establish file location and save
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data.save('test.xls')
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Intelligently add new rows: ::
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data.append('Bob', 'Dylan', 3.2)
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print data.headers
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# >>> ('first_name', 'last_name', 'gpa')
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Slice rows: ::
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print data[0:1]
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# >>> [('John', 'Adams', 4.0), ('George', 'Washington', 2.6)]
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.. Slice columns by header: ::
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..
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.. print data['first_name']
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.. # >>> ['John', 'George', 'Henry']
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..
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Manipulate rows by index: ::
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del data[0]
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print data[0:1]
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# >>> [('George', 'Washington', 2.6), ('Henry', 'Ford', 2.3)]
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.. # Update saved file
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.. data.save()
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.. Export to various formats: ::
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..
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.. # Save copy as CSV
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.. data.export('backup.csv') |