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Tutorial

This tutorial have a lot of examples (and some notes/implementation details in the end). All examples you'll read here are available to you - see examples folder. You can also learn more details about implementation reading the tests at tests/test_*.py.

If you find any kind of bug, error, have a suggestion, doubt or want to contribute with code or pay me a beer, please contact me. You can follow the development of this code in outputty on GitHub.

Enjoy! :-)

{{EXAMPLES}}

Character Encodings

Received strings are decoded using UTF-8 and output is encoded also using UTF-8 by default. You can change this behaviour with the parameters input_encoding and output_encoding to Table, for example:

my_table = Table(headers=['Column 1', 'Column 2'], input_encoding='iso-8859-1',
                 output_encoding='utf16')

You can also get the table string decoded, in unicode:

table_in_unicode = unicode(my_table)

Notes About Data Normalization

We have two kinds of normalization in Table:

  • .normalize_types(): used by default when importing from CSV, this method convert table rows to the types it identify. All data that in first moment are strings will be converted to unicode, int, float, datetime.date or datetime.datetime when identified.
  • unicode normalization: all operations in Table (import from some format, output table in some format, normalization and ordering) will convert data internally to unicode using input_encoding as codec (passed in Table.__init__). When convert_types=False, all row's values will be unicode, otherwise only types identified as string will be converted to unicode.

to_list_of_dicts and to_dict

If you want to access all table rows as dicts, just convert it using the method to_list_of_dicts. Using the same table from Example 1, if we execute:

rows = my_table.to_list_of_dicts()
print rows[1]['First Name']

...it'll print:

Flávio

You'll receive this data encoded with output_encoding. If you need it as unicode just pass unicode=True to this method.

You can also convert your table to a dict, with header names as keys and columns as values and filter which columns will go to the dictionary:

table_dict = my_table.to_dict()
print table_dict

table_dict_filtered = my_table.to_dict(only=['First Name', 'Last Name'])
print table_dict_filtered

...will print:

{'Last Name': (u'Justen', u'Amieiro'), 'First Name': (u'\xc1lvaro', u'Fl\xe1vio'), 'Main Language': (u'Python', u'Python')}
{'Last Name': (u'Justen', u'Amieiro'), 'First Name': (u'\xc1lvaro', u'Fl\xe1vio')}

And if you want to create a dict with some column value as key and other column value as value you can specify key and value parameters, as in:

other_table = Table(headers=['date', 'measure'])
other_table.append(('2011-12-01', 21))
other_table.append(('2011-12-02', 42))
other_table.append(('2011-12-03', 3.14))
other_table.append(('2011-12-04', 2.71))
values_as_dict = other_table.to_dict(key='date', value='measure')
print values_as_dict

...that produces:

{'2011-12-04': 2.71, '2011-12-03': 3.14, '2011-12-02': 42, '2011-12-01': 21}