How to use the petl.look function in petl

print('=' * len(repr(dbo)))
    print('EXERCISE UNICODE')
    print(repr(dbo))
    print('=' * len(repr(dbo)))
    print()
    expect = ((u'name', u'id'),
              (u'Արամ Խաչատրյան', 1),
              (u'Johann Strauß', 2),
              (u'Вагиф Сәмәдоғлу', 3),
              (u'章子怡', 4),
              )
    actual = etl.fromdb(dbo, 'SELECT * FROM test_unicode')
    print('write some data and verify...')
    etl.todb(expect, dbo, 'test_unicode')
    ieq(expect, actual)
    print(etl.look(actual))

def process_rows(resource_):
            for row_index, row in enumerate(resource_):
                if pass_row_index:
                    parameters.update(row_index=row_index)

                new_row = row_processor(row, **parameters)
                yield new_row

                if verbose and row_index < LOG_SAMPLE_SIZE:
                    sample_rows.append(new_row)

            if verbose:
                table = look(fromdicts(sample_rows), limit=LOG_SAMPLE_SIZE)
                message = 'Output of processor %s for resource %s is...\n%s'
                args = row_processor.__name__, resource_index, table
                logging.info(message, *args)

['M', 12],
          ['F', 34],
          ['-', 56]]

from petl import extend, look
look(table1)
# using a fixed value
table2 = extend(table1, 'baz', 42)
look(table2)
# calculating the value
table2 = extend(table1, 'baz', lambda rec: rec['bar'] * 2)
look(table2)
# an expression string can also be used via expr
from petl import expr
table3 = extend(table1, 'baz', expr('{bar} * 2'))
look(table3)
    

# rowslice

table1 = [['foo', 'bar'],
          ['a', 1],
          ['b', 2],
          ['c', 5],
          ['d', 7],
          ['f', 42]]

from petl import rowslice, look
look(table1)
table2 = rowslice(table1, 2)
look(table2)
table3 = rowslice(table1, 1, 4)

#!/usr/bin/env python

"""
TODO doc me
"""

from petl import cat, look

table1 = [['foo', 'bar'],
          [1, 'A'],
          [2, 'B']]
table2 = [['bar', 'baz'],
          ['C', True],
          ['D', False]]
table3 = cat(table1, table2, missing=None)
look(table3)

look(table2)


# tocsv

table = [['foo', 'bar'],
         ['a', 1],
         ['b', 2],
         ['c', 2]]

from petl import tocsv, look
look(table)
tocsv(table, 'test.csv', delimiter='\t')
# look what it did
from petl import fromcsv
look(fromcsv('test.csv', delimiter='\t'))


# appendcsv

table = [['foo', 'bar'],
         ['d', 7],
         ['e', 42],
         ['f', 12]]

# look at an existing CSV file
from petl import look, fromcsv
testcsv = fromcsv('test.csv', delimiter='\t')
look(testcsv)
# append some data
look(table)
from petl import appendcsv

# leftjoin

table1 = [['id', 'colour'],
          [1, 'blue'],
          [2, 'red'],
          [3, 'purple']]
table2 = [['id', 'shape'],
          [1, 'circle'],
          [3, 'square'],
          [4, 'ellipse']]

from petl import leftjoin, look
look(table1)
look(table2)
table3 = leftjoin(table1, table2, key='id')
look(table3)


# rightjoin

table1 = [['id', 'colour'],
          [1, 'blue'],
          [2, 'red'],
          [3, 'purple']]
table2 = [['id', 'shape'],
          [1, 'circle'],
          [3, 'square'],
          [4, 'ellipse']]

from petl import rightjoin, look

['B', 2]]
table7 = [['bar', 'foo'],
          ['A', 1],
          ['B', 2]]
table8 = [['bar', 'baz'],
          ['C', True],
          ['D', False]]

from petl import look, cat
look(table1)
look(table2)
table3 = cat(table1, table2)
look(table3)
# can also be used to square up a single table with uneven rows
look(table4)
look(cat(table4))
# use the header keyword argument to specify a fixed set of fields 
look(table5)
table6 = cat(table5, header=['A', 'foo', 'B', 'bar', 'C'])
look(table6)
# using the header keyword argument with two input tables
look(table7)
look(table8)
table9 = cat(table7, table8, header=['A', 'foo', 'B', 'bar', 'C'])
look(table9)


# convert

table1 = [['foo', 'bar'],
          ['A', '2.4'],
          ['B', '5.7'],

look(fromsqlite3('test.db', 'select * from foobar'))


# appendsqlite3

moredata = [['foo', 'bar'],
            ['d', 7],
            ['e', 9],
            ['f', 1]]

from petl import appendsqlite3, look
look(moredata)
appendsqlite3(moredata, 'test.db', 'foobar') 
# look what it did
from petl import look, fromsqlite3
look(fromsqlite3('test.db', 'select * from foobar'))


# mergesort

table1 = (('foo', 'bar'),
          ('A', 6),
          ('C', 2),
          ('D', 10),
          ('A', 9),
          ('F', 1))
table2 = (('foo', 'bar'),
          ('B', 3),
          ('D', 10),
          ('A', 10),
          ('F', 4))

# rowslice

table1 = [['foo', 'bar'],
          ['a', 1],
          ['b', 2],
          ['c', 5],
          ['d', 7],
          ['f', 42]]

from petl import rowslice, look
look(table1)
table2 = rowslice(table1, 2)
look(table2)
table3 = rowslice(table1, 1, 4)
look(table3)
table4 = rowslice(table1, 0, 5, 2)
look(table4)


# head

table1 = [['foo', 'bar'],
          ['a', 1],
          ['b', 2],
          ['c', 5],
          ['d', 7],
          ['f', 42],
          ['f', 3],
          ['h', 90]]

from petl import head, look

table3 = [['id', 'time', 'height', 'weight'],
          [1, 11, 66.4, 12.2],
          [2, 16, 53.2, 17.3],
          [3, 12, 34.5, 9.4]]

from petl import melt, look
look(table1)
table2 = melt(table1, 'id')
look(table2)
# compound keys are supported
look(table3)
table4 = melt(table3, key=['id', 'time'])
look(table4)
# a subset of variable fields can be selected
table5 = melt(table3, key=['id', 'time'], variables=['height'])    
look(table5)


# recast

table1 = [['id', 'variable', 'value'],
          [3, 'age', 16],
          [1, 'gender', 'F'],
          [2, 'gender', 'M'],
          [2, 'age', 17],
          [1, 'age', 12],
          [3, 'gender', 'M']]
table3 = [['id', 'vars', 'vals'],
          [3, 'age', 16],
          [1, 'gender', 'F'],
          [2, 'gender', 'M'],
          [2, 'age', 17],