Table¶

pyarrow.Table.column('nmi_code').unique()

table.select(['year'])

table.drop_columns('animals')
table.drop_columns(['n_legs', 'animals'])

table.take([1,3])

import pyarrow.compute as pc
expr = pc.field('year') <= 2020
table.filter(expr)

table.filter(pc.field('start_date') < pc.field('end_date'))

table.slice(offset=0, length=3)

table.sort_by('animal')

table.group_by('year').aggregate([('n_legs', 'sum')])