/
joins.py
931 lines (779 loc) · 31.2 KB
/
joins.py
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from __future__ import absolute_import, print_function, division
import itertools
import operator
from petl.compat import next
from petl.errors import ArgumentError
from petl.comparison import comparable_itemgetter, Comparable
from petl.util.base import Table, asindices, rowgetter, rowgroupby, \
header, data
from petl.transform.sorts import sort
from petl.transform.basics import cut, cutout
from petl.transform.dedup import distinct
def natural_key(left, right):
# determine key field or fields
lhdr = header(left)
lflds = list(map(str, lhdr))
rhdr = header(right)
rflds = list(map(str, rhdr))
key = [f for f in lflds if f in rflds]
assert len(key) > 0, 'no fields in common'
if len(key) == 1:
key = key[0] # deal with singletons
return key
def keys_from_args(left, right, key, lkey, rkey):
if key is lkey is rkey is None:
# no keys specified, attempt natural join
lkey = rkey = natural_key(left, right)
elif key is not None and lkey is rkey is None:
# common key specified
lkey = rkey = key
elif key is None and lkey is not None and rkey is not None:
# left and right keys specified
pass
else:
raise ArgumentError(
'bad key arguments: either specify key, or specify both lkey and '
'rkey, or provide no key/lkey/rkey arguments at all (natural join)'
)
return lkey, rkey
def join(left, right, key=None, lkey=None, rkey=None, presorted=False,
buffersize=None, tempdir=None, cache=True, lprefix=None, rprefix=None):
"""
Perform an equi-join on the given tables. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red'],
... [3, 'purple']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square'],
... [4, 'ellipse']]
>>> table3 = etl.join(table1, table2, key='id')
>>> table3
+----+----------+----------+
| id | colour | shape |
+====+==========+==========+
| 1 | 'blue' | 'circle' |
+----+----------+----------+
| 3 | 'purple' | 'square' |
+----+----------+----------+
>>> # if no key is given, a natural join is tried
... table4 = etl.join(table1, table2)
>>> table4
+----+----------+----------+
| id | colour | shape |
+====+==========+==========+
| 1 | 'blue' | 'circle' |
+----+----------+----------+
| 3 | 'purple' | 'square' |
+----+----------+----------+
>>> # note behaviour if the key is not unique in either or both tables
... table5 = [['id', 'colour'],
... [1, 'blue'],
... [1, 'red'],
... [2, 'purple']]
>>> table6 = [['id', 'shape'],
... [1, 'circle'],
... [1, 'square'],
... [2, 'ellipse']]
>>> table7 = etl.join(table5, table6, key='id')
>>> table7
+----+----------+-----------+
| id | colour | shape |
+====+==========+===========+
| 1 | 'blue' | 'circle' |
+----+----------+-----------+
| 1 | 'blue' | 'square' |
+----+----------+-----------+
| 1 | 'red' | 'circle' |
+----+----------+-----------+
| 1 | 'red' | 'square' |
+----+----------+-----------+
| 2 | 'purple' | 'ellipse' |
+----+----------+-----------+
>>> # compound keys are supported
... table8 = [['id', 'time', 'height'],
... [1, 1, 12.3],
... [1, 2, 34.5],
... [2, 1, 56.7]]
>>> table9 = [['id', 'time', 'weight'],
... [1, 2, 4.5],
... [2, 1, 6.7],
... [2, 2, 8.9]]
>>> table10 = etl.join(table8, table9, key=['id', 'time'])
>>> table10
+----+------+--------+--------+
| id | time | height | weight |
+====+======+========+========+
| 1 | 2 | 34.5 | 4.5 |
+----+------+--------+--------+
| 2 | 1 | 56.7 | 6.7 |
+----+------+--------+--------+
If `presorted` is True, it is assumed that the data are already sorted by
the given key, and the `buffersize`, `tempdir` and `cache` arguments are
ignored. Otherwise, the data are sorted, see also the discussion of the
`buffersize`, `tempdir` and `cache` arguments under the
:func:`petl.transform.sorts.sort` function.
Left and right tables with different key fields can be handled via the
`lkey` and `rkey` arguments.
"""
# TODO don't read data twice (occurs if using natural key)
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return JoinView(left, right, lkey=lkey, rkey=rkey,
presorted=presorted, buffersize=buffersize, tempdir=tempdir,
cache=cache, lprefix=lprefix, rprefix=rprefix)
Table.join = join
class JoinView(Table):
def __init__(self, left, right, lkey, rkey,
presorted=False, leftouter=False, rightouter=False,
missing=None, buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
self.lkey = lkey
self.rkey = rkey
if presorted:
self.left = left
self.right = right
else:
self.left = sort(left, lkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.right = sort(right, rkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.leftouter = leftouter
self.rightouter = rightouter
self.missing = missing
self.lprefix = lprefix
self.rprefix = rprefix
def __iter__(self):
return iterjoin(self.left, self.right, self.lkey, self.rkey,
leftouter=self.leftouter, rightouter=self.rightouter,
missing=self.missing, lprefix=self.lprefix,
rprefix=self.rprefix)
def leftjoin(left, right, key=None, lkey=None, rkey=None, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
"""
Perform a left outer join on the given tables. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red'],
... [3, 'purple']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square'],
... [4, 'ellipse']]
>>> table3 = etl.leftjoin(table1, table2, key='id')
>>> table3
+----+----------+----------+
| id | colour | shape |
+====+==========+==========+
| 1 | 'blue' | 'circle' |
+----+----------+----------+
| 2 | 'red' | None |
+----+----------+----------+
| 3 | 'purple' | 'square' |
+----+----------+----------+
If `presorted` is True, it is assumed that the data are already sorted by
the given key, and the `buffersize`, `tempdir` and `cache` arguments are
ignored. Otherwise, the data are sorted, see also the discussion of the
`buffersize`, `tempdir` and `cache` arguments under the
:func:`petl.transform.sorts.sort` function.
Left and right tables with different key fields can be handled via the
`lkey` and `rkey` arguments.
"""
# TODO don't read data twice (occurs if using natural key)
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return JoinView(left, right, lkey=lkey, rkey=rkey,
presorted=presorted, leftouter=True, rightouter=False,
missing=missing, buffersize=buffersize, tempdir=tempdir,
cache=cache, lprefix=lprefix, rprefix=rprefix)
Table.leftjoin = leftjoin
def rightjoin(left, right, key=None, lkey=None, rkey=None, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
"""
Perform a right outer join on the given tables. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red'],
... [3, 'purple']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square'],
... [4, 'ellipse']]
>>> table3 = etl.rightjoin(table1, table2, key='id')
>>> table3
+----+----------+-----------+
| id | colour | shape |
+====+==========+===========+
| 1 | 'blue' | 'circle' |
+----+----------+-----------+
| 3 | 'purple' | 'square' |
+----+----------+-----------+
| 4 | None | 'ellipse' |
+----+----------+-----------+
If `presorted` is True, it is assumed that the data are already sorted by
the given key, and the `buffersize`, `tempdir` and `cache` arguments are
ignored. Otherwise, the data are sorted, see also the discussion of the
`buffersize`, `tempdir` and `cache` arguments under the
:func:`petl.transform.sorts.sort` function.
Left and right tables with different key fields can be handled via the
`lkey` and `rkey` arguments.
"""
# TODO don't read data twice (occurs if using natural key)
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return JoinView(left, right, lkey=lkey, rkey=rkey,
presorted=presorted, leftouter=False, rightouter=True,
missing=missing, buffersize=buffersize,
tempdir=tempdir, cache=cache, lprefix=lprefix,
rprefix=rprefix)
Table.rightjoin = rightjoin
def outerjoin(left, right, key=None, lkey=None, rkey=None, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
"""
Perform a full outer join on the given tables. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red'],
... [3, 'purple']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square'],
... [4, 'ellipse']]
>>> table3 = etl.outerjoin(table1, table2, key='id')
>>> table3
+----+----------+-----------+
| id | colour | shape |
+====+==========+===========+
| 1 | 'blue' | 'circle' |
+----+----------+-----------+
| 2 | 'red' | None |
+----+----------+-----------+
| 3 | 'purple' | 'square' |
+----+----------+-----------+
| 4 | None | 'ellipse' |
+----+----------+-----------+
If `presorted` is True, it is assumed that the data are already sorted by
the given key, and the `buffersize`, `tempdir` and `cache` arguments are
ignored. Otherwise, the data are sorted, see also the discussion of the
`buffersize`, `tempdir` and `cache` arguments under the
:func:`petl.transform.sorts.sort` function.
Left and right tables with different key fields can be handled via the
`lkey` and `rkey` arguments.
"""
# TODO don't read data twice (occurs if using natural key)
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return JoinView(left, right, lkey=lkey, rkey=rkey,
presorted=presorted, leftouter=True, rightouter=True,
missing=missing, buffersize=buffersize, tempdir=tempdir,
cache=cache, lprefix=lprefix, rprefix=rprefix)
Table.outerjoin = outerjoin
def iterjoin(left, right, lkey, rkey, leftouter=False, rightouter=False,
missing=None, lprefix=None, rprefix=None):
lit = iter(left)
rit = iter(right)
lhdr = next(lit)
rhdr = next(rit)
# determine indices of the key fields in left and right tables
lkind = asindices(lhdr, lkey)
rkind = asindices(rhdr, rkey)
# construct functions to extract key values from both tables
lgetk = comparable_itemgetter(*lkind)
rgetk = comparable_itemgetter(*rkind)
# determine indices of non-key fields in the right table
# (in the output, we only include key fields from the left table - we
# don't want to duplicate fields)
rvind = [i for i in range(len(rhdr)) if i not in rkind]
rgetv = rowgetter(*rvind)
# determine the output fields
if lprefix is None:
outhdr = list(lhdr)
else:
outhdr = [(str(lprefix) + str(f)) for f in lhdr]
if rprefix is None:
outhdr.extend(rgetv(rhdr))
else:
outhdr.extend([(str(rprefix) + str(f)) for f in rgetv(rhdr)])
yield tuple(outhdr)
# define a function to join two groups of rows
def joinrows(_lrowgrp, _rrowgrp):
if _rrowgrp is None:
for lrow in _lrowgrp:
outrow = list(lrow) # start with the left row
# extend with missing values in place of the right row
outrow.extend([missing] * len(rvind))
yield tuple(outrow)
elif _lrowgrp is None:
for rrow in _rrowgrp:
# start with missing values in place of the left row
outrow = [missing] * len(lhdr)
# set key values
for li, ri in zip(lkind, rkind):
outrow[li] = rrow[ri]
# extend with non-key values from the right row
outrow.extend(rgetv(rrow))
yield tuple(outrow)
else:
_rrowgrp = list(_rrowgrp) # may need to iterate more than once
for lrow in _lrowgrp:
for rrow in _rrowgrp:
# start with the left row
outrow = list(lrow)
# extend with non-key values from the right row
outrow.extend(rgetv(rrow))
yield tuple(outrow)
# construct group iterators for both tables
lgit = itertools.groupby(lit, key=lgetk)
rgit = itertools.groupby(rit, key=rgetk)
lrowgrp = []
rrowgrp = []
# loop until *either* of the iterators is exhausted
# initialise here to handle empty tables
lkval, rkval = Comparable(None), Comparable(None)
try:
# pick off initial row groups
lkval, lrowgrp = next(lgit)
rkval, rrowgrp = next(rgit)
while True:
if lkval < rkval:
if leftouter:
for row in joinrows(lrowgrp, None):
yield tuple(row)
# advance left
lkval, lrowgrp = next(lgit)
elif lkval > rkval:
if rightouter:
for row in joinrows(None, rrowgrp):
yield tuple(row)
# advance right
rkval, rrowgrp = next(rgit)
else:
for row in joinrows(lrowgrp, rrowgrp):
yield tuple(row)
# advance both
lkval, lrowgrp = next(lgit)
rkval, rrowgrp = next(rgit)
except StopIteration:
pass
# make sure any left rows remaining are yielded
if leftouter:
if lkval > rkval:
# yield anything that got left hanging
for row in joinrows(lrowgrp, None):
yield tuple(row)
# yield the rest
for lkval, lrowgrp in lgit:
for row in joinrows(lrowgrp, None):
yield tuple(row)
# make sure any right rows remaining are yielded
if rightouter:
if lkval < rkval:
# yield anything that got left hanging
for row in joinrows(None, rrowgrp):
yield tuple(row)
# yield the rest
for rkval, rrowgrp in rgit:
for row in joinrows(None, rrowgrp):
yield tuple(row)
def crossjoin(*tables, **kwargs):
"""
Form the cartesian product of the given tables. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square']]
>>> table3 = etl.crossjoin(table1, table2)
>>> table3
+----+--------+----+----------+
| id | colour | id | shape |
+====+========+====+==========+
| 1 | 'blue' | 1 | 'circle' |
+----+--------+----+----------+
| 1 | 'blue' | 3 | 'square' |
+----+--------+----+----------+
| 2 | 'red' | 1 | 'circle' |
+----+--------+----+----------+
| 2 | 'red' | 3 | 'square' |
+----+--------+----+----------+
If `prefix` is `True` then field names in the output table header will be
prefixed by the index of the input table.
"""
return CrossJoinView(*tables, **kwargs)
Table.crossjoin = crossjoin
class CrossJoinView(Table):
def __init__(self, *sources, **kwargs):
self.sources = sources
self.prefix = kwargs.get('prefix', False)
def __iter__(self):
return itercrossjoin(self.sources, self.prefix)
def itercrossjoin(sources, prefix):
# construct fields
outhdr = list()
for i, s in enumerate(sources):
if prefix:
# use one-based numbering
outhdr.extend([str(i+1) + '_' + str(f) for f in header(s)])
else:
outhdr.extend(header(s))
yield tuple(outhdr)
datasrcs = [data(src) for src in sources]
for prod in itertools.product(*datasrcs):
outrow = list()
for row in prod:
outrow.extend(row)
yield tuple(outrow)
def antijoin(left, right, key=None, lkey=None, rkey=None, presorted=False,
buffersize=None, tempdir=None, cache=True):
"""
Return rows from the `left` table where the key value does not occur in
the `right` table. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [0, 'black'],
... [1, 'blue'],
... [2, 'red'],
... [4, 'yellow'],
... [5, 'white']]
>>> table2 = [['id', 'shape'],
... [1, 'circle'],
... [3, 'square']]
>>> table3 = etl.antijoin(table1, table2, key='id')
>>> table3
+----+----------+
| id | colour |
+====+==========+
| 0 | 'black' |
+----+----------+
| 2 | 'red' |
+----+----------+
| 4 | 'yellow' |
+----+----------+
| 5 | 'white' |
+----+----------+
If `presorted` is True, it is assumed that the data are already sorted by
the given key, and the `buffersize`, `tempdir` and `cache` arguments are
ignored. Otherwise, the data are sorted, see also the discussion of the
`buffersize`, `tempdir` and `cache` arguments under the
:func:`petl.transform.sorts.sort` function.
Left and right tables with different key fields can be handled via the
`lkey` and `rkey` arguments.
"""
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return AntiJoinView(left=left, right=right, lkey=lkey, rkey=rkey,
presorted=presorted, buffersize=buffersize,
tempdir=tempdir, cache=cache)
Table.antijoin = antijoin
class AntiJoinView(Table):
def __init__(self, left, right, lkey, rkey, presorted=False,
buffersize=None, tempdir=None, cache=True):
if presorted:
self.left = left
self.right = right
else:
self.left = sort(left, lkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.right = sort(right, rkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.lkey = lkey
self.rkey = rkey
def __iter__(self):
return iterantijoin(self.left, self.right, self.lkey, self.rkey)
def iterantijoin(left, right, lkey, rkey):
lit = iter(left)
rit = iter(right)
lhdr = next(lit)
rhdr = next(rit)
yield tuple(lhdr)
# determine indices of the key fields in left and right tables
lkind = asindices(lhdr, lkey)
rkind = asindices(rhdr, rkey)
# construct functions to extract key values from both tables
lgetk = comparable_itemgetter(*lkind)
rgetk = comparable_itemgetter(*rkind)
# construct group iterators for both tables
lgit = itertools.groupby(lit, key=lgetk)
rgit = itertools.groupby(rit, key=rgetk)
lrowgrp = []
# loop until *either* of the iterators is exhausted
lkval, rkval = Comparable(None), Comparable(None)
try:
# pick off initial row groups
lkval, lrowgrp = next(lgit)
rkval, _ = next(rgit)
while True:
if lkval < rkval:
for row in lrowgrp:
yield tuple(row)
# advance left
lkval, lrowgrp = next(lgit)
elif lkval > rkval:
# advance right
rkval, _ = next(rgit)
else:
# advance both
lkval, lrowgrp = next(lgit)
rkval, _ = next(rgit)
except StopIteration:
pass
# any left over?
if lkval > rkval:
# yield anything that got left hanging
for row in lrowgrp:
yield tuple(row)
# and the rest...
for lkval, lrowgrp in lgit:
for row in lrowgrp:
yield tuple(row)
def lookupjoin(left, right, key=None, lkey=None, rkey=None, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
"""
Perform a left join, but where the key is not unique in the right-hand
table, arbitrarily choose the first row and ignore others. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'color', 'cost'],
... [1, 'blue', 12],
... [2, 'red', 8],
... [3, 'purple', 4]]
>>> table2 = [['id', 'shape', 'size'],
... [1, 'circle', 'big'],
... [1, 'circle', 'small'],
... [2, 'square', 'tiny'],
... [2, 'square', 'big'],
... [3, 'ellipse', 'small'],
... [3, 'ellipse', 'tiny']]
>>> table3 = etl.lookupjoin(table1, table2, key='id')
>>> table3
+----+----------+------+-----------+---------+
| id | color | cost | shape | size |
+====+==========+======+===========+=========+
| 1 | 'blue' | 12 | 'circle' | 'big' |
+----+----------+------+-----------+---------+
| 2 | 'red' | 8 | 'square' | 'tiny' |
+----+----------+------+-----------+---------+
| 3 | 'purple' | 4 | 'ellipse' | 'small' |
+----+----------+------+-----------+---------+
See also :func:`petl.transform.joins.leftjoin`.
"""
lkey, rkey = keys_from_args(left, right, key, lkey, rkey)
return LookupJoinView(left, right, lkey, rkey, presorted=presorted,
missing=missing, buffersize=buffersize,
tempdir=tempdir, cache=cache,
lprefix=lprefix, rprefix=rprefix)
Table.lookupjoin = lookupjoin
class LookupJoinView(Table):
def __init__(self, left, right, lkey, rkey, presorted=False, missing=None,
buffersize=None, tempdir=None, cache=True,
lprefix=None, rprefix=None):
if presorted:
self.left = left
self.right = right
else:
self.left = sort(left, lkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.right = sort(right, rkey, buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.lkey = lkey
self.rkey = rkey
self.missing = missing
self.lprefix = lprefix
self.rprefix = rprefix
def __iter__(self):
return iterlookupjoin(self.left, self.right, self.lkey, self.rkey,
missing=self.missing, lprefix=self.lprefix,
rprefix=self.rprefix)
def iterlookupjoin(left, right, lkey, rkey, missing=None, lprefix=None,
rprefix=None):
lit = iter(left)
rit = iter(right)
lhdr = next(lit)
rhdr = next(rit)
# determine indices of the key fields in left and right tables
lkind = asindices(lhdr, lkey)
rkind = asindices(rhdr, rkey)
# construct functions to extract key values from both tables
lgetk = operator.itemgetter(*lkind)
rgetk = operator.itemgetter(*rkind)
# determine indices of non-key fields in the right table
# (in the output, we only include key fields from the left table - we
# don't want to duplicate fields)
rvind = [i for i in range(len(rhdr)) if i not in rkind]
rgetv = rowgetter(*rvind)
# determine the output fields
if lprefix is None:
outhdr = list(lhdr)
else:
outhdr = [(str(lprefix) + str(f)) for f in lhdr]
if rprefix is None:
outhdr.extend(rgetv(rhdr))
else:
outhdr.extend([(str(rprefix) + str(f)) for f in rgetv(rhdr)])
yield tuple(outhdr)
# define a function to join two groups of rows
def joinrows(_lrowgrp, _rrowgrp):
if _rrowgrp is None:
for lrow in _lrowgrp:
outrow = list(lrow) # start with the left row
# extend with missing values in place of the right row
outrow.extend([missing] * len(rvind))
yield tuple(outrow)
else:
rrow = next(iter(_rrowgrp)) # pick first arbitrarily
for lrow in _lrowgrp:
# start with the left row
outrow = list(lrow)
# extend with non-key values from the right row
outrow.extend(rgetv(rrow))
yield tuple(outrow)
# construct group iterators for both tables
lgit = itertools.groupby(lit, key=lgetk)
rgit = itertools.groupby(rit, key=rgetk)
lrowgrp = []
# loop until *either* of the iterators is exhausted
lkval, rkval = None, None # initialise here to handle empty tables
try:
# pick off initial row groups
lkval, lrowgrp = next(lgit)
rkval, rrowgrp = next(rgit)
while True:
if lkval < rkval:
for row in joinrows(lrowgrp, None):
yield tuple(row)
# advance left
lkval, lrowgrp = next(lgit)
elif lkval > rkval:
# advance right
rkval, rrowgrp = next(rgit)
else:
for row in joinrows(lrowgrp, rrowgrp):
yield tuple(row)
# advance both
lkval, lrowgrp = next(lgit)
rkval, rrowgrp = next(rgit)
except StopIteration:
pass
# make sure any left rows remaining are yielded
if lkval > rkval:
# yield anything that got left hanging
for row in joinrows(lrowgrp, None):
yield tuple(row)
# yield the rest
for lkval, lrowgrp in lgit:
for row in joinrows(lrowgrp, None):
yield tuple(row)
def unjoin(table, value, key=None, autoincrement=(1, 1), presorted=False,
buffersize=None, tempdir=None, cache=True):
"""
Split a table into two tables by reversing an inner join. E.g.::
>>> import petl as etl
>>> # join key is present in the table
... table1 = (('foo', 'bar', 'baz'),
... ('A', 1, 'apple'),
... ('B', 1, 'apple'),
... ('C', 2, 'orange'))
>>> table2, table3 = etl.unjoin(table1, 'baz', key='bar')
>>> table2
+-----+-----+
| foo | bar |
+=====+=====+
| 'A' | 1 |
+-----+-----+
| 'B' | 1 |
+-----+-----+
| 'C' | 2 |
+-----+-----+
>>> table3
+-----+----------+
| bar | baz |
+=====+==========+
| 1 | 'apple' |
+-----+----------+
| 2 | 'orange' |
+-----+----------+
>>> # an integer join key can also be reconstructed
... table4 = (('foo', 'bar'),
... ('A', 'apple'),
... ('B', 'apple'),
... ('C', 'orange'))
>>> table5, table6 = etl.unjoin(table4, 'bar')
>>> table5
+-----+--------+
| foo | bar_id |
+=====+========+
| 'A' | 1 |
+-----+--------+
| 'B' | 1 |
+-----+--------+
| 'C' | 2 |
+-----+--------+
>>> table6
+----+----------+
| id | bar |
+====+==========+
| 1 | 'apple' |
+----+----------+
| 2 | 'orange' |
+----+----------+
The `autoincrement` parameter controls how an integer join key is
reconstructed, and should be a tuple of (`start`, `step`).
"""
if key is None:
# first sort the table by the value field
if presorted:
tbl_sorted = table
else:
tbl_sorted = sort(table, value, buffersize=buffersize,
tempdir=tempdir, cache=cache)
# on the left, return the original table but with the value field
# replaced by an incrementing integer
left = ConvertToIncrementingCounterView(tbl_sorted, value,
autoincrement)
# on the right, return a new table with distinct values from the
# given field
right = EnumerateDistinctView(tbl_sorted, value, autoincrement)
else:
# on the left, return distinct rows from the original table
# with the value field cut out
left = distinct(cutout(table, value))
# on the right, return distinct rows from the original table
# with all fields but the key and value cut out
right = distinct(cut(table, key, value))
return left, right
class ConvertToIncrementingCounterView(Table):
def __init__(self, tbl, value, autoincrement):
self.table = tbl
self.value = value
self.autoincrement = autoincrement
def __iter__(self):
it = iter(self.table)
hdr = next(it)
table = itertools.chain([hdr], it)
value = self.value
vidx = hdr.index(value)
outhdr = list(hdr)
outhdr[vidx] = '%s_id' % value
yield tuple(outhdr)
offset, multiplier = self.autoincrement
for n, (_, group) in enumerate(rowgroupby(table, value)):
for row in group:
outrow = list(row)
outrow[vidx] = (n * multiplier) + offset
yield tuple(outrow)
Table.unjoin = unjoin
class EnumerateDistinctView(Table):
def __init__(self, tbl, value, autoincrement):
self.table = tbl
self.value = value
self.autoincrement = autoincrement
def __iter__(self):
offset, multiplier = self.autoincrement
yield ('id', self.value)
for n, (v, _) in enumerate(rowgroupby(self.table, self.value)):
yield ((n * multiplier) + offset, v)