/
reshape.py
681 lines (561 loc) · 21.6 KB
/
reshape.py
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from __future__ import absolute_import, print_function, division
import itertools
import collections
import operator
from petl.compat import next, text_type
from petl.comparison import comparable_itemgetter
from petl.util.base import Table, rowgetter, values, itervalues, \
header, data, asindices
from petl.transform.sorts import sort
def melt(table, key=None, variables=None, variablefield='variable',
valuefield='value'):
"""
Reshape a table, melting fields into data. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'gender', 'age'],
... [1, 'F', 12],
... [2, 'M', 17],
... [3, 'M', 16]]
>>> table2 = etl.melt(table1, 'id')
>>> table2.lookall()
+----+----------+-------+
| id | variable | value |
+====+==========+=======+
| 1 | 'gender' | 'F' |
+----+----------+-------+
| 1 | 'age' | 12 |
+----+----------+-------+
| 2 | 'gender' | 'M' |
+----+----------+-------+
| 2 | 'age' | 17 |
+----+----------+-------+
| 3 | 'gender' | 'M' |
+----+----------+-------+
| 3 | 'age' | 16 |
+----+----------+-------+
>>> # compound keys are supported
... table3 = [['id', 'time', 'height', 'weight'],
... [1, 11, 66.4, 12.2],
... [2, 16, 53.2, 17.3],
... [3, 12, 34.5, 9.4]]
>>> table4 = etl.melt(table3, key=['id', 'time'])
>>> table4.lookall()
+----+------+----------+-------+
| id | time | variable | value |
+====+======+==========+=======+
| 1 | 11 | 'height' | 66.4 |
+----+------+----------+-------+
| 1 | 11 | 'weight' | 12.2 |
+----+------+----------+-------+
| 2 | 16 | 'height' | 53.2 |
+----+------+----------+-------+
| 2 | 16 | 'weight' | 17.3 |
+----+------+----------+-------+
| 3 | 12 | 'height' | 34.5 |
+----+------+----------+-------+
| 3 | 12 | 'weight' | 9.4 |
+----+------+----------+-------+
>>> # a subset of variable fields can be selected
... table5 = etl.melt(table3, key=['id', 'time'],
... variables=['height'])
>>> table5.lookall()
+----+------+----------+-------+
| id | time | variable | value |
+====+======+==========+=======+
| 1 | 11 | 'height' | 66.4 |
+----+------+----------+-------+
| 2 | 16 | 'height' | 53.2 |
+----+------+----------+-------+
| 3 | 12 | 'height' | 34.5 |
+----+------+----------+-------+
See also :func:`petl.transform.reshape.recast`.
"""
return MeltView(table, key=key, variables=variables,
variablefield=variablefield,
valuefield=valuefield)
Table.melt = melt
class MeltView(Table):
def __init__(self, source, key=None, variables=None,
variablefield='variable', valuefield='value'):
self.source = source
self.key = key
self.variables = variables
self.variablefield = variablefield
self.valuefield = valuefield
def __iter__(self):
return itermelt(self.source, self.key, self.variables,
self.variablefield, self.valuefield)
def itermelt(source, key, variables, variablefield, valuefield):
if key is None and variables is None:
raise ValueError('either key or variables must be specified')
it = iter(source)
hdr = next(it)
# determine key and variable field indices
key_indices = variables_indices = None
if key is not None:
key_indices = asindices(hdr, key)
if variables is not None:
if not isinstance(variables, (list, tuple)):
variables = (variables,)
variables_indices = asindices(hdr, variables)
if key is None:
# assume key is fields not in variables
key_indices = [i for i in range(len(hdr))
if i not in variables_indices]
if variables is None:
# assume variables are fields not in key
variables_indices = [i for i in range(len(hdr))
if i not in key_indices]
variables = [hdr[i] for i in variables_indices]
getkey = rowgetter(*key_indices)
# determine the output fields
outhdr = [hdr[i] for i in key_indices]
outhdr.append(variablefield)
outhdr.append(valuefield)
yield tuple(outhdr)
# construct the output data
for row in it:
k = getkey(row)
for v, i in zip(variables, variables_indices):
try:
o = list(k) # populate with key values initially
o.append(v) # add variable
o.append(row[i]) # add value
yield tuple(o)
except IndexError:
# row is missing this value, and melt() should yield no row
pass
def recast(table, key=None, variablefield='variable', valuefield='value',
samplesize=1000, reducers=None, missing=None):
"""
Recast molten data. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'variable', 'value'],
... [3, 'age', 16],
... [1, 'gender', 'F'],
... [2, 'gender', 'M'],
... [2, 'age', 17],
... [1, 'age', 12],
... [3, 'gender', 'M']]
>>> table2 = etl.recast(table1)
>>> table2
+----+-----+--------+
| id | age | gender |
+====+=====+========+
| 1 | 12 | 'F' |
+----+-----+--------+
| 2 | 17 | 'M' |
+----+-----+--------+
| 3 | 16 | 'M' |
+----+-----+--------+
>>> # specifying variable and value fields
... table3 = [['id', 'vars', 'vals'],
... [3, 'age', 16],
... [1, 'gender', 'F'],
... [2, 'gender', 'M'],
... [2, 'age', 17],
... [1, 'age', 12],
... [3, 'gender', 'M']]
>>> table4 = etl.recast(table3, variablefield='vars', valuefield='vals')
>>> table4
+----+-----+--------+
| id | age | gender |
+====+=====+========+
| 1 | 12 | 'F' |
+----+-----+--------+
| 2 | 17 | 'M' |
+----+-----+--------+
| 3 | 16 | 'M' |
+----+-----+--------+
>>> # if there are multiple values for each key/variable pair, and no
... # reducers function is provided, then all values will be listed
... table6 = [['id', 'time', 'variable', 'value'],
... [1, 11, 'weight', 66.4],
... [1, 14, 'weight', 55.2],
... [2, 12, 'weight', 53.2],
... [2, 16, 'weight', 43.3],
... [3, 12, 'weight', 34.5],
... [3, 17, 'weight', 49.4]]
>>> table7 = etl.recast(table6, key='id')
>>> table7
+----+--------------+
| id | weight |
+====+==============+
| 1 | [66.4, 55.2] |
+----+--------------+
| 2 | [53.2, 43.3] |
+----+--------------+
| 3 | [34.5, 49.4] |
+----+--------------+
>>> # multiple values can be reduced via an aggregation function
... def mean(values):
... return float(sum(values)) / len(values)
...
>>> table8 = etl.recast(table6, key='id', reducers={'weight': mean})
>>> table8
+----+--------------------+
| id | weight |
+====+====================+
| 1 | 60.800000000000004 |
+----+--------------------+
| 2 | 48.25 |
+----+--------------------+
| 3 | 41.95 |
+----+--------------------+
>>> # missing values are padded with whatever is provided via the
... # missing keyword argument (None by default)
... table9 = [['id', 'variable', 'value'],
... [1, 'gender', 'F'],
... [2, 'age', 17],
... [1, 'age', 12],
... [3, 'gender', 'M']]
>>> table10 = etl.recast(table9, key='id')
>>> table10
+----+------+--------+
| id | age | gender |
+====+======+========+
| 1 | 12 | 'F' |
+----+------+--------+
| 2 | 17 | None |
+----+------+--------+
| 3 | None | 'M' |
+----+------+--------+
Note that the table is scanned once to discover variables, then a second
time to reshape the data and recast variables as fields. How many rows are
scanned in the first pass is determined by the `samplesize` argument.
See also :func:`petl.transform.reshape.melt`.
"""
return RecastView(table, key=key, variablefield=variablefield,
valuefield=valuefield, samplesize=samplesize,
reducers=reducers, missing=missing)
Table.recast = recast
class RecastView(Table):
def __init__(self, source, key=None, variablefield='variable',
valuefield='value', samplesize=1000, reducers=None,
missing=None):
self.source = source
self.key = key
self.variablefield = variablefield
self.valuefield = valuefield
self.samplesize = samplesize
if reducers is None:
self.reducers = dict()
else:
self.reducers = reducers
self.missing = missing
def __iter__(self):
return iterrecast(self.source, self.key, self.variablefield,
self.valuefield, self.samplesize, self.reducers,
self.missing)
def iterrecast(source, key, variablefield, valuefield,
samplesize, reducers, missing):
# TODO only make one pass through the data
it = iter(source)
hdr = next(it)
flds = list(map(text_type, hdr))
# normalise some stuff
keyfields = key
variablefields = variablefield # N.B., could be more than one
# normalise key fields
if keyfields and not isinstance(keyfields, (list, tuple)):
keyfields = (keyfields,)
# normalise variable fields
if variablefields:
if isinstance(variablefields, dict):
pass # handle this later
elif not isinstance(variablefields, (list, tuple)):
variablefields = (variablefields,)
# infer key fields
if not keyfields:
# assume keyfields is fields not in variables
keyfields = [f for f in flds
if f not in variablefields and f != valuefield]
# infer key fields
if not variablefields:
# assume variables are fields not in keyfields
variablefields = [f for f in flds
if f not in keyfields and f != valuefield]
# sanity checks
assert valuefield in flds, 'invalid value field: %s' % valuefield
assert valuefield not in keyfields, 'value field cannot be keyfields'
assert valuefield not in variablefields, \
'value field cannot be variable field'
for f in keyfields:
assert f in flds, 'invalid keyfields field: %s' % f
for f in variablefields:
assert f in flds, 'invalid variable field: %s' % f
# we'll need these later
valueindex = flds.index(valuefield)
keyindices = [flds.index(f) for f in keyfields]
variableindices = [flds.index(f) for f in variablefields]
# determine the actual variable names to be cast as fields
if isinstance(variablefields, dict):
# user supplied dictionary
variables = variablefields
else:
variables = collections.defaultdict(set)
# sample the data to discover variables to be cast as fields
for row in itertools.islice(it, 0, samplesize):
for i, f in zip(variableindices, variablefields):
variables[f].add(row[i])
for f in variables:
# turn from sets to sorted lists
variables[f] = sorted(variables[f])
# finished the first pass
# determine the output fields
outhdr = list(keyfields)
for f in variablefields:
outhdr.extend(variables[f])
yield tuple(outhdr)
# output data
source = sort(source, key=keyfields)
it = itertools.islice(source, 1, None) # skip header row
getsortablekey = comparable_itemgetter(*keyindices)
getactualkey = operator.itemgetter(*keyindices)
# process sorted data in newfields
groups = itertools.groupby(it, key=getsortablekey)
for _, group in groups:
# may need to iterate over the group more than once
group = list(group)
# N.B., key returned by groupby may be wrapped as SortableItem, we want
# to output the actual key value, get it from the first row in the group
key_value = getactualkey(group[0])
if len(keyfields) > 1:
out_row = list(key_value)
else:
out_row = [key_value]
for f, i in zip(variablefields, variableindices):
for variable in variables[f]:
# collect all values for the current variable
vals = [r[valueindex] for r in group if r[i] == variable]
if len(vals) == 0:
val = missing
elif len(vals) == 1:
val = vals[0]
else:
if variable in reducers:
redu = reducers[variable]
else:
redu = list # list all values
val = redu(vals)
out_row.append(val)
yield tuple(out_row)
def transpose(table):
"""
Transpose rows into columns. E.g.::
>>> import petl as etl
>>> table1 = [['id', 'colour'],
... [1, 'blue'],
... [2, 'red'],
... [3, 'purple'],
... [5, 'yellow'],
... [7, 'orange']]
>>> table2 = etl.transpose(table1)
>>> table2
+----------+--------+-------+----------+----------+----------+
| id | 1 | 2 | 3 | 5 | 7 |
+==========+========+=======+==========+==========+==========+
| 'colour' | 'blue' | 'red' | 'purple' | 'yellow' | 'orange' |
+----------+--------+-------+----------+----------+----------+
See also :func:`petl.transform.reshape.recast`.
"""
return TransposeView(table)
Table.transpose = transpose
class TransposeView(Table):
def __init__(self, source):
self.source = source
def __iter__(self):
return itertranspose(self.source)
def itertranspose(source):
hdr = header(source)
its = [iter(source) for _ in hdr]
for i in range(len(hdr)):
yield tuple(row[i] for row in its[i])
def pivot(table, f1, f2, f3, aggfun, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True):
"""
Construct a pivot table. E.g.::
>>> import petl as etl
>>> table1 = [['region', 'gender', 'style', 'units'],
... ['east', 'boy', 'tee', 12],
... ['east', 'boy', 'golf', 14],
... ['east', 'boy', 'fancy', 7],
... ['east', 'girl', 'tee', 3],
... ['east', 'girl', 'golf', 8],
... ['east', 'girl', 'fancy', 18],
... ['west', 'boy', 'tee', 12],
... ['west', 'boy', 'golf', 15],
... ['west', 'boy', 'fancy', 8],
... ['west', 'girl', 'tee', 6],
... ['west', 'girl', 'golf', 16],
... ['west', 'girl', 'fancy', 1]]
>>> table2 = etl.pivot(table1, 'region', 'gender', 'units', sum)
>>> table2
+--------+-----+------+
| region | boy | girl |
+========+=====+======+
| 'east' | 33 | 29 |
+--------+-----+------+
| 'west' | 35 | 23 |
+--------+-----+------+
>>> table3 = etl.pivot(table1, 'region', 'style', 'units', sum)
>>> table3
+--------+-------+------+-----+
| region | fancy | golf | tee |
+========+=======+======+=====+
| 'east' | 25 | 22 | 15 |
+--------+-------+------+-----+
| 'west' | 9 | 31 | 18 |
+--------+-------+------+-----+
>>> table4 = etl.pivot(table1, 'gender', 'style', 'units', sum)
>>> table4
+--------+-------+------+-----+
| gender | fancy | golf | tee |
+========+=======+======+=====+
| 'boy' | 15 | 29 | 24 |
+--------+-------+------+-----+
| 'girl' | 19 | 24 | 9 |
+--------+-------+------+-----+
See also :func:`petl.transform.reshape.recast`.
"""
return PivotView(table, f1, f2, f3, aggfun, missing=missing,
presorted=presorted, buffersize=buffersize,
tempdir=tempdir, cache=cache)
Table.pivot = pivot
class PivotView(Table):
def __init__(self, source, f1, f2, f3, aggfun, missing=None,
presorted=False, buffersize=None, tempdir=None, cache=True):
if presorted:
self.source = source
else:
self.source = sort(source, key=(f1, f2), buffersize=buffersize,
tempdir=tempdir, cache=cache)
self.f1, self.f2, self.f3 = f1, f2, f3
self.aggfun = aggfun
self.missing = missing
def __iter__(self):
return iterpivot(self.source, self.f1, self.f2, self.f3, self.aggfun,
self.missing)
def iterpivot(source, f1, f2, f3, aggfun, missing):
# first pass - collect fields
f2vals = set(itervalues(source, f2)) # TODO only make one pass
f2vals = list(f2vals)
f2vals.sort()
outhdr = [f1]
outhdr.extend(f2vals)
yield tuple(outhdr)
# second pass - generate output
it = iter(source)
hdr = next(it)
flds = list(map(text_type, hdr))
f1i = flds.index(f1)
f2i = flds.index(f2)
f3i = flds.index(f3)
for v1, v1rows in itertools.groupby(it, key=operator.itemgetter(f1i)):
outrow = [v1] + [missing] * len(f2vals)
for v2, v12rows in itertools.groupby(v1rows,
key=operator.itemgetter(f2i)):
aggval = aggfun([row[f3i] for row in v12rows])
outrow[1 + f2vals.index(v2)] = aggval
yield tuple(outrow)
def flatten(table):
"""
Convert a table to a sequence of values in row-major order. E.g.::
>>> import petl as etl
>>> table1 = [['foo', 'bar', 'baz'],
... ['A', 1, True],
... ['C', 7, False],
... ['B', 2, False],
... ['C', 9, True]]
>>> list(etl.flatten(table1))
['A', 1, True, 'C', 7, False, 'B', 2, False, 'C', 9, True]
See also :func:`petl.transform.reshape.unflatten`.
"""
return FlattenView(table)
Table.flatten = flatten
class FlattenView(Table):
def __init__(self, table):
self.table = table
def __iter__(self):
for row in data(self.table):
for value in row:
yield value
def unflatten(*args, **kwargs):
"""
Convert a sequence of values in row-major order into a table. E.g.::
>>> import petl as etl
>>> a = ['A', 1, True, 'C', 7, False, 'B', 2, False, 'C', 9]
>>> table1 = etl.unflatten(a, 3)
>>> table1
+-----+----+-------+
| f0 | f1 | f2 |
+=====+====+=======+
| 'A' | 1 | True |
+-----+----+-------+
| 'C' | 7 | False |
+-----+----+-------+
| 'B' | 2 | False |
+-----+----+-------+
| 'C' | 9 | None |
+-----+----+-------+
>>> # a table and field name can also be provided as arguments
... table2 = [['lines'],
... ['A'],
... [1],
... [True],
... ['C'],
... [7],
... [False],
... ['B'],
... [2],
... [False],
... ['C'],
... [9]]
>>> table3 = etl.unflatten(table2, 'lines', 3)
>>> table3
+-----+----+-------+
| f0 | f1 | f2 |
+=====+====+=======+
| 'A' | 1 | True |
+-----+----+-------+
| 'C' | 7 | False |
+-----+----+-------+
| 'B' | 2 | False |
+-----+----+-------+
| 'C' | 9 | None |
+-----+----+-------+
See also :func:`petl.transform.reshape.flatten`.
"""
return UnflattenView(*args, **kwargs)
Table.unflatten = unflatten
class UnflattenView(Table):
def __init__(self, *args, **kwargs):
if len(args) == 2:
self.input = args[0]
self.period = args[1]
elif len(args) == 3:
self.input = values(args[0], args[1])
self.period = args[2]
else:
assert False, 'invalid arguments'
self.missing = kwargs.get('missing', None)
def __iter__(self):
inpt = self.input
period = self.period
missing = self.missing
# generate header row
outhdr = tuple('f%s' % i for i in range(period))
yield outhdr
# generate data rows
row = list()
for v in inpt:
if len(row) < period:
row.append(v)
else:
yield tuple(row)
row = [v]
# deal with last row
if len(row) > 0:
if len(row) < period:
row.extend([missing] * (period - len(row)))
yield tuple(row)