def _get_py(self, key):
if isinstance(key, tuple):
assert len(key) == 2
rows, cols = key
usecols = cols
ds = self.dshape.subshape[rows, cols]
usecols = None if isinstance(usecols, slice) else listpack(usecols)
else:
rows = key
ds = self.dshape.subshape[rows]
usecols = None
if isinstance(ds, DataShape) and isdimension(ds[0]):
ds = ds.subshape[0]
seq = self._iter(usecols=usecols)
if isinstance(key, tuple) and isinstance(cols, _strtypes + _inttypes):
seq = pluck(0, seq)
seq = coerce(ds, seq)
if isinstance(rows, compatibility._inttypes):
line = nth(rows, seq)
try:
return next(line).item()
except TypeError:
try:
return line.item()
except AttributeError:
return line
elif isinstance(rows, list):
return nth_list(rows, seq)
elif isinstance(rows, slice):
return it.islice(seq, rows.start, rows.stop, rows.step)
else:
raise IndexError("key %r is not valid" % rows)
def _get_py(self, key):
if isinstance(key, tuple):
assert len(key) == 2
rows, cols = key
usecols = cols
ds = self.dshape.subshape[rows, cols]
usecols = None if isinstance(usecols, slice) else listpack(usecols)
else:
rows = key
ds = self.dshape.subshape[rows]
usecols = None
if isinstance(ds, DataShape) and isdimension(ds[0]):
ds = ds.subshape[0]
seq = self._iter(usecols=usecols)
if isinstance(key, tuple) and isinstance(cols, _strtypes + _inttypes):
seq = pluck(0, seq)
seq = coerce(ds, seq)
if isinstance(rows, compatibility._inttypes):
line = nth(rows, seq)
try:
return next(line).item()
except TypeError:
try:
return line.item()
except AttributeError:
return line
elif isinstance(rows, list):
return nth_list(rows, seq)
elif isinstance(rows, slice):
return it.islice(seq, rows.start, rows.stop, rows.step)
else:
raise IndexError("key %r is not valid" % rows)
def coerce_to_ordered(ds, data):
""" Coerce data with dicts into an ordered ND collection
>>> from datashape import dshape
>>> coerce_to_ordered('{x: int, y: int}', {'x': 1, 'y': 2})
(1, 2)
>>> coerce_to_ordered('var * {x: int, y: int}',
... [{'x': 1, 'y': 2}, {'x': 10, 'y': 20}])
((1, 2), (10, 20))
Idempotent
>>> coerce_to_ordered('var * {x: int, y: int}',
... ((1, 2), (10, 20)))
((1, 2), (10, 20))
"""
if isinstance(ds, _strtypes):
ds = dshape(ds)
if isinstance(ds[0], Record):
if isinstance(data, (list, tuple)):
return data
rec = ds[0]
return tuple(coerce_to_ordered(rec[name], data[name])
for name in rec.names)
if isdimension(ds[0]):
return tuple(coerce_to_ordered(ds.subarray(1), row)
for row in data)
return data
def coerce_to_ordered(ds, data):
""" Coerce data with dicts into an ordered ND collection
>>> from datashape import dshape
>>> coerce_to_ordered('{x: int, y: int}', {'x': 1, 'y': 2})
(1, 2)
>>> coerce_to_ordered('var * {x: int, y: int}',
... [{'x': 1, 'y': 2}, {'x': 10, 'y': 20}])
((1, 2), (10, 20))
Idempotent
>>> coerce_to_ordered('var * {x: int, y: int}',
... ((1, 2), (10, 20)))
((1, 2), (10, 20))
"""
if isinstance(ds, _strtypes):
ds = dshape(ds)
if isinstance(ds[0], Record):
if isinstance(data, (list, tuple)):
return data
rec = ds[0]
return tuple(
coerce_to_ordered(rec[name], data[name]) for name in rec.names)
if isdimension(ds[0]):
return tuple(coerce_to_ordered(ds.subarray(1), row) for row in data)
return data
def into_string(uri, b, dshape=None, **kwargs):
if dshape is None:
dshape = discover(b)
resource_ds = 0 * dshape.subshape[0] if isdimension(dshape[0]) else dshape
a = resource(uri, dshape=resource_ds, expected_dshape=dshape, **kwargs)
return into(a, b, dshape=dshape, **kwargs)
def into_string(uri, b, dshape=None, **kwargs):
if dshape is None:
dshape = discover(b)
resource_ds = 0 * dshape.subshape[0] if isdimension(dshape[0]) else dshape
a = resource(uri, dshape=resource_ds, expected_dshape=dshape, **kwargs)
return into(a, b, dshape=dshape, **kwargs)
def into_string(uri, b, **kwargs):
ds = kwargs.pop('dshape', None)
if not ds:
ds = discover(b)
if isdimension(ds[0]):
resource_ds = 0 * ds.subshape[0]
else:
resource_ds = ds
a = resource(uri, dshape=resource_ds, expected_dshape=ds, **kwargs)
return into(a, b, dshape=ds, **kwargs)
def into_string(uri, b, **kwargs):
try:
if 'dshape' not in kwargs:
ds = discover(b)
if isdimension(ds[0]):
ds = var * ds.subshape[0]
kwargs['dshape'] = ds
except NotImplementedError:
pass
a = resource(uri, **kwargs)
return into(a, b, **kwargs)
def into_string(uri, b, **kwargs):
ds = kwargs.pop('dshape', None)
if not ds:
ds = discover(b)
if isdimension(ds[0]):
resource_ds = 0 * ds.subshape[0]
else:
resource_ds = ds
a = resource(uri, dshape=resource_ds, expected_dshape=ds, **kwargs)
return into(a, b, dshape=ds, **kwargs)
def discover_pymongo_collection(coll, n=50):
items = list(take(n, coll.find()))
oid_cols = [k for k, v in items[0].items() if isinstance(v, ObjectId)]
for item in items:
for col in oid_cols:
del item[col]
ds = discover(items)
if isdimension(ds[0]):
return coll.count() * ds.subshape[0]
else:
raise ValueError("Consistent datashape not found")
def dshape_to_alchemy(dshape, primary_key=frozenset()):
"""
>>> dshape_to_alchemy('int')
<class 'sqlalchemy.sql.sqltypes.Integer'>
>>> dshape_to_alchemy('string')
<class 'sqlalchemy.sql.sqltypes.Text'>
>>> dshape_to_alchemy('{name: string, amount: int}')
[Column('name', Text(), table=None, nullable=False), Column('amount', Integer(), table=None, nullable=False)]
>>> dshape_to_alchemy('{name: ?string, amount: ?int}')
[Column('name', Text(), table=None), Column('amount', Integer(), table=None)]
"""
if isinstance(dshape, str):
dshape = datashape.dshape(dshape)
if isinstance(dshape, Map):
return dshape_to_alchemy(dshape.key.measure, primary_key=primary_key)
if isinstance(dshape, Option):
return dshape_to_alchemy(dshape.ty, primary_key=primary_key)
if str(dshape) in types:
return types[str(dshape)]
if isinstance(dshape, datashape.Record):
return [
sa.Column(name,
dshape_to_alchemy(getattr(typ, 'ty', typ),
primary_key=primary_key),
primary_key=name in primary_key,
nullable=isinstance(typ[0], Option))
for name, typ in dshape.parameters[0]
]
if isinstance(dshape, datashape.DataShape):
if isdimension(dshape[0]):
return dshape_to_alchemy(dshape[1], primary_key=primary_key)
else:
return dshape_to_alchemy(dshape[0], primary_key=primary_key)
if isinstance(dshape, datashape.String):
fixlen = dshape[0].fixlen
if fixlen is None:
return sa.TEXT
string_types = dict(U=sa.Unicode, A=sa.String)
assert dshape.encoding is not None
return string_types[dshape.encoding[0]](length=fixlen)
if isinstance(dshape, datashape.DateTime):
return sa.DATETIME(timezone=dshape.tz is not None)
if isinstance(dshape, datashape.Decimal):
return sa.NUMERIC(dshape.precision, dshape.scale)
raise NotImplementedError("No SQLAlchemy dtype match for datashape: %s" %
dshape)
def dshape_to_alchemy(dshape, primary_key=frozenset()):
"""
>>> dshape_to_alchemy('int')
<class 'sqlalchemy.sql.sqltypes.Integer'>
>>> dshape_to_alchemy('string')
<class 'sqlalchemy.sql.sqltypes.Text'>
>>> dshape_to_alchemy('{name: string, amount: int}')
[Column('name', Text(), table=None, nullable=False), Column('amount', Integer(), table=None, nullable=False)]
>>> dshape_to_alchemy('{name: ?string, amount: ?int}')
[Column('name', Text(), table=None), Column('amount', Integer(), table=None)]
"""
if isinstance(dshape, str):
dshape = datashape.dshape(dshape)
if isinstance(dshape, Map):
return dshape_to_alchemy(dshape.key.measure, primary_key=primary_key)
if isinstance(dshape, Option):
return dshape_to_alchemy(dshape.ty, primary_key=primary_key)
if str(dshape) in types:
return types[str(dshape)]
if isinstance(dshape, datashape.Record):
return [
sa.Column(
name,
dshape_to_alchemy(getattr(typ, "ty", typ), primary_key=primary_key),
primary_key=name in primary_key,
nullable=isinstance(typ[0], Option),
)
for name, typ in dshape.parameters[0]
]
if isinstance(dshape, datashape.DataShape):
if isdimension(dshape[0]):
return dshape_to_alchemy(dshape[1], primary_key=primary_key)
else:
return dshape_to_alchemy(dshape[0], primary_key=primary_key)
if isinstance(dshape, datashape.String):
fixlen = dshape[0].fixlen
if fixlen is None:
return sa.TEXT
string_types = dict(U=sa.Unicode, A=sa.String)
assert dshape.encoding is not None
return string_types[dshape.encoding[0]](length=fixlen)
if isinstance(dshape, datashape.DateTime):
return sa.DATETIME(timezone=dshape.tz is not None)
if isinstance(dshape, datashape.Decimal):
return sa.NUMERIC(dshape.precision, dshape.scale)
raise NotImplementedError("No SQLAlchemy dtype match for datashape: %s" % dshape)
def rdd_to_sqlcontext(ctx, rdd, name=None, dshape=None, **kwargs):
""" Convert a normal PySpark RDD to a SparkSQL RDD or Spark DataFrame
Schema inferred by ds_to_sparksql. Can also specify it explicitly with
schema keyword argument.
"""
# TODO: assumes that we don't have e.g., 10 * 10 * {x: int, y: int}
if isdimension(dshape.parameters[0]):
dshape = dshape.measure
sql_schema = dshape_to_schema(dshape)
sdf = ctx.applySchema(rdd, sql_schema)
if name is None:
name = next(_names)
register_table(ctx, sdf, name=name)
ctx.cacheTable(name)
return sdf
def dshape_to_alchemy(dshape):
"""
>>> dshape_to_alchemy('int')
<class 'sqlalchemy.sql.sqltypes.Integer'>
>>> dshape_to_alchemy('string')
<class 'sqlalchemy.sql.sqltypes.Text'>
>>> dshape_to_alchemy('{name: string, amount: int}')
[Column('name', Text(), table=None, nullable=False), Column('amount', Integer(), table=None, nullable=False)]
>>> dshape_to_alchemy('{name: ?string, amount: ?int}')
[Column('name', Text(), table=None), Column('amount', Integer(), table=None)]
"""
if isinstance(dshape, str):
dshape = datashape.dshape(dshape)
if isinstance(dshape, Option):
return dshape_to_alchemy(dshape.ty)
if str(dshape) in types:
return types[str(dshape)]
if isinstance(dshape, datashape.Record):
return [
sa.Column(name,
dshape_to_alchemy(typ),
nullable=isinstance(typ[0], Option))
for name, typ in dshape.parameters[0]
]
if isinstance(dshape, datashape.DataShape):
if isdimension(dshape[0]):
return dshape_to_alchemy(dshape[1])
else:
return dshape_to_alchemy(dshape[0])
if isinstance(dshape, datashape.String):
if dshape[0].fixlen is None:
return sa.types.Text
if 'U' in dshape.encoding:
return sa.types.Unicode(length=dshape[0].fixlen)
if 'A' in dshape.encoding:
return sa.types.String(length=dshape[0].fixlen)
if isinstance(dshape, datashape.DateTime):
if dshape.tz:
return sa.types.DateTime(timezone=True)
else:
return sa.types.DateTime(timezone=False)
raise NotImplementedError("No SQLAlchemy dtype match for datashape: %s" %
dshape)
def deoption(ds):
"""
>>> deoption('int32')
ctype("int32")
>>> deoption('?int32')
ctype("int32")
"""
if isinstance(ds, str):
ds = dshape(ds)
if isinstance(ds, DataShape) and not isdimension(ds[0]):
return deoption(ds[0])
if isinstance(ds, Option):
return ds.ty
else:
return ds
def dshape_to_alchemy(dshape):
"""
>>> dshape_to_alchemy('int')
<class 'sqlalchemy.sql.sqltypes.Integer'>
>>> dshape_to_alchemy('string')
<class 'sqlalchemy.sql.sqltypes.Text'>
>>> dshape_to_alchemy('{name: string, amount: int}')
[Column('name', Text(), table=None, nullable=False), Column('amount', Integer(), table=None, nullable=False)]
>>> dshape_to_alchemy('{name: ?string, amount: ?int}')
[Column('name', Text(), table=None), Column('amount', Integer(), table=None)]
"""
if isinstance(dshape, str):
dshape = datashape.dshape(dshape)
if isinstance(dshape, Option):
return dshape_to_alchemy(dshape.ty)
if str(dshape) in types:
return types[str(dshape)]
if isinstance(dshape, datashape.Record):
return [sa.Column(name,
dshape_to_alchemy(typ),
nullable=isinstance(typ[0], Option))
for name, typ in dshape.parameters[0]]
if isinstance(dshape, datashape.DataShape):
if isdimension(dshape[0]):
return dshape_to_alchemy(dshape[1])
else:
return dshape_to_alchemy(dshape[0])
if isinstance(dshape, datashape.String):
fixlen = dshape[0].fixlen
if fixlen is None:
return sa.types.Text
string_types = dict(U=sa.types.Unicode, A=sa.types.String)
assert dshape.encoding is not None
return string_types[dshape.encoding[0]](length=fixlen)
if isinstance(dshape, datashape.DateTime):
if dshape.tz:
return sa.types.DateTime(timezone=True)
else:
return sa.types.DateTime(timezone=False)
raise NotImplementedError("No SQLAlchemy dtype match for datashape: %s"
% dshape)
def dshape_to_schema(ds):
"""Convert datashape to SparkSQL type system.
Examples
--------
>>> print(dshape_to_schema('int32')) # doctest: +SKIP
IntegerType
>>> print(dshape_to_schema('5 * int32') # doctest: +SKIP
ArrayType(IntegerType,false)
>>> print(dshape_to_schema('5 * ?int32')) # doctest: +SKIP
ArrayType(IntegerType,true)
>>> print(dshape_to_schema('{name: string, amount: int32}')) # doctest: +SKIP
StructType(List(StructField(name,StringType,false),StructField(amount,IntegerType,false) # doctest: +SKIP))
>>> print(dshape_to_schema('10 * {name: string, amount: ?int32}')) # doctest: +SKIP
ArrayType(StructType(List(StructField(name,StringType,false),StructField(amount,IntegerType,true))),false)
"""
if isinstance(ds, str):
return dshape_to_schema(dshape(ds))
if isinstance(ds, Tuple):
raise TypeError('Please provide a Record dshape for these column '
'types: %s' % (ds.dshapes,))
if isinstance(ds, Record):
return StructType([
StructField(name,
dshape_to_schema(deoption(typ)),
isinstance(typ, datashape.Option))
for name, typ in ds.fields])
if isinstance(ds, DataShape):
if isdimension(ds[0]):
elem = ds.subshape[0]
if isinstance(elem, DataShape) and len(elem) == 1:
elem = elem[0]
return ArrayType(dshape_to_schema(deoption(elem)),
isinstance(elem, Option))
else:
return dshape_to_schema(ds[0])
if ds in dshape_to_sparksql:
return dshape_to_sparksql[ds]
raise NotImplementedError()
def ordered_index(ind, ds):
""" Transform a named index into an ordered one
>>> ordered_index(1, '3 * int')
1
>>> ordered_index('name', '{name: string, amount: int}')
0
>>> ordered_index((0, 0), '3 * {x: int, y: int}')
(0, 0)
>>> ordered_index([0, 1], '3 * {x: int, y: int}')
[0, 1]
>>> ordered_index(([0, 1], 'x'), '3 * {x: int, y: int}')
([0, 1], 0)
>>> ordered_index((0, 'x'), '3 * {x: int, y: int}')
(0, 0)
>>> ordered_index((0, [0, 1]), '3 * {x: int, y: int}')
(0, [0, 1])
>>> ordered_index((0, ['x', 'y']), '3 * {x: int, y: int}')
(0, [0, 1])
"""
if isinstance(ds, _strtypes):
ds = dshape(ds)
if isinstance(ind, (int, slice)):
return ind
if isinstance(ind, list):
return [ordered_index(i, ds) for i in ind]
if isinstance(ind, _strtypes) and isinstance(ds[0], Record):
return ds[0].names.index(ind)
if isinstance(ind, tuple) and not ind:
return ()
if isdimension(ds[0]):
return (ind[0],) + tupleit(ordered_index(ind[1:], ds.subshape[0]))
if isinstance(ind, tuple):
return ((ordered_index(ind[0], ds),)
+ tupleit(ordered_index(ind[1:], ds.subshape[0])))
raise NotImplementedError("Rule for ind: %s, ds: %ds not found"
% (str(ind), str(ds)))
def ordered_index(ind, ds):
""" Transform a named index into an ordered one
>>> ordered_index(1, '3 * int')
1
>>> ordered_index('name', '{name: string, amount: int}')
0
>>> ordered_index((0, 0), '3 * {x: int, y: int}')
(0, 0)
>>> ordered_index([0, 1], '3 * {x: int, y: int}')
[0, 1]
>>> ordered_index(([0, 1], 'x'), '3 * {x: int, y: int}')
([0, 1], 0)
>>> ordered_index((0, 'x'), '3 * {x: int, y: int}')
(0, 0)
>>> ordered_index((0, [0, 1]), '3 * {x: int, y: int}')
(0, [0, 1])
>>> ordered_index((0, ['x', 'y']), '3 * {x: int, y: int}')
(0, [0, 1])
"""
if isinstance(ds, _strtypes):
ds = dshape(ds)
if isinstance(ind, (int, slice)):
return ind
if isinstance(ind, list):
return [ordered_index(i, ds) for i in ind]
if isinstance(ind, _strtypes) and isinstance(ds[0], Record):
return ds[0].names.index(ind)
if isinstance(ind, tuple) and not ind:
return ()
if isdimension(ds[0]):
return (ind[0], ) + tupleit(ordered_index(ind[1:], ds.subshape[0]))
if isinstance(ind, tuple):
return ((ordered_index(ind[0], ds), ) +
tupleit(ordered_index(ind[1:], ds.subshape[0])))
raise NotImplementedError("Rule for ind: %s, ds: %ds not found" %
(str(ind), str(ds)))
def __init__(self, descriptors):
assert all(isdimension(ddesc.dshape[0]) for ddesc in descriptors)
self.descriptors = descriptors
def __init__(self, descriptors):
assert all(isdimension(ddesc.dshape[0]) for ddesc in descriptors)
self.descriptors = descriptors
