def guessdtype(table):
try:
import numpy as np
except ImportError as e:
raise UnsatisfiedDependency(e, dep_message)
else:
# get numpy to infer dtypes for each field individually
fields, table = iterpeek(table, 1)
cols = columns(table)
dtype = []
for f in fields:
a = np.array(cols[f]) # load into 1D array to get numpy to infer a dtype for the column
dtype.append((f, a.dtype))
return np.dtype(dtype)
def toarray(table, dtype=None, count=-1, sample=1000):
"""
Convenience function to load data from the given `table` into a numpy
structured array. E.g.::
>>> from petl import look
>>> from petlx.array import toarray
>>> look(table)
+-----------+-------+-------+
| 'foo' | 'bar' | 'baz' |
+===========+=======+=======+
| 'apples' | 1 | 2.5 |
+-----------+-------+-------+
| 'oranges' | 3 | 4.4 |
+-----------+-------+-------+
| 'pears' | 7 | 0.1 |
+-----------+-------+-------+
>>> a = toarray(table)
>>> a
array([('apples', 1, 2.5), ('oranges', 3, 4.4), ('pears', 7, 0.1)],
dtype=[('foo', '|S7'), ('bar', '<i8'), ('baz', '<f8')])
>>> a['foo']
array(['apples', 'oranges', 'pears'],
dtype='|S7')
>>> a['bar']
array([1, 3, 7])
>>> a['baz']
array([ 2.5, 4.4, 0.1])
>>> a['foo'][0]
'apples'
>>> a['bar'][1]
3
>>> a['baz'][2]
0.10000000000000001
If no datatype is specified, `sample` rows will be examined to infer an
appropriate datatype for each field.
The datatype can be specified as a string, e.g.:
>>> a = toarray(table, dtype='a4, i2, f4')
>>> a
array([('appl', 1, 2.5), ('oran', 3, 4.400000095367432),
('pear', 7, 0.10000000149011612)],
dtype=[('foo', '|S4'), ('bar', '<i2'), ('baz', '<f4')])
The datatype can also be partially specified, in which case datatypes will
be inferred for other fields, e.g.:
>>> a = toarray(table, dtype={'foo': 'a4'})
>>> a
array([('appl', 1, 2.5), ('oran', 3, 4.4), ('pear', 7, 0.1)],
dtype=[('foo', '|S4'), ('bar', '<i8'), ('baz', '<f8')])
"""
try:
import numpy as np
except ImportError as e:
raise UnsatisfiedDependency(e, dep_message)
else:
it = iter(table)
peek, it = iterpeek(it, sample)
fields = it.next()
if dtype is None:
dtype = guessdtype(peek)
elif isinstance(dtype, basestring):
# insert field names from source table
typestrings = [s.strip() for s in dtype.split(',')]
dtype = [(f, t) for f, t in zip(fields, typestrings)]
elif isinstance(dtype, dict) and ('names' not in dtype or 'formats' not in dtype):
# allow for partial specification of dtype
cols = columns(peek)
newdtype = {'names': [], 'formats': []}
for f in fields:
newdtype['names'].append(f)
if f in dtype and isinstance(dtype[f], tuple):
# assume fully specified
newdtype['formats'].append(dtype[f][0])
elif f not in dtype:
# not specified at all
a = np.array(cols[f])
newdtype['formats'].append(a.dtype)
else:
# assume directly specified, just need to add offset
newdtype['formats'].append(dtype[f])
dtype = newdtype
else:
pass # leave dtype as-is
it = (tuple(row) for row in it) # numpy is fussy about having tuples, need to make sure
sa = np.fromiter(it, dtype=dtype, count=count)
return sa
def toarray(table, dtype=None, count=-1, sample=1000):
"""
Convenience function to load data from the given `table` into a numpy
structured array. E.g.::
>>> from petl import look
>>> from petlx.array import toarray
>>> look(table)
+-----------+-------+-------+
| 'foo' | 'bar' | 'baz' |
+===========+=======+=======+
| 'apples' | 1 | 2.5 |
+-----------+-------+-------+
| 'oranges' | 3 | 4.4 |
+-----------+-------+-------+
| 'pears' | 7 | 0.1 |
+-----------+-------+-------+
>>> a = toarray(table)
>>> a
array([('apples', 1, 2.5), ('oranges', 3, 4.4), ('pears', 7, 0.1)],
dtype=[('foo', '|S7'), ('bar', '<i8'), ('baz', '<f8')])
>>> a['foo']
array(['apples', 'oranges', 'pears'],
dtype='|S7')
>>> a['bar']
array([1, 3, 7])
>>> a['baz']
array([ 2.5, 4.4, 0.1])
>>> a['foo'][0]
'apples'
>>> a['bar'][1]
3
>>> a['baz'][2]
0.10000000000000001
If no datatype is specified, `sample` rows will be examined to infer an
appropriate datatype for each field.
The datatype can be specified as a string, e.g.:
>>> a = toarray(table, dtype='a4, i2, f4')
>>> a
array([('appl', 1, 2.5), ('oran', 3, 4.400000095367432),
('pear', 7, 0.10000000149011612)],
dtype=[('foo', '|S4'), ('bar', '<i2'), ('baz', '<f4')])
The datatype can also be partially specified, in which case datatypes will
be inferred for other fields, e.g.:
>>> a = toarray(table, dtype={'foo': 'a4'})
>>> a
array([('appl', 1, 2.5), ('oran', 3, 4.4), ('pear', 7, 0.1)],
dtype=[('foo', '|S4'), ('bar', '<i8'), ('baz', '<f8')])
"""
try:
import numpy as np
except ImportError as e:
raise UnsatisfiedDependency(e, dep_message)
else:
it = iter(table)
peek, it = iterpeek(it, sample)
fields = it.next()
if dtype is None:
dtype = guessdtype(peek)
elif isinstance(dtype, basestring):
# insert field names from source table
typestrings = [s.strip() for s in dtype.split(',')]
dtype = [(f, t) for f, t in zip(fields, typestrings)]
elif (isinstance(dtype, dict)
and ('names' not in dtype or 'formats' not in dtype)):
# allow for partial specification of dtype
cols = columns(peek)
newdtype = {'names': [], 'formats': []}
for f in fields:
newdtype['names'].append(f)
if f in dtype and isinstance(dtype[f], tuple):
# assume fully specified
newdtype['formats'].append(dtype[f][0])
elif f not in dtype:
# not specified at all
a = np.array(cols[f])
newdtype['formats'].append(a.dtype)
else:
# assume directly specified, just need to add offset
newdtype['formats'].append(dtype[f])
dtype = newdtype
else:
pass # leave dtype as-is
# numpy is fussy about having tuples, need to make sure
it = (tuple(row) for row in it)
sa = np.fromiter(it, dtype=dtype, count=count)
return sa
def test_columns():
table = [['foo', 'bar'], ['a', 1], ['b', 2], ['b', 3]]
cols = columns(table)
eq_(['a', 'b', 'b'], cols['foo'])
eq_([1, 2, 3], cols['bar'])
