def testArrayNumpyLabelled(self):
input = {"a": []}
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
self.assertTrue((np.empty((1, 0)) == output[0]).all())
self.assertTrue((np.array(["a"]) == output[1]).all())
self.assertTrue(output[2] is None)
input = [{"a": 42}]
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
self.assertTrue((np.array([42]) == output[0]).all())
self.assertTrue(output[1] is None)
self.assertTrue((np.array([u"a"]) == output[2]).all())
input = [{"a": 42, "b": 31}, {"a": 24, "c": 99}, {"a": 2.4, "b": 78}]
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
expectedvals = np.array([42, 31, 24, 99, 2.4, 78], dtype=int).reshape((3, 2))
self.assertTrue((expectedvals == output[0]).all())
self.assertTrue(output[1] is None)
self.assertTrue((np.array([u"a", "b"]) == output[2]).all())
input = {1: {"a": 42, "b": 31}, 2: {"a": 24, "c": 99}, 3: {"a": 2.4, "b": 78}}
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
expectedvals = np.array([42, 31, 24, 99, 2.4, 78], dtype=int).reshape((3, 2))
self.assertTrue((expectedvals == output[0]).all())
self.assertTrue((np.array(["1", "2", "3"]) == output[1]).all())
self.assertTrue((np.array(["a", "b"]) == output[2]).all())
def testArrayNumpyLabelled(self):
input = {'a': []}
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
self.assertTrue((np.empty((1, 0)) == output[0]).all())
self.assertTrue((np.array(['a']) == output[1]).all())
self.assertTrue(output[2] is None)
input = [{'a': 42}]
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
self.assertTrue((np.array([42]) == output[0]).all())
self.assertTrue(output[1] is None)
self.assertTrue((np.array([u'a']) == output[2]).all())
input = [{'a': 42, 'b':31}, {'a': 24, 'c': 99}, {'a': 2.4, 'b': 78}]
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
expectedvals = np.array([42, 31, 24, 99, 2.4, 78], dtype=int).reshape((3,2))
self.assertTrue((expectedvals == output[0]).all())
self.assertTrue(output[1] is None)
self.assertTrue((np.array([u'a', 'b']) == output[2]).all())
input = {1: {'a': 42, 'b':31}, 2: {'a': 24, 'c': 99}, 3: {'a': 2.4, 'b': 78}}
output = ujson.loads(ujson.dumps(input), numpy=True, labelled=True)
expectedvals = np.array([42, 31, 24, 99, 2.4, 78], dtype=int).reshape((3,2))
self.assertTrue((expectedvals == output[0]).all())
self.assertTrue((np.array(['1','2','3']) == output[1]).all())
self.assertTrue((np.array(['a', 'b']) == output[2]).all())
def from_json(cls, json, orient="columns", dtype=None, numpy=True):
"""
Convert JSON string to DataFrame
Parameters
----------
json : The JSON string to parse.
orient : {'split', 'records', 'index', 'columns', 'values'},
default 'columns'
The format of the JSON string
split : dict like
{index -> [index], columns -> [columns], data -> [values]}
records : list like [{column -> value}, ... , {column -> value}]
index : dict like {index -> {column -> value}}
columns : dict like {column -> {index -> value}}
values : just the values array
dtype : dtype of the resulting DataFrame
nupmpy: direct decoding to numpy arrays. default True but falls back
to standard decoding if a problem occurs.
Returns
-------
result : DataFrame
"""
df = None
if dtype is not None and orient == "split":
numpy = False
if numpy:
try:
if orient == "columns":
args = loads(json, dtype=dtype, numpy=True, labelled=True)
if args:
args = (args[0].T, args[2], args[1])
df = DataFrame(*args)
elif orient == "split":
decoded = loads(json, dtype=dtype, numpy=True)
decoded = dict((str(k), v) for k, v in decoded.iteritems())
df = DataFrame(**decoded)
elif orient == "values":
df = DataFrame(loads(json, dtype=dtype, numpy=True))
else:
df = DataFrame(*loads(json, dtype=dtype, numpy=True,
labelled=True))
except ValueError:
numpy = False
if not numpy:
if orient == "columns":
df = DataFrame(loads(json), dtype=dtype)
elif orient == "split":
decoded = dict((str(k), v)
for k, v in loads(json).iteritems())
df = DataFrame(dtype=dtype, **decoded)
elif orient == "index":
df = DataFrame(loads(json), dtype=dtype).T
else:
df = DataFrame(loads(json), dtype=dtype)
return df
def from_json(cls, json, orient="columns", dtype=None, numpy=True):
"""
Convert JSON string to DataFrame
Parameters
----------
json : The JSON string to parse.
orient : {'split', 'records', 'index', 'columns', 'values'},
default 'columns'
The format of the JSON string
split : dict like
{index -> [index], columns -> [columns], data -> [values]}
records : list like [{column -> value}, ... , {column -> value}]
index : dict like {index -> {column -> value}}
columns : dict like {column -> {index -> value}}
values : just the values array
dtype : dtype of the resulting DataFrame
nupmpy: direct decoding to numpy arrays. default True but falls back
to standard decoding if a problem occurs.
Returns
-------
result : DataFrame
"""
df = None
if dtype is not None and orient == "split":
numpy = False
if numpy:
try:
if orient == "columns":
args = loads(json, dtype=dtype, numpy=True, labelled=True)
if args:
args = (args[0].T, args[2], args[1])
df = DataFrame(*args)
elif orient == "split":
decoded = loads(json, dtype=dtype, numpy=True)
decoded = dict((str(k), v) for k, v in decoded.iteritems())
df = DataFrame(**decoded)
elif orient == "values":
df = DataFrame(loads(json, dtype=dtype, numpy=True))
else:
df = DataFrame(
*loads(json, dtype=dtype, numpy=True, labelled=True))
except ValueError:
numpy = False
if not numpy:
if orient == "columns":
df = DataFrame(loads(json), dtype=dtype)
elif orient == "split":
decoded = dict((str(k), v) for k, v in loads(json).iteritems())
df = DataFrame(dtype=dtype, **decoded)
elif orient == "index":
df = DataFrame(loads(json), dtype=dtype).T
else:
df = DataFrame(loads(json), dtype=dtype)
return df
def from_json(cls, json, orient="index", dtype=None, numpy=True):
"""
Convert JSON string to Series
Parameters
----------
json : The JSON string to parse.
orient : {'split', 'records', 'index'}, default 'index'
The format of the JSON string
split : dict like
{index -> [index], name -> name, data -> [values]}
records : list like [value, ... , value]
index : dict like {index -> value}
dtype : dtype of the resulting Series
nupmpy: direct decoding to numpy arrays. default True but falls back
to standard decoding if a problem occurs.
Returns
-------
result : Series
"""
s = None
if dtype is not None and orient == "split":
numpy = False
if numpy:
try:
if orient == "split":
decoded = loads(json, dtype=dtype, numpy=True)
decoded = dict((str(k), v) for k, v in decoded.iteritems())
s = Series(**decoded)
elif orient == "columns" or orient == "index":
s = Series(*loads(json, dtype=dtype, numpy=True,
labelled=True))
else:
s = Series(loads(json, dtype=dtype, numpy=True))
except ValueError:
numpy = False
if not numpy:
if orient == "split":
decoded = dict((str(k), v)
for k, v in loads(json).iteritems())
s = Series(dtype=dtype, **decoded)
else:
s = Series(loads(json), dtype=dtype)
return s
def from_json(cls, json, orient="index", dtype=None, numpy=True):
"""
Convert JSON string to Series
Parameters
----------
json : The JSON string to parse.
orient : {'split', 'records', 'index'}, default 'index'
The format of the JSON string
split : dict like
{index -> [index], name -> name, data -> [values]}
records : list like [value, ... , value]
index : dict like {index -> value}
dtype : dtype of the resulting Series
nupmpy: direct decoding to numpy arrays. default True but falls back
to standard decoding if a problem occurs.
Returns
-------
result : Series
"""
s = None
if dtype is not None and orient == "split":
numpy = False
if numpy:
try:
if orient == "split":
decoded = loads(json, dtype=dtype, numpy=True)
decoded = dict((str(k), v) for k, v in decoded.iteritems())
s = Series(**decoded)
elif orient == "columns" or orient == "index":
s = Series(
*loads(json, dtype=dtype, numpy=True, labelled=True))
else:
s = Series(loads(json, dtype=dtype, numpy=True))
except ValueError:
numpy = False
if not numpy:
if orient == "split":
decoded = dict((str(k), v) for k, v in loads(json).iteritems())
s = Series(dtype=dtype, **decoded)
else:
s = Series(loads(json), dtype=dtype)
return s
def on_message(self, message):
request = json.loads(message)
handler = self._get_handler(request)
frame = SOURCES[request['source']]
resp = handler(request, frame)
return self._respond(resp, request['handler'])