def _read_table(self, qtype = QTABLE, options = READER_CONFIGURATION):
if options.pandas:
self._buffer.skip() # ignore attributes
self._buffer.skip() # ignore dict type stamp
columns = self._read_object(options = options)
data = self._read_object(options = options)
odict = OrderedDict()
meta = MetaData(qtype = QTABLE)
for i in xrange(len(columns)):
if isinstance(data[i], str):
# convert character list (represented as string) to numpy representation
meta[columns[i]] = QSTRING
odict[columns[i]] = numpy.array(list(data[i]), dtype = numpy.str)
elif isinstance(data[i], (list, tuple)):
# convert character list (represented as string) to numpy representation
meta[columns[i]] = QGENERAL_LIST
odict[columns[i]] = numpy.array(list(data[i]))
else:
meta[columns[i]] = data[i].meta.qtype
odict[columns[i]] = data[i]
df = pandas.DataFrame(odict)
df.meta = meta
return df
else:
return QReader._read_table(self, qtype = qtype, options = options)
def _read_table(self, qtype=QTABLE, options=READER_CONFIGURATION):
if options.pandas:
self._buffer.skip() # ignore attributes
self._buffer.skip() # ignore dict type stamp
columns = self._read_object(options=options)
data = self._read_object(options=options)
odict = OrderedDict()
meta = MetaData(qtype=QTABLE)
for i in xrange(len(columns)):
if isinstance(data[i], str):
# convert character list (represented as string) to numpy representation
meta[columns[i]] = QSTRING
odict[columns[i]] = pandas.Series(list(data[i]),
dtype=numpy.str).replace(
' ', numpy.nan)
elif isinstance(data[i], (list, tuple)):
meta[columns[i]] = QGENERAL_LIST
tarray = numpy.ndarray(shape=len(data[i]),
dtype=numpy.dtype('O'))
for j in xrange(len(data[i])):
tarray[j] = data[i][j]
odict[columns[i]] = tarray
else:
meta[columns[i]] = data[i].meta.qtype
odict[columns[i]] = data[i]
df = pandas.DataFrame(odict)
df.meta = meta
return df
else:
return QReader._read_table(self, qtype=qtype, options=options)
def _read_table(self, qtype = QTABLE, options = READER_CONFIGURATION):
if options.pandas:
self._buffer.skip() # ignore attributes
self._buffer.skip() # ignore dict type stamp
columns = self._read_object(options = options)
data = self._read_object(options = options)
odict = OrderedDict()
meta = MetaData(qtype = QTABLE)
strcols = []
for i in xrange(len(columns)):
if isinstance(data[i], str):
# convert character list (represented as string) to numpy representation
meta[columns[i]] = QSTRING
odict[columns[i]] = pandas.Series(list(data[i]), dtype = numpy.str).replace(' ', numpy.nan)
elif isinstance(data[i], (list, tuple)):
meta[columns[i]] = QGENERAL_LIST
tarray = numpy.ndarray(shape = len(data[i]), dtype = numpy.dtype('O'))
for j in xrange(len(data[i])):
tarray[j] = data[i][j]
odict[columns[i]] = tarray
else:
meta[columns[i]] = data[i].meta.qtype
odict[columns[i]] = data[i]
df = pandas.DataFrame(odict)
df.meta = meta
for column in strcols:
# q uses the space character as the NULL value for strings
df[column] = df[column].replace([' ', ''], numpy.nan)
return df
else:
return QReader._read_table(self, qtype = qtype, options = options)
def _read_table(self, qtype = QTABLE):
if self._options.pandas:
self._buffer.skip() # ignore attributes
self._buffer.skip() # ignore dict type stamp
columns = self._read_object()
data = self._read_object()
odict = OrderedDict()
meta = MetaData(qtype = QTABLE)
for i in xrange(len(columns)):
if isinstance(data[i], str):
# convert character list (represented as string) to numpy representation
meta[columns[i]] = QSTRING
odict[columns[i]] = pandas.Series(list(data[i]), dtype = numpy.str).replace(' ', numpy.nan)
elif isinstance(data[i], (list, tuple)):
meta[columns[i]] = QGENERAL_LIST
tarray = numpy.ndarray(shape = len(data[i]), dtype = numpy.dtype('O'))
for j in xrange(len(data[i])):
tarray[j] = data[i][j]
odict[columns[i]] = tarray
else:
meta[columns[i]] = data[i].meta.qtype
odict[columns[i]] = data[i]
df = pandas.DataFrame(odict)
df.meta = meta
return df
else:
return QReader._read_table(self, qtype = qtype)
def _read_table(self, qtype=QTABLE):
if self._options.pandas:
self._buffer.skip() # ignore attributes
self._buffer.skip() # ignore dict type stamp
columns = self._read_object()
self._buffer.skip() # ignore generic list type indicator
data = QReader._read_general_list(self, qtype)
odict = OrderedDict()
meta = MetaData(qtype=QTABLE)
for i in range(len(columns)):
column_name = columns[i] if isinstance(
columns[i], str) else columns[i].decode("utf-8")
if isinstance(data[i], str):
# convert character list (represented as string) to numpy representation
meta[column_name] = QSTRING
odict[column_name] = pandas.Series(
list(data[i]),
dtype=numpy.str).replace(b' ', numpy.nan)
elif isinstance(data[i], bytes):
# convert character list (represented as string) to numpy representation
meta[column_name] = QSTRING
odict[column_name] = pandas.Series(list(data[i].decode()),
dtype=str).replace(
b' ', numpy.nan)
elif isinstance(data[i], (list, tuple)):
meta[column_name] = QGENERAL_LIST
tarray = numpy.ndarray(shape=len(data[i]),
dtype=numpy.dtype('O'))
for j in range(len(data[i])):
tarray[j] = data[i][j]
odict[column_name] = tarray
else:
meta[column_name] = data[i].meta.qtype
odict[column_name] = data[i]
df = pandas.DataFrame(odict)
df._metadata = ["meta"]
df.meta = meta
return df
else:
return QReader._read_table(self, qtype=qtype)
