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)