def _determine_types(self):
column_data = OrderedDict()
self.columns = OrderedDict()
if hasattr(self.data.generator, 'sqla_columns'):
for col in self.data.generator.sqla_columns:
self.columns[col.name] = {'is_nullable': col.nullable,
'is_unique': col.unique,
'satype': col.type,
'pytype': col.pytype}
return
self.comments = {}
rowcount = 0
for row in self.data:
rowcount += 1
keys = row.keys()
for col_name in self.columns:
if col_name not in keys:
self.columns[col_name]['is_nullable'] = True
if not isinstance(row, OrderedDict):
keys = sorted(keys)
for k in keys:
v_raw = row[k]
if not th.is_scalar(v_raw):
v = str(v_raw)
self.comments[k] = 'nested values! example:\n%s' % \
pprint.pformat(v)
logging.warning('in %s: %s' % (k, self.comments[k]))
v = th.coerce_to_specific(v_raw)
if k not in self.columns:
self.columns[k] = {'sample_datum': v,
'str_length': len(str(v_raw)),
'is_nullable': not (rowcount == 1 and
v is not None and
str(v).strip()
),
'is_unique': set([v, ])}
else:
col = self.columns[k]
col['str_length'] = max(col['str_length'], len(str(v_raw)))
old_sample_datum = col.get('sample_datum')
col['sample_datum'] = th.best_representative(
col['sample_datum'], v)
if (v is None) or (not str(v).strip()):
col['is_nullable'] = True
if (col['is_unique'] != False):
if v in col['is_unique']:
col['is_unique'] = False
else:
col['is_unique'].add(v)
for col_name in self.columns:
col = self.columns[col_name]
self._fill_metadata_from_sample(col)
col['is_unique'] = bool(col['is_unique'])
def _determine_types(self, varying_length_text=False, uniques=False):
column_data = OrderedDict()
self.columns = OrderedDict()
self.comments = {}
rowcount = 0
for row in self.data:
rowcount += 1
keys = row.keys()
for col_name in self.columns:
if col_name not in keys:
self.columns[col_name]['is_nullable'] = True
if not isinstance(row, OrderedDict):
keys = sorted(keys)
for k in keys:
v = row[k]
if not th.is_scalar(v):
v = str(v)
self.comments[
k] = 'nested values! example:\n%s' % pprint.pformat(v)
logging.warning('in %s: %s' % (k, self.comments[k]))
v = th.coerce_to_specific(v)
if k not in self.columns:
self.columns[k] = {
'sample_datum': v,
'is_unique': True,
'is_nullable': not (rowcount == 1 and v is not None),
'is_unique': set([
v,
])
}
else:
col = self.columns[k]
col['sample_datum'] = th.best_representative(
col['sample_datum'], v)
if (v is None):
col['is_nullable'] = True
if (col['is_unique'] != False):
if v in col['is_unique']:
col['is_unique'] = False
else:
col['is_unique'].add(v)
for col_name in self.columns:
col = self.columns[col_name]
self._fill_metadata_from_sample(col)
col['is_unique'] = bool(col['is_unique'])
def _determine_types(self):
column_data = OrderedDict()
self.columns = OrderedDict()
self.comments = {}
rowcount = 0
for row in self.data:
rowcount += 1
keys = row.keys()
for col_name in self.columns:
if col_name not in keys:
self.columns[col_name]['is_nullable'] = True
if not isinstance(row, OrderedDict):
keys = sorted(keys)
for k in keys:
v = row[k]
if not th.is_scalar(v):
v = str(v)
self.comments[k] = 'nested values! example:\n%s' % \
pprint.pformat(v)
logging.warning('in %s: %s' % (k, self.comments[k]))
v = th.coerce_to_specific(v)
if k not in self.columns:
self.columns[k] = {'sample_datum': v,
'is_nullable': not (rowcount == 1 and
v is not None),
'is_unique': set([v, ])}
else:
col = self.columns[k]
col['sample_datum'] = th.best_representative(
col['sample_datum'], v)
if (v is None):
col['is_nullable'] = True
if (col['is_unique'] != False):
if v in col['is_unique']:
col['is_unique'] = False
else:
col['is_unique'].add(v)
for col_name in self.columns:
col = self.columns[col_name]
self._fill_metadata_from_sample(col)
col['is_unique'] = bool(col['is_unique'])
def _determine_types(self):
column_data = OrderedDict()
self.columns = OrderedDict()
if hasattr(self.data, 'generator') and hasattr(self.data.generator,
'sqla_columns'):
for col in self.data.generator.sqla_columns:
self.columns[col.name] = {
'is_nullable': col.nullable,
'is_unique': col.unique,
'satype': col.type,
'pytype': col.pytype
}
return
self.comments = {}
rowcount = 0
for row in self.data:
rowcount += 1
keys = row.keys()
for col_name in self.columns:
if col_name not in keys:
self.columns[col_name]['is_nullable'] = True
if not isinstance(row, OrderedDict):
keys = sorted(keys)
for k in keys:
v_raw = row[k]
if not th.is_scalar(v_raw):
v = str(v_raw)
self.comments[k] = 'nested values! example:\n%s' % \
pprint.pformat(v)
logging.warning('in %s: %s' % (k, self.comments[k]))
v = th.coerce_to_specific(v_raw)
if k not in self.columns:
self.columns[k] = {
'sample_datum':
v,
'str_length':
len(str(v_raw)),
'is_nullable':
not (rowcount == 1 and v is not None
and str(v).strip()),
'is_unique':
set([
v,
])
}
else:
col = self.columns[k]
col['str_length'] = max(col['str_length'], len(str(v_raw)))
old_sample_datum = col.get('sample_datum')
col['sample_datum'] = th.best_representative(
col['sample_datum'], v)
if (v is None) or (not str(v).strip()):
col['is_nullable'] = True
if (col['is_unique'] != False):
if v in col['is_unique']:
col['is_unique'] = False
else:
col['is_unique'].add(v)
for col_name in self.columns:
col = self.columns[col_name]
self._fill_metadata_from_sample(col)
col['is_unique'] = bool(col['is_unique'])
