本文整理汇总了Python中tablib.Dataset.load方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.load方法的具体用法？Python Dataset.load怎么用？Python Dataset.load使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tablib.Dataset的用法示例。
在下文中一共展示了Dataset.load方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: ExcelUploader
# 需要导入模块: from tablib import Dataset [as 别名]
# 或者: from tablib.Dataset import load [as 别名]
class ExcelUploader(BaseManager):
"""Manage general uploading."""
response = {'success': False, 'desc': ''}
def __init__(self, request, **kwargs):
"""Instantiate class ."""
self.request = request
self.request.user = extendUser(request.user)
self.model = kwargs['model']
self.compulsatory_fields = kwargs['compulsatory_fields']
self.optional_fields = kwargs['optional_fields']
self.template_location = kwargs['template_location']
self.template_zipped_name = kwargs['template_zipped_name']
self.form_input_name = kwargs['form_input_name']
def get_template(self):
"""Return the template used to import storm drain data."""
if self.request.user.is_epidemiologist_editor():
in_memory = StringIO()
zip = ZipFile(in_memory, "a")
# Add files to zip
for dirname, subdirs, files in os.walk(self.template_location):
for filename in files:
zip.write(os.path.join(dirname, filename), filename)
# close zip
zip.close()
# prepare response object
response = HttpResponse(content_type="application/zip")
response["Content-Disposition"] = ("attachment;filename=" +
self.template_zipped_name)
# write files into the response
in_memory.seek(0)
response.write(in_memory.read())
return response
else:
return self._end_unauthorized()
def _match_column_to_field(self, fields, file_headers, key):
"""Match each column name with the model fields."""
for field in fields:
header_position = 0
for header in file_headers:
if header == field:
self.read_dataset.headers[header_position] = key
else:
header_position = header_position + 1
def _get_missing_fields(self, file_headers):
"""Return all missing headers."""
# has_missing = False
missing_headers = []
for key, fieldVariants in (
self.compulsatory_fields.iteritems()
):
if not bool(set(fieldVariants) & set(file_headers)):
missing_headers.append(key)
else:
# find out the match between column name and model field
self._match_column_to_field(
fieldVariants,
file_headers,
key
)
return missing_headers
def _get_optional_fields(self, file_headers):
for key, fieldVariants in self.optional_fields.iteritems():
# find out the match between column name and model field
self._match_column_to_field(
fieldVariants,
file_headers,
key
)
def _prepare_dataset_to_import(self):
# Add column 'user_id'
self.read_dataset.insert_col(
0,
col=([self.request.user.id, ]
* self.read_dataset.height),
header="user_id"
)
booleans = []
numerics = []
for field in Epidemiology._meta.fields:
if field.get_internal_type() == 'NullBooleanField':
booleans.append(field.name)
elif field.get_internal_type() in [
'FloatField',
'DecimalField',
'IntegerField'
]:
numerics.append(field.name)
#.........这里部分代码省略.........
示例2: StormDrainUploader
# 需要导入模块: from tablib import Dataset [as 别名]
# 或者: from tablib.Dataset import load [as 别名]
class StormDrainUploader(StormDrainVersioningMixin, BaseManager):
"""Manage the storm drains data uploading."""
response = {'success': False, 'desc': ''}
def get_template(self):
"""Return the template used to import storm drain data."""
if self.request.user.is_authorized():
in_memory = StringIO()
zip = ZipFile(in_memory, "a")
# Add files to zip
for dirname, subdirs, files in os.walk(stormdrain_templates_path):
for filename in files:
zip.write(os.path.join(dirname, filename), filename)
# close zip
zip.close()
# prepare response object
response = HttpResponse(content_type="application/zip")
response["Content-Disposition"] = ("attachment;filename="
"mosquito_alert_template.zip")
# write files into the response
in_memory.seek(0)
response.write(in_memory.read())
return response
else:
return self._end_unauthorized()
def _match_column_to_field(self, fields, file_headers, key):
"""Match each column name with the model fields."""
for field in fields:
header_position = 0
for header in file_headers:
if header == field:
self.read_dataset.headers[header_position] = key
else:
header_position = header_position + 1
def _get_missing_fields(self, file_headers):
"""Return all missing headers."""
# has_missing = False
missing_headers = []
for key, fieldVariants in (
compulsatory_stormdrain_fields.iteritems()
):
if not bool(set(fieldVariants) & set(file_headers)):
missing_headers.append(key)
else:
# find out the match between column name and model field
self._match_column_to_field(
fieldVariants,
file_headers,
key
)
return missing_headers
def _get_optional_fields(self, file_headers):
for key, fieldVariants in optional_stormdrain_fields.iteritems():
# find out the match between column name and model field
self._match_column_to_field(
fieldVariants,
file_headers,
key
)
def _prepare_dataset_to_import(self):
# Add column 'user_id'
self.read_dataset.insert_col(
0,
col=([self.request.user.id, ]
* self.read_dataset.height),
header="user_id"
)
# Add column 'version'
self.last_ver = self._get_last_version_id(self.request)
self.read_dataset.insert_col(
0,
col=[self.last_ver+1, ]*self.read_dataset.height,
header="version"
)
# Define index position of headers
headers = {k: v for v, k in
enumerate(self.read_dataset.headers)}
# Add original lat, lon columns
self.read_dataset.append_col(
self.read_dataset.get_col(headers['lon']),
header="original_lon"
)
self.read_dataset.append_col(
self.read_dataset.get_col(headers['lat']),
header="original_lat"
)
booleans = []
numerics = []
for field in StormDrain._meta.fields:
if field.get_internal_type() == 'NullBooleanField':
booleans.append(field.name)
elif field.get_internal_type() in [
#.........这里部分代码省略.........