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31,258 | quandl.errors.quandl_error | ColumnNotFound | null | class ColumnNotFound(QuandlError):
pass
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|
31,261 | quandl.model.data | Data | null | class Data(DataListOperation, DataMixin, ModelBase):
def __init__(self, data, **options):
self.meta = options['meta']
self._raw_data = Util.convert_to_dates(data)
# Optimization for when a list of data points are created from a
# dataset (via the model_list class)
if 'converted_column_names' in options.keys():
self._converted_column_names = options['converted_column_names']
# Need to override data_fields incase the way the Data class was populated
# that it did not contain a converted_column_names option passed in when it was created.
def data_fields(self):
if not self._converted_column_names and self.meta:
self._converted_column_names = Util.convert_column_names(self.meta)
return self._converted_column_names
def __getattr__(self, k):
if k[0] == '_' and k != '_raw_data':
raise AttributeError(k)
elif k in self.meta:
return self.meta[k]
# Convenience method for accessing individual data point columns by name.
elif k in self.data_fields():
return self._raw_data[self.data_fields().index(k)]
return super(Data, self).__getattr__(k)
| (data, **options) | [
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|
31,262 | quandl.model.model_base | __get_raw_data__ | null | def __get_raw_data__(self):
return self._raw_data
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|
31,263 | quandl.model.data | __getattr__ | null | def __getattr__(self, k):
if k[0] == '_' and k != '_raw_data':
raise AttributeError(k)
elif k in self.meta:
return self.meta[k]
# Convenience method for accessing individual data point columns by name.
elif k in self.data_fields():
return self._raw_data[self.data_fields().index(k)]
return super(Data, self).__getattr__(k)
| (self, k) | [
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|
31,264 | quandl.model.model_base | __getitem__ | null | def __getitem__(self, k):
return self.__get_raw_data__()[k]
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|
31,265 | quandl.model.data | __init__ | null | def __init__(self, data, **options):
self.meta = options['meta']
self._raw_data = Util.convert_to_dates(data)
# Optimization for when a list of data points are created from a
# dataset (via the model_list class)
if 'converted_column_names' in options.keys():
self._converted_column_names = options['converted_column_names']
| (self, data, **options) | [
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|
31,266 | quandl.model.data_mixin | _validate_col_index | null | def _validate_col_index(self, df, keep_column_indexes):
num_columns = len(df.columns)
for col_index in keep_column_indexes:
if col_index > num_columns or col_index < 1:
raise ColumnNotFound('Requested column index %s does not exist'
% col_index)
| (self, df, keep_column_indexes) | [
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|
31,267 | quandl.model.data | data_fields | null | def data_fields(self):
if not self._converted_column_names and self.meta:
self._converted_column_names = Util.convert_column_names(self.meta)
return self._converted_column_names
| (self) | [
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|
31,268 | quandl.model.data_mixin | to_csv | null | def to_csv(self):
return self.to_pandas().to_csv()
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|
31,269 | quandl.model.model_base | to_list | null | def to_list(self):
if isinstance(self.__get_raw_data__(), dict):
return list(self.__get_raw_data__().values())
return self.__get_raw_data__()
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|
31,270 | quandl.model.data_mixin | to_numpy | null | def to_numpy(self):
return self.to_pandas().to_records()
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|
31,271 | quandl.model.data_mixin | to_pandas | null | def to_pandas(self, keep_column_indexes=[]):
data = self.to_list()
# ensure pandas gets a list of lists
if data and isinstance(data, list) and not isinstance(data[0], list):
data = [data]
if 'columns' in self.meta.keys():
df = pd.DataFrame(data=data, columns=self.columns)
for index, column_type in enumerate(self.column_types):
if column_type == 'Date':
df[self.columns[index]] = df[self.columns[index]].apply(pd.to_datetime)
else:
df = pd.DataFrame(data=data, columns=self.column_names)
# ensure our first column of time series data is of pd.datetime
df[self.column_names[0]] = df[self.column_names[0]].apply(pd.to_datetime)
df.set_index(self.column_names[0], inplace=True)
# unfortunately to_records() cannot handle unicode in 2.7
df.index.name = str(df.index.name)
# keep_column_indexes are 0 based, 0 is the first column
if len(keep_column_indexes) > 0:
self._validate_col_index(df, keep_column_indexes)
# need to decrement all our indexes by 1 because
# Date is considered a column by our API, but in pandas,
# it is the index, so column 0 is the first column after Date index
keep_column_indexes = list([x - 1 for x in keep_column_indexes])
df = df.iloc[:, keep_column_indexes]
return df
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|
31,272 | quandl.model.database | Database | null | class Database(GetOperation, ListOperation, ModelBase):
BULK_CHUNK_SIZE = 512
@classmethod
def get_code_from_meta(cls, metadata):
return metadata['database_code']
def bulk_download_url(self, **options):
url = self._bulk_download_path()
url = ApiConfig.api_base + '/' + url
if 'params' not in options:
options['params'] = {}
if ApiConfig.api_key:
options['params']['api_key'] = ApiConfig.api_key
if ApiConfig.api_version:
options['params']['api_version'] = ApiConfig.api_version
if list(options.keys()):
url += '?' + urlencode(options['params'])
return url
def bulk_download_to_file(self, file_or_folder_path, **options):
if not isinstance(file_or_folder_path, str):
raise QuandlError(Message.ERROR_FOLDER_ISSUE)
path_url = self._bulk_download_path()
options['stream'] = True
r = Connection.request('get', path_url, **options)
file_path = file_or_folder_path
if os.path.isdir(file_or_folder_path):
file_path = file_or_folder_path + '/' + os.path.basename(urlparse(r.url).path)
with open(file_path, 'wb') as fd:
for chunk in r.iter_content(self.BULK_CHUNK_SIZE):
fd.write(chunk)
return file_path
def _bulk_download_path(self):
url = self.default_path() + '/data'
url = Util.constructed_path(url, {'id': self.code})
return url
def datasets(self, **options):
params = {'database_code': self.code, 'query': '', 'page': 1}
options = Util.merge_options('params', params, **options)
return quandl.model.dataset.Dataset.all(**options)
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|
31,273 | quandl.operations.get | __get_raw_data__ | null | def __get_raw_data__(self):
if self._raw_data:
return self._raw_data
cls = self.__class__
params = {'id': str(self.code)}
options = Util.merge_options('params', params, **self.options)
path = Util.constructed_path(cls.get_path(), options['params'])
r = Connection.request('get', path, **options)
response_data = r.json()
Util.convert_to_dates(response_data)
self._raw_data = response_data[singularize(cls.lookup_key())]
return self._raw_data
| (self) | [
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|
31,274 | quandl.model.model_base | __getattr__ | null | def __getattr__(self, k):
if k[0] == '_':
raise AttributeError(k)
elif k in self.__get_raw_data__():
return self.__get_raw_data__()[k]
else:
raise AttributeError(k)
| (self, k) | [
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|
31,276 | quandl.model.model_base | __init__ | null | def __init__(self, code, raw_data=None, **options):
self.code = code
self._raw_data = raw_data
self.options = options
| (self, code, raw_data=None, **options) | [
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|
31,277 | quandl.model.database | _bulk_download_path | null | def _bulk_download_path(self):
url = self.default_path() + '/data'
url = Util.constructed_path(url, {'id': self.code})
return url
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|
31,278 | quandl.model.database | bulk_download_to_file | null | def bulk_download_to_file(self, file_or_folder_path, **options):
if not isinstance(file_or_folder_path, str):
raise QuandlError(Message.ERROR_FOLDER_ISSUE)
path_url = self._bulk_download_path()
options['stream'] = True
r = Connection.request('get', path_url, **options)
file_path = file_or_folder_path
if os.path.isdir(file_or_folder_path):
file_path = file_or_folder_path + '/' + os.path.basename(urlparse(r.url).path)
with open(file_path, 'wb') as fd:
for chunk in r.iter_content(self.BULK_CHUNK_SIZE):
fd.write(chunk)
return file_path
| (self, file_or_folder_path, **options) | [
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|
31,279 | quandl.model.database | bulk_download_url | null | def bulk_download_url(self, **options):
url = self._bulk_download_path()
url = ApiConfig.api_base + '/' + url
if 'params' not in options:
options['params'] = {}
if ApiConfig.api_key:
options['params']['api_key'] = ApiConfig.api_key
if ApiConfig.api_version:
options['params']['api_version'] = ApiConfig.api_version
if list(options.keys()):
url += '?' + urlencode(options['params'])
return url
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|
31,280 | quandl.model.model_base | data_fields | null | def data_fields(self):
return list(self.__get_raw_data__().keys())
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|
31,281 | quandl.model.database | datasets | null | def datasets(self, **options):
params = {'database_code': self.code, 'query': '', 'page': 1}
options = Util.merge_options('params', params, **options)
return quandl.model.dataset.Dataset.all(**options)
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|
31,283 | quandl.model.dataset | Dataset | null | class Dataset(GetOperation, ListOperation, ModelBase):
@classmethod
def get_path(cls):
return "%s/metadata" % cls.default_path()
@classmethod
def get_code_from_meta(cls, metadata):
return "%s/%s" % (metadata['database_code'], metadata['dataset_code'])
def __init__(self, code, raw_data=None, **options):
ModelBase.__init__(self, code, raw_data)
parsed_code = self.code.split("/")
if len(parsed_code) < 2:
raise SyntaxError(Message.ERROR_INVALID_DATABASE_CODE_FORMAT)
self.database_code = parsed_code[0]
self.dataset_code = parsed_code[1]
self.options = options
def data(self, **options):
# handle_not_found_error if set to True will add an empty DataFrame
# for a non-existent dataset instead of raising an error
handle_not_found_error = options.pop('handle_not_found_error', False)
handle_column_not_found = options.pop('handle_column_not_found', False)
# default order to ascending, and respect whatever user passes in
params = {
'database_code': self.database_code,
'dataset_code': self.dataset_code,
'order': 'asc'
}
updated_options = Util.merge_options('params', params, **options)
try:
return Data.all(**updated_options)
except NotFoundError:
if handle_not_found_error:
return DataList(Data, [], {'column_names': [six.u('None'), six.u('Not Found')]})
raise
except ColumnNotFound:
if handle_column_not_found:
return DataList(Data, [], {'column_names': [six.u('None'), six.u('Not Found')]})
raise
def database(self):
return quandl.model.database.Database(self.database_code)
| (code, raw_data=None, **options) | [
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|
31,287 | quandl.model.dataset | __init__ | null | def __init__(self, code, raw_data=None, **options):
ModelBase.__init__(self, code, raw_data)
parsed_code = self.code.split("/")
if len(parsed_code) < 2:
raise SyntaxError(Message.ERROR_INVALID_DATABASE_CODE_FORMAT)
self.database_code = parsed_code[0]
self.dataset_code = parsed_code[1]
self.options = options
| (self, code, raw_data=None, **options) | [
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|
31,288 | quandl.model.dataset | data | null | def data(self, **options):
# handle_not_found_error if set to True will add an empty DataFrame
# for a non-existent dataset instead of raising an error
handle_not_found_error = options.pop('handle_not_found_error', False)
handle_column_not_found = options.pop('handle_column_not_found', False)
# default order to ascending, and respect whatever user passes in
params = {
'database_code': self.database_code,
'dataset_code': self.dataset_code,
'order': 'asc'
}
updated_options = Util.merge_options('params', params, **options)
try:
return Data.all(**updated_options)
except NotFoundError:
if handle_not_found_error:
return DataList(Data, [], {'column_names': [six.u('None'), six.u('Not Found')]})
raise
except ColumnNotFound:
if handle_column_not_found:
return DataList(Data, [], {'column_names': [six.u('None'), six.u('Not Found')]})
raise
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|
31,290 | quandl.model.dataset | database | null | def database(self):
return quandl.model.database.Database(self.database_code)
| (self) | [
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|
31,292 | quandl.model.datatable | Datatable | null | class Datatable(GetOperation, ListOperation, ModelBase):
BULK_CHUNK_SIZE = 16 * 1024
WAIT_GENERATION_INTERVAL = 30
@classmethod
def get_path(cls):
return "%s/metadata" % cls.default_path()
def data(self, **options):
if not options:
options = {'params': {}}
return Data.page(self, **options)
def download_file(self, file_or_folder_path, **options):
if not isinstance(file_or_folder_path, str):
raise QuandlError(Message.ERROR_FOLDER_ISSUE)
file_is_ready = False
while not file_is_ready:
file_is_ready = self._request_file_info(file_or_folder_path, params=options)
if not file_is_ready:
log.debug(Message.LONG_GENERATION_TIME)
sleep(self.WAIT_GENERATION_INTERVAL)
def _request_file_info(self, file_or_folder_path, **options):
url = self._download_request_path()
code_name = self.code
options['params']['qopts.export'] = 'true'
request_type = RequestType.get_request_type(url, **options)
updated_options = Util.convert_options(request_type=request_type, **options)
r = Connection.request(request_type, url, **updated_options)
response_data = r.json()
file_info = response_data['datatable_bulk_download']['file']
status = file_info['status']
if status == 'fresh':
file_link = file_info['link']
self._download_file_with_link(file_or_folder_path, file_link, code_name)
return True
else:
return False
def _download_file_with_link(self, file_or_folder_path, file_link, code_name):
file_path = file_or_folder_path
if os.path.isdir(file_or_folder_path):
file_path = os.path.join(file_or_folder_path,
'{}.{}'.format(code_name.replace('/', '_'), 'zip'))
res = urlopen(file_link)
with open(file_path, 'wb') as fd:
while True:
chunk = res.read(self.BULK_CHUNK_SIZE)
if not chunk:
break
fd.write(chunk)
log.debug(
"File path: %s",
file_path
)
def _download_request_path(self):
url = self.default_path()
url = Util.constructed_path(url, {'id': self.code})
url += '.json'
return url
| (code, raw_data=None, **options) | [
0.004022766370326281,
-0.056323446333408356,
-0.09263923764228821,
0.011268464848399162,
0.01923377811908722,
-0.0032724791672080755,
-0.05670095235109329,
0.005964547395706177,
-0.045224860310554504,
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-0.014194113202393055,
-0.041412077844142914,
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|
31,297 | quandl.model.datatable | _download_file_with_link | null | def _download_file_with_link(self, file_or_folder_path, file_link, code_name):
file_path = file_or_folder_path
if os.path.isdir(file_or_folder_path):
file_path = os.path.join(file_or_folder_path,
'{}.{}'.format(code_name.replace('/', '_'), 'zip'))
res = urlopen(file_link)
with open(file_path, 'wb') as fd:
while True:
chunk = res.read(self.BULK_CHUNK_SIZE)
if not chunk:
break
fd.write(chunk)
log.debug(
"File path: %s",
file_path
)
| (self, file_or_folder_path, file_link, code_name) | [
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|
31,298 | quandl.model.datatable | _download_request_path | null | def _download_request_path(self):
url = self.default_path()
url = Util.constructed_path(url, {'id': self.code})
url += '.json'
return url
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|
31,299 | quandl.model.datatable | _request_file_info | null | def _request_file_info(self, file_or_folder_path, **options):
url = self._download_request_path()
code_name = self.code
options['params']['qopts.export'] = 'true'
request_type = RequestType.get_request_type(url, **options)
updated_options = Util.convert_options(request_type=request_type, **options)
r = Connection.request(request_type, url, **updated_options)
response_data = r.json()
file_info = response_data['datatable_bulk_download']['file']
status = file_info['status']
if status == 'fresh':
file_link = file_info['link']
self._download_file_with_link(file_or_folder_path, file_link, code_name)
return True
else:
return False
| (self, file_or_folder_path, **options) | [
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|
31,300 | quandl.model.datatable | data | null | def data(self, **options):
if not options:
options = {'params': {}}
return Data.page(self, **options)
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|
31,302 | quandl.model.datatable | download_file | null | def download_file(self, file_or_folder_path, **options):
if not isinstance(file_or_folder_path, str):
raise QuandlError(Message.ERROR_FOLDER_ISSUE)
file_is_ready = False
while not file_is_ready:
file_is_ready = self._request_file_info(file_or_folder_path, params=options)
if not file_is_ready:
log.debug(Message.LONG_GENERATION_TIME)
sleep(self.WAIT_GENERATION_INTERVAL)
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|
31,304 | quandl.errors.quandl_error | ForbiddenError | null | class ForbiddenError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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|
31,307 | quandl.errors.quandl_error | InternalServerError | null | class InternalServerError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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|
31,310 | quandl.errors.quandl_error | InvalidDataError | null | class InvalidDataError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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0.014926753006875515,
0.018444523215293884
]
|
31,313 | quandl.errors.quandl_error | InvalidRequestError | null | class InvalidRequestError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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]
|
31,316 | quandl.errors.quandl_error | LimitExceededError | null | class LimitExceededError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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|
31,319 | quandl.model.merged_dataset | MergedDataset | null | class MergedDataset(ModelBase):
def __init__(self, dataset_codes, **options):
self.dataset_codes = dataset_codes
self._datasets = None
self._raw_data = None
self.options = options
@property
def column_names(self):
return self._merged_column_names_from(self.__dataset_objects__())
@property
def oldest_available_date(self):
return min(self._get_dataset_attribute('oldest_available_date'))
@property
def newest_available_date(self):
return max(self._get_dataset_attribute('newest_available_date'))
def data(self, **options):
# if there is only one column_index, use the api to fetch
# else fetch all the data and filter column indexes requested locally
dataset_data_list = [self._get_dataset_data(dataset, **options)
for dataset in self.__dataset_objects__()]
# build data frames and filter locally when necessary
data_frames = [dataset_data.to_pandas(
keep_column_indexes=self._keep_column_indexes(index))
for index, dataset_data in enumerate(dataset_data_list)]
merged_data_frame = pd.DataFrame()
for index, data_frame in enumerate(data_frames):
metadata = self.__dataset_objects__()[index]
# use code to prevent metadata api call
data_frame.rename(
columns=lambda x: self._rename_columns(metadata.code, x), inplace=True)
merged_data_frame = pd.merge(
merged_data_frame, data_frame, right_index=True, left_index=True, how='outer')
merged_data_metadata = self._build_data_meta(dataset_data_list, merged_data_frame)
# check if descending was explicitly set
# if set we need to sort in descending order
# since panda merged dataframe will
# by default sort everything in ascending
return MergedDataList(
Data, merged_data_frame, merged_data_metadata,
ascending=self._order_is_ascending(**options))
# for MergeDataset data calls
def _get_dataset_data(self, dataset, **options):
updated_options = options
# if we have only one column index, let the api
# handle the column filtering since the api supports this
if len(dataset.requested_column_indexes) == 1:
params = {'column_index': dataset.requested_column_indexes[0]}
# only change the options per request
updated_options = options.copy()
updated_options = Util.merge_options('params', params, **updated_options)
return dataset.data(**updated_options)
def _build_data_meta(self, dataset_data_list, df):
merged_data_metadata = {}
# for sanity check if list has items
if dataset_data_list:
# meta should be the same for every individual Dataset
# request, just take the first one
merged_data_metadata = dataset_data_list[0].meta.copy()
# set the start_date and end_date to
# the actual values we got back from data
num_rows = len(df.index)
if num_rows > 0:
merged_data_metadata['start_date'] = df.index[0].date()
merged_data_metadata['end_date'] = df.index[num_rows - 1].date()
# remove column_index if it exists because this would be per request data
merged_data_metadata.pop('column_index', None)
# don't use self.column_names to prevent metadata api call
# instead, get the column_names from the dataset_data_objects
merged_data_metadata['column_names'] = self._merged_column_names_from(dataset_data_list)
return merged_data_metadata
def _keep_column_indexes(self, index):
# no need to filter if we only have one column_index
# since leveraged the server to do the filtering
col_index = self.__dataset_objects__()[index].requested_column_indexes
if len(self.__dataset_objects__()[index].requested_column_indexes) == 1:
# empty array for no filtering
col_index = []
return col_index
def _rename_columns(self, code, original_column_name):
return code + ' - ' + original_column_name
def _get_dataset_attribute(self, k):
elements = []
for dataset in self.__dataset_objects__():
elements.append(dataset.__get_raw_data__()[k])
return list(unique_everseen(elements))
def _order_is_ascending(self, **options):
return not (self._in_query_param('order', **options) and
options['params']['order'] == 'desc')
def _in_query_param(self, name, **options):
return ('params' in options and
name in options['params'])
# can take in a list of dataset_objects
# or a list of dataset_data_objects
def _merged_column_names_from(self, dataset_list):
elements = []
for idx_dataset, dataset in enumerate(dataset_list):
# require getting the code from the dataset object always
code = self.__dataset_objects__()[idx_dataset].code
for index, column_name in enumerate(dataset.column_names):
# only include column names that are not filtered out
# by specification of the column_indexes list
if self._include_column(dataset, index):
# first index is the date, don't modify the date name
if index > 0:
elements.append(self._rename_columns(code, column_name))
else:
elements.append(column_name)
return list(unique_everseen(elements))
def _include_column(self, dataset_metadata, column_index):
# non-pandas/dataframe:
# keep column 0 around because we want to keep Date
if (hasattr(dataset_metadata, 'requested_column_indexes') and
len(dataset_metadata.requested_column_indexes) > 0 and
column_index != 0):
return column_index in dataset_metadata.requested_column_indexes
return True
def _initialize_raw_data(self):
datasets = self.__dataset_objects__()
self._raw_data = {}
if not datasets:
return self._raw_data
self._raw_data = datasets[0].__get_raw_data__().copy()
for k, v in list(self._raw_data.items()):
self._raw_data[k] = getattr(self, k)
return self._raw_data
def _build_dataset_object(self, dataset_code, **options):
options_copy = options.copy()
# data_codes are tuples
# e.g., ('WIKI/AAPL', {'column_index": [1,2]})
# or strings
# e.g., 'NSE/OIL'
code = self._get_request_dataset_code(dataset_code)
dataset = Dataset(code, None, **options_copy)
# save column_index param requested dynamically
# used later on to determine:
# if column_index is an array, fetch all data and use locally to filter columns
# if column_index is an empty array, fetch all data and don't filter columns
dataset.requested_column_indexes = self._get_req_dataset_col_indexes(dataset_code, code)
return dataset
def _get_req_dataset_col_indexes(self, dataset_code, code_str):
# ensure if column_index dict is specified, value is a list
params = self._get_request_params(dataset_code)
if 'column_index' in params:
column_index = params['column_index']
if not isinstance(column_index, list):
raise ValueError(
Message.ERROR_COLUMN_INDEX_LIST % code_str)
return column_index
# default, no column indexes to filter
return []
def _get_request_dataset_code(self, dataset_code):
if isinstance(dataset_code, tuple):
return dataset_code[0]
elif isinstance(dataset_code, string_types):
return dataset_code
else:
raise ValueError(Message.ERROR_ARGUMENTS_LIST_FORMAT)
def _get_request_params(self, dataset_code):
if isinstance(dataset_code, tuple):
| (dataset_codes, **options) | [
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|
31,320 | quandl.model.merged_dataset | __dataset_objects__ | null | def __dataset_objects__(self):
if self._datasets:
return self._datasets
if not isinstance(self.dataset_codes, list):
raise ValueError('dataset codes must be specified in a list')
# column_index is handled by individual dataset get's
if 'params' in self.options:
self.options['params'].pop("column_index", None)
self._datasets = list([self._build_dataset_object(dataset_code, **self.options)
for dataset_code in self.dataset_codes])
return self._datasets
| (self) | [
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|
31,321 | quandl.model.merged_dataset | __get_raw_data__ | null | def __get_raw_data__(self):
if self._raw_data is None:
self._initialize_raw_data()
return ModelBase.__get_raw_data__(self)
| (self) | [
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|
31,322 | quandl.model.merged_dataset | __getattr__ | null | def __getattr__(self, k):
if k[0] == '_' and k != '_raw_data':
raise AttributeError(k)
elif hasattr(MergedDataset, k):
return super(MergedDataset, self).__getattr__(k)
elif k in self.__dataset_objects__()[0].__get_raw_data__():
return self._get_dataset_attribute(k)
return super(MergedDataset, self).__getattr__(k)
| (self, k) | [
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|
31,324 | quandl.model.merged_dataset | __init__ | null | def __init__(self, dataset_codes, **options):
self.dataset_codes = dataset_codes
self._datasets = None
self._raw_data = None
self.options = options
| (self, dataset_codes, **options) | [
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|
31,325 | quandl.model.merged_dataset | _build_data_meta | null | def _build_data_meta(self, dataset_data_list, df):
merged_data_metadata = {}
# for sanity check if list has items
if dataset_data_list:
# meta should be the same for every individual Dataset
# request, just take the first one
merged_data_metadata = dataset_data_list[0].meta.copy()
# set the start_date and end_date to
# the actual values we got back from data
num_rows = len(df.index)
if num_rows > 0:
merged_data_metadata['start_date'] = df.index[0].date()
merged_data_metadata['end_date'] = df.index[num_rows - 1].date()
# remove column_index if it exists because this would be per request data
merged_data_metadata.pop('column_index', None)
# don't use self.column_names to prevent metadata api call
# instead, get the column_names from the dataset_data_objects
merged_data_metadata['column_names'] = self._merged_column_names_from(dataset_data_list)
return merged_data_metadata
| (self, dataset_data_list, df) | [
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|
31,326 | quandl.model.merged_dataset | _build_dataset_object | null | def _build_dataset_object(self, dataset_code, **options):
options_copy = options.copy()
# data_codes are tuples
# e.g., ('WIKI/AAPL', {'column_index": [1,2]})
# or strings
# e.g., 'NSE/OIL'
code = self._get_request_dataset_code(dataset_code)
dataset = Dataset(code, None, **options_copy)
# save column_index param requested dynamically
# used later on to determine:
# if column_index is an array, fetch all data and use locally to filter columns
# if column_index is an empty array, fetch all data and don't filter columns
dataset.requested_column_indexes = self._get_req_dataset_col_indexes(dataset_code, code)
return dataset
| (self, dataset_code, **options) | [
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|
31,327 | quandl.model.merged_dataset | _get_dataset_attribute | null | def _get_dataset_attribute(self, k):
elements = []
for dataset in self.__dataset_objects__():
elements.append(dataset.__get_raw_data__()[k])
return list(unique_everseen(elements))
| (self, k) | [
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|
31,328 | quandl.model.merged_dataset | _get_dataset_data | null | def _get_dataset_data(self, dataset, **options):
updated_options = options
# if we have only one column index, let the api
# handle the column filtering since the api supports this
if len(dataset.requested_column_indexes) == 1:
params = {'column_index': dataset.requested_column_indexes[0]}
# only change the options per request
updated_options = options.copy()
updated_options = Util.merge_options('params', params, **updated_options)
return dataset.data(**updated_options)
| (self, dataset, **options) | [
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|
31,329 | quandl.model.merged_dataset | _get_req_dataset_col_indexes | null | def _get_req_dataset_col_indexes(self, dataset_code, code_str):
# ensure if column_index dict is specified, value is a list
params = self._get_request_params(dataset_code)
if 'column_index' in params:
column_index = params['column_index']
if not isinstance(column_index, list):
raise ValueError(
Message.ERROR_COLUMN_INDEX_LIST % code_str)
return column_index
# default, no column indexes to filter
return []
| (self, dataset_code, code_str) | [
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|
31,330 | quandl.model.merged_dataset | _get_request_dataset_code | null | def _get_request_dataset_code(self, dataset_code):
if isinstance(dataset_code, tuple):
return dataset_code[0]
elif isinstance(dataset_code, string_types):
return dataset_code
else:
raise ValueError(Message.ERROR_ARGUMENTS_LIST_FORMAT)
| (self, dataset_code) | [
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|
31,331 | quandl.model.merged_dataset | _get_request_params | null | def _get_request_params(self, dataset_code):
if isinstance(dataset_code, tuple):
return dataset_code[1]
return {}
| (self, dataset_code) | [
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|
31,332 | quandl.model.merged_dataset | _in_query_param | null | def _in_query_param(self, name, **options):
return ('params' in options and
name in options['params'])
| (self, name, **options) | [
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|
31,333 | quandl.model.merged_dataset | _include_column | null | def _include_column(self, dataset_metadata, column_index):
# non-pandas/dataframe:
# keep column 0 around because we want to keep Date
if (hasattr(dataset_metadata, 'requested_column_indexes') and
len(dataset_metadata.requested_column_indexes) > 0 and
column_index != 0):
return column_index in dataset_metadata.requested_column_indexes
return True
| (self, dataset_metadata, column_index) | [
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|
31,334 | quandl.model.merged_dataset | _initialize_raw_data | null | def _initialize_raw_data(self):
datasets = self.__dataset_objects__()
self._raw_data = {}
if not datasets:
return self._raw_data
self._raw_data = datasets[0].__get_raw_data__().copy()
for k, v in list(self._raw_data.items()):
self._raw_data[k] = getattr(self, k)
return self._raw_data
| (self) | [
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|
31,335 | quandl.model.merged_dataset | _keep_column_indexes | null | def _keep_column_indexes(self, index):
# no need to filter if we only have one column_index
# since leveraged the server to do the filtering
col_index = self.__dataset_objects__()[index].requested_column_indexes
if len(self.__dataset_objects__()[index].requested_column_indexes) == 1:
# empty array for no filtering
col_index = []
return col_index
| (self, index) | [
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|
31,336 | quandl.model.merged_dataset | _merged_column_names_from | null | def _merged_column_names_from(self, dataset_list):
elements = []
for idx_dataset, dataset in enumerate(dataset_list):
# require getting the code from the dataset object always
code = self.__dataset_objects__()[idx_dataset].code
for index, column_name in enumerate(dataset.column_names):
# only include column names that are not filtered out
# by specification of the column_indexes list
if self._include_column(dataset, index):
# first index is the date, don't modify the date name
if index > 0:
elements.append(self._rename_columns(code, column_name))
else:
elements.append(column_name)
return list(unique_everseen(elements))
| (self, dataset_list) | [
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|
31,337 | quandl.model.merged_dataset | _order_is_ascending | null | def _order_is_ascending(self, **options):
return not (self._in_query_param('order', **options) and
options['params']['order'] == 'desc')
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|
31,338 | quandl.model.merged_dataset | _rename_columns | null | def _rename_columns(self, code, original_column_name):
return code + ' - ' + original_column_name
| (self, code, original_column_name) | [
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|
31,339 | quandl.model.merged_dataset | data | null | def data(self, **options):
# if there is only one column_index, use the api to fetch
# else fetch all the data and filter column indexes requested locally
dataset_data_list = [self._get_dataset_data(dataset, **options)
for dataset in self.__dataset_objects__()]
# build data frames and filter locally when necessary
data_frames = [dataset_data.to_pandas(
keep_column_indexes=self._keep_column_indexes(index))
for index, dataset_data in enumerate(dataset_data_list)]
merged_data_frame = pd.DataFrame()
for index, data_frame in enumerate(data_frames):
metadata = self.__dataset_objects__()[index]
# use code to prevent metadata api call
data_frame.rename(
columns=lambda x: self._rename_columns(metadata.code, x), inplace=True)
merged_data_frame = pd.merge(
merged_data_frame, data_frame, right_index=True, left_index=True, how='outer')
merged_data_metadata = self._build_data_meta(dataset_data_list, merged_data_frame)
# check if descending was explicitly set
# if set we need to sort in descending order
# since panda merged dataframe will
# by default sort everything in ascending
return MergedDataList(
Data, merged_data_frame, merged_data_metadata,
ascending=self._order_is_ascending(**options))
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|
31,342 | quandl.errors.quandl_error | NotFoundError | null | class NotFoundError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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|
31,345 | quandl.model.point_in_time | PointInTime | null | class PointInTime(GetOperation, ListOperation, ModelBase):
def data(self, **options):
if not options:
options = {'params': {}}
return Data.page(self, **options)
def default_path(self):
return "%s/:id/%s" % (self.lookup_key(), self.pit_url(),)
def lookup_key(self):
return 'pit'
def pit_url(self):
interval = self.options['pit']['interval']
if interval in ['asofdate']:
if 'date' not in self.options['pit'].keys():
date_replace = date.today()
else:
date_replace = self.options['pit']['date']
return "%s/%s" % (interval, date_replace, )
else:
start_date = self.options['pit']['start_date']
end_date = self.options['pit']['end_date']
if interval == 'between':
return "%s/%s/%s" % (interval, start_date, end_date, )
else:
return "%s/%s/to/%s" % (interval, start_date, end_date, )
| (code, raw_data=None, **options) | [
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|
31,352 | quandl.model.point_in_time | default_path | null | def default_path(self):
return "%s/:id/%s" % (self.lookup_key(), self.pit_url(),)
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|
31,353 | quandl.model.point_in_time | lookup_key | null | def lookup_key(self):
return 'pit'
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|
31,354 | quandl.model.point_in_time | pit_url | null | def pit_url(self):
interval = self.options['pit']['interval']
if interval in ['asofdate']:
if 'date' not in self.options['pit'].keys():
date_replace = date.today()
else:
date_replace = self.options['pit']['date']
return "%s/%s" % (interval, date_replace, )
else:
start_date = self.options['pit']['start_date']
end_date = self.options['pit']['end_date']
if interval == 'between':
return "%s/%s/%s" % (interval, start_date, end_date, )
else:
return "%s/%s/to/%s" % (interval, start_date, end_date, )
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|
31,356 | quandl.errors.quandl_error | QuandlError | null | class QuandlError(RuntimeError):
GENERIC_ERROR_MESSAGE = 'Something went wrong. Please try again. \
If you continue to have problems, please contact us at [email protected].'
def __init__(self, quandl_message=None, http_status=None, http_body=None, http_headers=None,
quandl_error_code=None, response_data=None):
self.http_status = http_status
self.http_body = http_body
self.http_headers = http_headers if http_headers is not None else {}
self.quandl_error_code = quandl_error_code
self.quandl_message = quandl_message if quandl_message is not None \
else self.GENERIC_ERROR_MESSAGE
self.response_data = response_data
def __str__(self):
if self.http_status is None:
status_string = ''
else:
status_string = "(Status %(http_status)s) " % {"http_status": self.http_status}
if self.quandl_error_code is None:
quandl_error_string = ''
else:
quandl_error_string = "(Quandl Error %(quandl_error_code)s) " % {
"quandl_error_code": self.quandl_error_code}
return "%(ss)s%(qes)s%(qm)s" % {
"ss": status_string, "qes": quandl_error_string, "qm": self.quandl_message
}
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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|
31,359 | quandl.errors.quandl_error | ServiceUnavailableError | null | class ServiceUnavailableError(QuandlError):
pass
| (quandl_message=None, http_status=None, http_body=None, http_headers=None, quandl_error_code=None, response_data=None) | [
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]
|
31,363 | quandl.bulkdownload | bulkdownload | Downloads an entire database.
:param str database: The database code to download
:param str filename: The filename for the download. If not specified, will download to the current working directory
:param str api_key: Most databases require api_key for bulk download
:param str download_type: 'partial' or 'complete'. See: https://www.quandl.com/docs/api#database-metadata
| def bulkdownload(database, **kwargs):
"""Downloads an entire database.
:param str database: The database code to download
:param str filename: The filename for the download. \
If not specified, will download to the current working directory
:param str api_key: Most databases require api_key for bulk download
:param str download_type: 'partial' or 'complete'. \
See: https://www.quandl.com/docs/api#database-metadata
"""
# discourage users from using authtoken
if 'authtoken' in kwargs:
raise InvalidRequestError(Message.ERROR_AUTHTOKEN_NOT_SUPPORTED)
ApiKeyUtil.init_api_key_from_args(kwargs)
filename = kwargs.pop('filename', '.')
return Database(database).bulk_download_to_file(filename, params=kwargs)
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|
31,366 | quandl.export_table | export_table | Downloads an entire table as a zip file.
:param str datatable_code: The datatable code to download, such as MER/F1
:param str filename: The filename for the download. If not specified, will download to the current working directory
:param str api_key: Most databases require api_key for bulk download
| def export_table(datatable_code, **kwargs):
"""Downloads an entire table as a zip file.
:param str datatable_code: The datatable code to download, such as MER/F1
:param str filename: The filename for the download. \
If not specified, will download to the current working directory
:param str api_key: Most databases require api_key for bulk download
"""
# discourage users from using authtoken
if 'authtoken' in kwargs:
raise InvalidRequestError(Message.ERROR_AUTHTOKEN_NOT_SUPPORTED)
ApiKeyUtil.init_api_key_from_args(kwargs)
filename = kwargs.pop('filename', '.')
return Datatable(datatable_code).download_file(filename, **kwargs)
| (datatable_code, **kwargs) | [
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|
31,367 | quandl.get | get | Return dataframe of requested dataset from Quandl.
:param dataset: str or list, depending on single dataset usage or multiset usage
Dataset codes are available on the Quandl website
:param str api_key: Downloads are limited to 50 unless api_key is specified
:param str start_date, end_date: Optional datefilers, otherwise entire
dataset is returned
:param str collapse: Options are daily, weekly, monthly, quarterly, annual
:param str transform: options are diff, rdiff, cumul, and normalize
:param int rows: Number of rows which will be returned
:param str order: options are asc, desc. Default: `asc`
:param str returns: specify what format you wish your dataset returned as,
either `numpy` for a numpy ndarray or `pandas`. Default: `pandas`
:returns: :class:`pandas.DataFrame` or :class:`numpy.ndarray`
Note that Pandas expects timeseries data to be sorted ascending for most
timeseries functionality to work.
Any other `kwargs` passed to `get` are sent as field/value params to Quandl
with no interference.
| def get(dataset, **kwargs):
"""Return dataframe of requested dataset from Quandl.
:param dataset: str or list, depending on single dataset usage or multiset usage
Dataset codes are available on the Quandl website
:param str api_key: Downloads are limited to 50 unless api_key is specified
:param str start_date, end_date: Optional datefilers, otherwise entire
dataset is returned
:param str collapse: Options are daily, weekly, monthly, quarterly, annual
:param str transform: options are diff, rdiff, cumul, and normalize
:param int rows: Number of rows which will be returned
:param str order: options are asc, desc. Default: `asc`
:param str returns: specify what format you wish your dataset returned as,
either `numpy` for a numpy ndarray or `pandas`. Default: `pandas`
:returns: :class:`pandas.DataFrame` or :class:`numpy.ndarray`
Note that Pandas expects timeseries data to be sorted ascending for most
timeseries functionality to work.
Any other `kwargs` passed to `get` are sent as field/value params to Quandl
with no interference.
"""
_convert_params_to_v3(kwargs)
data_format = kwargs.pop('returns', 'pandas')
ApiKeyUtil.init_api_key_from_args(kwargs)
# Check whether dataset is given as a string
# (for a single dataset) or an array (for a multiset call)
# Unicode String
if isinstance(dataset, string_types):
dataset_args = _parse_dataset_code(dataset)
if dataset_args['column_index'] is not None:
kwargs.update({'column_index': dataset_args['column_index']})
data = Dataset(dataset_args['code']).data(params=kwargs, handle_column_not_found=True)
# Array
elif isinstance(dataset, list):
args = _build_merged_dataset_args(dataset)
# handle_not_found_error if set to True will add an empty DataFrame
# for a non-existent dataset instead of raising an error
data = MergedDataset(args).data(params=kwargs,
handle_not_found_error=True,
handle_column_not_found=True)
# If wrong format
else:
raise InvalidRequestError(Message.ERROR_DATASET_FORMAT)
if data_format == 'numpy':
return data.to_numpy()
return data.to_pandas()
| (dataset, **kwargs) | [
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|
31,368 | quandl.get_point_in_time | get_point_in_time | null | def get_point_in_time(datatable_code, **options):
validate_pit_options(options)
pit_options = {}
# Remove the PIT params/keys from the options to not send it as a query params
for k in ['interval', 'date', 'start_date', 'end_date']:
if k in options.keys():
pit_options[k] = options.pop(k)
if 'paginate' in options.keys():
paginate = options.pop('paginate')
else:
paginate = None
data = None
page_count = 0
while True:
next_options = copy.deepcopy(options)
next_data = PointInTime(datatable_code, pit=pit_options).data(params=next_options)
if data is None:
data = next_data
else:
data.extend(next_data)
if page_count >= ApiConfig.page_limit:
raise LimitExceededError(
Message.WARN_DATA_LIMIT_EXCEEDED % (datatable_code,
ApiConfig.api_key
)
)
next_cursor_id = next_data.meta['next_cursor_id']
if next_cursor_id is None:
break
elif paginate is not True and next_cursor_id is not None:
warnings.warn(Message.WARN_PAGE_LIMIT_EXCEEDED, UserWarning)
break
page_count = page_count + 1
options['qopts.cursor_id'] = next_cursor_id
return data.to_pandas()
| (datatable_code, **options) | [
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|
31,369 | quandl.get_table | get_table | null | def get_table(datatable_code, **options):
if 'paginate' in options.keys():
paginate = options.pop('paginate')
else:
paginate = None
data = None
page_count = 0
while True:
next_options = copy.deepcopy(options)
next_data = Datatable(datatable_code).data(params=next_options)
if data is None:
data = next_data
else:
data.extend(next_data)
if page_count >= ApiConfig.page_limit:
raise LimitExceededError(
Message.WARN_DATA_LIMIT_EXCEEDED % (datatable_code,
ApiConfig.api_key
)
)
next_cursor_id = next_data.meta['next_cursor_id']
if next_cursor_id is None:
break
elif paginate is not True and next_cursor_id is not None:
warnings.warn(Message.WARN_PAGE_LIMIT_EXCEEDED, UserWarning)
break
page_count = page_count + 1
options['qopts.cursor_id'] = next_cursor_id
return data.to_pandas()
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|
31,373 | quandl.api_config | read_key | null | def read_key(filename=None):
if filename is None:
filename = os.path.join(os.path.expanduser('~'), '.quandl_apikey')
with open(filename, 'r') as f:
apikey = f.read()
if not apikey:
raise ValueError("File '{:s}' is empty.".format(filename))
ApiConfig.api_key = apikey
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|
31,374 | quandl.api_config | save_key | null | def save_key(apikey, filename=None):
if filename is None:
filename = os.path.join(os.path.expanduser('~'), '.quandl_apikey')
fileptr = open(filename, 'w')
fileptr.write(apikey)
fileptr.close()
ApiConfig.api_key = apikey
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|
31,378 | prefect_sqlalchemy.credentials | AsyncDriver |
Known dialects with their corresponding async drivers.
Attributes:
POSTGRESQL_ASYNCPG (Enum): [postgresql+asyncpg](https://docs.sqlalchemy.org/en/14/dialects/postgresql.html#module-sqlalchemy.dialects.postgresql.asyncpg)
SQLITE_AIOSQLITE (Enum): [sqlite+aiosqlite](https://docs.sqlalchemy.org/en/14/dialects/sqlite.html#module-sqlalchemy.dialects.sqlite.aiosqlite)
MYSQL_ASYNCMY (Enum): [mysql+asyncmy](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#module-sqlalchemy.dialects.mysql.asyncmy)
MYSQL_AIOMYSQL (Enum): [mysql+aiomysql](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#module-sqlalchemy.dialects.mysql.aiomysql)
| class AsyncDriver(Enum):
"""
Known dialects with their corresponding async drivers.
Attributes:
POSTGRESQL_ASYNCPG (Enum): [postgresql+asyncpg](https://docs.sqlalchemy.org/en/14/dialects/postgresql.html#module-sqlalchemy.dialects.postgresql.asyncpg)
SQLITE_AIOSQLITE (Enum): [sqlite+aiosqlite](https://docs.sqlalchemy.org/en/14/dialects/sqlite.html#module-sqlalchemy.dialects.sqlite.aiosqlite)
MYSQL_ASYNCMY (Enum): [mysql+asyncmy](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#module-sqlalchemy.dialects.mysql.asyncmy)
MYSQL_AIOMYSQL (Enum): [mysql+aiomysql](https://docs.sqlalchemy.org/en/14/dialects/mysql.html#module-sqlalchemy.dialects.mysql.aiomysql)
""" # noqa
POSTGRESQL_ASYNCPG = "postgresql+asyncpg"
SQLITE_AIOSQLITE = "sqlite+aiosqlite"
MYSQL_ASYNCMY = "mysql+asyncmy"
MYSQL_AIOMYSQL = "mysql+aiomysql"
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|
31,379 | prefect_sqlalchemy.credentials | ConnectionComponents |
Parameters to use to create a SQLAlchemy engine URL.
Attributes:
driver: The driver name to use.
database: The name of the database to use.
username: The user name used to authenticate.
password: The password used to authenticate.
host: The host address of the database.
port: The port to connect to the database.
query: A dictionary of string keys to string values to be passed to the dialect
and/or the DBAPI upon connect.
| class ConnectionComponents(BaseModel):
"""
Parameters to use to create a SQLAlchemy engine URL.
Attributes:
driver: The driver name to use.
database: The name of the database to use.
username: The user name used to authenticate.
password: The password used to authenticate.
host: The host address of the database.
port: The port to connect to the database.
query: A dictionary of string keys to string values to be passed to the dialect
and/or the DBAPI upon connect.
"""
driver: Union[AsyncDriver, SyncDriver, str] = Field(
default=..., description="The driver name to use."
)
database: str = Field(default=..., description="The name of the database to use.")
username: Optional[str] = Field(
default=None, description="The user name used to authenticate."
)
password: Optional[SecretStr] = Field(
default=None, description="The password used to authenticate."
)
host: Optional[str] = Field(
default=None, description="The host address of the database."
)
port: Optional[str] = Field(
default=None, description="The port to connect to the database."
)
query: Optional[Dict[str, str]] = Field(
default=None,
description=(
"A dictionary of string keys to string values to be passed to the dialect "
"and/or the DBAPI upon connect. To specify non-string parameters to a "
"Python DBAPI directly, use connect_args."
),
)
def create_url(self) -> URL:
"""
Create a fully formed connection URL.
Returns:
The SQLAlchemy engine URL.
"""
driver = self.driver
drivername = driver.value if isinstance(driver, Enum) else driver
password = self.password.get_secret_value() if self.password else None
url_params = dict(
drivername=drivername,
username=self.username,
password=password,
database=self.database,
host=self.host,
port=self.port,
query=self.query,
)
return URL.create(
**{
url_key: url_param
for url_key, url_param in url_params.items()
if url_param is not None
}
)
| (*, driver: Union[prefect_sqlalchemy.credentials.AsyncDriver, prefect_sqlalchemy.credentials.SyncDriver, str], database: str, username: Optional[str] = None, password: Optional[pydantic.v1.types.SecretStr] = None, host: Optional[str] = None, port: Optional[str] = None, query: Optional[Dict[str, str]] = None) -> None | [
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|
31,380 | pydantic.v1.main | __eq__ | null | def __eq__(self, other: Any) -> bool:
if isinstance(other, BaseModel):
return self.dict() == other.dict()
else:
return self.dict() == other
| (self, other: Any) -> bool | [
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31,381 | pydantic.v1.main | __getstate__ | null | def __getstate__(self) -> 'DictAny':
private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__)
return {
'__dict__': self.__dict__,
'__fields_set__': self.__fields_set__,
'__private_attribute_values__': {k: v for k, v in private_attrs if v is not Undefined},
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|
31,382 | pydantic.v1.main | __init__ |
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
| def __init__(__pydantic_self__, **data: Any) -> None:
"""
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
"""
# Uses something other than `self` the first arg to allow "self" as a settable attribute
values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
if validation_error:
raise validation_error
try:
object_setattr(__pydantic_self__, '__dict__', values)
except TypeError as e:
raise TypeError(
'Model values must be a dict; you may not have returned a dictionary from a root validator'
) from e
object_setattr(__pydantic_self__, '__fields_set__', fields_set)
__pydantic_self__._init_private_attributes()
| (__pydantic_self__, **data: Any) -> NoneType | [
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|
31,383 | pydantic.v1.main | __iter__ |
so `dict(model)` works
| def __iter__(self) -> 'TupleGenerator':
"""
so `dict(model)` works
"""
yield from self.__dict__.items()
| (self) -> 'TupleGenerator' | [
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|
31,384 | pydantic.v1.json | pydantic_encoder | null | def pydantic_encoder(obj: Any) -> Any:
from dataclasses import asdict, is_dataclass
from .main import BaseModel
if isinstance(obj, BaseModel):
return obj.dict()
elif is_dataclass(obj):
return asdict(obj)
# Check the class type and its superclasses for a matching encoder
for base in obj.__class__.__mro__[:-1]:
try:
encoder = ENCODERS_BY_TYPE[base]
except KeyError:
continue
return encoder(obj)
else: # We have exited the for loop without finding a suitable encoder
raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
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|
31,385 | pydantic.v1.utils | __pretty__ |
Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
| def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
"""
Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
"""
yield self.__repr_name__() + '('
yield 1
for name, value in self.__repr_args__():
if name is not None:
yield name + '='
yield fmt(value)
yield ','
yield 0
yield -1
yield ')'
| (self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, NoneType, NoneType] | [
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|
31,387 | pydantic.v1.main | __repr_args__ | null | def __repr_args__(self) -> 'ReprArgs':
return [
(k, v)
for k, v in self.__dict__.items()
if k not in DUNDER_ATTRIBUTES and (k not in self.__fields__ or self.__fields__[k].field_info.repr)
]
| (self) -> 'ReprArgs' | [
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|
31,388 | pydantic.v1.utils | __repr_name__ |
Name of the instance's class, used in __repr__.
| def __repr_name__(self) -> str:
"""
Name of the instance's class, used in __repr__.
"""
return self.__class__.__name__
| (self) -> str | [
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|
31,390 | pydantic.v1.utils | __rich_repr__ | Get fields for Rich library | def __rich_repr__(self) -> 'RichReprResult':
"""Get fields for Rich library"""
for name, field_repr in self.__repr_args__():
if name is None:
yield field_repr
else:
yield name, field_repr
| (self) -> 'RichReprResult' | [
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|
31,391 | pydantic.v1.main | __setattr__ | null | @no_type_check
def __setattr__(self, name, value): # noqa: C901 (ignore complexity)
if name in self.__private_attributes__ or name in DUNDER_ATTRIBUTES:
return object_setattr(self, name, value)
if self.__config__.extra is not Extra.allow and name not in self.__fields__:
raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
elif not self.__config__.allow_mutation or self.__config__.frozen:
raise TypeError(f'"{self.__class__.__name__}" is immutable and does not support item assignment')
elif name in self.__fields__ and self.__fields__[name].final:
raise TypeError(
f'"{self.__class__.__name__}" object "{name}" field is final and does not support reassignment'
)
elif self.__config__.validate_assignment:
new_values = {**self.__dict__, name: value}
for validator in self.__pre_root_validators__:
try:
new_values = validator(self.__class__, new_values)
except (ValueError, TypeError, AssertionError) as exc:
raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], self.__class__)
known_field = self.__fields__.get(name, None)
if known_field:
# We want to
# - make sure validators are called without the current value for this field inside `values`
# - keep other values (e.g. submodels) untouched (using `BaseModel.dict()` will change them into dicts)
# - keep the order of the fields
if not known_field.field_info.allow_mutation:
raise TypeError(f'"{known_field.name}" has allow_mutation set to False and cannot be assigned')
dict_without_original_value = {k: v for k, v in self.__dict__.items() if k != name}
value, error_ = known_field.validate(value, dict_without_original_value, loc=name, cls=self.__class__)
if error_:
raise ValidationError([error_], self.__class__)
else:
new_values[name] = value
errors = []
for skip_on_failure, validator in self.__post_root_validators__:
if skip_on_failure and errors:
continue
try:
new_values = validator(self.__class__, new_values)
except (ValueError, TypeError, AssertionError) as exc:
errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
if errors:
raise ValidationError(errors, self.__class__)
# update the whole __dict__ as other values than just `value`
# may be changed (e.g. with `root_validator`)
object_setattr(self, '__dict__', new_values)
else:
self.__dict__[name] = value
self.__fields_set__.add(name)
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|
31,392 | pydantic.v1.main | __setstate__ | null | def __setstate__(self, state: 'DictAny') -> None:
object_setattr(self, '__dict__', state['__dict__'])
object_setattr(self, '__fields_set__', state['__fields_set__'])
for name, value in state.get('__private_attribute_values__', {}).items():
object_setattr(self, name, value)
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|
31,394 | pydantic.v1.main | _calculate_keys | null | def _calculate_keys(
self,
include: Optional['MappingIntStrAny'],
exclude: Optional['MappingIntStrAny'],
exclude_unset: bool,
update: Optional['DictStrAny'] = None,
) -> Optional[AbstractSet[str]]:
if include is None and exclude is None and exclude_unset is False:
return None
keys: AbstractSet[str]
if exclude_unset:
keys = self.__fields_set__.copy()
else:
keys = self.__dict__.keys()
if include is not None:
keys &= include.keys()
if update:
keys -= update.keys()
if exclude:
keys -= {k for k, v in exclude.items() if ValueItems.is_true(v)}
return keys
| (self, include: Optional[ForwardRef('MappingIntStrAny')], exclude: Optional[ForwardRef('MappingIntStrAny')], exclude_unset: bool, update: Optional[ForwardRef('DictStrAny')] = None) -> Optional[AbstractSet[str]] | [
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|
31,395 | pydantic.v1.main | _copy_and_set_values | null | def _copy_and_set_values(self: 'Model', values: 'DictStrAny', fields_set: 'SetStr', *, deep: bool) -> 'Model':
if deep:
# chances of having empty dict here are quite low for using smart_deepcopy
values = deepcopy(values)
cls = self.__class__
m = cls.__new__(cls)
object_setattr(m, '__dict__', values)
object_setattr(m, '__fields_set__', fields_set)
for name in self.__private_attributes__:
value = getattr(self, name, Undefined)
if value is not Undefined:
if deep:
value = deepcopy(value)
object_setattr(m, name, value)
return m
| (self: 'Model', values: 'DictStrAny', fields_set: 'SetStr', *, deep: bool) -> 'Model' | [
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|
31,396 | pydantic.v1.main | _init_private_attributes | null | def _init_private_attributes(self) -> None:
for name, private_attr in self.__private_attributes__.items():
default = private_attr.get_default()
if default is not Undefined:
object_setattr(self, name, default)
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|
31,397 | pydantic.v1.main | _iter | null | def _iter(
self,
to_dict: bool = False,
by_alias: bool = False,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> 'TupleGenerator':
# Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
# The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
if exclude is not None or self.__exclude_fields__ is not None:
exclude = ValueItems.merge(self.__exclude_fields__, exclude)
if include is not None or self.__include_fields__ is not None:
include = ValueItems.merge(self.__include_fields__, include, intersect=True)
allowed_keys = self._calculate_keys(
include=include, exclude=exclude, exclude_unset=exclude_unset # type: ignore
)
if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
# huge boost for plain _iter()
yield from self.__dict__.items()
return
value_exclude = ValueItems(self, exclude) if exclude is not None else None
value_include = ValueItems(self, include) if include is not None else None
for field_key, v in self.__dict__.items():
if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
continue
if exclude_defaults:
model_field = self.__fields__.get(field_key)
if not getattr(model_field, 'required', True) and getattr(model_field, 'default', _missing) == v:
continue
if by_alias and field_key in self.__fields__:
dict_key = self.__fields__[field_key].alias
else:
dict_key = field_key
if to_dict or value_include or value_exclude:
v = self._get_value(
v,
to_dict=to_dict,
by_alias=by_alias,
include=value_include and value_include.for_element(field_key),
exclude=value_exclude and value_exclude.for_element(field_key),
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
yield dict_key, v
| (self, to_dict: bool = False, by_alias: bool = False, include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) -> 'TupleGenerator' | [
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|
31,398 | pydantic.v1.main | copy |
Duplicate a model, optionally choose which fields to include, exclude and change.
:param include: fields to include in new model
:param exclude: fields to exclude from new model, as with values this takes precedence over include
:param update: values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
:param deep: set to `True` to make a deep copy of the model
:return: new model instance
| def copy(
self: 'Model',
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
update: Optional['DictStrAny'] = None,
deep: bool = False,
) -> 'Model':
"""
Duplicate a model, optionally choose which fields to include, exclude and change.
:param include: fields to include in new model
:param exclude: fields to exclude from new model, as with values this takes precedence over include
:param update: values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
:param deep: set to `True` to make a deep copy of the model
:return: new model instance
"""
values = dict(
self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False),
**(update or {}),
)
# new `__fields_set__` can have unset optional fields with a set value in `update` kwarg
if update:
fields_set = self.__fields_set__ | update.keys()
else:
fields_set = set(self.__fields_set__)
return self._copy_and_set_values(values, fields_set, deep=deep)
| (self: 'Model', *, include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, update: Optional[ForwardRef('DictStrAny')] = None, deep: bool = False) -> 'Model' | [
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|
31,399 | prefect_sqlalchemy.credentials | create_url |
Create a fully formed connection URL.
Returns:
The SQLAlchemy engine URL.
| def create_url(self) -> URL:
"""
Create a fully formed connection URL.
Returns:
The SQLAlchemy engine URL.
"""
driver = self.driver
drivername = driver.value if isinstance(driver, Enum) else driver
password = self.password.get_secret_value() if self.password else None
url_params = dict(
drivername=drivername,
username=self.username,
password=password,
database=self.database,
host=self.host,
port=self.port,
query=self.query,
)
return URL.create(
**{
url_key: url_param
for url_key, url_param in url_params.items()
if url_param is not None
}
)
| (self) -> sqlalchemy.engine.url.URL | [
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|
31,400 | pydantic.v1.main | dict |
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
| def dict(
self,
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
by_alias: bool = False,
skip_defaults: Optional[bool] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> 'DictStrAny':
"""
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
"""
if skip_defaults is not None:
warnings.warn(
f'{self.__class__.__name__}.dict(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
DeprecationWarning,
)
exclude_unset = skip_defaults
return dict(
self._iter(
to_dict=True,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
)
| (self, *, include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) -> 'DictStrAny' | [
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]
|
31,401 | pydantic.v1.main | json |
Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`.
| def json(
self,
*,
include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None,
by_alias: bool = False,
skip_defaults: Optional[bool] = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: Optional[Callable[[Any], Any]] = None,
models_as_dict: bool = True,
**dumps_kwargs: Any,
) -> str:
"""
Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`.
"""
if skip_defaults is not None:
warnings.warn(
f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"',
DeprecationWarning,
)
exclude_unset = skip_defaults
encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
# We don't directly call `self.dict()`, which does exactly this with `to_dict=True`
# because we want to be able to keep raw `BaseModel` instances and not as `dict`.
# This allows users to write custom JSON encoders for given `BaseModel` classes.
data = dict(
self._iter(
to_dict=models_as_dict,
by_alias=by_alias,
include=include,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
)
if self.__custom_root_type__:
data = data[ROOT_KEY]
return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)
| (self, *, include: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, exclude: Union[ForwardRef('AbstractSetIntStr'), ForwardRef('MappingIntStrAny'), NoneType] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) -> str | [
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|
31,402 | prefect_sqlalchemy.credentials | DatabaseCredentials |
Block used to manage authentication with a database.
Attributes:
driver: The driver name, e.g. "postgresql+asyncpg"
database: The name of the database to use.
username: The user name used to authenticate.
password: The password used to authenticate.
host: The host address of the database.
port: The port to connect to the database.
query: A dictionary of string keys to string values to be passed to
the dialect and/or the DBAPI upon connect. To specify non-string
parameters to a Python DBAPI directly, use connect_args.
url: Manually create and provide a URL to create the engine,
this is useful for external dialects, e.g. Snowflake, because some
of the params, such as "warehouse", is not directly supported in
the vanilla `sqlalchemy.engine.URL.create` method; do not provide
this alongside with other URL params as it will raise a `ValueError`.
connect_args: The options which will be passed directly to the
DBAPI's connect() method as additional keyword arguments.
Example:
Load stored database credentials:
```python
from prefect_sqlalchemy import DatabaseCredentials
database_block = DatabaseCredentials.load("BLOCK_NAME")
```
| class DatabaseCredentials(Block):
"""
Block used to manage authentication with a database.
Attributes:
driver: The driver name, e.g. "postgresql+asyncpg"
database: The name of the database to use.
username: The user name used to authenticate.
password: The password used to authenticate.
host: The host address of the database.
port: The port to connect to the database.
query: A dictionary of string keys to string values to be passed to
the dialect and/or the DBAPI upon connect. To specify non-string
parameters to a Python DBAPI directly, use connect_args.
url: Manually create and provide a URL to create the engine,
this is useful for external dialects, e.g. Snowflake, because some
of the params, such as "warehouse", is not directly supported in
the vanilla `sqlalchemy.engine.URL.create` method; do not provide
this alongside with other URL params as it will raise a `ValueError`.
connect_args: The options which will be passed directly to the
DBAPI's connect() method as additional keyword arguments.
Example:
Load stored database credentials:
```python
from prefect_sqlalchemy import DatabaseCredentials
database_block = DatabaseCredentials.load("BLOCK_NAME")
```
"""
_block_type_name = "Database Credentials"
_logo_url = "https://cdn.sanity.io/images/3ugk85nk/production/fb3f4debabcda1c5a3aeea4f5b3f94c28845e23e-250x250.png" # noqa
_documentation_url = "https://prefecthq.github.io/prefect-sqlalchemy/credentials/#prefect_sqlalchemy.credentials.DatabaseCredentials" # noqa
driver: Optional[Union[AsyncDriver, SyncDriver, str]] = Field(
default=None, description="The driver name to use."
)
username: Optional[str] = Field(
default=None, description="The user name used to authenticate."
)
password: Optional[SecretStr] = Field(
default=None, description="The password used to authenticate."
)
database: Optional[str] = Field(
default=None, description="The name of the database to use."
)
host: Optional[str] = Field(
default=None, description="The host address of the database."
)
port: Optional[str] = Field(
default=None, description="The port to connect to the database."
)
query: Optional[Dict[str, str]] = Field(
default=None,
description=(
"A dictionary of string keys to string values to be passed to the dialect "
"and/or the DBAPI upon connect. To specify non-string parameters to a "
"Python DBAPI directly, use connect_args."
),
)
url: Optional[AnyUrl] = Field(
default=None,
description=(
"Manually create and provide a URL to create the engine, this is useful "
"for external dialects, e.g. Snowflake, because some of the params, "
"such as 'warehouse', is not directly supported in the vanilla "
"`sqlalchemy.engine.URL.create` method; do not provide this "
"alongside with other URL params as it will raise a `ValueError`."
),
)
connect_args: Optional[Dict[str, Any]] = Field(
default=None,
description=(
"The options which will be passed directly to the DBAPI's connect() "
"method as additional keyword arguments."
),
)
def block_initialization(self):
"""
Initializes the engine.
"""
warnings.warn(
"DatabaseCredentials is now deprecated and will be removed March 2023; "
"please use SqlAlchemyConnector instead.",
DeprecationWarning,
)
if isinstance(self.driver, AsyncDriver):
drivername = self.driver.value
self._driver_is_async = True
elif isinstance(self.driver, SyncDriver):
drivername = self.driver.value
self._driver_is_async = False
else:
drivername = self.driver
self._driver_is_async = drivername in AsyncDriver._value2member_map_
url_params = dict(
drivername=drivername,
username=self.username,
password=self.password.get_secret_value() if self.password else None,
database=self.database,
host=self.host,
port=self.port,
query=self.query,
)
if not self.url:
required_url_keys = ("drivername", "database")
if not all(url_params[key] for key in required_url_keys):
required_url_keys = ("driver", "database")
raise ValueError(
f"If the `url` is not provided, "
f"all of these URL params are required: "
f"{required_url_keys}"
)
self.rendered_url = URL.create(
**{
url_key: url_param
for url_key, url_param in url_params.items()
if url_param is not None
}
) # from params
else:
if any(val for val in url_params.values()):
raise ValueError(
f"The `url` should not be provided "
f"alongside any of these URL params: "
f"{url_params.keys()}"
)
self.rendered_url = make_url(str(self.url))
def get_engine(self) -> Union["Connection", "AsyncConnection"]:
"""
Returns an authenticated engine that can be
used to query from databases.
Returns:
The authenticated SQLAlchemy Connection / AsyncConnection.
Examples:
Create an asynchronous engine to PostgreSQL using URL params.
```python
from prefect import flow
from prefect_sqlalchemy import DatabaseCredentials, AsyncDriver
@flow
def sqlalchemy_credentials_flow():
sqlalchemy_credentials = DatabaseCredentials(
driver=AsyncDriver.POSTGRESQL_ASYNCPG,
username="prefect",
password="prefect_password",
database="postgres"
)
print(sqlalchemy_credentials.get_engine())
sqlalchemy_credentials_flow()
```
Create a synchronous engine to Snowflake using the `url` kwarg.
```python
from prefect import flow
from prefect_sqlalchemy import DatabaseCredentials, AsyncDriver
@flow
def sqlalchemy_credentials_flow():
url = (
"snowflake://<user_login_name>:<password>"
"@<account_identifier>/<database_name>"
"?warehouse=<warehouse_name>"
)
sqlalchemy_credentials = DatabaseCredentials(url=url)
print(sqlalchemy_credentials.get_engine())
sqlalchemy_credentials_flow()
```
"""
engine_kwargs = dict(
url=self.rendered_url,
connect_args=self.connect_args or {},
poolclass=NullPool,
)
if self._driver_is_async:
engine = create_async_engine(**engine_kwargs)
else:
engine = create_engine(**engine_kwargs)
return engine
class Config:
"""Configuration of pydantic."""
# Support serialization of the 'URL' type
arbitrary_types_allowed = True
json_encoders = {URL: lambda u: u.render_as_string()}
def dict(self, *args, **kwargs) -> Dict:
"""
Convert to a dictionary.
"""
# Support serialization of the 'URL' type
d = super().dict(*args, **kwargs)
d["rendered_url"] = SecretStr(
self.rendered_url.render_as_string(hide_password=False)
)
return d
| (*args, driver: Union[prefect_sqlalchemy.credentials.AsyncDriver, prefect_sqlalchemy.credentials.SyncDriver, str, NoneType] = None, username: Optional[str] = None, password: Optional[pydantic.v1.types.SecretStr] = None, database: Optional[str] = None, host: Optional[str] = None, port: Optional[str] = None, query: Optional[Dict[str, str]] = None, url: Optional[pydantic.v1.networks.AnyUrl] = None, connect_args: Optional[Dict[str, Any]] = None, **kwargs) -> None | [
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|
31,405 | prefect.blocks.core | __init__ | null | def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.block_initialization()
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|
31,407 | prefect.blocks.core | __new__ |
Create an instance of the Block subclass type if a `block_type_slug` is
present in the data payload.
| def __new__(cls: Type[Self], **kwargs) -> Self:
"""
Create an instance of the Block subclass type if a `block_type_slug` is
present in the data payload.
"""
block_type_slug = kwargs.pop("block_type_slug", None)
if block_type_slug:
subcls = lookup_type(cls, dispatch_key=block_type_slug)
m = super().__new__(subcls)
# NOTE: This is a workaround for an obscure issue where copied models were
# missing attributes. This pattern is from Pydantic's
# `BaseModel._copy_and_set_values`.
# The issue this fixes could not be reproduced in unit tests that
# directly targeted dispatch handling and was only observed when
# copying then saving infrastructure blocks on deployment models.
object.__setattr__(m, "__dict__", kwargs)
object.__setattr__(m, "__fields_set__", set(kwargs.keys()))
return m
else:
m = super().__new__(cls)
object.__setattr__(m, "__dict__", kwargs)
object.__setattr__(m, "__fields_set__", set(kwargs.keys()))
return m
| (cls: Type[typing_extensions.Self], **kwargs) -> typing_extensions.Self | [
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|
31,410 | prefect.blocks.core | __repr_args__ | null | def __repr_args__(self):
repr_args = super().__repr_args__()
data_keys = self.schema()["properties"].keys()
return [
(key, value) for key, value in repr_args if key is None or key in data_keys
]
| (self) | [
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|
31,416 | prefect.blocks.core | __str__ | null | def __str__(self) -> str:
return self.__repr__()
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|
31,419 | prefect.blocks.core | _define_metadata_on_nested_blocks |
Recursively populates metadata fields on nested blocks based on the
provided block document references.
| def _define_metadata_on_nested_blocks(
self, block_document_references: Dict[str, Dict[str, Any]]
):
"""
Recursively populates metadata fields on nested blocks based on the
provided block document references.
"""
for item in block_document_references.items():
field_name, block_document_reference = item
nested_block = getattr(self, field_name)
if isinstance(nested_block, Block):
nested_block_document_info = block_document_reference.get(
"block_document", {}
)
nested_block._define_metadata_on_nested_blocks(
nested_block_document_info.get("block_document_references", {})
)
nested_block_document_id = nested_block_document_info.get("id")
nested_block._block_document_id = (
UUID(nested_block_document_id) if nested_block_document_id else None
)
nested_block._block_document_name = nested_block_document_info.get(
"name"
)
nested_block._is_anonymous = nested_block_document_info.get(
"is_anonymous"
)
| (self, block_document_references: Dict[str, Dict[str, Any]]) | [
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|
31,420 | prefect.blocks.core | _event_kind | null | def _event_kind(self) -> str:
return f"prefect.block.{self.get_block_type_slug()}"
| (self) -> str | [
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|
31,421 | prefect.blocks.core | _event_method_called_resources | null | def _event_method_called_resources(self) -> Optional[ResourceTuple]:
if not (self._block_document_id and self._block_document_name):
return None
return (
{
"prefect.resource.id": (
f"prefect.block-document.{self._block_document_id}"
),
"prefect.resource.name": self._block_document_name,
},
[
{
"prefect.resource.id": (
f"prefect.block-type.{self.get_block_type_slug()}"
),
"prefect.resource.role": "block-type",
}
],
)
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|
31,423 | prefect.blocks.core | _iter | null | def _iter(self, *, include=None, exclude=None, **kwargs):
# Injects the `block_type_slug` into serialized payloads for dispatch
for key_value in super()._iter(include=include, exclude=exclude, **kwargs):
yield key_value
# Respect inclusion and exclusion still
if include and "block_type_slug" not in include:
return
if exclude and "block_type_slug" in exclude:
return
yield "block_type_slug", self.get_block_type_slug()
| (self, *, include=None, exclude=None, **kwargs) | [
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|
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