def verify(dataset, publication_date, source, refernce_url):
"""Use this function to verify a dataset."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
client.verify(dataset, publication_date, source, refernce_url)
def get(dataset=None,
include_metadata=False,
mnemonics=None,
transform=None,
**dim_values):
"""Use this function to get data from Knoema dataset."""
if not dataset and not mnemonics:
raise ValueError('Dataset id is not specified')
if mnemonics and dim_values:
raise ValueError(
'The function does not support specifying mnemonics and selection in a single call'
)
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
ds = client.get_dataset(dataset) if dataset else None
reader = MnemonicsDataReader(client, mnemonics, transform) if mnemonics \
else TransformationDataReader(client, dim_values, transform) if ds.type != 'Regular' or 'frequency' in dim_values or transform\
else StreamingDataReader(client, dim_values)
reader.include_metadata = include_metadata
reader.dataset = ds
return reader.get_pandasframe()
def delete(dataset):
"""Use this function to delete dataset by it's id."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
client.delete(dataset)
return ('Dataset {} has been deleted successfully'.format(dataset))
def ticker(ticker):
"""Use this function to get data about company"""
if not ticker:
raise ValueError('Ticker or company name is not specified')
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
company_int = client.get_company_info(ticker)
return Company(company_int, client)
def upload(file_path_or_frame, dataset=None, public=False, name = None):
"""Use this function to upload data to Knoema dataset."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
if isinstance(file_path_or_frame, str):
return client.upload(file_path_or_frame, dataset, public)
frame_transformer = FrameTransformerFactory(file_path_or_frame).get_transformer()
with FileLayerWrapper() as fw:
file = frame_transformer.prepare(fw, dataset, name)
return client.upload(file, dataset, public, name)
def get(dataset, **dim_values):
"""Use this function to get data from Knoema dataset."""
if not dataset:
raise ValueError('Dataset id is not specified')
if not dim_values:
raise ValueError('Dimensions members are not specified')
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
dataset = client.get_dataset(dataset)
data_reader = DataReader(client, dataset, dim_values)
return data_reader.get_pandasframe()
def dimension(dataset, dimension):
"""Use this function to get dimension metadata"""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
if not dataset:
raise ValueError('Dataset id is not specified')
if not dimension:
raise ValueError('Dimension id is not specified')
reader = DimensionMetadataReader(client, dataset, dimension)
return reader.get()
def dataset(id):
"""Use this function to get dataset metadata."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
ds = client.get_dataset_meta(id)
res = {}
res.update(ds.data)
range = client.get_daterange(id)
res['startDate'] = range.start_date
res['endDate'] = range.end_date
res['frequencies'] = range.frequencies
return res
def get(dataset=None,
include_metadata=False,
mnemonics=None,
transform=None,
separator=None,
**dim_values):
"""Use this function to get data from Knoema dataset."""
if not dataset and not mnemonics:
raise ValueError('Dataset id is not specified')
frequency = None
for name, value in dim_values.copy().items():
if is_equal_strings_ignore_case(name, 'frequency'):
frequency = value
del dim_values[name]
continue
if is_equal_strings_ignore_case(name, 'transform'):
transform = value
del dim_values[name]
continue
if mnemonics and dim_values:
raise ValueError(
'The function does not support specifying mnemonics and selection in a single call'
)
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
ds = client.get_dataset(dataset) if dataset else None
reader = MnemonicsDataReader(client, mnemonics, transform, frequency) if mnemonics \
else TransformationDataReader(client, dim_values, transform, frequency) if ds.type != 'Regular' or frequency or transform\
else StreamingDataReader(client, dim_values)
reader.include_metadata = include_metadata
reader.dataset = ds
if separator:
reader.separator = separator
return reader.get_pandasframe()
def search(query):
"""Use this function to make search request"""
if not query:
raise ValueError('Query is not specified')
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
search_results_int = client.search(query)
search_results = SearchResults(search_results_int, client)
if search_results.instant != None:
if search_results.instant.type == 'ConceptBind':
company_int = client.get_company_info(search_results.instant.id)
search_results.instant.company = Company(company_int, client)
return search_results
def upload(file_path, dataset=None, public=False):
"""Use this function to upload data to Knoema dataset."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
return client.upload(file_path, dataset, public)
def delete(dataset):
"""Use this function to delete dataset by it's id."""
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.delete(dataset)
def get(dataset = None, include_metadata = False, mnemonics = None, transform = None, separator = None, group_by = None, columns = None, **dim_values):
"""Use this function to get data from Knoema dataset."""
if not dataset and not mnemonics:
raise ValueError('Dataset id is not specified')
config = ApiConfig()
client = ApiClient(config.host, config.app_id, config.app_secret)
client.check_correct_host()
ds = client.get_dataset(dataset) if dataset else None
if columns is not None and isinstance(columns, str):
columns = columns.split(';')
frequency = None
timerange = None
has_agg = False
for name, value in dim_values.copy().items():
if is_equal_strings_ignore_case(name, 'frequency'):
frequency = value
del dim_values[name]
continue
if is_equal_strings_ignore_case(name, 'transform'):
transform = value
del dim_values[name]
continue
if is_equal_strings_ignore_case(name, 'timerange'):
timerange = value
continue
if len(value) > 0 and value[0].startswith('@'):
has_agg = True
if mnemonics:
reader = MnemonicsDataReader(client, mnemonics, transform, frequency)
reader.columns = columns
reader.include_metadata = include_metadata
reader.dataset = ds
if separator:
reader.separator = separator
return reader.get_pandasframe()
if not dataset:
raise ValueError('Dataset id is not specified')
if ds.type == 'Regular' and not has_agg and group_by:
if frequency != None: dim_values['frequency'] = frequency
metadata_reader = StreamingDataReader(client, dim_values)
metadata_reader.columns = columns
metadata_reader.dataset = ds
if separator:
metadata_reader.separator = separator
metadata = metadata_reader.get_series_metadata()
reader = StreamingDataReader(client, None, transform, group_by)
reader.columns = columns
reader.include_metadata = include_metadata
reader.dataset = ds
return reader.get_pandasframe_by_metadata_grouped(metadata, frequency, timerange)
if ds.type == 'Regular' and frequency != None:
dim_values['frequency'] = frequency
if ds.type == 'Regular' and columns != None:
reader = StreamingDataReader(client, dim_values, transform)
else:
reader = TransformationDataReader(client, dim_values, transform, frequency)
reader.columns = columns
reader.include_metadata = include_metadata
reader.dataset = ds
if separator:
reader.separator = separator
return reader.get_pandasframe()
