def write_rates_df_to_table(table_name: str,
table: pd.DataFrame,
account_name: str = None,
account_key: str = None,
connection_string: str = None):
# Set up connection to table service
table_service = __connect(table_name, account_name, account_key,
connection_string)
# Specify PartitonKey and RowKey
table["PartitionKey"] = table["Date"]
table["Date"] = pd.to_datetime(table["Date"])
table["RowKey"] = table["country_code"] + "_" + table["Maturity"].astype(
str)
# Iterate through each PartitionKey and insert the rows into batch and submit to table service
for _, partition_df in table.groupby(
["PartitionKey", np.arange(len(table)) // 100]):
batch = TableBatch()
rates_list = json.loads(
partition_df.to_json(date_format="iso", orient="records"))
for rate in rates_list:
batch.insert_or_replace_entity(rate)
table_service.commit_batch(table_name, batch)
def uploadToTable():
connection_string, container_name = get_credentials()
blob = BlobClient.from_connection_string(conn_str=connection_string,
container_name=container_name,
blob_name=filename)
blob_data = blob.download_blob().readall()
json_file_content = blob_data.decode('utf8')
jlist = json.loads(json_file_content)
table_service = TableService(connection_string=connection_string)
table_service.create_table(table_name=fixed_tablename)
partition_key = 'Fishlanding'
row = 1
batch_list = []
for jdict in jlist:
task = {'PartitionKey': partition_key, 'RowKey': str(row)}
for key in jdict:
keyVal = key.replace(' ', '')
if keyVal == '':
pass
task[keyVal] = jdict[key]
batch_list.append(task)
row += 1
seperated_batch_list = list_of_groups(init_list=batch_list,
childern_list_len=50)
for children_list in seperated_batch_list:
batch = TableBatch()
for task in children_list:
batch.insert_or_replace_entity(task)
table_service.commit_batch(table_name=tablename, batch=batch)
return batch_list
def main(event: func.EventGridEvent):
event_type = event.event_type
event_subject = event.subject
containername = event_subject.split("/")[-3]
# credentials needed
connection_string = os.getenv('AZURE_CONNECTION_STRING')
container_name = os.getenv('ContainerName')
# Only blob creations at container would trigger this function
if event_type == "Microsoft.Storage.BlobCreated" and containername == container_name:
filename = event_subject.split("/")[-1]
tablename = gettablename(
filename=filename) + datetime.now().strftime("%H%M%S")
table_service = TableService(connection_string=connection_string)
table_service.create_table(table_name=tablename)
# Download the blob data
blob = BlobClient.from_connection_string(conn_str=connection_string,
container_name=container_name,
blob_name=filename)
blob_data = blob.download_blob().readall()
json_file_content = blob_data.decode('utf8').replace("'", '"')
jlist = json.loads(json_file_content)
# The partition key might be changed later. This is only for DEVELOPMENT purpose
partition_key = tablename
row = 1
batch_list = []
for jdict in jlist:
if 'Cruise_ID' in jdict:
partition_key = jdict['Cruise_ID']
task = {'PartitionKey': partition_key, 'RowKey': str(row)}
for key in jdict:
keyVal = key.replace(' ', '')
if keyVal == '':
continue
task[keyVal] = jdict[key]
batch_list.append(task)
row += 1
seperated_batch_list = list_of_groups(init_list=batch_list,
childern_list_len=50)
for children_list in seperated_batch_list:
batch = TableBatch()
for task in children_list:
batch.insert_or_replace_entity(task)
table_service.commit_batch(table_name=tablename, batch=batch)
def sendEntities():
table_service = TableService(
connection_string='DefaultEndpointsProtocol=https;AccountName=sauokgp;AccountKey=113mdwUqIiqt4K2HonK80HakIOplxYZINmQME5KB1IZfP+v3JHZK64wpoTP5NBFaG0MaO/TVqA0nW4KuCINTow==;EndpointSuffix=core.windows.net')
batch = TableBatch()
inBatch = 0
for a in bitcoins:
batch.insert_or_replace_entity(a)
inBatch += 1
if inBatch > 99:
table_service.commit_batch('HelpPage', batch)
batch = TableBatch()
inBatch = 0
table_service.commit_batch('HelpPage', batch)
batch = TableBatch()
inBatch = 0
def sendEntities():
table_service = TableService(
connection_string='')
batch = TableBatch()
inBatch = 0
for a in bitcoins:
batch.insert_or_replace_entity(a)
inBatch += 1
if inBatch > 99:
table_service.commit_batch('CryptoCompareAPIData', batch)
batch = TableBatch()
inBatch = 0
table_service.commit_batch('CryptoCompareAPIData', batch)
batch = TableBatch()
inBatch = 0
def crawl_and_store(table_service, symbol):
batch = TableBatch()
batch_size = 0
for each_trend in fetch(symbol):
batch.insert_or_replace_entity(each_trend)
batch_size = batch_size + 1
if batch_size >= 75:
table_service.commit_batch('stocktrend', batch)
batch = TableBatch()
print("stored a batch, size:", batch_size)
batch_size = 0
if batch_size > 0:
table_service.commit_batch('stocktrend', batch)
print("stored a batch, size:", batch_size)
def uploadentities():
table_service = TableService(
connection_string=tablestorageconnectionstring)
batch = TableBatch()
inBatch = 0
for e in entities:
batch.insert_or_replace_entity(e)
inBatch += 1
if inBatch > 99:
print("Sending batch...")
table_service.commit_batch('PredictionData', batch)
print("Batch sent!")
batch = TableBatch()
inBatch = 0
print("Sending batch...")
table_service.commit_batch('PredictionData', batch)
print("Batch sent!")
batch = TableBatch()
inBatch = 0
def main(myblob: func.InputStream):
logging.info(
f"Python blob trigger function processed blob \n"
f"Name: {myblob.name}\n"
f"Blob Size: {myblob.length} bytes"
)
postcode_data = pandas.read_csv(
"https://raw.githubusercontent.com/Elkfox/Australian-Postcode-Data/master/au_postcodes.csv",
dtype={"postcode": str},
)
df = pandas.read_excel(
io.BytesIO(myblob.read()),
engine="openpyxl",
true_values=("Y",),
false_values=("n"),
)
df.dropna(subset=["ABN"], inplace=True) # Remove entries with no ABN
df.drop_duplicates(
subset=["ABN"], keep="last", inplace=True
) # Remove duplicate entries and keep the last
df["Town_City"] = df["Town_City"].apply(lambda x: str(x).title())
df["Reporting_hours___Paid"].fillna(0, inplace=True)
df["Reporting_hours___Unpaid"].fillna(0, inplace=True)
df["Reporting_hours___Total"].fillna(0, inplace=True)
check_columns = (
"Operates_in_ACT",
"Operates_in_NSW",
"Operates_in_NT",
"Operates_in_QLD",
"Operates_in_SA",
"Operates_in_TAS",
"Operates_in_VIC",
"Operates_in_WA",
"Relief_of_poverty_sickness_or_the_needs_of_the_aged",
"The_advancement_of_education",
"The_advancement_of_religion",
"The_provision_of_child_care_services",
"Other_purposes_beneficial_to_the_community",
"BASIC_RELIGIOUS",
"Conducted_Activities",
"Animal_Protection",
"Aged_Care_Activities",
"Civic_and_advocacy_activities",
"Culture_and_arts",
"Economic_social_and_community_development",
"Emergency_Relief",
"Employment_and_training",
"Environmental_activities",
"Grant_making_activities",
"Higher_education",
"Hospital_services_and_rehabilitation_activities",
"Housing_activities",
"Income_support_and_maintenance",
"International_activities",
"Law_and_legal_services",
"Mental_health_and_crisis_intervention",
"Political_activities",
"Primary_and_secondary_education",
"Religious_activities",
"Research",
"Social_services",
"Sports",
"Other_Educations",
"other_health_service_delivery",
"Other_recreation_and_social_club_activity",
"Other",
"Aboriginal_or_TSI",
"Aged_Persons",
"Children",
"Communities_Overseas",
"Ethnic_Groups",
"Gay_Lesbian_Bisexual",
"General_Community_in_Australia",
"men",
"Migrants_Refugees_or_Asylum_Seekers",
"Pre_Post_Release_Offenders",
"People_with_Chronic_Illness",
"People_with_Disabilities",
"People_at_risk_of_homelessness",
"Unemployed_Persons",
"Veterans_or_their_families",
"Victims_of_crime",
"Victims_of_Disasters",
"Women",
"Youth",
"Other_charities",
"Other_beneficiaries_not_listed",
"Reporting_Obligations___ACT",
"Reporting_Obligations___NSW",
"Reporting_Obligations___NT",
"Reporting_Obligations___QLD",
"Reporting_Obligations___SA",
"Reporting_Obligations___TAS",
"Reporting_Obligations___VIC",
"Reporting_Obligations___WA",
)
col_names = list(df.columns)
for col in check_columns:
if col in col_names:
df[col].fillna(False, inplace=True)
df["State"] = df["Postcode"].apply(map_postcode)
df = pandas.merge(
df,
postcode_data,
left_on=["Postcode", "Town_City"],
right_on=["postcode", "place_name"],
how="left",
).drop(columns=["postcode", "place_name", "state_name", "state_code", "accuracy"])
# Connect to Azure Table Store
table_service = TableService(
account_name=os.getenv("TABLE_STORAGE_ACCOUNT_NAME"),
account_key=os.getenv("TABLE_STORAGE_KEY"),
)
# Convert dataframe to list of dictionary
records = df.to_dict(orient="records")
# Batch in 100 rows
for record_batch in (
records[pos : pos + 100] for pos in range(0, len(records), 100)
):
batch = TableBatch()
for record in record_batch:
entry = {
"PartitionKey": f"ais",
"RowKey": str(record["ABN"]),
}
for k, v in record.items():
cleanCamelKey = camelKey(k)
# Check that removing those characters didn't create a duplicate key
if cleanCamelKey in entry:
raise ValueError(f"Key collision on {cleanCamelKey}")
entry[cleanCamelKey] = v
batch.insert_or_replace_entity(entry)
table_service.commit_batch("ACN", batch)
