def btc_sort():
btc = read_to_df('xbtusd', '1d', update=True)
btc = add_opens_days(btc)
btc = apply_WO(btc)
btc = apply_MO(btc)
btc = find_body_ratio(btc)
btc = find_volume_ratio(btc)
btc = calc_ma(btc)
btc = find_gradient(btc)
btc_1m = read_to_df('xbtusd', '5m', update=True)
btc_1m = add_opens_days(btc_1m)
btc_1m = apply_WO(btc_1m)
btc_1m = apply_MO(btc_1m)
vwaps = calc_vwap(btc_1m)
btc = btc.assign(W_vwap=vwaps[['W_vwap']])
btc = btc.assign(M_vwap=vwaps[['M_vwap']])
btc = find_returns(btc)
table_btc, btc, signals_test, columns = create_table(btc)
table_btc.insert(0, columns)
btc = find_percentiles(btc)
btc = find_body_ratio(btc)
btc = find_volume_ratio(btc)
btc = calc_ma(btc)
btc = find_gradient(btc)
fig_btc = dash_table.DataTable
fig_btc = ff.create_table(table_btc)
return btc, fig_btc
def main():
conn, cur = functions.connect()
status = True
functions.print_menu()
while status:
choice = int(input('Choice: '))
if choice == 1:
functions.create_table(conn, cur, 'teachers')
elif choice == 2:
functions.insert_data(conn, cur, 'teachers')
elif choice == 3:
keys = input('SELECT BY: ')
records = functions.select_from_db(conn, cur, 'teachers', keys)
functions.print_selected_data(records, keys)
elif choice == 4:
records = functions.select_from_db(conn, cur, 'teachers')
functions.print_selected_data(records)
elif choice == 5:
functions.disconnect(conn, cur)
break
functions.print_menu()
print(database_name)
break
else:
database_name += '.db'
print(database_name)
if database_name in os.listdir():
print(
'This database already exists. Please select another name. \n'
)
else:
break
with sql.connect(database_name) as conn: # connection to DB.
c = conn.cursor()
# Create DB table for Ad Groups
functions.create_table(c, 'Ad Groups', {
'Campaign Name': 'TEXT',
'Ad Group Name': 'TEXT'
})
print()
# Open reader to extract a list of headers & create table based on it
with open('Ad_Report2.csv', 'r') as csv_file:
csv_reader = csv.reader(csv_file)
# Removing all "Responsive Search Ad" from headers
# headers = [x for x in next(csv_reader) if x.startswith('Responsive Search Ad') is False]
headers = [x for x in next(csv_reader)]
# Creating a table with each header as a text column
dict_columns = {
k: v
for k, v in zip(headers, ('' for x in range(len(headers))))
}
functions.create_table(c, 'Ads', dict_columns)
from functions import create_table, insert_table, view_table
import argparse
import sys
#Argparse Config
parser = argparse.ArgumentParser(description='TY Investments \
Database Script')
parser.add_argument('-i',
'--insert',
help='Input data \
into database',
action='store_true')
parser.add_argument('-v',
'--view',
help='Show all data \
inside money table',
action='store_true')
args = parser.parse_args()
if __name__ == "__main__":
print("running app.py")
if args.insert:
create_table()
insert_table()
elif args.view:
view_table()
else:
pass
def main():
f.create_db()
f.create_table()
f.insert_data()
'client_secrets.json', scopes=["https://www.googleapis.com/auth/cloud-platform"],
)
client = bigquery.Client(credentials=credentials, project=credentials.project_id,)
# The initial tables will live in the bronze Dataset
dataset = 'bronze_warehouse'
# Define table name and schema
table_id_warehouse = 'warehouse_overview'
warehouse_schema = [
bigquery.SchemaField("warehouse_name", bigquery.enums.SqlTypeNames.STRING),
bigquery.SchemaField("warehouse_id", bigquery.enums.SqlTypeNames.STRING),
bigquery.SchemaField("country", bigquery.enums.SqlTypeNames.STRING),
bigquery.SchemaField("update_time", bigquery.enums.SqlTypeNames.TIMESTAMP),
]
# Define table name and schema
table_id_product = 'warehouse_product'
product_schema = [
bigquery.SchemaField("sku", bigquery.enums.SqlTypeNames.STRING),
bigquery.SchemaField("inventory_quantity", bigquery.enums.SqlTypeNames.INTEGER),
bigquery.SchemaField("update_time", bigquery.enums.SqlTypeNames.TIMESTAMP),
]
# Function to create Dataset (if dataset exists this wont do anything)
f.create_dataset(client,dataset)
# Function to create Table (if Table exists this wont do anything)
f.create_table(client,dataset,table_id_warehouse,warehouse_schema)
f.create_table(client,dataset,table_id_product,product_schema)
def main(database, table):
create_database(database)
cur = connect_to_database(database)
create_table(cur, table)