def table_archiver(server, database, source_table, destination_table, timestamp_column_name='ArchivedDTS'):
"""
Takes a table and archives a complete copy of it with the addition of a timestamp of when the archive occurred to a
given destination table on the same database.
This should build a new table if the table doesn't exist.
Args:
server (str): Server name
database (str): Database name
source_table (str): Source table name
destination_table (str): Destination table name
timestamp_column_name (str): New timestamp column name
Returns:
(str): A string with details on records archived.
Example usage:
from healthcareai.common.table_archiver import table_archiver
table_archiver('localhost', 'SAM_123', 'RiskScores', 'RiskScoreArchive', 'ArchiveDTS')
```
"""
# Basic input validation
if type(server) is not str:
raise HealthcareAIError('Please specify a server address')
if type(database) is not str:
raise HealthcareAIError('Please specify a database name')
if type(source_table) is not str:
raise HealthcareAIError('Please specify a source table name')
if type(destination_table) is not str:
raise HealthcareAIError('Please specify a destination table name')
start_time = time.time()
connection_string = 'mssql+pyodbc://{}/{}?driver=SQL+Server+Native+Client+11.0'.format(server, database)
# Load the table to be archived
df = pd.read_sql_table(source_table, connection_string)
number_records_to_add = len(df)
# Add timestamp to dataframe
df[timestamp_column_name] = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')
# Save the new dataframe out to the db without the index, appending values
df.to_sql(destination_table, connection_string, index=False, if_exists='append')
end_time = time.time()
delta_time = end_time - start_time
result = 'Archived {0} records from {1}/{2}/{3} to {4} in {5} seconds'.format(
number_records_to_add,
server,
database,
source_table,
destination_table,
delta_time)
return result```
