data-transport

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None <html lang="en"> <head> </head>

Writing to Microsoft SQLServer¶

Insure the Microsoft SQL Server is installed and you have access i.e account information
The target database must be created before hand.
We created an authentication file that will contain user account and location of the database

The cell below creates a dataframe that will be stored in a Microsoft SQL Server database.

NOTE This was not tested with a cloud instance

In [1]:

#
# Writing to Google Bigquery database
#
import transport
from transport import providers
import pandas as pd
import os

AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key
MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])

_data = pd.DataFrame({"name":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})
msw = transport.factory.instance(provider=providers.MSSQL,table='friends',context='write',auth_file=MSSQL_AUTH_FILE)
msw.write(_data,if_exists='replace') #-- default is append
print (['data transport version ', transport.__version__])

['data transport version ', '2.0.0']

Reading from Microsoft SQL Server database¶

The cell below reads the data that has been written by the cell above and computes the average age within an MS SQL Server (simple query).

Basic read of the designated table (friends) created above
Execute an aggregate SQL against the table

NOTE

It is possible to use transport.factory.instance or transport.instance they are the same. It allows the maintainers to know that we used a factory design pattern.

In [5]:

import transport
from transport import providers
import os
AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key
MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])

msr = transport.instance(provider=providers.MSSQL,table='friends',auth_file=MSSQL_AUTH_FILE)
_df = msr.read()
_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'
_sdf = msr.read(sql=_query)
print (_df)
print ('\n--------- STATISTICS ------------\n')
print (_sdf)

           name  age
0    James Bond   55
1  Steve Rogers  150
2  Steve Nyemba   44

--------- STATISTICS ------------

   _counts    
0        3  83

The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.

NOTE:

The auth_file is intended to be JSON formatted

In [3]:

{
    
    "dataset":"demo","table":"friends","username":"<username>","password":"<password>"
}

Out[3]:

{'dataset': 'demo', 'table': 'friends'}

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4.4 KiB Raw Blame History

Writing to Microsoft SQLServer¶

Reading from Microsoft SQL Server database¶

4.4 KiB

Raw Blame History