IOPro - MySQL

[Larry Green]

Very simply, I need to understand how to setup the connection string for use of IOPro with a MySQL database.

iopro seems to piggyback pyodbc. 

The documentation says -

connect_string = 'DSN=myodbc3;UID=devel;PWD=XXX;DATABASE=test'  # MySQL

But there is zero explanation of this, or relevant examples. 

Do I need to have pyodbc installed in conda?

I use sqlalchemy and PyMySQL.  is the connection the same?

I also see references to Drivers etc., but no explanation as to when,why and how to use...

To make simple, my sqlalchemy connection is:

engine_str = 'mysql+pymysql://{}:{}@{}/{}'.format(login, passwd, server, db)

engine = sqlalchemy.create_engine(engine_str, echo=False, encoding='utf-8', pool_recycle=3600)

conn = engine.connect()   

Please help...

[Ilan Schnell]

IOPro comes with it's own version of pyodbc, which may be imported as iopro.pyodbc.

I'm CCing Jay, who is the main developer of IOPro.

- Ilan

--
Anaconda Community Support Group Brought to you by Continuum Analytics
---
You received this message because you are subscribed to the Google Groups "Anaconda - Public" group.
To unsubscribe from this group and stop receiving emails from it, send an email to anaconda+u...@continuum.io.
To post to this group, send email to anac...@continuum.io.
Visit this group at http://groups.google.com/a/continuum.io/group/anaconda/.

[Larry Green]

Hi ,

I found the MySQL Driver in the Windows 7 administrative settings.  Then used that in the connection string, and it worked.  Thanks.

I will say that the IOPro fetchdictarray from a MySQL table seems to return some strange results/characters.  

Is there any way to bring the table directly into a pandas DataFrame?

________________________________

Larry Green, CFA

SmartMarketData, LLC

lgr...@SmartMarketData.com

203.829.3000 (m)

[Oscar Villellas]

There is no call in IOPro.pyodbc that returns a dataframe directly. However, a single call is enough to make a dataframe out of the results of fetch_dictarray or fetch_sarray in a quite efficient way.

For fetch_dictarray you can use "pandas.DataFrame.from_dict", while for fetch_sarray just pass the result to the DataFrame constructor. In our tests the conversions are very efficient.

Can you develop a bit more on the strange results/characters? Are you using an unicode driver? what are the actual types used in those fields? what is the NumPy dtype returned for those character fields?

Thanks,

  Oscar