Continuum Analytics: Documentation

# iopro.pyodbc Enhanced Capabilities

## Demo code showing the enhanced capabilities of iopro.pyodbc submodule

This demo shows the basic capabilities for the iopro.pyodbc module. It first will connect with the database of your choice by ODBC, create and fill a new table (market) and then retrieve data with different methods (fetchall(), fetchdictarray() and fetchsarray()).

Author: Francesc Alted, Continuum Analytics

    >>> import iopro.pyodbc as pyodbc
    >>> # Open the database (use the most appropriate for you)
    >>> connect_string = 'DSN=odbcsqlite;DATABASE=market.sqlite'  # SQLite
    >>> #connect_string = 'Driver={SQL Server};SERVER=MyWinBox;DATABASE=Test;USER=Devel;PWD=XXX'  # SQL Server
    >>> #connect_string = 'DSN=myodbc3;UID=devel;PWD=XXX;DATABASE=test'  # MySQL
    >>> #connect_string = 'DSN=PSQL;UID=devel;PWD=XXX;DATABASE=test'   # PostgreSQL
    >>> connection = pyodbc.connect(connect_string)
    >>> cursor = connection.cursor()

## Create the test table (optional if already done)

    >>> try:
    ...     cursor.execute('drop table market')
    ... except:
    ...     pass
    >>> cursor.execute('create table market (symbol_ varchar(5), open_ float, low_ float, high_ float, close_ float, volume_ int)')

## Fill the test table (optional if already done)

    >>> from time import time
    >>> t0 = time()
    >>> N = 1000*1000
    >>> for i in xrange(N):
    ...     cursor.execute(
    ...         "insert into market(symbol_, open_, low_, high_, close_, volume_)"
    ...         " values (?, ?, ?, ?, ?, ?)",
    ...         (str(i), float(i), float(2*i), None, float(4*i), i))
    >>> cursor.execute("commit")             # not supported by SQLite
    >>> t1 = time() - t0
    >>> print "Stored %d rows in %.3fs" % (N, t1)

## Do the query in the traditional way

    >>> # Query of the full table using the traditional fetchall
    >>> query = "select * from market"
    >>> cursor.execute(query)
    >>> %time all = cursor.fetchall()
    CPU times: user 5.23 s, sys: 0.56 s, total: 5.79 s
    Wall time: 7.09 s

## Do the query and get a dictionary of NumPy arrays

    >>> # Query of the full table using the fetchdictarray (retrieve a dictionary of arrays)
    >>> cursor.execute(query)
    >>> %time dictarray = cursor.fetchdictarray()
    CPU times: user 0.92 s, sys: 0.10 s, total: 1.02 s
    Wall time: 1.44 s

## Peek into the retrieved data

    >>> dictarray.keys()
    ['high_', 'close_', 'open_', 'low_', 'volume_', 'symbol_']
    >>> dictarray['high_']
    array([ nan,  nan,  nan, ...,  nan,  nan,  nan])
    >>> dictarray['symbol_']
    array(['0', '1', '2', ..., '99999', '99999', '99999'], dtype='|S6')

## Do the query and get a NumPy structured array

    >>> # Query of the full table using the fetchsarray (retrieve a structured array)
    >>> cursor.execute(query)
    >>> %time sarray = cursor.fetchsarray()
    CPU times: user 1.08 s, sys: 0.11 s, total: 1.20 s
    Wall time: 1.99 s

## Peek into retrieved data

    >>> sarray.dtype
    dtype([('symbol_', 'S6'), ('open_', '&lt;f8'), ('low_', '&lt;f8'), ('high_', '&lt;f8'), ('close_', '&lt;f8'), ('volume_', '&lt;i4')])
    >>> sarray[0:10]
    array([('0', 0.0, 0.0, nan, 0.0, 0), ('1', 1.0, 2.0, nan, 4.0, 1),
           ('2', 2.0, 4.0, nan, 8.0, 2), ('3', 3.0, 6.0, nan, 12.0, 3),
           ('4', 4.0, 8.0, nan, 16.0, 4), ('5', 5.0, 10.0, nan, 20.0, 5),
           ('6', 6.0, 12.0, nan, 24.0, 6), ('7', 7.0, 14.0, nan, 28.0, 7),
           ('8', 8.0, 16.0, nan, 32.0, 8), ('9', 9.0, 18.0, nan, 36.0, 9)],
          dtype=[('symbol_', 'S6'), ('open_', '&lt;f8'), ('low_', '&lt;f8'), ('high_', '&lt;f8'), ('close_', '&lt;f8'), ('volume_', '&lt;i4')])
    >>> sarray['symbol_']
    array(['0', '1', '2', ..., '99999', '99999', '99999'], dtype='|S6')