Pandas Tutorial | Hedaro

Lesson 8



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How to pull data from a microsoft sql database

In [1]:
# Import libraries
import pandas as pd
import sys
from sqlalchemy import create_engine, MetaData, Table, select, engine
In [2]:
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
Python version 3.5.1 |Anaconda custom (64-bit)| (default, Feb 16 2016, 09:49:46) [MSC v.1900 64 bit (AMD64)]
Pandas version 0.18.1

Version 1

In this section we use the sqlalchemy library to grab data from a sql database. Make sure to use your own ServerName, Database, TableName.

In [3]:
# Parameters
TableName = "data"

DB = {
    'drivername': 'mssql+pyodbc',
    'servername': 'DAVID-THINK',
    #'port': '5432',
    #'username': 'lynn',
    #'password': '',
    'database': 'BizIntel',
    'driver': 'SQL Server Native Client 11.0',
    'trusted_connection': 'yes',  
    'legacy_schema_aliasing': False
}

# Create the connection
engine = create_engine(DB['drivername'] + '://' + DB['servername'] + '/' + DB['database'] + '?' + 'driver=' + DB['driver'] + ';' + 'trusted_connection=' + DB['trusted_connection'], legacy_schema_aliasing=DB['legacy_schema_aliasing'])
conn = engine.connect()

# Required for querying tables
metadata = MetaData(conn)

# Table to query
tbl = Table(TableName, metadata, autoload=True, schema="dbo")
#tbl.create(checkfirst=True)

# Select all
sql = tbl.select()

# run sql code
result = conn.execute(sql)

# Insert to a dataframe
df = pd.DataFrame(data=list(result), columns=result.keys())

# Close connection
conn.close()

print('Done')
Done

Select the contents in the dataframe.

In [4]:
df.head()
Out[4]:
Date Symbol Volume
0 2013-01-01 A 0.00
1 2013-01-02 A 200.00
2 2013-01-03 A 1200.00
3 2013-01-04 A 1001.00
4 2013-01-05 A 1300.00
In [5]:
df.dtypes
Out[5]:
Date      datetime64[ns]
Symbol            object
Volume            object
dtype: object

Convert to specific data types. The code below will have to be modified to match your table.

Version 2

In [6]:
import pandas.io.sql
import pyodbc
In [7]:
# Parameters
server = 'DAVID-THINK'
db = 'BizIntel'

# Create the connection
conn = pyodbc.connect('DRIVER={SQL Server};SERVER=' + DB['servername'] + ';DATABASE=' + DB['database'] + ';Trusted_Connection=yes')

# query db
sql = """

SELECT top 5 *
FROM data

"""
df = pandas.io.sql.read_sql(sql, conn)
df.head()
Out[7]:
Date Symbol Volume
0 2013-01-01 A 0.0
1 2013-01-02 A 200.0
2 2013-01-03 A 1200.0
3 2013-01-04 A 1001.0
4 2013-01-05 A 1300.0

Version 3

In [8]:
from sqlalchemy import create_engine
In [9]:
# Parameters
ServerName = "DAVID-THINK"
Database = "BizIntel"
Driver = "driver=SQL Server Native Client 11.0"

# Create the connection
engine = create_engine('mssql+pyodbc://' + ServerName + '/' + Database + "?" + Driver)

df = pd.read_sql_query("SELECT top 5 * FROM data", engine)
df
Out[9]:
Date Symbol Volume
0 2013-01-01 A 0.0
1 2013-01-02 A 200.0
2 2013-01-03 A 1200.0
3 2013-01-04 A 1001.0
4 2013-01-05 A 1300.0

This tutorial was created by HEDARO