第八课： - 从Microsoft SQL数据库读取

第 8 课

如何从Microsoft SQL数据库中提取数据

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 0.20.1 

版本1

在本节中，我们使用sqlalchemy库从sql数据库中获取数据。确保使用您自己的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

 选择数据帧中的内容。

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

 

转换为特定的数据类型。下面的代码必须修改成符合你的表。

 

版本 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

 

版本 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 rewrited by CDS.