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，咱们使用sqlalchemy库从sql数据库中获取数据。确保使用您本身的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为符合你的表。

 

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.