第九課： - 導出到CSV / EXCEL / TXT

第 9 課

將數據從microdost sql數據庫導出到cvs，excel和txt文件。

In [1]:

# Import libraries
import pandas as pd import sys from sqlalchemy import create_engine, MetaData, Table, select 

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

 

從SQL獲取數據

在本節中，咱們使用sqlalchemy庫從sql數據庫中獲取數據。請注意，參數部分須要根據你的環境修改。

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

 

下面的全部文件將保存到當前的文件夾中。

 

導出到 CSV文件

In [4]:

df.to_csv('DimDate.csv', index=False) print('Done') 

 

Done

 

導出到 EXCEL文件

In [5]:

df.to_excel('DimDate.xls', index=False) print('Done') 

 

Done

 

導出到 TXT文件

In [6]:

df.to_csv('DimDate.txt', index=False) print('Done') 

 

Done

 

This tutorial was rewrited by CDS