第九课： - 导出到CSV / EXCEL / TXT

第 9 课

将数据从microdost sql数据库导出到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

从SQL获取数据

在本节中，我们使用sqlalchemy库从sql数据库中获取数据。请注意，参数部分需要根据你的环境修改。

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

下面的所有文件将保存到当前的文件夹中。

导出到 CSV文件

In [4]:

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

Done

导出到 EXCEL文件

In [5]:

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

Done

导出到 TXT文件

In [6]:

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

Done

This tutorial was rewrited by CDS