Pandas Tutorial | Hedaro

Lesson 9



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Export data from a microdost sql database to cvs, excel, and txt.

In [7]:
# Import libraries
import pandas as pd
import sys
from sqlalchemy import create_engine, MetaData, Table, select
In [8]:
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

Grab Data from SQL

In this section we use the sqlalchemy library to grab data from a sql database. Note that the parameter section will need to be modified.

In [9]:
# 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

All the files below will be saved to the same folder the notebook resides in.

Export to CSV

In [10]:
df.to_csv('DimDate.csv', index=False)
print('Done')
Done

Export to EXCEL

In [11]:
df.to_excel('DimDate.xls', index=False)
print('Done')
Done

Export to TXT

In [12]:
df.to_csv('DimDate.txt', index=False)
print('Done')
Done

This tutorial was created by HEDARO