Extract, Transform, and Load Excel Data in Python

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Microsoft Excel Python Connector

Python Connector Libraries for Microsoft Excel Data Connectivity. Integrate Microsoft Excel with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Excel enables you to create ETL applications and pipelines for Excel data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Excel and the petl framework, you can build Excel-connected applications and pipelines for extracting, transforming, and loading Excel data. This article shows how to connect to Excel with the CData Python Connector and use petl and pandas to extract, transform, and load Excel data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Excel data in Python. When you issue complex SQL queries from Excel, the driver pushes supported SQL operations, like filters and aggregations, directly to Excel and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Excel Data

Connecting to Excel data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The ExcelFile, under the Authentication section, must be set to a valid Excel File.

After installing the CData Excel Connector, follow the procedure below to install the other required modules and start accessing Excel through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Excel Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.excel as mod

You can now connect with a connection string. Use the connect function for the CData Excel Connector to create a connection for working with Excel data.

cnxn = mod.connect("Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx';")

Create a SQL Statement to Query Excel

Use SQL to create a statement for querying Excel. In this article, we read data from the Sheet entity.

sql = "SELECT Name, Revenue FROM Sheet WHERE Name = 'Bob'"

Extract, Transform, and Load the Excel Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Excel data. In this example, we extract Excel data, sort the data by the Revenue column, and load the data into a CSV file.

Loading Excel Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Revenue')

etl.tocsv(table2,'sheet_data.csv')

In the following example, we add new rows to the Sheet table.

Adding New Rows to Excel

table1 = [ ['Name','Revenue'], ['NewName1','NewRevenue1'], ['NewName2','NewRevenue2'], ['NewName3','NewRevenue3'] ]

etl.appenddb(table1, cnxn, 'Sheet')

With the CData Python Connector for Excel, you can work with Excel data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Excel Python Connector to start building Python apps and scripts with connectivity to Excel data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.excel as mod

cnxn = mod.connect("Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx';")

sql = "SELECT Name, Revenue FROM Sheet WHERE Name = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Revenue')

etl.tocsv(table2,'sheet_data.csv')

table3 = [ ['Name','Revenue'], ['NewName1','NewRevenue1'], ['NewName2','NewRevenue2'], ['NewName3','NewRevenue3'] ]

etl.appenddb(table3, cnxn, 'Sheet')