Extract, Transform, and Load Tally Data in Python

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Tally Python Connector

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



The CData Python Connector for Tally enables you to create ETL applications and pipelines for Tally 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 Tally and the petl framework, you can build Tally-connected applications and pipelines for extracting, transforming, and loading Tally data. This article shows how to connect to Tally with the CData Python Connector and use petl and pandas to extract, transform, and load Tally data.

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

Connecting to Tally Data

Connecting to Tally 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.

Set the following connection properties to connect to Tally Instance:

  • Url: Set this to the URL for your Tally instance. For example: http://localhost:9000.

After installing the CData Tally Connector, follow the procedure below to install the other required modules and start accessing Tally 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 Tally 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.tally as mod

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

cnxn = mod.connect("Url='http://localhost:9000'")

Create a SQL Statement to Query Tally

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

sql = "SELECT Name, Address FROM Company WHERE CompanyNumber = '1000'"

Extract, Transform, and Load the Tally Data

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

Loading Tally Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Tally, you can work with Tally 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 Tally Python Connector to start building Python apps and scripts with connectivity to Tally 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.tally as mod

cnxn = mod.connect("Url='http://localhost:9000'")

sql = "SELECT Name, Address FROM Company WHERE CompanyNumber = '1000'"

table1 = etl.fromdb(cnxn,sql)

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

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