CSV Connector for Python CSV Connector can be used to extract and output CSV data from Web URL/REST API web service calls (Web URL) or direct CSV String (variables or DB columns) or local CSV files data. In this article you will learn how to quickly and efficiently integrate CSV data in Python. We will use high-performance CSV Connector to easily connect to CSV and then access the data inside Python. Let's follow the steps below to see how we can accomplish that! Download Documentation CSV Connector for Python is based on ZappySys CSV Driver which is part of ODBC PowerPack. It is a collection of high-performance ODBC drivers that enable you to integrate data in SQL Server, SSIS, a programming language, or any other ODBC-compatible application. ODBC PowerPack supports various file formats, sources and destinations, including REST/SOAP API, SFTP/FTP, storage services, and plain files, to mention a few.

Connect to CSV in other apps Connect to CSV in SSIS Connect to CSV in SQL Server Connect to CSV in Power BI Connect to CSV in SSRS Connect to CSV in Informatica Connect to CSV in MS Access Connect to CSV in MS Excel Connect to CSV in SSAS Connect to CSV in C# Connect to CSV in JAVA Connect to CSV in Tableau Connect to CSV in SAP Crystal Reports Connect to CSV in Azure Data Factory (Pipeline) Connect to CSV in Azure Data Factory (SSIS) Connect to CSV in Talend Studio Connect to CSV in UiPath Connect to CSV in PowerShell Connect to CSV in ODBC

Create ODBC Data Source (DSN) based on ZappySys CSV Driver

Step-by-step instructions

To get data from CSV using Python we first need to create a DSN (Data Source) which will access data from CSV. We will later be able to read data using Python. Perform these steps:

1. Download and install ODBC PowerPack.

2. Open ODBC Data Sources (x64):

   

3. Create a User data source (User DSN) based on ZappySys CSV Driver

   ZappySys CSV Driver

   
   • Create and use User DSN if the client application is run under a User Account. This is an ideal option in design-time, when developing a solution, e.g. in Visual Studio 2019. Use it for both type of applications - 64-bit and 32-bit.
   • Create and use System DSN if the client application is launched under a System Account, e.g. as a Windows Service. Usually, this is an ideal option to use in a production environment. Use ODBC Data Source Administrator (32-bit), instead of 64-bit version, if Windows Service is a 32-bit application.

4. Select Url or File.
   

   Read CSV API in Python

   • Paste the following Url. In this example, We are using Zip format CSV File URL, but you need to refer your CSV File/URL.

     https://zappysys.com/downloads/files/test/cust-1.csv.zip
     Click on Test Connection button to view whether the Test Connection is SUCCESSFUL or Not.

   Read CSV File in Python

   • You can use pass single file or multiple file path using wildcard pattern in path and you can use select single file by clicking [...] path button or multiple file using wildcard pattern in path.
     

     Note: If you want to operation with multiple files then use wild card pattern as below
     (when you use wild card pattern in source path then system will treat target path as folder regardless you end with slash)
     
     C:\SSIS\Test\reponse.csv (will read only single reponse.csv file)
     C:\SSIS\Test\j*.csv (all files starting with file name j)
     C:\SSIS\Test\*.csv (all files with .csv Extension and located under folder subfolder)
     

     Click on Test Connection button to view whether the Test Connection is SUCCESSFUL or Not.

5. Once you configured a data source, you can preview data. Hit Preview tab, and use similar settings to preview data:
   

6. Click OK to finish creating the data source

7. That's it; we are done. In a few clicks we configured the read the CSV data using ZappySys CSV Connector.

Read data in Python

Using ODBC DSN

1. Python code to get the data:
   

   CsvDSN')

   

2. When you run the code it will make the API call and read the data:
   

3. Here is Python program's code in text format:

   
       import pyodbc
       conn = pyodbc.connect('DSN=CsvDSN')
   
       cursor = conn.cursor()
   
       #execute query to fetch data from API service
       cursor.execute("SELECT id,title FROM products")
   
       row = cursor.fetchone()
       while row:
           print(row)
           row = cursor.fetchone()
   
       ##For loop example
       #for row in cursor:
       #    print(row)
   

Using a full ODBC connection string

If you want to avoid being dependent on a DSN and creating multiple DSNs for each platform (x86, x64), then you can use a fully qualified connection string. Simply go to your DSN and copy the Connection String:

1. Open ODBC data source configuration and click Copy settings:
   

   ZappySys CSV Driver - CSV

   CSV Connector can be used to extract and output CSV data from Web URL/REST API web service calls (Web URL) or direct CSV String (variables or DB columns) or local CSV files data.

   CsvDSN

   
2. The window opens, telling us the connection string was successfully copied to the clipboard:

3. Then in your Python code use Connection String when initializing OdbcConnection object, for example:

   conn = pyodbc.connect('DRIVER={ZappySys CSV Driver};ServiceUrl=https://yourservices.provider.com/api/xxxx....;AuthName=Http;')

How to install `pyodbc` in the Python?

Configuring pagination in the CSV Driver

ZappySys CSV Driver equips users with powerful tools for seamless data extraction and management from REST APIs, leveraging advanced pagination methods for enhanced efficiency. These options are designed to handle various types of pagination structures commonly used in APIs. Below are the detailed descriptions of these options:

1. Page-based Pagination: This method works by retrieving data in fixed-size pages from the Rest API. It allows you to specify the page size and navigate through the results by requesting different page numbers, ensuring that you can access all the data in a structured manner.

2. Offset-based Pagination: With this approach, you can extract data by specifying the starting point or offset from which to begin retrieving data. It allows you to define the number of records to skip and fetch subsequent data accordingly, providing precise control over the data extraction process.

3. Cursor-based Pagination: This technique involves using a cursor or a marker that points to a specific position in the dataset. It enables you to retrieve data starting from the position indicated by the cursor and proceed to subsequent segments, ensuring that you capture all the relevant information without missing any records.

4. Token-based Pagination: In this method, a token serves as a unique identifier for a specific data segment. It allows you to access the next set of data by using the token provided in the response from the previous request. This ensures that you can systematically retrieve all the data segments without duplication or omission.

Utilizing these comprehensive pagination features in the ZappySys CSV Driver facilitates efficient data management and extraction from REST APIs, optimizing the integration and analysis of extensive datasets.

For more detailed steps, please refer to this link: How to do REST API Pagination in SSIS / ODBC Drivers

Authentication

ZappySys offers various authentication methods to securely access data from various sources. These authentication methods include OAuth, Basic Authentication, Token-based Authentication, and more, allowing users to connect to a wide range of data sources securely.

ZappySys Authentication is a robust system that facilitates secure access to data from a diverse range of sources. It includes a variety of authentication methods tailored to meet the specific requirements of different data platforms and services. These authentication methods may involve:

1. OAuth: ZappySys supports OAuth for authentication, which allows users to grant limited access to their data without revealing their credentials. It's commonly used for applications that require access to user account information.

2. Basic Authentication: This method involves sending a username and password with every request. ZappySys allows users to securely access data using this traditional authentication approach.

3. Token-based Authentication: ZappySys enables users to utilize tokens for authentication. This method involves exchanging a unique token with each request to authenticate the user's identity without revealing sensitive information.

By implementing these authentication methods, ZappySys ensures the secure and reliable retrieval of data from various sources, providing users with the necessary tools to access and integrate data securely and efficiently. For more comprehensive details on the authentication process, please refer to the official ZappySys documentation or reach out to their support team for further assistance.

For more details, please refer to this link: ZappySys Connections

Advanced topics

Create Custom Stored Procedure in ZappySys Driver

You can create procedures to encapsulate custom logic and then only pass handful parameters rather than long SQL to execute your API call.

Steps to create Custom Stored Procedure in ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here

1. Go to Custom Objects Tab and Click on Add button and Select Add Procedure:
   

2. Enter the desired Procedure name and click on OK:
   

3. Select the created Stored Procedure and write the your desired stored procedure and Save it and it will create the custom stored procedure in the ZappySys Driver:
   Here is an example stored procedure for ZappySys Driver. You can insert Placeholders anywhere inside Procedure Body. Read more about placeholders here
   

   CREATE PROCEDURE [usp_get_orders]
       @fromdate = '<<yyyy-MM-dd,FUN_TODAY>>'
    AS
       SELECT * FROM Orders where OrderDate >= '<@fromdate>';
   

   
   

4. That's it now go to Preview Tab and Execute your Stored Procedure using Exec Command. In this example it will extract the orders from the date 1996-01-01:
   

   Exec usp_get_orders '1996-01-01';

   
   

5. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([LS_TO_CSV_IN_GATEWAY], 'EXEC usp_get_orders @fromdate=''1996-07-30''')

   
   

6. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Create Custom Virtual Table in ZappySys Driver

ZappySys API Drivers support flexible Query language so you can override Default Properties you configured on Data Source such as URL, Body. This way you don't have to create multiple Data Sources if you like to read data from multiple EndPoints. However not every application support supplying custom SQL to driver so you can only select Table from list returned from driver.

If you're dealing with Microsoft Access and need to import data from an SQL query, it's important to note that Access doesn't allow direct import of SQL queries. Instead, you can create custom objects (Virtual Tables) to handle the import process.

Many applications like MS Access, Informatica Designer wont give you option to specify custom SQL when you import Objects. In such case Virtual Table is very useful. You can create many Virtual Tables on the same Data Source (e.g. If you have 50 URLs with slight variations you can create virtual tables with just URL as Parameter setting.

1. Go to Custom Objects Tab and Click on Add button and Select Add Table:
   

2. Enter the desired Table name and click on OK:
   

3. And it will open the New Query Window Click on Cancel to close that window and go to Custom Objects Tab.

4. Select the created table, Select Text Type AS SQL and write the your desired SQL Query and Save it and it will create the custom table in the ZappySys Driver:
   Here is an example SQL query for ZappySys Driver. You can insert Placeholders also. Read more about placeholders here
   

   SELECT
     "ShipCountry",
     "OrderID",
     "CustomerID",
     "EmployeeID",
     "OrderDate",
     "RequiredDate",
     "ShippedDate",
     "ShipVia",
     "Freight",
     "ShipName",
     "ShipAddress",
     "ShipCity",
     "ShipRegion",
     "ShipPostalCode"
   FROM "Orders"
   Where "ShipCountry"='USA'

   
   

5. That's it now go to Preview Tab and Execute your custom virtual table query. In this example it will extract the orders for the USA Shipping Country only:
   

   SELECT * FROM "vt__usa_orders_only"

   
   

6. Let's generate the SQL Server Query Code to make the API call using stored procedure. Go to Code Generator Tab, select language as SQL Server and click on Generate button the generate the code.
   As we already created the linked server for this Data Source, in that you just need to copy the Select Query and need to use the linked server name which we have apply on the place of [MY_API_SERVICE] placeholder.
   

   SELECT * FROM OPENQUERY([LS_TO_CSV_IN_GATEWAY], 'EXEC [usp_get_orders] ''1996-01-01''')

   
   

7. Now go to SQL served and execute that query and it will make the API call using stored procedure and provide you the response.
   

Conclusion

In this article we showed you how to connect to CSV in Python and integrate data without any coding, saving you time and effort. It's worth noting that ZappySys CSV Driver allows you to connect not only to CSV, but to any Java application that supports JDBC (just use a different JDBC driver and configure it appropriately).

We encourage you to download CSV Connector for Python and see how easy it is to use it for yourself or your team.

If you have any questions, feel free to contact ZappySys support team. You can also open a live chat immediately by clicking on the chat icon below.

Download CSV Connector for Python Documentation

More integrations

Other connectors for Python

Amazon Ads

Amazon Athena

Amazon Selling Partner (SP-API)

Apache Derby

Asana

Azure DevOps

Cosmos DB

DropBox

ElasticSearch

FastSpring

Google Ads

Google BigQuery

Google Calendar

Google Drive

Google Sheets

Hubspot

Infor Data Lake

Jira

Mailchimp

ManagedEngine ServiceDesk Plus (Zoho)

MariaDB

OData

OneDrive

Outlook Mail (Office 365)

Power BI

ServiceNow

SharePoint Online

Shopify

Smartsheet

Starburst

Stripe

Trino

YouTube

Zendesk

Zoho CRM

Zoho SalesIQ

Zoom

REST API

JSON

JSON File

XML

SOAP

XML File

JDBC-ODBC Bridge

Salesforce

CSV

Amazon S3 JSON File

Amazon S3 XML File

Amazon S3 CSV File

Azure Blob JSON File

Azure Blob XML File

Azure Blob CSV File

FTP/SFTP JSON File

FTP/SFTP XML File

FTP/SFTP CSV File

Build your own Custom API Connector

Other application integration scenarios for CSV

SSIS

SQL Server

Power BI

SSRS

Informatica

MS Access

MS Excel

SSAS

C#

JAVA

Tableau

SAP Crystal Reports

Azure Data Factory (Pipeline)

Azure Data Factory (SSIS)

Talend Studio

UiPath

PowerShell

ODBC

Common Searches: