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Pandas

The Pandas connector is a Nexla-managed destination that exports the records from a Nexset to a file storage location so they can be loaded directly into a Jupyter notebook as a pandas DataFrame. When you send data to this destination, Nexla runs a packaged export script that writes the Nexset to an intermediate file store — Amazon S3, FTP, Dropbox, Box, or Google Cloud Storage — in a format ready for analysis in Python. This is a utility connector for data science workflows rather than a third-party API integration.

Pandas icon

Power end-to-end data operations for your Pandas API with Nexla. Our bi-directional Pandas connector is purpose-built for Pandas, making it simple to ingest data, sync it across systems, and deliver it anywhere — all with no coding required. Nexla turns API-sourced data into ready-to-use, reusable data products and makes it easy to send data to Pandas or any other destination. With comprehensive monitoring, lineage tracking, and access controls, Nexla keeps your Pandas workflows fast, secure, and fully governed.

Features

Type: API

SourceDestination

  • Seamless API Integration: Connect to any endpoint as source or destination without coding, with automatic data product creation
  • Visual Composition & Chaining: Build complex integrations using visual templates, chain API calls, and compose workflows with data validation and filtering
  • API Proxy: Expose curated slices of your data securely with a secure and customizable API proxy that validates and transforms data on the fly
  • Request optimization with intelligent batching, retry, and caching to minimize API calls and costs

Prerequisites​

The Pandas connector does not authenticate against a Pandas service. Instead, it writes exported data to an intermediate file storage location that you own, from which a Jupyter notebook can read it. Before configuring a Pandas destination, make sure you have the following:

  • A supported file storage location — Pandas can stage exported data in Amazon S3, FTP, Dropbox, Box, or Google Cloud Storage. Choose the storage type you want to use and note the path where the exported data should be written.

  • A Nexla credential for that storage location — Nexla connects to the chosen file store using a stored credential for that provider (for example, an Amazon S3 access key and secret, or an authorized Dropbox, Box, or GCS account). You can select an existing credential or create a new one while configuring the destination.

  • A Jupyter notebook environment — To read the exported data back into a pandas DataFrame, you need a Python environment with the pandas library installed and access to the same storage location. See the Nexla Access Data in a Jupyter Notebook tutorial for a step-by-step walkthrough and a sample notebook.

Authenticate​

The Pandas connector has no credential fields of its own. Authentication is handled entirely by the credential for the intermediate file storage location that you select when configuring the destination.

Create a credential in Nexla​

  1. When configuring the Pandas destination, choose the file storage type for the intermediate data store (Amazon S3, FTP, Dropbox, Box, or Google Cloud Storage).

  2. Select an existing credential for that storage type, or click the Add Credential tile to open the Add New Credential overlay and create a new one.

  3. Enter a name in the Credential Name field and a short, meaningful description in the Credential Description field.

  4. Provide the connection details for your chosen storage provider (for example, the access key and secret for Amazon S3, or the authorized account for Dropbox, Box, or Google Cloud Storage). The fields shown depend on the storage type you selected.

  5. Click the Save button at the bottom of the overlay. The credential will now appear as a tile and can be selected for use with the Pandas destination.

Use as a destination​

Click the + icon on the Nexset that will be sent to the Pandas destination, and select the Send to Destination option from the menu. Select the Pandas connector from the list of available destination connectors, then configure the export settings described below.

Endpoint templates​

Pandas

Export Data to Pandas Jupyter Notebook. This template writes the records from the Nexset to an intermediate file storage location in a format that can be loaded directly into a pandas DataFrame from a Jupyter notebook.

  • File Storage For Intermediate Data Store — Select the type of file storage where the exported data will be written. Supported options are FTP, Amazon S3, Dropbox, Box, and Google Cloud Storage.
  • Credential — Select the credential for the storage location you chose. Nexla shows the credential selector that matches the selected storage type (for example, an FTP, S3, Dropbox, Box, or GCS credential).
  • Sink Path — Enter the path in the storage location where the exported data should be written.

The Pandas destination writes exported data to the storage location you configure so it can be read back into a pandas DataFrame. For a step-by-step walkthrough and a sample notebook, see the Nexla Access Data in a Jupyter Notebook tutorial.

Save & activate​

Once all destination settings have been configured, click the Done button in the upper right corner of the screen to save and create the destination. To export the data to the configured storage location, open the destination resource menu, and select Activate.

The Nexset data will not be written to the storage location until the destination is activated. Destinations can be activated immediately or at a later time, providing full control over data movement.