The CSV Reader node is designed to read data from a CSV file and convert each column into a separate output list based on the file’s headers. This node simplifies working with structured data, making it ideal for flows that involve CSV file data extraction.

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Node Inputs

  • CSV File Name
    • Type: File
    • Description: The CSV file to be processed by the CSV Reader node. This file should have column headers in the first row.

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Node Outputs

The CSV Reader dynamically creates an output for each column in the CSV file, using the column headers as identifiers. Each output will contain the data from one column, making it easy to access and manipulate the data programmatically.

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Node Functionality

The CSV Reader node reads the content from a CSV file and distributes each column’s data based on the headers in the first row. This node outputs a structured list for each column, which can be used in downstream steps within your flow.

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When To Use

Use the CSV Reader node whenever you need to retrieve and process data stored in a CSV format. CSV files are widely used for storing tabular data, such as contact lists, sales records, and product inventories. By converting CSV columns into structured lists, the CSV Reader node makes this data accessible for automation, data analysis, or further transformation.

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Common Use Cases

  • Data Importing: Loading contact information from a CSV into a CRM system.
  • Sales Analysis: Reading and analyzing sales data exported from an e-commerce platform.
  • Inventory Management: Importing product details from a CSV into a database or inventory management system.

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Key Features

  • Automatic Header Recognition: The node reads the headers from the first row of the CSV and uses them to label the outputs for each column.
  • Column-Based Output: Each column of data is output as a separate list, allowing for easy access and manipulation of individual data fields.
  • Batch Processing Support: Supports Loop Mode, enabling batch processing of multiple CSV files in a single flow.

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Important Notes

  1. Headers Required: The CSV file must contain headers in the first row for the node to function correctly.
  2. Dynamic Outputs: Outputs are generated dynamically based on the headers, allowing flexibility in handling CSV files with varying structures.
  3. Ideal for Data Ingestion: This node is essential for workflows that start with CSV files, especially when dealing with structured data that requires column-based manipulation.

The CSV Reader node is a powerful tool for automating data ingestion tasks involving CSV files, enabling you to easily access and process structured data across a variety of applications.

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