KNIME Analytics Platform is a powerful tool for data analytics, and integrating it with Anaconda allows users to leverage Python scripts within their workflows. Here’s a step-by-step guide to set up this integration:​

1. Install KNIME Python Integration: To run Python scripts in KNIME, you need to install the KNIME Python Integration extension. In KNIME Analytics Platform, navigate to File > Install Extensions, search for “KNIME Python Integration”, and proceed with the installation.
2. Install Anaconda or Miniconda: Anaconda and Miniconda are popular distributions for managing Python environments. Anaconda comes with a wide array of pre-installed packages, while Miniconda offers a minimal setup, allowing you to install only the packages you need. Download and install the distribution that best fits your requirements.
3. Create a Python Environment: Open your terminal or Anaconda Prompt and execute the following command to create a new Python environment named knime_env with the necessary packages for KNIME:

conda create -n knime_env -c knime -c conda-forge knime-python-scripting

This command sets up an environment tailored for KNIME’s Python scripting needs.

4. Configure Python Environment in KNIME: Within KNIME Analytics Platform, go to File > Preferences > KNIME > Python. Select the Conda option and choose the knime_env environment you created earlier. To determine the base environment path of your Anaconda installation, you can run the following command in your terminal or Anaconda Prompt:

conda info | grep -i "base environment"

This will display the base environment path, which you can use to set the correct Conda executable path in KNIME’s preferences.

5. Utilize Python Nodes in KNIME: You can now add Python Script nodes to your KNIME workflows to execute Python code. These nodes accept input data, run the specified Python code, and return the results as output. For example, to divide two columns using Python, you can use the following script:

import pandas as pd

# Assuming input_table is the DataFrame received from KNIME
input_table = knime.input_tables[0]

# Perform the division
input_table['result'] = input_table['Column1'] / input_table['Column2']

# Return the modified table to KNIME
knime.output_tables[0] = input_table

This script takes two columns, ‘Column1’ and ‘Column2’, divides them, and stores the result in a new column ‘result’. Ensure that you handle any potential division by zero errors or missing values as needed.

Additional Tips:

• Managing Dependencies: Regularly update your Conda environment to include the latest versions of packages. This helps in maintaining compatibility and leveraging new features.​
• Testing Scripts Independently: Before integrating Python scripts into KNIME, test them in a standalone environment to ensure they function correctly. This practice can save time troubleshooting within KNIME.​
• Utilizing Virtual Environments: For specific projects, consider creating dedicated Conda environments. This approach isolates dependencies and minimizes conflicts between packages.​

By following these steps and tips, you can effectively integrate Anaconda with KNIME, enhancing your data analysis capabilities by incorporating Python scripts into your workflows.