knime(py): Python toolkit for KNIME

knime(py) provides tools for reading and executing KNIME workflows from Python. It is distributable as a single file module and has no requirements beyond Python 3.6+ and the Python Standard Library. Optionally, if the pandas module is also installed, then pandas DataFrames are supported for both input and output to KNIME workflows executed through this toolkit.

Example: Execute a KNIME Workflow

import knime

with knime.Workflow("DemoWorkflow01") as wf:
    wf.execute()
    results = wf.data_table_outputs[:]

Download and Install

Install the latest stable release with pip install knime (or pip3 install knime if you have both Python 2 and 3 installed). Alternatively, download knime.py (unstable) into your project directory. There are no hard dependencies other than Python 3.6+ and the Python standard library itself.

License

Code and documentation are available according to the LICENSE.

Expanded Example: Multiple Inputs/Outputs when Executing a KNIME Workflow

import knime
import pandas as pd

# Change the executable_path to point at a particular KNIME install.
# May alternatively be set via OS Environment Variable, 'KNIME_EXEC'.
knime.executable_path = r"C:\Program Files\KNIME\knime.exe"

# Prepare input data tables as DataFrames or regular dicts (in KNIME's
# required schema) to be read by the "Container Input (Table)" nodes in
# the KNIME Workflow.
input_table_1 = pd.DataFrame([["blau", -273.15], ["gelb", 100.0]], columns=["color", "temp"])
input_table_2 = {
    "table-spec": [{"color": "string"}, {"size": "long"}],
    "table-data": [["blue", 42], ["yellow", 8675309]]
}

# Use a with-statement to set the inputs, execute, and get the results.
with knime.Workflow(r"C:\Users\berthold\knime-workspace\ExploreData01") as wf:
    wf.data_table_inputs[0] = input_table_1
    wf.data_table_inputs[1] = input_table_2
    wf.execute()
    output_table = wf.data_table_outputs[0]  # output_table will be a pd.DataFrame

KNIME Server Example: Execute a KNIME Workflow on a remote KNIME Server

import knime
import pandas as pd

input_table_1 = pd.DataFrame([["blau", -273.15], ["gelb", 100.0]], columns=["color", "temp"])

# Requires a valid user account on a running KNIME Server instance.
with knime.Workflow(
    "https://your_server.your_company.com/knime/your_directory/your_workflow_name",
    username="arthur.dent",
    password="forty-two"
) as wf:
    wf.data_table_inputs[:] = [input_table_1]
    wf.execute(reset=True, timeout_ms=10000)  # Default timeout is usually plenty.
    output_table = wf.data_table_outputs[0]

# Alternative syntax for specifying the Workflow, keeping everything else the same:
with knime.Workflow(
    workspace_path="https://your_server.your_company.com/knime",
    workflow_path="/your_directory/your_workflow_name",
    username="arthur.dent",
    password="forty-two"
) as wf:
    wf.data_table_inputs[:] = [input_table_1]
    wf.execute(reset=True, timeout_ms=10000)
    output_table = wf.data_table_outputs[0]