You can enable an API endpoint that converts the your recipe steps to generate a specific output into Python Pandas code. When executed, this generated Python Pandas code applies transformations to your specified dataset and generates the specified output. 

Alpha Release

This endpoint is the beginning of enabling the 

This documentation provides information on how to explore the capabilities of the 

Enable

To enable generation of Python Pandas code, please complete the following:

Steps:

1. D s config
   method ws

2. Locate the following setting and set it to Enabled:
   
   

   Code Block
   Wrangle to Python Conversion

3. The feature is now enabled.

Limitations

• This endpoint does not currently support multi-dataset operations.
• The generated Python code does not yield readable columns in the output code.
• Conversion of Wrangle script to Python code is supported for CSV files only.

v4 OutputObjects WrangleToPython Create

This section contains reference documentation on the API endpoint. This endpoint method is applied to a specified outputObject. This outputObject is the result of execution of a specific recipe (wrangledDataset). That recipe has references to its source importedDatasets and connections.

This API enables generation of Python Pandas code for the Wrangle recipe associated with an output object.

Version:  v4

Required Permissions

Request

Request Type: POST

Endpoint:

/v4/outputObjects/<id>/wrangleToPython

Request URI - Example:

/v4/outputObjects/3/wrangleToPython

Request Body:

The following defines the running environment used for the outputObject. 

{
    "execution": "spark"
}

Response

Response Status Code - Success:  200 - OK

Response Body Example: 

The generated response is the Python Pandas code:

{
from trifacta.transform_functions.function_definitions import Replace
import pandas as pd
import numpy as np

def run_transforms(df0=None):
  if (df0 is None):
    df0 = pd.read_csv('input.csv', skip_blank_lines=False, lineterminator='\n', dtype=str, encoding='UTF-8')
  replace1 = Replace('x', 'u', False, False)
  replace2 = Replace('x', 'u', True, False)
  replace3 = Replace('x', 'u', True, True)
  replace4 = Replace('x', '\\\\', True, False)
  df3 = pd.DataFrame({'new_column1': df0['col1'].apply((lambda x: replace1.exec(x))), 'new_column2': df0['col1'].apply((lambda x: replace2.exec(x))), 'new_column3': df0['col1'].apply((lambda x: replace3.exec(x))), 'new_column4': df0['col1'].apply((lambda x: replace4.exec(x)))})
  return df3
}

Reference

For more information , see 

operation/getPythonScriptForOutputObjectInput