Python SDK
Note
As of Release 9.7, Wrangle to Python conversion has been deprecated. For more information, please see End of Life and Deprecated Features.
The Alteryx Python SDK enables you to integrate the Trifacta Application into your Python pipelines. When your Python environment has been integrated with the Trifacta Application, you can leverage the visual tools in the application to rapidly construct your transformation steps on exampled data that you upload. When you have finished building your recipe, you can invoke a function in your Python environment to download the recipe as Python Pandas code for use in your data pipelines.
Basic task:
Through your Python notebook:
Upload example data to your Alteryx workspace.
Launch the Trifacta Application.
In the Trifacta Application:
Use the transformation tools in the application to transform your example data using a series of recipe steps.
Iterate on your recipe. Generate results through the Trifacta Application to verify that you have transformed your data correctly.
In your Python notebook:
Invoke a function to translate the recipe into Python Pandas and download it to your local Python environment.
Deploy this recipe into other Python pipelines to transform other datasets as needed.
Prerequisites
Alteryx prerequisites
Note
This feature may not be available in all product editions. For more information on available features, see Compare Editions.
A workspace administrator must enable the Python to Wrangle feature in your workspace. For more information, see Workspace Settings Page.
You must have a valid API access token. For more information, see Manage API Access Tokens.
Python prerequisites
Note
If you receive the following error message: PermissionError: You must setup a trifacta configuration, use tfconfig.setup_configuration(user, pwd), then you must deploy .trifacta.py.conf in the directory where the software is located.
Please see the installation instructions available at the download URL listed below.
Limitations
Note
This is an Alpha release. Do not use the Python SDK in a production environment.
Some Wrangle functions and transformations are not supported by Python Pandas. Known limitations:
NUMFORMAT function
String comparison functions
Transformations that use Array or Map data types are not supported for Python Pandas generation.
Uploaded files must be in CSV file format.
Download and Install
For more information on downloading and installing the Python SDK, see https://pypi.org/project/trifacta/.
Examples
For a basic example, please see https://pypi.org/project/trifacta/.
Wrangle function reference
The following wrangling functions are available through the SDK.
Alteryx module functions
tf is an alias to the Alteryx module.
Function Name | Description | Arguments |
|---|---|---|
tf.wrangle(*datasets) | Upload one ore more datasets to the Trifacta Application and create a flow for it. This flow is then available through the Trifacta Application, where you can transform the dataset through the user interface. See https://pypi.org/project/trifacta/. | *datasets: Pandas DataFrames to be wrangled. It could also be a tuple, where the first element in the tuple is a Pandas DataFrame, and second element is the reference name (string) for the DataFrame. |
WrangleFlow module functions
All the below functions are available for the WrangleFlowobject in your Python environment. So, you must call them using a WrangleFlow object.
wf is a reference to the WrangleFlow object.
Function Name | Description | Arguments |
|---|---|---|
wf.add_datasets(*datasets) | Add Pandas DataFrames to a flow, where | *datasets: Pandas DataFrames to be added to a flow. It could also be a tuple, where the first element in the tuple is a Pandas DataFrame, and second element is the reference name (string) for the DataFrame. |
| Generates Python Pandas code for your Wrangle recipe. | add_to_next_cell: Set it to True, if you're using Jupyter Notebook and would like to add the generated Pandas code to be added to next cell. If False, the Pandas code is returned as string. recipe_name: Recipe for which you want to get the Pandas code. If not specified, the default recipe is used. Use |
wf.run_job(pbar=None, execution='photon', recipe_name=None) | Run a job for a specified recipe. | pbar: can be ignored. execution: Running environment in Alteryx Analytics Cloud where you want to execute the job. Possible values: recipe_name: Recipe for which you want to execute the job. If set to |
wf.profile(recipe_name=None) | Generate a profile for a specified recipe. | recipe_name: Recipe for which you want to generate profile. If set to |
wf.recipe_names() | Lists the recipe names for the recipe present in Alteryx flow. | N/A |
wf.open_profile(recipe_name=None) | Open a profile that you have previously generated for the specified recipe. | recipe_name: Recipe for which you want to open the profile. If set to |
Data profiling functions
Function Name | Description | Arguments |
|---|---|---|
wf.summary(recipe_name=None) | Returns a table of summary statistics per column | recipe_name: Recipe name for which you want to generate the summary. If set to |
wf.dq_bars(show_types=True, recipe_name=None) | Returns the valid/invalid/missing ratio per column | show_types: Show column types information along with data quality bars for the column. recipe_name: Recipe name for which you want to generate the data quality bar. If set to |
wf.col_types(recipe_name=None) | Lists the inferred data type for each column | recipe_name: Recipe name for which you want to infer data types for each column. If set to |
wf.bars_df_list(recipe_name=None) | Returns a list of dataframes, one per column, representing a bar-chart for that column | recipe_name: Recipe name for which you want to generate the bar-chart. If set to |
wf.pdf_profile(filename=None, recipe_name=None) | Returns a snazzy PDF report with all the statistics | filename: Name of the file to which PDF profile results are written. If set to recipe_name: Recipe for which you want to generate PDF profile results. If set to |