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Alteryx Designer Knowledge Base

Definitive answers from Designer experts.
Error when using the Assisted Modeling tool on a Proxy environment
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This article will review how to install and uninstall Python packages without using the command prompt or the built in installPackages method. This method will also provide better messages if something does error out.
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Making a copy of a workflow that contains a Python tool will cause the JupyterGuidDir to be duplicated. Because of this reason, both copies will point to the same Jupyter Notebook document. If the user makes changes on one copy, the other will be changed too which is not desirable.
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This article will review the method of creating a requirements text file that can be used to reinstall the same packages. Then a process to reinstall the same packages.
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Failed to find virtual env named: “designerbasetools_venv” occurs if the Server version is on a lower minor version than the Designer.
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This article will walk through how to have different Module versions for different workflows. This example will use PyPDF2 as the Module
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Here is a list of items that we suggest get whitelisted for antivirus software to reduce unexpected behavior.
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Installing a package from the Python tool is an important task. In this article, we will review all the possible functionality included with the Python method Alteryx.installPackages().
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ERROR: "Could not build wheels for cryptography which use PEP 517 and cannot be installed directly" while installing OneDrive tools in Alteryx Designer
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This is article will show you the "DataWrap2WrigleyDb::GoRecord: Attempt to seek past the end of the file" error and how to solve it.
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A workflow that includes a Python tool may throw an error of the form "Error: unable to read data (C:\AppData\Alteryx\Engine\Engine_23200_be6a9480b4fc4e038a8668b82debdf74_\aa37b5ac-6323-472b-8f0f-5cb0b95b822e\4460abb7be83bae8f01b9bf1238a923c.yxdb)"
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Learn about the new 20.4 changes to Python.
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Steps on how to reinstall a Python-based .yxi tool.
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With the release of 2018.3 comes the long-awaited and highly anticipated Python Tool! This article is a general introduction to using the tool.
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Instructions for installing Python packages for Alteryx Designer in the command line
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When using a Python-based connector (e.g. Salesforce Input, Azure Data Lake File Input), an error message referring to CERTIFICATE_VERIFY_FAILED appears.
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Fix Python tool installation error: An error occurred while installing package 'defaults::vs2015_runtime-14.16.27012-hf0eaf9b_1'. AssertionError()
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When your Python libraries don't work the way they should in Python tool, restoring the tool to it's original state could be the solution. This article walks through how to restore Python libraries and the virtual environment associated with the Python tool.
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With the Python Tool, Alteryx can manipulate your data using everyone’s favorite programming language - Python! Included with the tool are a few of pre-built libraries that extend past even the native Python download. This allows you to extend your data manipulation even further than one could ever imagine. The libraries installed are listed here - and below I’ll go into a bit more detail on what and why these libraries are so useful.   Each library is well documented, and there’s usually an introduction or examples on their sites to get you started on how a basic function in their library works.     ayx – Alteryx API – simply enough, we’re using Alteryx, sooo yea, kind of a requirement for the translation between Alteryx and Python.   jupyter – Jupyter metapackage – If you’ve used a Jupyter notebook in the past, you’ll notice the interface for the Python Tool is similar. This interface allows you to run sections of code outside of actually running the workflow, which makes understanding and testing your data that much easier. http://jupyter.org/index.html   matplotlib – Python plotting package – Any charting, plotting, or graphical needs you would want will be in this package. This provides a great deal of flexibility for whatever you want to visualize. https://matplotlib.org/   numPy – NumPy, array processing for numbers, strings, records, and objects – Native Python processes data in what some would call a cumbersome way. For instance, if you wanted to make a matrix, a.k.a. a 4x4 table, you would need to create a list within a list, which can slow processing a bit. However, NumPy has its own “array” type that fits the data in this matrix pattern that allows for faster processing. Additionally, it has a bunch of methods of handling numbers, strings, and objects that make processing a whole lot easier and a whole lot faster. http://www.numpy.org/   pandas – Powerful data structures for data analysis, time series, and statistics – This is your staple for handling data within Alteryx. Those who have used Python, but never pandas, will enter a whole new beautiful world of data handling and structure. Data manipulation within Python is faster, cleaner, and easier to code with. The best part about it is that the Python Tool will read in your Alteryx data as a pandas data frame! Understanding this library should be one of the first things to know when tackling the Python code. https://pandas.pydata.org/   geopandas – Extends the data types used by pandas to allow spatial operations on geometric types. Are you interested in geospatial analysis using Python? Try this package.  It makes working with geospatial data in Python much easier and faster.  http://geopandas.org/   requests – Python HTTP for Humans – for all the connector/Download Tool fans out there. If any of you are familiar with making HTTP requests (API calls and the like), then you should introduce yourselves to this package and explore how Python performs these requests. http://docs.python-requests.org/en/master/   scikit-learn – a set of Python modules for machine learning and data mining – Welcome to the world of machine learning in Python! This library is your go-to for statistical and predictive modeling and evaluation. Any crazy and wild methods you’ve learned for machine learning will most likely be found here and can really push the boundaries of data science. http://scikit-learn.org/stable/   scipy – Scientific Library for Python – all your scientific and technical computing can be found here. This library builds off the packages already installed here, like numPy, pandas, and matplotlib. Dealing with mathematical models and formulae are usually located within this library and can help provide that higher level analysis of your data. https://www.scipy.org/   six – Python 2 and 3 compatibility utilities – For those who are unfamiliar, Python versions come in 2 forms, version 2.x and 3.x (with 3.x being the most recent). Now, even though Python 3 is supposed to be the latest and greatest, there are still many users out there who prefer using Python 2. Therefore, integration between the two is a bit tricky with syntax differences, etc. The six module provides functions that are usable between the two so everyone can remain calm and happy! Their documentation is usually coupled with which version the functions most closely align to, so a user can get a better idea to its functionality. https://pypi.org/project/six/   SQLAlchemy – Database Abstraction Library – SQL in Python! Covers all your database needs from connecting to and extracting data, allowing it to interact with your Python code and thus, Alteryx itself. https://www.sqlalchemy.org/   statsmodels – statistical computations and models for Python – This library builds off sci-kit learn but focuses more on statistical tests and data exploration. Additionally, it utilizes R-style formulae with pandas data frames to fit models! https://www.statsmodels.org/stable/index.html   These are the libraries installed with the Python Tool, which can do almost any data function imaginable. Of course, if you’re looking to do something that these libraries don’t provide, there are myriad other Python libraries that I’m sure will help you with your use case. Most of these are also well documented in how to use so search away and let your mind float away in the beautiful cosmos created by Python.
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SnakePlane is an abstraction layer that makes working with the Python SDK easy! This three-part tutorial breaks down building a tool using SnakePlane and SnakePlane Pilot.
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