Altreryx and Python are great tools for data analysis. In this post, I would like to compare these tools, describe their similarities and differences, look at their pros and cons. I will focus mainly on data analysis as well as some data science and machine learning; however, both tools have a broader range of applications.

Availability and General Information

To start with, Python is a high-level multipurpose language that solves problems in data analysis, data science and machine learning, web design and games development, automation and blockchain. Python is an easily readable language and designed in the way that it can be simply inferred just knowing English. The Python philosophy is described in Wikipedia as:

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Readability counts.

Python is a free open source solution meaning that everyone and any organization can download and install it from However, it is more common to install a special package called Anaconda, which comes with different preinstalled programs for data scientists and analysts as well as a number of libraries in Python. It is free for individuals, but some fees might be applied for commercial uses.

Python libraries are also free and are developed for many different applications. If the Python version you use does not have the library you need, you can simply install it. Most libraries can be installed from PyPI using pip install library_name in the command line interface. It is better to check the website to find instructions on installing a specific library.

On the other hand, Alteryx is proprietary software available for free only for 30 days of the trial period. A user must have a license after the trial period. The installed version of Alteryx includes a wide variety of packages like Preparation, Transformation and Reporting. However, to use machine learning and text mining capabilities, one has to have the Alteryx Intelligence Suite license to activate these capabilities. To download and instal an Alteryx trial version, press here.

Learning path 

The learning path would be easier for those who are learning Alteryx. One can compare Alteryx with LEGO, where you can use tools to build a workflow and connect tools properly. Each block carries its purpose, such as filter, join or order. The data flows from one tool to another via connectors like water pipes. The tools can be considered as water valves or filters so that you can manipulate the water flow in the desired way.

Alteryx does not require knowledge of any programming language; however, some tools such as Filter or Formula, for instance, will require some understanding of programming concepts like the IF structure, for example. However, if you are still reading this post then you should not worry about it.

On the other hand, we can use Python. However, it is all about coding. Nevertheless, because Python is a high-level language, it is easy to learn Python. Pythonists often say that when you write a code in Python, one literally writes a text in English. You will not be able to work with Python without a basic understanding of IF statements, loops, functions and probably object-oriented programming (OOP). There are a lot of free courses as well as ones you have to pay for. When I had just started my carrier in data, I took a class at Udacity named Programming for Data Science with Python. I really like this well-structured course. This course covered not only Python but also SQL, version control and Tableau; however, the course structure could have been changed since I completed the course.

If you have already got your feet wet and have learned Python, it would make the transition from Python to Alteryx a lot easier, as well as SQL knowledge would make it easier too. So, if you do not have permanent access to Alteryx, learn Python, and when you need to know Alteryx, it would be easier for you to learn it.

Even though the Alteryx community is big and very supportive, the Python community is more extensive because it is open-source, and everyone has access to it.

The efficiency of Working with Python vs Alteryx 

As was abovementioned, Alteryx looks more like LEGO. It is effortless to pull a tool from a pallet and put it on canvas. Set it up, then connect it to another tool and so on. I found input and output tools very efficient. The input tool has different options to download datasets from various file types or databases to Alteryx. When you are ready to save your dataset, you can easily do so using the output tool with different options of downloading the dataset to the file or uploading it to the database.

Because Python is open-source, you can probably find more data science and machine learning libraries in Python than predictive tools in Alteteryx. Due to this fact, Alteryx has a handy tool called Python where you can write a Python script, install libraries and more. I have already dedicated several posts to this topic, so please check the following posts: Can you use Python in Alteryx? and Installing libraries in Alteryx Python Tool.

Alteryx has a few tools designed specifically for Spatial data analysis. They are very advanced and easy to use. I believe you can find libraries that can do spatial analysis in Python, but I have never explored this topic yet.

Anaconda also comes with Jupyter Notebook. This is an excellent tool for data analysis and data exploration. It opens in a browser. You can write a Python script and run them in cells. At the same time, you can describe your findings and process along with your data analysis. It makes it easy to follow your analysis and share your thoughts with your colleagues and stakeholders in pdf or HTML formats, for instance. So they do not need to have installed Python or Jupyter Notebook on their computers. Data analysts can also use the presentation mode to convey the information to your audience, and this is a fantastic feature. I found this tool very neat.

In Conclusion

Alteryx and Python are great tools. Both of them can be used in industry but as an individual, you most likely choose Python because it is free. If you do not have access to Alteryx at the moment and want to explore the data world, do not hesitate to learn Python and, when you are ready, transition to Alteryx.


Boris Kushnarev
Author: Boris Kushnarev