Month: May 2023

  • Does a Power BI developer need Python?

    Does a Power BI developer need Python?

    In this episode of the Explicit Measures Podcast, we discuss the values of adding Python knowledge. It is well known that more organizations are moving to cloud platforms. Because of this, we should ask the following questions:

    • What will be the needed skills for Power BI professionals?
    • Is this for the Business or for a Data Engineer?

    We invite you to jump in and listen to the podcast as we discuss these topics. Enjoy!

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    Ask Chat GPT

    As in all our episodes we like to end with what does chat GPT say. Thus, our question for this episode is

    Should I learn Python for Power BI development?

    Chat GPT response

    The response was quite interesting, discussing the various factors as to why Python is valuable. Overall, we gave chat GPT a better than average score of about 6.5 out of 7.

    Learning Python can be beneficial for Power BI development. Python is a versatile programming language that is widely used in data analysis, data manipulation, and data visualization tasks. Power BI has its own built-in capabilities for data processing and visualization. Python can extend its functionality and provide additional flexibility.

    Here are a few reasons why learning Python for Power BI development can be advantageous:

    Data Manipulation

    Python has a rich ecosystem of libraries such as Pandas, NumPy, and scikit-learn. These libraries are excellent for data manipulation, cleaning, and transformation. Use Python to preprocess and shape your data before loading it into Power BI.

    Advanced Analytics

    Python offers a range of libraries for advanced analytics and machine learning, such as TensorFlow, Keras, and PyTorch. Integrating Python code into Power BI, you can leverage these libraries to perform complex calculations. Additional features could include predictive modeling, or create custom machine learning models to enhance your data analysis capabilities.

    Custom Visualizations

    Power BI provides a variety of built-in visualizations. But, if you have specific visualization requirements or want to create custom charts. Python can be a powerful tool for visual development. Libraries like Matplotlib and Seaborn offer extensive options for data visualization. Enabling you to generate interactive and customized charts and graphs to present your insights effectively.

    Automation and Integration

    Python can help automate repetitive tasks and streamline your Power BI workflows. You can write Python scripts to extract data from different sources, transform it, and load it into Power BI automatically. Python’s integration capabilities enables connections with other tools and platforms, such as web APIs, databases, or cloud services. Python expands the scope of your data analysis.

    Community Support

    Python has a large and active community, with abundant resources, tutorials, and forums available. Learning Python can provide you with access to a wealth of knowledge and expertise. A Large community makes it easier to find solutions to specific challenges.

    In conclusion, while it is not necessary to learn Python for Power BI development. It can certainly enhance your capabilities and offer more flexibility in data manipulation, advanced analytics, custom visualizations, and automation. Consider your specific requirements and goals for a project. Your project requirements may determine whether investing time in learning Python would be beneficial.

    Chat GPT – May 12 Version

    Not bad Chat GPT. I would disagree with a couple points made on how easy it is to integrate python into Power BI. Two options I am aware of today using Python in Power Query.

    Option 1 – Using Python in Power Query

    Using Python in Power Query from Microsoft Learn

    Option 2 – Using Python for Visuals

    Using Python to develop a Visual

    I feel that more of python is used in cloud services. This was a major point brought by Seth in the podcast. Very valid point.

    Overall, we hope you enjoyed the podcast! See you next time.

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