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Category: Python

Thoughts on Fabric Data Wrangler

Published 2023-06-30 by Kevin Feasel

Gilbert Quevauvilliers tries out a tool:

I was going through my twitter feed and I came across this tweet where they spoke about the Data Wrangler Calling all #Python users! Have you tried Data Wrangler in #MicrosoftFabric?

I thought I would give this a try and that was the idea for my blog post. I honestly had no idea that firstly was this possible, but second that it is so easy for data wrangler to do all the hard work for me

I am going to demonstrate 2 transformations in this blog post, the first will be changing the d_date from date to datetime and then using the columns from examples I am going to create a new column where it concatenates 2 columns delimited with a double pipe command.

Read on for Gilbert’s thoughts.

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Read and Write Data with PySpark

Published 2023-06-22 by Kevin Feasel

Dustin Vannnoy has two of the three R’s down:

Every Spark pipeline involves reading data from a data source or table. For data engineers we usually end the pipelines by writing the transformed data. In this tutorial we walk through some of the most common format and cloud storage locations for reading and writing with Spark. We’ll save some of the advanced Delta Lake capabilities for another tutorial.

Click through to see how to read from and write to CSV, JSON, and Parquet formats. Dustin has examples of working with Azure Blob Storage, S3, and Google Cloud Storage, and even some database examples with JDBC.

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Using SHAP to Gauge Geographic Effects in R or Python

Published 2023-06-08 by Kevin Feasel

Michael Mayer runs an analysis:

This is the next article in our series “Lost in Translation between R and Python”. The aim of this series is to provide high-quality R and Python code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, the Python tab will be your friend.

This post is heavily based on the new {shapviz} vignette.

I appreciate the effort to include both R and Python code in this analysis, and recommend you peruse both sets of code listings.

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PyPI and Malicious Code

Published 2023-06-02 by Kevin Feasel

Steven Vaughan-Nichols gives us the story:

The Python Package Index (PyPI), is the most popular Python programming language software repository. It’s also a mess. Earlier this year, the FortiGuard team discovered zero-day malware in three PyPI packages called “colorslib,” “httpslib,” and “libhttps.”  Before that, 2022 closed with  PyTorch-nightly on Linux being poisoned with a fake dependency. More recently, PyPI had to stop new user registrations and project creations because of a flood of malicious users. PyPI isn’t the only one to notice the user trouble. The Python Software Foundation (PSF) received three subpoenas for PyPI user data. What is going on here!?

Read on to learn more about what’s happening with the most popular Python repository.

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Diffify Updates

Published 2023-05-03 by Kevin Feasel

Myles Mitchell celebrates a year of diffify:

We’ve just passed an important milestone for diffify: our app for tracking Python and R package releases has just turned 1 year old! To mark this exciting occasion we are delighted to announce an “anniversary update” featuring numerous quality of life improvements. This post will outline the latest changes and tease at some exciting developments in the works…

Check out these recent changes and a little bit of what’s on the horizon.

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Creating Your First PySpark Application

Published 2023-05-02 by Kevin Feasel

Dustin Vannoy gives us a primer on Apache Spark:

Get hands on with Python and PySpark to build your first data pipeline. In this video I walk you through how to read, transform, and write the NYC Taxi dataset which can be found on Databricks, Azure Synapse, or downloaded from the web to wherever you run Apache Spark. Once you have watched and followed along with this tutorial, go find a free dataset and try to write your own PySpark application. Pro tip: Search for the Spark equivalent of functions you use in other programming languages (including SQL). Many will exist in the pyspark.sql.functions module.

In addition to the code listing, Dustin has a video walking us through the process.

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Hybrid ML and Rules-Based Fraud Detection

Published 2023-04-28 by Kevin Feasel

Ayodeji Ogunlami mixes approaches:

In developing this hybrid system, sets of rules are required as well as a machine learning model. I would be making use of a vehicle insurance dataset from Kaggle in this demonstration.

The dataset can be downloaded from this link: https://www.kaggle.com/datasets/shivamb/vehicle-claim-fraud-detection

The ML model would be built using a random forest classifier on Azure Databricks using Pyspark.

This seems to be the most sensible approach, especially given how rare actual fraud incidents are and what that imbalance does to classification algorithms.

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Common Challenges Implementing PySpark Code

Published 2023-04-27 by Kevin Feasel

Amlan Patnaik looks at some common implementation problems:

Pyspark has become one of the most popular tools for data processing and data engineering applications. It is a fast and efficient tool that can handle large volumes of data and provide scalable data processing capabilities. However, Pyspark applications also come with their own set of challenges that data engineers face on a day-to-day basis. In this article, we will discuss some of the common challenges faced by data engineers in Pyspark applications and the possible solutions to overcome these challenges.

Read on for five such challenges.

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Improving the Robustness of ML Model Deployment

Published 2023-04-04 by Kevin Feasel

Alexander Billington shares a few tools and tips:

Machine learning (ML) model deployment is a critical part of the MLOps lifecycle, and it can be a challenging process. In the previous blog, we explored how Azure Functions can simplify the deployment process. However, there are many other factors to consider when deploying ML models to production environments. In this blog, we’ll delve deeper into some of the essential hints and tips for more robust model deployments. We’ll look at topics such as proper model versioning and packaging, data validation, and performative code optimisations. By implementing these practices, data scientists and ML engineers can ensure their models are deployed efficiently, accurately, and with minimal downtime.

MLflow is definitely a good recommendation, as is Pydantic (which is on my to-learn list…one of these days).

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Generating Artificial Data in Databricks

Published 2023-03-29 by Kevin Feasel

Ben Hazan needs some fake data:

While attending the SQLBits 2023, I took part in André Kamman’s session about “Generate test data quick, easy and lots of it with the Databricks Labs Data Generator”.

In this blog, I will share with you my insights about the DataBricks Data Generator library and I’ll give an example.

Synthetic data is a valuable resource for data scientists, engineers, and analysts who need to test, benchmark, or demonstrate their solutions without compromising sensitive or confidential information. However, generating realistic and relevant synthetic data can be challenging and time-consuming.

That’s why Databricks Labs has developed a Python library called dbldatagen that can help you create large-scale synthetic data sets using Spark.

Click through to learn more about the library and see how you can use to to generate arbitrary amounts of artificial data following certain constraints.

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