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Category: Python

Incremental Data Load into Parquet Files from Python

Published 2025-07-18 by Kevin Feasel

Lee Asher loads some data:

Parquet is a column-oriented open-source storage format increasingly used for “big data” analytics. Yet despite its growing popularity as a native format for data lakes and data warehouses, tools for maintaining these environments remain scarce. Getting data from a SQL environment into Parquet isn’t difficult – but how do we maintain that data over time, keeping it current? In other words, if we already have an existing Parquet file, how can we efficiently append new data to it?

In this article, we’ll introduce the Parquet format, explain some strategies for incrementally updating a Parquet repository, and, with a simple Python script, implement a nightly-feed update process.

Not listed in here is one word that I expected: Delta. Because that’s how we normally do incremental data modification in Parquet data. Either that or Apache Iceberg. Lee shows us a different route that can work.

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Data Cleansing Tips in Pandas

Published 2025-07-15 by Kevin Feasel

Jayita Gulati shares some tips:

Data preparation is one of the most time-consuming parts of any data science or analytics project, but it doesn’t have to be. With the proper techniques, Pandas can help you quickly transform messy and complex datasets into clean, ready-to-analyze formats. From handling missing data to reshaping and optimizing your DataFrames, a few tricks can save you hours of work.

In this article, you will discover seven practical Pandas tips that can speed up your data prep process and help you focus more on analysis and less on cleanup.

Two of the tips are basically “use functional programming techniques,” and I’m okay with that.

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Decision Trees and Non-Tabular Data

Published 2025-07-11 by Kevin Feasel

Ivan Palomares Carrascosa explains that you can use more than standard structured data against decision trees:

Versatile, interpretable, and effective for a variety of use cases, decision trees have been among the most well-established machine learning techniques for decades, widely used for classification and regression tasks. Yet, they are still widely used — whether as standalone models or as components of more powerful ensemble methods like random forests and gradient boosting machines.

And there is one more attractive feature that pushes the boundaries of their versatility even further: they can accommodate data in diverse formats, beyond just fully structured, tabular data. This article examines this facet of decision trees from a balanced theoretical and practical approach.

Click through for an example.

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From Pandas to Polars

Published 2025-06-19 by Kevin Feasel

Ivan Palomares Carrascosa provides an introduction to the polars library:

Polars is currently one of the fastest open-source libraries for data manipulation and processing on a single machine, featuring an intuitive and user-friendly API. Natively built in Rust, it is designed to optimize low memory consumption and speed while working with DataFrames.

This article takes a tour of Polars library in Python and illustrates how it can be seamlessly used similarly to Pandas to efficiently manipulate large datasets.

My experience with polars is that it’s not a 1:1 replacement for pandas, but the interfaces are similar enough that a lot of code can swap over without much effort. And yes, it’s typically faster.

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Multithreading and Multiprocessing in Python

Published 2025-06-17 by Kevin Feasel

Jessica Wachtel explains how two systems work in Python:

Let’s use a simple example to understand them: a mechanics shop. Concurrency happens when one mechanic works on several cars by switching between them. For example, the mechanic changes the oil in one car while waiting for a part for another. They don’t finish one car before starting the next, but they can’t do two tasks at exactly the same time. The tasks overlap in time but don’t happen simultaneously.

Click through for the analogy, how it applies to Python, and tips and tricks around each.

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Loading Data into Snowflake via Python

Published 2025-06-13 by Kevin Feasel

Anil Kumar Moka does a bit of data loading:

In our ongoing exploration of Snowflake data loading strategies, we’ve previously examined how to use pandas with SQLAlchemy to efficiently move data into Snowflake tables. That approach leverages pandas’ intuitive DataFrame handling and works well for many common scenarios where you’re already manipulating data in Python before loading it to Snowflake.

In this article, we’re diving deeper into the Snowflake toolbox by exploring the native Snowflake Connector for Python. While pandas offers simplicity and familiarity, the native connector provides a different set of capabilities focused on precision control and Snowflake-specific optimizations. This article explains you when and how to use this more direct approach for everything from small CSV files to massive datasets that would overwhelm pandas.

Click through for the full article.

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Handling Imbalanced Data in Python

Published 2025-06-13 by Kevin Feasel

Ivan Palomares Carrascosa gives three ways to deal with imbalanced data:

Here’s the catch: having imbalanced data usually makes analysis processes more difficult, especially for machine learning models that can easily get biased toward the majority class as a result of dealing with data with a remarkably unequal class distribution, thereby ending up becoming an almost “dummy classifier” that assigns the same class to virtually everything — in the most extreme case.

This article shows several strategies to navigate and handle imbalanced datasets using two of Python’s most stellar libraries for “all things data”: Pandas and Scikit-learn.

Click through for those ways, including sample code.

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Custom Libraries in Microsoft Fabric Data Engineering

Published 2025-06-13 by Kevin Feasel

Gerhard Brueckl isn’t content with the defaults:

When working with Spark or data engineering in general in Microsoft Fabric, you will sooner or later come to the point where you need to reuse some of the code that you have already written in another notebook. Best practice is to put these code pieces into a central place from where it can be referenced and reused. This way you can make sure all notebooks always use the very same code and it is also easy to develop, update and test the common functions.

As Gerhard mentions, having common notebooks with utilities is fine for when you’re getting started with development, but being able to centralize functions in proper libraries can make that code a lot more useful, not just in the context of the single notebook.

I believe that this does allow for arbitrary code execution, so someone with sufficient permissions to create a notebook and import code from arbitrary locations would be able to execute that code. I think there are ways of limiting this risk (such as not allowing your Fabric hosts to connect to any remote servers other than ones you explicitly allow), but it’s something I’d have to puzzle through.

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Vector Search from Scratch

Published 2025-06-11 by Kevin Feasel

Kanwai Mehreen does a bit of searching:

In this article, I’ll walk you through every step from generating vector representations to searching using cosine similarity, and we’ll even visualize what’s happening behind the scenes. By the end, you’ll not only understand how vector search works but also have a working implementation you can build on. So, let’s get started.

It’s kind of funny how simple this is, but it is. A lot of the complexity is around data quality operations, as well as optimizing the search process.

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