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την Οκτωβρίου 12, 2017

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This Is Where You Should Be

"Golden Retriever" TL;DR The post demonstrates a reusable DataProcessor class on a synthetic sales dataset (standing in for a proprietary one), with support for generic datasets like the included “foo”. It loads, inspects, cleans, and partially visualizes data—particularly focusing on outlier detection via boxplots and histogram comparisons using Z-score and/or IQR methods to confirm the cleanup. Finally it saves the cleaned data to .csv or a .xlsx . Call me! In the previous posts Class of the Process , Raw Power - Data Processor Pt. 2 , we implemented our DataProcessor class — so far, so good! Now it's time to call it from our main.py program and test its functionality! Dataset It would be ideal for me to demonstrate the data I’ve extracted from my job — but there’s a catch. The data I’ve collected is proprietary ! Unfortunately, it’s not mine to share. To work around this l...

Class of the Process

"Tortoise, Calico, sometimes known as the 'divas' of the feline world " TL;DR Writing a reusable class like DataProcessor can turn repeated, error-prone tasks into clean, modular, and maintainable code — giving you more time to focus on the things that actually matter in your project. From Repetition to Reusability: How I Ended Up Writing My First Class In my data projects, the biggest hurdle was always the time it took to prepare the data and get it ready for analysis. After writing the same code over and over again—with only minor changes each time—I realized it was time to create a function (or a small collection of them) and just call it when needed. It made sense, since the overall procedure didn’t vary that much. So, I got started. At first, all these functions lived inside main.py . But as the project grew, that file became bloated and harder to manage. I quickly realized how important it was to keep main.py clean and simple so I could easily...

Raw Power - Data Processor Pt. 2

"Bombay" TL;DR This blog post explains DataProcessor , a Python class designed for processing raw data: loading files, checking data types, dropping columns, identifying categorical data, and managing datetime indexes. It emphasizes flexibility, clean error handling, and modular design using libraries lik e pandas , numpy , and scikit-learn . The focus is on preparing data for analysis or visualization — powerful stuff for any data judoka! Introduction - Data Processor Pt. 2 From the previous post — Class of the Process — we introduce the idea of a class that could handle raw data and prepare it for further analysis. This includes data ingestion, cleaning, transformation, and visualization support , making the data ready to use! Originally, I intended to present the whole concept in one article, but despite my efforts, it became too large. Both the article and the code! You can find full code on m...

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