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This large wooden tray provides the ideal work surface for building LEGO models and other construction sets. Measuring 41.3″ wide by 21.6″ deep, it has 11 trays for organizing parts and a spacious work area. A smooth-spinning lazy susan lets you access your creations from all sides. At 12.1 lb., it’s easy to move around and is thin enough to store behind the couch.
The Awesomer
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The YData Profiling package generates an exploratory data analysis (EDA) report with a few lines of code. The report provides dataset and column-level analysis, including plots and summary statistics to help you quickly understand your dataset. These reports can be exported to HTML or JSON so you can share them with other stakeholders.
By the end of this tutorial, you’ll understand that:
ProfileReport creates a profile you can save with .to_file() for HTML or JSON export, or display inline with .to_notebook_iframe().tsmode=True and specifying a date column with sortby enables time series analysis, including stationarity tests and seasonality detection..compare() method generates side-by-side reports highlighting distribution shifts and statistical differences between datasets.To get the most out of this tutorial, you’ll benefit from having knowledge of pandas.
Note: The examples in this tutorial were tested using Python 3.13. Additionally, you may need to install setuptools<81 for backward compatibility.
You can install this package using pip:
$ python -m pip install ydata-profiling
Once installed, you’re ready to transform any pandas DataFrame into an interactive report. To follow along, download the example dataset you’ll work with by clicking the link below:
Get Your Code: Click here to download the free sample code and start automating Python data analysis with YData Profiling.
The following example generates a profiling report from the 2024 flight delay dataset and saves it to disk:
flight_report.py
import pandas as pd
from ydata_profiling import ProfileReport
df = pd.read_csv("flight_data_2024_sample.csv")
profile = ProfileReport(df)
profile.to_file("flight_report.html")
This code generates an HTML file containing interactive visualizations, statistical summaries, and data quality warnings:
You can open the file in any browser to explore your data’s characteristics without writing additional analysis code.
There are a number of tools available for high-level dataset exploration, but not all are built for the same purpose. The following table highlights a few common options and when each one is a good fit:
| Use case | Pick | Best for |
|---|---|---|
| You want to quickly generate an exploratory report | ydata-profiling |
Generating exploratory data analysis reports with visualizations |
| You want an overview of a large dataset | skimpy or df.describe() |
Providing fast, lightweight summaries in the console |
| You want to enforce data quality | pandera |
Validating schemas and catching errors in data pipelines |
Overall, YData Profiling is best used as an exploratory report creation tool. If you’re looking to generate an overview for a large dataset, using SkimPy or a built-in DataFrame library method may be more efficient. Other tools, like Pandera, are more appropriate for data validation.
If YData Profiling looks like the right choice for your use case, then keep reading to learn about its most important features.
A YData Profiling report is composed of several sections that summarize different aspects of your dataset. Before customizing a report, it helps to understand the main components it includes and what each one is designed to show.
[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
Planet Python
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In this quiz, you’ll test your understanding of the
pandas DataFrame.
By working through this quiz, you’ll review how to create pandas DataFrames, access and modify columns, insert and sort data, extract values as NumPy arrays, and how pandas handles missing data.
[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]
Planet Python
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Has your terminal app ever crashed mid-op? Ever wish you didn’t have to juggle multiple terminal tabs or deal with failed processes caused by terminal connection drops? If any of that sounds relatable, multiplexing, which isn’t as complicated as it sounds, can save you from the tab chaos and turn your Linux terminal into a productivity dashboard.
How-To Geek
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A new study finds that feeding mice with hyperglycemia a high-fat, low-carbohydrate diet lowered their blood sugar and improved their bodies’ response to exercise.
To be healthy, conventional wisdom tells us to exercise and limit fatty foods. Exercise helps us lose weight and build muscle. It makes our hearts stronger and boosts how we take in and use oxygen for energy—one of the strongest predictors of health and longevity.
But people with high blood sugar often don’t achieve those benefits from exercise, especially the ability to use oxygen efficiently. They’re at higher risk for heart and kidney disease, but high blood sugar can prevent their muscles from taking up oxygen more effectively in response to exercise.
For them, the new study suggests the answer could be eating not less fat, but more.
The study by exercise medicine scientist Sarah Lessard in Nature Communications, found that a high-fat, ketogenic diet reduced high blood sugar, or hyperglycemia, in mice, and their bodies were more responsive to exercise.
“After one week on the ketogenic diet, their blood sugar was completely normal, as though they didn’t have diabetes at all,” says Lessard, associate professor at Virginia Tech’s Fralin Biomedical Research Institute at VTC’s Center for Exercise Medicine Research.
“Over time, the diet caused remodeling of the mice’s muscles, making them more oxidative and making them react better to aerobic exercise.”
The ketogenic diet is named for its ability to induce ketosis, a metabolic state that shifts the body to burning fat for fuel instead of sugar. The diet is controversial because it calls for eating high-fat, very low-carbohydrate foods, which is counter to the low-fat diet historically urged by health advocates.
However, the keto diet has been linked to benefits for people with some diseases, including epilepsy and Parkinson’s disease. In the 1920s, before the discovery of insulin, it was a way to manage diabetes because of its ability to lower blood sugar.
In earlier research, Lessard found that people with high blood sugar had lower exercise capacity. She wondered if the diet might improve the response to exercise, leading to higher exercise capacity.
Mice were fed a high-fat, low-carbohydrate diet and exercised on running wheels. The mice developed more slow-twitch muscle fibers, which give better endurance.
“Their bodies were more efficiently using oxygen, which is a sign of higher aerobic capacity,” Lessard says.
Lessard says exercise positively affects virtually every tissue in our body, even fat tissue, but she and others are seeing that the greatest health improvements won’t come with diet or exercise alone.
“What we’re really finding from this study and from our other studies is that diet and exercise aren’t simply working in isolation,” says Lessard, who also holds an appointment in the Department of Human Foods, Nutrition, and Exercise in Virginia Tech’s College of Agriculture and Life Sciences.
“There are a lot of combined effects, and so we can get the most benefits from exercise if we eat a healthy diet at the same time.”
Next, Lessard would like to continue her research in human subjects to see if they gain the same benefits from the keto diet seen in mice.
She also notes that the keto diet is challenging to follow. A less restrictive regimen, such as the Mediterranean diet, might be easier for people to follow and still be effective. That diet can also keep blood sugar low, while including carbohydrates from unprocessed fruits, vegetables, and whole grains rather than restricting carbohydrates altogether.
“Our previous studies have shown that any strategy you and your doctor have arrived at to reduce your blood sugar could work,” she says.
Source: Virginia Tech
The post Keto diet could improve response to exercise in people with high blood sugar appeared first on Futurity.
Futurity
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Well, I hope my wife misses this story, because I’m totally going to pull this one on her.
Not the Bee
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Have you been experiencing frequent internet connection issues, but whenever you do an internet speed test, the results show you’re getting the speeds your internet service provider promised? If you can relate to that, consider building an automatic internet speed tracker and logger.
How-To Geek
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The Laravel ecosystem continues to lean into the agent-driven future with the launch of Laravel Skills, an open directory of reusable AI agent skills designed specifically for Laravel and PHP developers.
Available at https://skills.laravel.cloud/, the new site makes it easy to discover, share, and install skills that help AI tools better understand your codebase, workflows, and best practices.
Laravel Skills is described as an open directory of reusable AI agent skills for Laravel and PHP, where developers can browse and install skills with a single command.
These skills are designed to work with popular AI coding environments including Claude Code, Cursor, Windsurf, Copilot, and others, helping agents perform tasks like:
Instead of repeatedly explaining your stack or preferences, you can load a skill that teaches your agent how to behave.
One of the highlights is how lightweight the workflow is. Skills can be installed using a simple command:
npx skills add <owner/repo>
The directory already includes skills covering areas like:
Because it’s community powered, developers can submit their own skills and contribute patterns that reflect real-world experience.
Laravel News
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Last night, Trump gave the longest, most incredible State of the Union address in American history — almost two hours, a record, nobody’s ever done anything like it.
Not the Bee
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Most organizations are sitting on MySQL deployments they can’t easily change — a mix of community editions, managed cloud services, and legacy versions. Teams want RAG pipelines and natural language querying, but adding AI capabilities typically means schema migrations, new vector database infrastructure, dual-write synchronization headaches, and AI logic sprawled across every application layer. The operational cost is real, and the governance risk is worse.
ProxySQL v4.0 takes a different approach: don’t touch your database at all.
The core thesis is elegant — put the intelligence at the proxy layer, not in the database or the application. ProxySQL already sits between every client and every MySQL backend, which makes it a natural choke point for centralized governance, auth, auditing, and now AI capabilities. No connection string changes, no schema migrations, no new infrastructure your DBA team has to babysit.
The comparison with app-layer integration is stark. Where app-layer AI means fragmented governance across every service, schema changes, and multiple network hops, the ProxySQL AI layer provides unified query rules, zero schema changes, and a single enforced access path.
The MCP (Model Context Protocol) server is the centerpiece. Running on port 6071 over HTTPS with bearer token auth, it exposes 30+ tools that any MCP-compatible agent — Claude Code, GitHub Copilot, Cursor, Warp — can discover and call via standard JSON-RPC. Tools span schema discovery (list_schemas, list_tables, list_columns), safe read-only execution (run_sql_readonly, explain_sql), and full RAG search capabilities (rag.search_fts, rag.search_vector, rag.search_hybrid).
All MCP requests pass through MCP Query Rules — analogous to ProxySQL’s existing mysql_query_rules — which can allow, block, rewrite, or timeout requests before they ever reach MySQL. This is where you enforce read-only access, prevent data exfiltration, and audit everything agents are doing.
The Autodiscovery system (discovery.run_static) runs a two-phase process: static schema harvesting followed by LLM-enriched metadata including summaries and domain analysis. Everything lands in a local SQLite catalog (mcp_catalog.db) that agents can then search semantically via llm.search.
The NL2SQL workflow builds on this: agents search the catalog for relevant schemas, synthesize or reuse SQL templates, execute safely via run_sql_readonly, and optionally store successful query patterns as templates for future reuse — a continuous learning loop that improves accuracy over time.
The presentation is upfront that this is a prototype showcase. Still on the roadmap: automatic embedding generation (with local or external model options), real-time indexing via MySQL replication/binlog without touching source tables, DISTANCE() SQL semantics for vector search on AI-blind MySQL backends, and additional MCP endpoints for config management, cache inspection, and observability.
The proxy layer argument is compelling: it’s operationally mature, protocol-aware, already deployed in front of critical databases, and has a battle-tested policy engine. Adding AI there rather than in application code means one place to enforce rules, one place to audit, and zero changes to the workloads that depend on MySQL stability.
The Code is on the v4.0 branch at github.com/sysown/proxysql, and the Generative AI documentation section at sysown.github.io/proxysql covers the new features.
Download the Bringing GenAI to every MySQL Instance Presentation given by
René Cannaò at preFOSDEM MySQL Belgian Days 2026 in Brussels.
The post Bringing GenAI to Every MySQL Instance: ProxySQL v4.0 appeared first on ProxySQL.
Planet MySQL