Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
phyphox turns your smartphone into a mobile lab, allowing you to use its sensors for physics experiments. It offers data export, remote control, and the ability to create custom experiments. The project has received several teaching awards and is supported by various organizations.
A technical blog post about setting up JupyterLab and integrating it with OpenWebUI's code interpreter feature, enabling the LLM to execute and generate code for tasks such as exploratory data analysis.
Google has enhanced Google Sheets with an AI-powered upgrade using its Gemini technology. This update allows users to automatically convert spreadsheets into charts, identify trends, and create advanced visualizations like heatmaps. Users can interact with the Gemini feature directly through a chat interface within Sheets.
Hex introduces Advanced Compute Profiles for demanding workflows, offering more CPU, RAM, and GPUs. It also features Explore, a fast, flexible no-code data analysis tool. Hex emphasizes collaboration, AI integration, and a wide range of use cases including data science, operational reporting, and self-serve data tools.
A comprehensive guide to understanding the correlation matrix, including its use in identifying and quantifying correlations between variables for future predictions, and how to create such matrices in Python.
A Docker container for quickly standing up a Splunk instance, complete with Eventgen and Splunk's Machine Learning app for testing and training purposes.
This article demonstrates how basic statistics and techniques like PCA can be used to analyze tabular datasets, highlighting the importance of data preprocessing, statistical tests, and handling multicollinearity.
Learn how to use Autoencoders to detect anomalies in time series data in a few lines of code.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
