From Static Plots to Interactive Data Exploration The post Matplotlib vs Plotly: Which Python Chart Tool Should You Choose? appeared first on Towards Data Science .
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.
Exploratory data analysis on the US Census Dataset The post Exploring Income Patterns with Python Pandas, Matplotlib, and Seaborn appeared first on Towards Data Science .
A technical overview and some benchmarks The post Python 3. 14 and its New JIT Compiler appeared first on Towards Data Science .
Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart semantics, and place appropriate textual or graphical elements.
arXiv:2608. 03464v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored.
Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head. The post The Problem with pandas Isn’t Performance.
arXiv:2607. 04727v1 Announce Type: cross Abstract: Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration.