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 .
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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 .
arXiv:2608.30514v1 Announce Type: cross Abstract: We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-d...
arXiv:2608.03464v2 Announce Type: replace Abstract: Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large...
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.
ChartRevise is a new dataset and evaluation protocol designed for exact chart editing via code. It contains 92,438 records covering 344 edit types across 20 chart types and three plotting libraries, built using the grammar of graphics and source‑program checks to ensure applicability. The protocol measures atomic requirement completion, detects gratuitous changes and missed coupled updates, and combines these with execution and rendering success to determine exact‑edit success.