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 .
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.
Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort.
arXiv:2606. 24236v1 Announce Type: cross Abstract: Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts.
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. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.
Not all Python data libraries are created equal! The post Should AI Developers Make the Switch from Polars to Pandas?
arXiv:2601. 04390v2 Announce Type: replace Abstract: High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create.
arXiv:2607. 04726v1 Announce Type: cross Abstract: Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target.