Lux is a Python library designed to automate data visualization within Pandas DataFrames, streamlining the exploratory data analysis (EDA) process. It automatically generates insightful charts like distributions, correlations, and temporal trends upon displaying a DataFrame, reducing the need for manual plotting code. Users can also save visualizations as interactive HTML reports or export individual charts for further customization using tools like Matplotlib, Seaborn, or Altair. While best suited for Jupyter Notebook environments and smaller datasets, Lux aims to accelerate data understanding and hypothesis building, particularly for learners and researchers.
An easy starting point for larger and more complicated Dash dashboards. This article presents a sensible, and fully functional, multi-file project structure, containing all the essentials to get started.
This article details a hands-on approach to modeling rare events in time series data using Python. It covers data exploration, defining extreme events, fitting distributions (GEV, Weibull, Gumbel), and evaluating model performance using metrics like log-likelihood, AIC, and BIC. The example uses weather data and provides code snippets for implementation.
This article explores how different decision tree hyperparameters affect performance and visual structure, using scikit-learn's DecisionTreeRegressor and the California housing dataset. It examines the impact of max_depth, ccp_alpha, min_samples_split, min_samples_leaf, and max_leaf_nodes, and demonstrates the use of cross-validation and BayesSearchCV for optimal hyperparameter tuning.
This article explores the impact of hyperparameters on random forests, both in terms of performance and visual representation. It compares the performance of a default random forest with tuned decision trees and examines the effects of various hyperparameters like `n_estimators`, `max_depth`, and `ccp_alpha` using visualizations of individual trees, predictions, and errors.
Learn how to connect several essential tools to develop a simple yet intuitive dashboard using Streamlit, Plotly, DuckDB, and Pandas to visualize data from a JSON file.
This article explores gamma spectroscopy using a Radiacode 103G detector and Python, detailing data collection, analysis, and experiments with various objects to identify radioactive elements.
A summary of Antranig Basman's projects, including work with the Oregon Bee Atlas, the Infusion framework, spherical turtle graphics for NOAA's Science on a Sphere, the GPII/Morphic autopersonalization project, Darwin Online, and the RSF framework.
The article uses a WSJ measles heatmap to illustrate heatmaps' effectiveness in displaying vaccine impacts on infectious diseases. It guides creating custom colormaps with Matplotlib’s LinearSegmentedColormap and pcolormesh function.
A tutorial on creating a data dashboard prototype using Goodreads reading data and generative AI tools like Vizro-AI. The process includes chart generation, setup of a Jupyter Notebook, and deployment on PyCafe.
