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VAEX
Explore and visualize billion-row datasets on a laptop
MIT
ABOUT
Data scientists regularly work with tabular datasets too large to fit in RAM — logs, time-series data, clickstreams, and scientific measurements — but most tools either sample the data (losing detail) or require a cluster. Vaex solves this with an out-of-core, lazy-evaluation DataFrame that memory-maps data from disk and only computes what is needed for the current query, enabling interactive exploration, aggregation, and visualization of billion-row datasets on a single machine without specialized infrastructure.
INSTALL
pip install vaexINTEGRATION GUIDE
1. Interactively explore and visualize billion-row log and event datasets without loading them into RAM
2. Compute summary statistics, histograms, and heatmaps on large time-series data with sub-second latency
3. Filter, group, and aggregate streaming or archival datasets using lazy evaluation for instant response
4. Display interactive scatter plots and density maps of large geospatial or ML feature datasets
5. Preprocess and clean massive tabular datasets for downstream ML training pipelines efficiently
TAGS
dataframevisualizationout-of-corebig-datapythonexplorationlazy-evaluationmemory-mapping