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NVEIL Python SDK

Data science made easy.

From raw data to processed insights and production visuals — just describe what you need.

NVEIL Dashboard — multi-panel visualization with heatmaps, charts, and flow diagrams

Two Engines, One SDK

Most tools do one thing. NVEIL handles both halves of the data science workflow:

  • Data Processing Engine

    Describe your data pipeline in plain language. NVEIL plans and executes it locally: joins, aggregations, pivots, time series analysis, geocoding, feature engineering, and more. No pandas code to write, no pipeline to maintain.

  • Visualization Engine

    NVEIL generates production code for Plotly, VTK, and DeckGL so you don't have to write a single line of chart configuration. 50+ visualization types, each handcrafted to get the most out of these libraries.

You describe the goal, NVEIL handles the rest

"Compare revenue vs targets by region" produces a join across DataFrames, an aggregation by region, and a grouped bar chart. One line of code.

Quick Example

import nveil
import pandas as pd

nveil.configure(api_key="nveil_...")

df = pd.read_csv("sales.csv")

# NVEIL processes your data AND generates the visualization
spec = nveil.generate_spec("Revenue by region, colored by quarter", df)

fig = spec.render(df)       # 100% local, no API call
nveil.show(fig)              # opens in browser
nveil.save_image(fig, "chart.png")

How It Works

graph LR
    A[Your Data] --> B[SDK]
    B -- metadata only --> C[NVEIL AI]
    C -- processing plan --> B
    B -- runs locally --> D[Result]

    style A fill:#1a1a2e,stroke:#f7941d,color:#fff
    style B fill:#5c2d91,stroke:#e91e8c,color:#fff
    style C fill:#e91e8c,stroke:#f7941d,color:#fff
    style D fill:#1a1a2e,stroke:#f7941d,color:#fff

The SDK sends only metadata (column names, types, statistics) to the server. Your raw data stays on your machine. The AI plans the processing and visualization, then the SDK executes everything locally.

Beyond Simple Charts

NVEIL AI Chat — conversational data exploration with geospatial heatmaps

  • Geospatial

    Heatmaps, choropleths, and point clouds on interactive maps. Automatic geocoding from city names or coordinates.

  • 3D & Scientific

    Surfaces, volumes, point clouds, and meshes. Medical imaging (DICOM), scientific data, and engineering models.

  • Time Series

    Trends, forecasting, rolling statistics, seasonal decomposition. Automatic date parsing and resampling.

  • Auditable Results

    Every output is deterministic, backed by constraint solving instead of random generation. Same input, same result, every time.

Save, Reload, Render Anywhere

# Generate once (API call)
spec = nveil.generate_spec("Monthly trend by category", df)
spec.save("trend.nveil")

# Reload later — no API call, no server needed
spec = nveil.load_spec("trend.nveil")
fig = spec.render(fresh_data)  # new data, same pipeline + visualization
nveil.save_image(fig, "report.png")

.nveil files are encrypted and portable. Share them with colleagues, use them in CI/CD pipelines, or embed in automated reports.

  • Get Started

    Install the SDK and create your first visualization in minutes.

    Quickstart

  • Concepts

    Understand sessions, specs, and the processing pipeline.

    Concepts

  • API Reference

    Full reference for all public functions and classes.

    API Reference

  • Examples

    Recipes for charts, multi-dataset processing, and offline rendering.

    Examples