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Python Packages for BDXpy

BDXpy users can benefit from a variety of Python packages to enhance data analysis, visualization, automation, and machine learning. Python has numerous packages than what is listed below for almost anything. Youtube, Google, and other search options can help provide a lot of free examples to beginners. For those getting started or unfamiliar below are some packages that you might find useful working with BDXpy:

📊 Data Analysis & Processing

Pandas (Docs)

  • Essential for data manipulation and analysis, Pandas makes it easy to handle time-series data retrieved from BDX.
  • Supports powerful data aggregation, filtering, and statistical operations.

NumPy (Docs)

  • Provides fast array operations, mathematical functions, and numerical processing capabilities.
  • Helpful for working with large datasets from BDX efficiently.

ReportLab (Docs)

  • A powerful library for generating PDFs from data.
  • Useful for exporting BDX reports, trend summaries, and energy analysis into professional-grade PDFs.

📈 Data Visualization

Plotly (Docs)

  • Ideal for creating interactive charts, including line charts, scatter plots, heatmaps, and dashboards.
  • Works well with BDX trend data and supports rendering inside web applications.

Dash (Docs)

  • A great framework for building interactive web applications without needing JavaScript.
  • Useful for creating dynamic visualizations with BDX data.

Matplotlib (Docs)

  • A powerful library for static, animated, and interactive visualizations.
  • Useful for quick visual analysis of BDX data without interactive elements.

Seaborn (Docs)

  • Enhances Matplotlib with advanced statistical visualizations and better default aesthetics.
  • Great for analyzing trends in energy usage and equipment performance.

Plotly Mapbox (Docs)

  • Enables map-based visualization of BDX data, such as energy consumption across multiple buildings.
  • Supports overlaying real-time data on campus maps.

🔮 Machine Learning & Forecasting

Scikit-learn (Docs)

  • A machine learning library with tools for regression, classification, clustering, and anomaly detection.
  • Can be used to analyze energy trends, detect anomalies, and forecast consumption.

XGBoost (Docs)

  • A high-performance machine learning library optimized for structured data.
  • Useful for predictive modeling of energy consumption and HVAC system behaviors.

Prophet (Docs)

  • A time-series forecasting tool developed by Facebook.
  • Useful for predicting energy usage patterns and trends in BDX.

Statsmodels (Docs)

  • Provides statistical models, hypothesis testing, and forecasting.
  • Useful for analyzing historical building energy consumption and HVAC efficiency.

PyCaret (Docs)

  • A low-code machine learning library that automates model training, selection, and tuning.
  • Great for quickly developing predictive models for BDX data.

🛠 Automation & Optimization

NetworkX (Docs)

  • Helps model and analyze complex relationships within BDX hierarchical components.
  • Useful for understanding interdependencies between buildings, sensors, and control points.

PuLP (Docs)

  • A linear programming and optimization library.
  • Can optimize HVAC schedules or chiller plant operations.

Schedule (Docs)

  • A lightweight job scheduling library for Python.
  • Useful for automating BDX data retrieval and reporting.

Flask (Docs)

  • A lightweight web framework for building BDX-powered APIs and dashboards.
  • Can serve energy reports, analytics, and visualizations.

FastAPI (Docs)

  • A high-performance web framework for building APIs with Python.
  • Faster and more scalable than Flask, making it great for handling large BDX requests.

🚀 Got more package recommendations? Let us know on the discussion board User Creations