Vaex — Анализ и визуализация больших табличных данных

--- name: vaex description: Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory. license: MIT license metadata: skill-author: K-Dense Inc. --- # Vaex ## Overview Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows. ## When to Use This Skill Use Vaex when: - Processing tabular datasets larger than available RAM (gigabytes to terabytes) - Performing fast statistical aggregations on massive datasets - Creating visualizations and heatmaps of large datasets - Building machine learning pipelines on big data - Converting between data formats (CSV, HDF5, Arrow, Parquet) - Needing lazy evaluation and virtual columns to avoid memory overhead - Working with astronomical data, financial time series, or other large-scale scientific datasets ## Core Capabilities Vaex provides six primary capability areas, each documented in detail in the references directory: ### 1. DataFrames and Data Loading Load and create Vaex DataFrames from various sources including files (HDF5, CSV, Arrow, Parquet), pandas DataFrames, NumPy arrays, and dictionaries. Reference `references/core_dataframes.md` for: - Opening large files efficiently - Converting from pandas/NumPy/Arrow - Working with example datasets - Understanding DataFrame structure ### 2. Data Processing and Manipulation Perform filtering, create virtual columns, use expressions, and aggregate data without loading everything into memory. Reference `references/data_processing.md` for: - Filtering and selections - Virtual columns and expressions - Groupby operations and aggregations - String operations and datetime handling - Working with missing data ### 3. Performance and Optimization Leverage Vaex's lazy evaluation, caching strategies, and memory-efficient operations. Reference `references/performance.md` for: - Understanding lazy evaluation - Using `delay=True` for batching operations - Materializing columns when needed - Caching strategies - Asynchronous operations ### 4. Data Visualization Create interactive visualizations of large datasets including heatmaps, histograms, and scatter plots. Reference `references/visualization.md` for: - Creating 1D and 2D plots - Heatmap visualizations - Working with selections - Customizing plots and subplots ### 5. Machine Learning Integration Build ML pipelines with transformers, encoders, and integration with scikit-learn, XGBoost, and other frameworks. Reference `references/machine_learning.md` for: - Feature scaling and encoding - PCA and dimensionality reduction - K-means clustering - Integration with scikit-learn/XGBoost/CatBoost - Model serialization and deployment ### 6. I/O Operations Efficiently read and write data in various formats with optimal performance. Reference `references/io_operations.md` for: - File format recommendations - Export strategies - Working with Apache Arrow - CSV handling for large files - Server and remote data access ## Quick Start Pattern For most Vaex tasks, follow this pattern: ```python import vaex # 1. Open or create DataFrame df = vaex.open('large_file.hdf5') # or .csv, .arrow, .parquet # OR df = vaex.from_pandas(pandas_df) # 2. Explore the data print(df) # Shows first/last rows and column info df.describe() # Statistical summary # 3. Create virtual columns (no memory overhead) df['new_column'] = df.x ** 2 + df.y # 4. Filter with selections df_filtered = df[df.age > 25] # 5. Compute statistics (fast, lazy evaluation) mean_val = df.x.mean() stats = df.groupby('category').agg({'value': 'sum'}) # 6. Visualize df.plot1d(df.x, limits=[0, 100]) df.plot(df.x, df.y, limits='99.7%') # 7. Export if needed df.export_hdf5('output.hdf5') ``` ## Working with References The reference files contain detailed information about each capability area. Load references into context based on the specific task: - **Basic operations**: Start with `references/core_dataframes.md` and `references/data_processing.md` - **Performance issues**: Check `references/performance.md` - **Visualization tasks**: Use `references/visualization.md` - **ML pipelines**: Reference `references/machine_learning.md` - **File I/O**: Consult `references/io_operations.md` ## Best Practices 1. **Use HDF5 or Apache Arrow formats** for optimal performance with large datasets 2. **Leverage virtual columns** instead of materializing data to save memory 3. **Batch operations** using `delay=True` when performing multiple calculations 4. **Export to efficient formats** rather than keeping data in CSV 5. **Use expressions** for complex calculations without intermediate storage 6. **Profile with `df.stat()`** to understand memory usage and optimize operations ## Common Patterns ### Pattern: Converting Large CSV to HDF5 ```python import vaex # Open large CSV (processes in chunks automatically) df = vaex.from_csv('large_file.csv') # Export to HDF5 for faster future access df.export_hdf5('large_file.hdf5') # Future loads are instant df = vaex.open('large_file.hdf5') ``` ### Pattern: Efficient Aggregations ```python # Use delay=True to batch multiple operations mean_x = df.x.mean(delay=True) std_y = df.y.std(delay=True) sum_z = df.z.sum(delay=True) # Execute all at once results = vaex.execute([mean_x, std_y, sum_z]) ``` ### Pattern: Virtual Columns for Feature Engineering ```python # No memory overhead - computed on the fly df['age_squared'] = df.age ** 2 df['full_name'] = df.first_name + ' ' + df.last_name df['is_adult'] = df.age >= 18 ``` ## Resources This skill includes reference documentation in the `references/` directory: - `core_dataframes.md` - DataFrame creation, loading, and basic structure - `data_processing.md` - Filtering, expressions, aggregations, and transformations - `performance.md` - Optimization strategies and lazy evaluation - `visualization.md` - Plotting and interactive visualizations - `machine_learning.md` - ML pipelines and model integration - `io_operations.md` - File formats and data import/export