vaex

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.

Installation

Install the full meta-package (recommended):

uv pip install vaex

Minimal install (pick only what you need):

uv pip install vaex-core vaex-viz vaex-hdf5 vaex-ml

The vaex package is a meta-package that pulls in vaex-core, vaex-viz, vaex-hdf5, vaex-ml, and other sub-packages. Arrow support is built into vaex-core (the separate vaex-arrow package is deprecated). vaex-distributed is deprecated in favor of vaex-enterprise.

Version notes (vaex 4.19.0+): Python 3.12 and NumPy v2 require vaex >= 4.19.0. On Windows, you may need Python dev headers to build the annoy dependency.

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

Vaex vs alternatives: Use polars when data fits in RAM and you need maximum in-memory speed. Use dask when you need distributed pandas/NumPy across a cluster. Use vaex for single-machine, out-of-core analytics on tabular data that exceeds RAM via memory-mapped HDF5/Arrow files.

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:

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.viz is the recommended accessor since vaex 4.0)
df.viz.heatmap(df.x, df.y, limits='99.7%', show=True)
# Legacy: df.plot1d() and df.plot() still work on the DataFrame

# 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.describe() and df.nbytes to understand data shape and memory usage

Common Patterns

Pattern: Converting Large CSV to HDF5

import vaex

# Open large CSV lazily (vaex 4.14+), or use from_csv to convert to HDF5
df = vaex.open('large_file.csv')
# df = vaex.from_csv('large_file.csv', convert='large_file.hdf5')

# 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

# 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

# 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

Agent operating procedure

1. Check the environment. Confirm the Python environment and library versions (python -c "import pkg; print(pkg.__version__)") and inspect the data's shape, types and missing values.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run on a sample or a single fold first and check runtime and memory.
4. Execute the full task using the instructions and references above.
5. Validate the result. Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Out-of-memory or very slow execution	Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never report a metric you did not compute in this session; show the code path that produced every number.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• polars: High-performance DataFrame library for Python ETL, analytics, and pandas migration.
• polars-bio: Python library polars-bio for genomic interval operations and bioinformatics file I/O on Polars DataFrames, built on Arrow and DataFusion.
• dask: Distributed computing for larger-than-RAM pandas/NumPy workflows.