What it is
Vaex is a high-performance Python library for out-of-core DataFrames, enabling visualization and exploration of datasets larger than memory. It allows fast filtering, grouping, aggregations, and statistical computations without loading the full dataset into RAM.
Vaex allows you to manipulate, filter, group, and aggregate large datasets efficiently. It uses lazy evaluations to compute results only when needed, which minimizes memory usage. Vaex integrates well with NumPy and Pandas-like syntax.
Installation
pip install vaexGetting started
The smallest useful thing you can do with it, and what each part means.
import vaex
df = vaex.from_csv('data.csv', convert=True)
print(df.head())filtered = df[df['age'] > 30]
print(filtered.head())Advanced usage
Where the library earns its place over a simpler alternative.
agg = df.groupby('department', agg={'avg_salary': vaex.agg.mean('salary')})
print(agg)df['bmi'] = df['weight'] / (df['height']/100)**2
print(df[['weight','height','bmi']].head())import matplotlib.pyplot as plt
agg = df.count(binby=df['age'], limits=[0,100], shape=100)
plt.plot(agg)
plt.show()df.export_csv('filtered.csv')
df.export_hdf5('filtered.hdf5')Errors and fixes
The failures you are most likely to hit, and what actually resolves them.
- ValueError: Column not found
- Check that the column name exists in the DataFrame. Use `df.columns` to list available columns.
- MemoryError
- Ensure you use out-of-core processing features and avoid loading extremely large datasets fully into memory.
- FileNotFoundError
- Verify the path to the CSV or HDF5 file is correct before loading.
Best practices
- Use HDF5 or Arrow format for very large datasets for faster access.
- Leverage virtual columns to avoid unnecessary memory usage.
- Apply filtering and aggregation lazily to scale computations efficiently.
- Use `vaex.open()` or `vaex.from_csv(convert=True)` to optimize repeated data loads.
- Combine with visualization tools like Matplotlib or Bokeh for interactive plotting of large datasets.
Background
Why it exists, and what it was reacting to.
Vaex was created by Jovan Popovic in 2015 to handle very large tabular datasets efficiently. It leverages memory mapping, lazy evaluations, and optimized algorithms to provide a pandas-like interface while scaling to billions of rows, making it ideal for big data analysis and visualization.