To help you get started, we've collected the most common ways that vaex is being used within popular public projects.

vaexio / vaex / test / ui.py View on Github

def test_select_by_expression(self):
		self.window.xlabel = "x"
		self.window.ylabel = "y"
		##self.window._wait() # TODO: is this a bug? if we don't wait and directly do the selection, the ThreadPoolIndex
		## is entered twice, not sure this can happen from the gui
		vaex.ui.qt.set_choose("x < 0", True)
		logger.debug("click mouse")
		QtTest.QTest.mouseClick(self.layer.button_selection_expression, QtCore.Qt.LeftButton)
		logger.debug("clicked mouse")
		return
		self.window._wait()
		self.assertTrue(self.no_exceptions)

		filename = self.window.plot_to_png()
		self.compare(filename, get_comparison_image("example_xy_selection_on_x"))

View more ways to use vaex

What is Vaex?

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

Installing

With pip:

$ pip install vaex

Or conda:

$ conda install -c conda-forge vaex

For more details, see the documentation

Key features

Instant opening of Huge data files (memory mapping)

HDF5 and Apache Arrow supported.

Read the documentation on how to efficiently convert your data from CSV files, Pandas DataFrames, or other sources.

Lazy streaming from S3 supported in combination with memory mapping.

Expression system

Don't waste memory or time with feature engineering, we (lazily) transform your data when needed.

Out-of-core DataFrame

Filtering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.

Fast groupby / aggregations

Vaex implements parallelized, highly performant groupby operations, especially when using categories (>1 billion/second).

Fast and efficient join

Vaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!

More features

• Remote DataFrames (documentation coming soon)
• Integration into Jupyter and Voila for interactive notebooks and dashboards
• Machine Learning without (explicit) pipelines

Contributing

See contributing page.

Slack

Join the discussion in our Slack channel!

Learn more about Vaex

• Articles

  • Beyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head (includes benchmarks)
  • 7 reasons why I love Vaex for data science (tips and trics)
  • ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn
  • How to analyse 100 GB of data on your laptop with Python
  • Flying high with Vaex: analysis of over 30 years of flight data in Python
  • Vaex: A DataFrame with super strings - Speed up your text processing up to a 1000x
  • Vaex: Out of Core Dataframes for Python and Fast Visualization - 1 billion row datasets on your laptop

• Follow our tutorials

• Watch our more recent talks:

  • PyData London 2019
  • SciPy 2019

• Contact us for data science solutions, training, or enterprise support at https://vaex.io/