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Out-of-Core DataFrames to visualize and explore big tabular datasets

Project description

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Vaex uses several sites:

* Main page:
* Documentation:
* Github:
* PyPi:

Vaex is open source software, if you need support, contact us at

What is Vaex?

Vaex is a python library for lazy **Out-of-Core DataFrames** (similar to
Pandas), to visualize and explore big tabular datasets. It can calculate
*statistics* such as mean, sum, count, standard deviation etc, on an
*N-dimensional grid* for more than **a billion** (10^9) objects/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).

Why vaex

- **Performance:** Works with huge tabular data, process
more than a *billion* rows/second
- **Lazy / Virtual columns:** compute on the fly, without wasting ram
- **Memory efficient** no memory copies when doing
- **Visualization:** directly supported, a one-liner is often enough.
- **User friendly API:** You will only need to deal with a Dataset
object, and tab completion + docstring will help you out:
``ds.mean<tab>``, feels very similar to Pandas.
- **Lean:** separated into multiple packages

- ``vaex-core``: Dataset and core algorithms, takes numpy arrays as
input columns.
- ``vaex-hdf5``: Provides memory mapped numpy arrays to a Dataset.
- ``vaex-arrow``: `Arrow <>`__ support for
cross language data sharing.
- ``vaex-viz``: Visualization based on matplotlib.
- ``vaex-jupyter``: Interactive visualization based on Jupyter
widgets / ipywidgets, bqplot, ipyvolume and ipyleaflet.
- ``vaex-astro``: Astronomy related transformations and FITS file
- ``vaex-server``: Provides a server to access a dataset remotely.
- ``vaex-distributed``: (Proof of concept) combined multiple servers
/ cluster into a single dataset for distributed computations.
- ``vaex-qt``: Program written using Qt GUI.
- ``vaex``: meta package that installs all of the above.
- ``vaex-ml``: `Machine learning <>`__ with automatic pipelines.

- **Jupyter integration**: vaex-jupyter will give you interactive
visualization and selection in the Jupyter notebook and Jupyter lab.


Using conda:

- ``conda install -c conda-forge vaex``

Using pip:

- ``pip install vaex``

Or read the `detailed instructions <>`__

Getting started

We assuming you have installed vaex, and are running a `Jupyter notebook
server <>`__. We
start by importing vaex and ask it to give us sample example dataset.

.. code:: ipython3

import vaex
ds = vaex.example() # open the example dataset provided with vaex

Instead, you can `download some larger datasets <>`__, or
`read in your csv file <>`__.

.. code:: ipython3

ds # will pretty print a table

Using `square brackets[] <>`__,
we can easily filter or get different views on the dataset.

.. code:: ipython3

ds_negative = ds[ds.x < 0] # easily filter your dataset, without making a copy
ds_negative[:5][['x', 'y']] # take the first five rows, and only the 'x' and 'y' column (no memory copy!)

When dealing with huge datasets, say a billion rows (10^9),
computations with the data can waste memory, up to 8 GB for a new
column. Instead, vaex uses lazy computation, only a representation of
the computation is stored, and computations done on the fly when needed.
Even though, you can just many of the numpy functions, as if it was a
normal array.

.. code:: ipython3

import numpy as np
# creates an expression (nothing is computed)
r = np.sqrt(ds.x**2 + ds.y**2 + ds.z**2)
r # for convenience, we print out some values

.. parsed-literal::

<vaex.expression.Expression(expressions='sqrt((((x ** 2) + (y ** 2)) + (z ** 2)))')> instance at 0x11bcc4780 values=[2.9655450396553587, 5.77829281049018, 6.99079603950256, 9.431842752707537, 0.8825613121347967 ... (total 330000 values) ... 7.453831761514681, 15.398412491068198, 8.864250273925633, 17.601047186042507, 14.540181524970293]

These expressions can be added to the dataset, creating what we call a
*virtual column*. These virtual columns are simular to normal columns,
except they do not waste memory.

.. code:: ipython3

ds['r'] = r # add a (virtual) column that will be computed on the fly
ds.mean(ds.x), ds.mean(ds.r) # calculate statistics on normal and virtual columns

.. parsed-literal::

(-0.06713149126400597, 9.407082338299773)

One of the core features of vaex is its ability to calculate statistics
on a regular (N-dimensional) grid. The dimensions of the grid are
specified by the binby argument (analogous to SQL's grouby), and the
shape and limits.

.. code:: ipython3

ds.mean(ds.r, binby=ds.x, shape=32, limits=[-10, 10]) # create statistics on a regular grid (1d)

.. parsed-literal::

array([15.01058183, 14.43693006, 13.72923338, 12.90294499, 11.86615103,
11.03563695, 10.12162553, 9.2969267 , 8.58250973, 7.86602644,
7.19568442, 6.55738773, 6.01942499, 5.51462457, 5.15798991,
4.8274218 , 4.7346551 , 5.1343761 , 5.46017944, 6.02199777,
6.54132124, 7.27025256, 7.99780777, 8.55188217, 9.30286584,
9.97067561, 10.81633293, 11.60615795, 12.33813552, 13.10488982,
13.86868565, 14.60577266])

.. code:: ipython3

ds.mean(ds.r, binby=[ds.x, ds.y], shape=32, limits=[-10, 10]) # or 2d
ds.count(ds.r, binby=[ds.x, ds.y], shape=32, limits=[-10, 10]) # or 2d counts/histogram

.. parsed-literal::

array([[22., 33., 37., ..., 58., 38., 45.],
[37., 36., 47., ..., 52., 36., 53.],
[34., 42., 47., ..., 59., 44., 56.],
[73., 73., 84., ..., 41., 40., 37.],
[53., 58., 63., ..., 34., 35., 28.],
[51., 32., 46., ..., 47., 33., 36.]])

These one and two dimensional grids can be visualized using any plotting
library, such as matplotlib, but the setup can be tedious. For
convenience we can use `plot1d <>`__,
`plot <>`__, or see the `list of
plotting commands <>`__


`Continue the tutorial <>`__ or check the
`examples <>`__

If you like vaex, please let us know by giving us a star on GitHub,


The team

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