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Vaex uses several sites:
- Main page: https://vaex.io/
- Documentation: https://docs.vaex.io/
- Github: https://github.com/vaexio/vaex
- PyPi: https://pypi.python.org/pypi/vaex/
Vaex is open source software, if you need support, contact us at https://vaex.io
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 (:math: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
filtering/selections/subsets. -
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 <https://arrow.apache.org/>__ 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
support.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 <http://docs.vaex.io/en/latest/ml.html>__ with automatic pipelines.
-
Jupyter integration: vaex-jupyter will give you interactive
visualization and selection in the Jupyter notebook and Jupyter lab.
Installation
Using conda:
conda install -c conda-forge vaex
Using pip:
pip install vaex
Or read the detailed instructions <https://docs.vaex.io/en/latest/installing.html>__
Getting started
We assuming you have installed vaex, and are running a Jupyter notebook server <https://jupyter.readthedocs.io/en/latest/running.html>__. 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 <https://docs.vaex.io/en/latest/datasets.html>, or
read in your csv file <https://docs.vaex.io/en/latest/api.html#vaex.from_csv>.
.. code:: ipython3
ds # will pretty print a table
.. raw:: html
<table>
<thead>
<tr><th># </th><th>x </th><th>y </th><th>z </th><th>vx </th><th>vy </th><th>vz </th><th>E </th><th>L </th><th>Lz </th><th>FeH </th></tr>
</thead>
<tbody>
<tr><td>0 </td><td>-0.777470767</td><td>2.10626292 </td><td>1.93743467 </td><td>53.276722 </td><td>288.386047 </td><td>-95.2649078</td><td>-121238.171875 </td><td>831.0799560546875 </td><td>-336.426513671875 </td><td>-2.309227609164518 </td></tr>
<tr><td>1 </td><td>3.77427316 </td><td>2.23387194 </td><td>3.76209331 </td><td>252.810791 </td><td>-69.9498444</td><td>-56.3121033</td><td>-100819.9140625</td><td>1435.1839599609375</td><td>-828.7567749023438 </td><td>-1.788735491591229 </td></tr>
<tr><td>2 </td><td>1.3757627 </td><td>-6.3283844 </td><td>2.63250017 </td><td>96.276474 </td><td>226.440201 </td><td>-34.7527161</td><td>-100559.9609375</td><td>1039.2989501953125</td><td>920.802490234375 </td><td>-0.7618109022478798</td></tr>
<tr><td>3 </td><td>-7.06737804 </td><td>1.31737781 </td><td>-6.10543537 </td><td>204.968842 </td><td>-205.679016</td><td>-58.9777031</td><td>-70174.8515625 </td><td>2441.724853515625 </td><td>1183.5899658203125 </td><td>-1.5208778422936413</td></tr>
<tr><td>4 </td><td>0.243441463 </td><td>-0.822781682</td><td>-0.206593871</td><td>-311.742371</td><td>-238.41217 </td><td>186.824127 </td><td>-144138.75 </td><td>374.8164367675781 </td><td>-314.5353088378906 </td><td>-2.655341358427361 </td></tr>
<tr><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td><td>... </td></tr>
<tr><td>329995</td><td>3.76883793 </td><td>4.66251659 </td><td>-4.42904139 </td><td>107.432999 </td><td>-2.13771296</td><td>17.5130272 </td><td>-119687.3203125</td><td>746.8833618164062 </td><td>-508.96484375 </td><td>-1.6499842518381402</td></tr>
<tr><td>329996</td><td>9.17409325 </td><td>-8.87091351 </td><td>-8.61707687 </td><td>32.0 </td><td>108.089264 </td><td>179.060638 </td><td>-68933.8046875 </td><td>2395.633056640625 </td><td>1275.490234375 </td><td>-1.4336036247720836</td></tr>
<tr><td>329997</td><td>-1.14041007 </td><td>-8.4957695 </td><td>2.25749826 </td><td>8.46711349 </td><td>-38.2765236</td><td>-127.541473</td><td>-112580.359375 </td><td>1182.436279296875 </td><td>115.58557891845703 </td><td>-1.9306227597361942</td></tr>
<tr><td>329998</td><td>-14.2985935 </td><td>-5.51750422 </td><td>-8.65472317 </td><td>110.221558 </td><td>-31.3925591</td><td>86.2726822 </td><td>-74862.90625 </td><td>1324.5926513671875</td><td>1057.017333984375 </td><td>-1.225019818838568 </td></tr>
<tr><td>329999</td><td>10.5450506 </td><td>-8.86106777 </td><td>-4.65835428 </td><td>-2.10541415</td><td>-27.6108856</td><td>3.80799961 </td><td>-95361.765625 </td><td>351.0955505371094 </td><td>-309.81439208984375</td><td>-2.5689636894079477</td></tr>
</tbody>
</table>
Using square brackets[] <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.__getitem__>__,
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] # take the first five rows, and only the 'x' and 'y' column (no memory copy!)
.. raw:: html
<table>
<thead>
<tr><th style="text-align: right;"> #</th><th style="text-align: right;"> x</th><th style="text-align: right;"> y</th></tr>
</thead>
<tbody>
<tr><td style="text-align: right;"> 0</td><td style="text-align: right;"> -0.777471</td><td style="text-align: right;"> 2.10626</td></tr>
<tr><td style="text-align: right;"> 1</td><td style="text-align: right;"> -7.06738 </td><td style="text-align: right;"> 1.31738</td></tr>
<tr><td style="text-align: right;"> 2</td><td style="text-align: right;"> -5.17174 </td><td style="text-align: right;"> 7.82915</td></tr>
<tr><td style="text-align: right;"> 3</td><td style="text-align: right;">-15.9539 </td><td style="text-align: right;"> 5.77126</td></tr>
<tr><td style="text-align: right;"> 4</td><td style="text-align: right;">-12.3995 </td><td style="text-align: right;">13.9182 </td></tr>
</tbody>
</table>
When dealing with huge datasets, say a billion rows (:math: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 <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.plot1d>,
plot <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.plot>, or see the list of plotting commands <https://docs.vaex.io/en/latest/api.html#visualization>__
Continue
Continue the tutorial <https://docs.vaex.io/en/latest/tutorial.html>__ or check the
examples <https://docs.vaex.io/en/latest/examples.html>__
If you like vaex, please let us know by giving us a star on GitHub,
Regards,
The vaex.io team
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