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        "author": "Maarten A. Breddels",
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        "description": "|Travis| |Conda| |Chat| \n\nVaex uses several sites:\n\n* Main page: https://vaex.io/\n* Documentation: https://docs.vaex.io/\n* Github: https://github.com/vaexio/vaex\n* PyPi: https://pypi.python.org/pypi/vaex/\n\n\nVaex is open source software, if you need support, contact us at https://vaex.io\n\n\n\nWhat is Vaex?\n-------------\n\nVaex is a python library for lazy **Out-of-Core DataFrames** (similar to\nPandas), to visualize and explore big tabular datasets. It can calculate\n*statistics* such as mean, sum, count, standard deviation etc, on an\n*N-dimensional grid* for more than **a billion** (10^9) objects/rows\n**per second**. Visualization is done using **histograms**, **density\nplots** and **3d volume rendering**, allowing interactive exploration of\nbig data. Vaex uses memory mapping, zero memory copy policy and lazy\ncomputations for best performance (no memory wasted).\n\n\nWhy vaex\n========\n\n-  **Performance:** Works with huge tabular data, process\n   more than a *billion* rows/second\n-  **Lazy / Virtual columns:** compute on the fly, without wasting ram\n-  **Memory efficient** no memory copies when doing\n   filtering/selections/subsets.\n-  **Visualization:** directly supported, a one-liner is often enough.\n-  **User friendly API:** You will only need to deal with a Dataset\n   object, and tab completion + docstring will help you out:\n   ``ds.mean<tab>``, feels very similar to Pandas.\n-  **Lean:** separated into multiple packages\n\n   -  ``vaex-core``: Dataset and core algorithms, takes numpy arrays as\n      input columns.\n   -  ``vaex-hdf5``: Provides memory mapped numpy arrays to a Dataset.\n   -  ``vaex-arrow``: `Arrow <https://arrow.apache.org/>`__ support for\n      cross language data sharing.\n   -  ``vaex-viz``: Visualization based on matplotlib.\n   -  ``vaex-jupyter``: Interactive visualization based on Jupyter\n      widgets / ipywidgets, bqplot, ipyvolume and ipyleaflet.\n   -  ``vaex-astro``: Astronomy related transformations and FITS file\n      support.\n   -  ``vaex-server``: Provides a server to access a dataset remotely.\n   -  ``vaex-distributed``: (Proof of concept) combined multiple servers\n      / cluster into a single dataset for distributed computations.\n   -  ``vaex-qt``: Program written using Qt GUI.\n   -  ``vaex``: meta package that installs all of the above.\n   -  ``vaex-ml``: `Machine learning <http://docs.vaex.io/en/latest/ml.html>`__ with automatic pipelines.\n\n-  **Jupyter integration**: vaex-jupyter will give you interactive\n   visualization and selection in the Jupyter notebook and Jupyter lab.\n\nInstallation\n------------\n\nUsing conda:\n\n-  ``conda install -c conda-forge vaex``\n\nUsing pip:\n\n-  ``pip install vaex``\n\nOr read the `detailed instructions <https://docs.vaex.io/en/latest/installing.html>`__\n\nGetting started\n===============\n\nWe assuming you have installed vaex, and are running a `Jupyter notebook\nserver <https://jupyter.readthedocs.io/en/latest/running.html>`__. We\nstart by importing vaex and ask it to give us sample example dataset.\n\n.. code:: ipython3\n\n    import vaex\n    ds = vaex.example()  # open the example dataset provided with vaex\n\n\nInstead, you can `download some larger datasets <https://docs.vaex.io/en/latest/datasets.html>`__, or\n`read in your csv file <https://docs.vaex.io/en/latest/api.html#vaex.from_csv>`__.\n\n.. code:: ipython3\n\n    ds  # will pretty print a table\n\n\n\n\n\nUsing `square brackets[] <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.__getitem__>`__,\nwe can easily filter or get different views on the dataset.\n\n.. code:: ipython3\n\n    ds_negative = ds[ds.x < 0]  # easily filter your dataset, without making a copy\n    ds_negative[:5][['x', 'y']]  # take the first five rows, and only the 'x' and 'y' column (no memory copy!)\n\n\n\n\n\n\nWhen dealing with huge datasets, say a billion rows (10^9),\ncomputations with the data can waste memory, up to 8 GB for a new\ncolumn. Instead, vaex uses lazy computation, only a representation of\nthe computation is stored, and computations done on the fly when needed.\nEven though, you can just many of the numpy functions, as if it was a\nnormal array.\n\n.. code:: ipython3\n\n    import numpy as np\n    # creates an expression (nothing is computed)\n    r = np.sqrt(ds.x**2 + ds.y**2 + ds.z**2)\n    r  # for convenience, we print out some values\n\n\n\n\n.. parsed-literal::\n\n    <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] \n\n\n\nThese expressions can be added to the dataset, creating what we call a\n*virtual column*. These virtual columns are simular to normal columns,\nexcept they do not waste memory.\n\n.. code:: ipython3\n\n    ds['r'] = r  # add a (virtual) column that will be computed on the fly\n    ds.mean(ds.x), ds.mean(ds.r)  # calculate statistics on normal and virtual columns\n\n\n\n\n.. parsed-literal::\n\n    (-0.06713149126400597, 9.407082338299773)\n\n\n\nOne of the core features of vaex is its ability to calculate statistics\non a regular (N-dimensional) grid. The dimensions of the grid are\nspecified by the binby argument (analogous to SQL's grouby), and the\nshape and limits.\n\n.. code:: ipython3\n\n    ds.mean(ds.r, binby=ds.x, shape=32, limits=[-10, 10]) # create statistics on a regular grid (1d)\n\n\n\n\n.. parsed-literal::\n\n    array([15.01058183, 14.43693006, 13.72923338, 12.90294499, 11.86615103,\n           11.03563695, 10.12162553,  9.2969267 ,  8.58250973,  7.86602644,\n            7.19568442,  6.55738773,  6.01942499,  5.51462457,  5.15798991,\n            4.8274218 ,  4.7346551 ,  5.1343761 ,  5.46017944,  6.02199777,\n            6.54132124,  7.27025256,  7.99780777,  8.55188217,  9.30286584,\n            9.97067561, 10.81633293, 11.60615795, 12.33813552, 13.10488982,\n           13.86868565, 14.60577266])\n\n\n\n.. code:: ipython3\n\n    ds.mean(ds.r, binby=[ds.x, ds.y], shape=32, limits=[-10, 10]) # or 2d\n    ds.count(ds.r, binby=[ds.x, ds.y], shape=32, limits=[-10, 10]) # or 2d counts/histogram\n\n\n\n\n.. parsed-literal::\n\n    array([[22., 33., 37., ..., 58., 38., 45.],\n           [37., 36., 47., ..., 52., 36., 53.],\n           [34., 42., 47., ..., 59., 44., 56.],\n           ...,\n           [73., 73., 84., ..., 41., 40., 37.],\n           [53., 58., 63., ..., 34., 35., 28.],\n           [51., 32., 46., ..., 47., 33., 36.]])\n\n\n\nThese one and two dimensional grids can be visualized using any plotting\nlibrary, such as matplotlib, but the setup can be tedious. For\nconvenience we can use `plot1d <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.plot1d>`__,\n`plot <https://docs.vaex.io/en/latest/api.html#vaex.dataset.Dataset.plot>`__, or see the `list of\nplotting commands <https://docs.vaex.io/en/latest/api.html#visualization>`__\n\n\n\nContinue\n--------\n\n`Continue the tutorial <https://docs.vaex.io/en/latest/tutorial.html>`__ or check the\n`examples <https://docs.vaex.io/en/latest/examples.html>`__\n\nIf you like vaex, please let us know by giving us a star on GitHub,\n\nRegards,\n\nThe vaex.io team\n\n.. |Travis| image:: https://travis-ci.org/vaexio/vaex.svg?branch=master\n   :target: https://travis-ci.org/vaexio/vaex.svg?branch=master\n.. |Chat| image:: https://badges.gitter.im/maartenbreddels/vaex.svg\n   :alt: Join the chat at https://gitter.im/maartenbreddels/vaex\n   :target: https://gitter.im/maartenbreddels/vaex?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n.. |Conda| image:: https://anaconda.org/conda-forge/vaex/badges/downloads.svg\n   :target: https://anaconda.org/conda-forge/vaex",
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