%global _empty_manifest_terminate_build 0 Name: python-vaex Version: 4.16.0 Release: 1 Summary: Out-of-Core DataFrames to visualize and explore big tabular datasets License: MIT URL: https://www.github.com/vaexio/vaex Source0: https://mirrors.nju.edu.cn/pypi/web/packages/62/fd/061dcce6ee7211f32b28aa6b49f8a19ece9619535b43ee3171b25c001711/vaex-4.16.0.tar.gz BuildArch: noarch Requires: python3-vaex-core Requires: python3-vaex-astro Requires: python3-vaex-hdf5 Requires: python3-vaex-viz Requires: python3-vaex-server Requires: python3-vaex-jupyter Requires: python3-vaex-ml %description [![Documentation](https://readthedocs.org/projects/vaex/badge/?version=latest)](https://docs.vaex.io) [![Slack](https://img.shields.io/badge/slack-chat-green.svg)](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) # 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](https://docs.vaex.io/en/latest/installing.html) # Key features ## Instant opening of Huge data files (memory mapping) [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) and [Apache Arrow](https://arrow.apache.org/) supported. ![opening1a](https://user-images.githubusercontent.com/1765949/82818563-31c1e200-9e9f-11ea-9ee0-0a8c1994cdc9.png) ![opening1b](https://user-images.githubusercontent.com/1765949/82820352-49e73080-9ea2-11ea-9153-d73aa399d329.png) [Read the documentation on how to efficiently convert your data](https://docs.vaex.io/en/latest/example_io.html) from CSV files, Pandas DataFrames, or other sources. Lazy streaming from S3 supported in combination with memory mapping. ![opening1c](https://user-images.githubusercontent.com/1765949/82820516-a21e3280-9ea2-11ea-948b-07df26c4b5d3.png) ## Expression system Don't waste memory or time with feature engineering, we (lazily) transform your data when needed. ![expression](https://user-images.githubusercontent.com/1765949/82818733-70f03300-9e9f-11ea-80b0-ab28e7950b5c.png) ## 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. ![occ-animated](https://user-images.githubusercontent.com/1765949/82821111-c6c6da00-9ea3-11ea-9f9e-498de8133cc2.gif) ## Fast groupby / aggregations Vaex implements parallelized, highly performant `groupby` operations, especially when using categories (>1 billion/second). ![groupby](https://user-images.githubusercontent.com/1765949/82818807-97ae6980-9e9f-11ea-8820-41dd4441057a.png) ## 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! ![join](https://user-images.githubusercontent.com/1765949/82818840-a268fe80-9e9f-11ea-8ba2-6a6d52c4af88.png) ## More features * Remote DataFrames (documentation coming soon) * Integration into [Jupyter and Voila for interactive notebooks and dashboards](https://vaex.readthedocs.io/en/latest/tutorial_jupyter.html) * [Machine Learning without (explicit) pipelines](https://vaex.readthedocs.io/en/latest/tutorial_ml.html) ## Contributing See [contributing](CONTRIBUTING.md) page. ## Slack Join the discussion in our [Slack](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) channel! # Learn more about Vaex * Articles * [Beyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head](https://towardsdatascience.com/beyond-pandas-spark-dask-vaex-and-other-big-data-technologies-battling-head-to-head-a453a1f8cc13) (includes benchmarks) * [7 reasons why I love Vaex for data science](https://towardsdatascience.com/7-reasons-why-i-love-vaex-for-data-science-99008bc8044b) (tips and trics) * [ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn](https://towardsdatascience.com/ml-impossible-train-a-1-billion-sample-model-in-20-minutes-with-vaex-and-scikit-learn-on-your-9e2968e6f385) * [How to analyse 100 GB of data on your laptop with Python](https://towardsdatascience.com/how-to-analyse-100s-of-gbs-of-data-on-your-laptop-with-python-f83363dda94) * [Flying high with Vaex: analysis of over 30 years of flight data in Python](https://towardsdatascience.com/https-medium-com-jovan-veljanoski-flying-high-with-vaex-analysis-of-over-30-years-of-flight-data-in-python-b224825a6d56) * [Vaex: A DataFrame with super strings - Speed up your text processing up to a 1000x ](https://towardsdatascience.com/vaex-a-dataframe-with-super-strings-789b92e8d861) * [Vaex: Out of Core Dataframes for Python and Fast Visualization - 1 billion row datasets on your laptop](https://towardsdatascience.com/vaex-out-of-core-dataframes-for-python-and-fast-visualization-12c102db044a) * [Follow our tutorials](https://docs.vaex.io/en/latest/tutorials.html) * Watch our more recent talks: * [PyData London 2019](https://www.youtube.com/watch?v=2Tt0i823-ec) * [SciPy 2019](https://www.youtube.com/watch?v=ELtjRdPT8is) * Contact us for data science solutions, training, or enterprise support at https://vaex.io/ %package -n python3-vaex Summary: Out-of-Core DataFrames to visualize and explore big tabular datasets Provides: python-vaex BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-vaex [![Documentation](https://readthedocs.org/projects/vaex/badge/?version=latest)](https://docs.vaex.io) [![Slack](https://img.shields.io/badge/slack-chat-green.svg)](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) # 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](https://docs.vaex.io/en/latest/installing.html) # Key features ## Instant opening of Huge data files (memory mapping) [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) and [Apache Arrow](https://arrow.apache.org/) supported. ![opening1a](https://user-images.githubusercontent.com/1765949/82818563-31c1e200-9e9f-11ea-9ee0-0a8c1994cdc9.png) ![opening1b](https://user-images.githubusercontent.com/1765949/82820352-49e73080-9ea2-11ea-9153-d73aa399d329.png) [Read the documentation on how to efficiently convert your data](https://docs.vaex.io/en/latest/example_io.html) from CSV files, Pandas DataFrames, or other sources. Lazy streaming from S3 supported in combination with memory mapping. ![opening1c](https://user-images.githubusercontent.com/1765949/82820516-a21e3280-9ea2-11ea-948b-07df26c4b5d3.png) ## Expression system Don't waste memory or time with feature engineering, we (lazily) transform your data when needed. ![expression](https://user-images.githubusercontent.com/1765949/82818733-70f03300-9e9f-11ea-80b0-ab28e7950b5c.png) ## 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. ![occ-animated](https://user-images.githubusercontent.com/1765949/82821111-c6c6da00-9ea3-11ea-9f9e-498de8133cc2.gif) ## Fast groupby / aggregations Vaex implements parallelized, highly performant `groupby` operations, especially when using categories (>1 billion/second). ![groupby](https://user-images.githubusercontent.com/1765949/82818807-97ae6980-9e9f-11ea-8820-41dd4441057a.png) ## 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! ![join](https://user-images.githubusercontent.com/1765949/82818840-a268fe80-9e9f-11ea-8ba2-6a6d52c4af88.png) ## More features * Remote DataFrames (documentation coming soon) * Integration into [Jupyter and Voila for interactive notebooks and dashboards](https://vaex.readthedocs.io/en/latest/tutorial_jupyter.html) * [Machine Learning without (explicit) pipelines](https://vaex.readthedocs.io/en/latest/tutorial_ml.html) ## Contributing See [contributing](CONTRIBUTING.md) page. ## Slack Join the discussion in our [Slack](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) channel! # Learn more about Vaex * Articles * [Beyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head](https://towardsdatascience.com/beyond-pandas-spark-dask-vaex-and-other-big-data-technologies-battling-head-to-head-a453a1f8cc13) (includes benchmarks) * [7 reasons why I love Vaex for data science](https://towardsdatascience.com/7-reasons-why-i-love-vaex-for-data-science-99008bc8044b) (tips and trics) * [ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn](https://towardsdatascience.com/ml-impossible-train-a-1-billion-sample-model-in-20-minutes-with-vaex-and-scikit-learn-on-your-9e2968e6f385) * [How to analyse 100 GB of data on your laptop with Python](https://towardsdatascience.com/how-to-analyse-100s-of-gbs-of-data-on-your-laptop-with-python-f83363dda94) * [Flying high with Vaex: analysis of over 30 years of flight data in Python](https://towardsdatascience.com/https-medium-com-jovan-veljanoski-flying-high-with-vaex-analysis-of-over-30-years-of-flight-data-in-python-b224825a6d56) * [Vaex: A DataFrame with super strings - Speed up your text processing up to a 1000x ](https://towardsdatascience.com/vaex-a-dataframe-with-super-strings-789b92e8d861) * [Vaex: Out of Core Dataframes for Python and Fast Visualization - 1 billion row datasets on your laptop](https://towardsdatascience.com/vaex-out-of-core-dataframes-for-python-and-fast-visualization-12c102db044a) * [Follow our tutorials](https://docs.vaex.io/en/latest/tutorials.html) * Watch our more recent talks: * [PyData London 2019](https://www.youtube.com/watch?v=2Tt0i823-ec) * [SciPy 2019](https://www.youtube.com/watch?v=ELtjRdPT8is) * Contact us for data science solutions, training, or enterprise support at https://vaex.io/ %package help Summary: Development documents and examples for vaex Provides: python3-vaex-doc %description help [![Documentation](https://readthedocs.org/projects/vaex/badge/?version=latest)](https://docs.vaex.io) [![Slack](https://img.shields.io/badge/slack-chat-green.svg)](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) # 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](https://docs.vaex.io/en/latest/installing.html) # Key features ## Instant opening of Huge data files (memory mapping) [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) and [Apache Arrow](https://arrow.apache.org/) supported. ![opening1a](https://user-images.githubusercontent.com/1765949/82818563-31c1e200-9e9f-11ea-9ee0-0a8c1994cdc9.png) ![opening1b](https://user-images.githubusercontent.com/1765949/82820352-49e73080-9ea2-11ea-9153-d73aa399d329.png) [Read the documentation on how to efficiently convert your data](https://docs.vaex.io/en/latest/example_io.html) from CSV files, Pandas DataFrames, or other sources. Lazy streaming from S3 supported in combination with memory mapping. ![opening1c](https://user-images.githubusercontent.com/1765949/82820516-a21e3280-9ea2-11ea-948b-07df26c4b5d3.png) ## Expression system Don't waste memory or time with feature engineering, we (lazily) transform your data when needed. ![expression](https://user-images.githubusercontent.com/1765949/82818733-70f03300-9e9f-11ea-80b0-ab28e7950b5c.png) ## 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. ![occ-animated](https://user-images.githubusercontent.com/1765949/82821111-c6c6da00-9ea3-11ea-9f9e-498de8133cc2.gif) ## Fast groupby / aggregations Vaex implements parallelized, highly performant `groupby` operations, especially when using categories (>1 billion/second). ![groupby](https://user-images.githubusercontent.com/1765949/82818807-97ae6980-9e9f-11ea-8820-41dd4441057a.png) ## 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! ![join](https://user-images.githubusercontent.com/1765949/82818840-a268fe80-9e9f-11ea-8ba2-6a6d52c4af88.png) ## More features * Remote DataFrames (documentation coming soon) * Integration into [Jupyter and Voila for interactive notebooks and dashboards](https://vaex.readthedocs.io/en/latest/tutorial_jupyter.html) * [Machine Learning without (explicit) pipelines](https://vaex.readthedocs.io/en/latest/tutorial_ml.html) ## Contributing See [contributing](CONTRIBUTING.md) page. ## Slack Join the discussion in our [Slack](https://join.slack.com/t/vaexio/shared_invite/zt-shhxzf5i-Cf5n2LtkoYgUjOjbB3bGQQ) channel! # Learn more about Vaex * Articles * [Beyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head](https://towardsdatascience.com/beyond-pandas-spark-dask-vaex-and-other-big-data-technologies-battling-head-to-head-a453a1f8cc13) (includes benchmarks) * [7 reasons why I love Vaex for data science](https://towardsdatascience.com/7-reasons-why-i-love-vaex-for-data-science-99008bc8044b) (tips and trics) * [ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn](https://towardsdatascience.com/ml-impossible-train-a-1-billion-sample-model-in-20-minutes-with-vaex-and-scikit-learn-on-your-9e2968e6f385) * [How to analyse 100 GB of data on your laptop with Python](https://towardsdatascience.com/how-to-analyse-100s-of-gbs-of-data-on-your-laptop-with-python-f83363dda94) * [Flying high with Vaex: analysis of over 30 years of flight data in Python](https://towardsdatascience.com/https-medium-com-jovan-veljanoski-flying-high-with-vaex-analysis-of-over-30-years-of-flight-data-in-python-b224825a6d56) * [Vaex: A DataFrame with super strings - Speed up your text processing up to a 1000x ](https://towardsdatascience.com/vaex-a-dataframe-with-super-strings-789b92e8d861) * [Vaex: Out of Core Dataframes for Python and Fast Visualization - 1 billion row datasets on your laptop](https://towardsdatascience.com/vaex-out-of-core-dataframes-for-python-and-fast-visualization-12c102db044a) * [Follow our tutorials](https://docs.vaex.io/en/latest/tutorials.html) * Watch our more recent talks: * [PyData London 2019](https://www.youtube.com/watch?v=2Tt0i823-ec) * [SciPy 2019](https://www.youtube.com/watch?v=ELtjRdPT8is) * Contact us for data science solutions, training, or enterprise support at https://vaex.io/ %prep %autosetup -n vaex-4.16.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-vaex -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 4.16.0-1 - Package Spec generated