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|
%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
[](https://docs.vaex.io)
[](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.
[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.
## 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](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
[](https://docs.vaex.io)
[](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.
[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.
## 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](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
[](https://docs.vaex.io)
[](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.
[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.
## 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](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
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 4.16.0-1
- Package Spec generated
|
