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|
%global _empty_manifest_terminate_build 0
Name: python-retentioneering
Version: 3.0.1
Release: 1
Summary: Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis. You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
License: Retentioneering Software Non-Exclusive License
URL: https://pypi.org/project/retentioneering/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/bb/e8/01215716af29fa457f8097cb0a544a33d9be0708fd99963b2a647b71197c/retentioneering-3.0.1.tar.gz
BuildArch: noarch
Requires: python3-virtualenv
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-jupyterlab
Requires: python3-notebook
Requires: python3-pandas-stubs
Requires: python3-pydantic
Requires: python3-networkx
Requires: python3-plotly
Requires: python3-seaborn
Requires: python3-umap-learn
Requires: python3-statsmodels
Requires: python3-scipy
Requires: python3-ipywidgets
%description
[](https://github.com/retentioneering/retentioneering-tools)
[](https://discord.com/invite/hBnuQABEV2)
[](https://t.me/retentioneering_support)
[](https://pypi.org/project/retentioneering/)
[](https://pypi.org/project/retentioneering/)
[](https://pepy.tech/project/retentioneering)
[](https://pepy.tech/project/retentioneering)
## What is Retentioneering?
Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis.
You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
Retentioneering uses clickstream data to build behavioral segments, highlighting the events and patterns in user behavior that impact your conversion rates, retention, and revenue. The Retentioneering library is created for data analysts, marketing analysts, product owners, managers, and anyone else whose job is to improve a product’s quality.
[](https://github.com/retentioneering/retentioneering-tools)
As a natural part of the [Jupyter](https://jupyter.org/) environment, Retentioneering extends the abilities of [pandas](https://pandas.pydata.org), [NetworkX](https://networkx.org/), [scikit-learn](https://scikit-learn.org) libraries to process sequential events data more efficiently. Retentioneering tools are interactive and tailored for analytical research, so you do not have to be a Python expert to use it. With just a few lines of code, you can wrangle data, explore customer journey maps, and make visualizations.
### Retentioneering structure
Retentioneering consists of two major parts: [the preprocessing module](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-preprocessing) and [the path analysis tools](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-rete-tools).
The **preprocessing module** provides a wide range of hands-on methods specifically designed for processing clickstream data, which can be called either using code, or via the preprocessing GUI. With separate methods for grouping or filtering events, splitting a clickstream into sessions, and much more, the Retentioneering preprocessing module enables you to dramatically reduce the amount of code, and therefore potential errors. Plus, if you’re dealing with a branchy analysis, which often happens, the preprocessing methods will help you make the calculations structured and reproducible, and organize them as a calculation graph. This is especially helpful for working with a team.
The **path analysis tools** bring behavior-driven segmentation of users to product analysis by providing a powerful set of techniques for performing in-depth analysis of customer journey maps. The tools feature informative and interactive visualizations that make it possible to quickly understand in very high resolution the complex structure of a clickstream.
## Documentation
Complete documentation is available [here](https://doc.retentioneering.com/3.0/doc/index.html).
## Installation
Retentioneering can be installed via pip using [PyPI](https://pypi.org/project/retentioneering/).
```bash
pip install retentioneering
```
Or directly from Jupyter notebook or [google.colab](https://colab.research.google.com/).
```bash
!pip install retentioneering
```
## Quick start
We recommend starting your Retentioneering journey with the [Quick Start document](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html).
## Step-by-step guides
- [Eventstream](https://doc.retentioneering.com/3.0/doc/user_guides/eventstream.html)
### Preprocessing
- [Data processors](https://doc.retentioneering.com/3.0/doc/user_guides/dataprocessors.html)
- [Preprocessing graph](https://doc.retentioneering.com/3.0/doc/user_guides/preprocessing.html)
- [Preprocessing tutorial](https://colab.research.google.com/drive/1WwVI5oQF81xp9DJ6rP5HyM_UjuNPjUk0?usp=sharing)
### Path analysis tools
- [Transition graph](https://doc.retentioneering.com/3.0/doc/user_guides/transition_graph.html)
- [Step matrix](https://doc.retentioneering.com/3.0/doc/user_guides/step_matrix.html)
- [Step Sankey](https://doc.retentioneering.com/3.0/doc/user_guides/step_sankey.html)
- [Clusters](https://doc.retentioneering.com/3.0/doc/user_guides/clusters.html)
- [Funnel](https://doc.retentioneering.com/3.0/doc/user_guides/funnel.html)
- [Cohorts](https://doc.retentioneering.com/3.0/doc/user_guides/cohorts.html)
- [Stattests](https://doc.retentioneering.com/3.0/doc/user_guides/stattests.html)
## Raw data type
Raw data can be downloaded from Google Analytics BigQuery stream, or any other such streams. Just convert that data to the list of triples - user_id, event, and timestamp - and pass it to Retentioneering tools. The package also includes some datasets for a quick start.
## Changelog
This is new major release Retentioneering 3.0. Change log is available [here](https://doc.retentioneering.com/3.0/doc/whatsnew/v3.0.0.html).
The Retentioneering 2.0 (archive) is available [here](https://github.com/retentioneering/retentioneering-tools-2-archive).
## Contributing
This is community-driven open source project in active development. Any contributions,
new ideas, bug reports, bug fixes, documentation improvements are very welcome.
Retentioneering now provides several opensource solutions for data-driven product
analytics and web analytics. Please checkout [this repository](https://github.com/retentioneering/retentioneering-dom-observer) for JS library to track the mutations of the website elements.
Apps are better with math! :)
Retentioneering is a research laboratory, analytics methodology and opensource
tools founded by [Maxim Godzi](https://www.linkedin.com/in/godsie/) and
[Anatoly Zaytsev](https://www.linkedin.com/in/anatoly-zaytsev/) in 2015.
Please feel free to contact us at retentioneering@gmail.com if you have any
questions regarding this repo.
%package -n python3-retentioneering
Summary: Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis. You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
Provides: python-retentioneering
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-retentioneering
[](https://github.com/retentioneering/retentioneering-tools)
[](https://discord.com/invite/hBnuQABEV2)
[](https://t.me/retentioneering_support)
[](https://pypi.org/project/retentioneering/)
[](https://pypi.org/project/retentioneering/)
[](https://pepy.tech/project/retentioneering)
[](https://pepy.tech/project/retentioneering)
## What is Retentioneering?
Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis.
You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
Retentioneering uses clickstream data to build behavioral segments, highlighting the events and patterns in user behavior that impact your conversion rates, retention, and revenue. The Retentioneering library is created for data analysts, marketing analysts, product owners, managers, and anyone else whose job is to improve a product’s quality.
[](https://github.com/retentioneering/retentioneering-tools)
As a natural part of the [Jupyter](https://jupyter.org/) environment, Retentioneering extends the abilities of [pandas](https://pandas.pydata.org), [NetworkX](https://networkx.org/), [scikit-learn](https://scikit-learn.org) libraries to process sequential events data more efficiently. Retentioneering tools are interactive and tailored for analytical research, so you do not have to be a Python expert to use it. With just a few lines of code, you can wrangle data, explore customer journey maps, and make visualizations.
### Retentioneering structure
Retentioneering consists of two major parts: [the preprocessing module](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-preprocessing) and [the path analysis tools](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-rete-tools).
The **preprocessing module** provides a wide range of hands-on methods specifically designed for processing clickstream data, which can be called either using code, or via the preprocessing GUI. With separate methods for grouping or filtering events, splitting a clickstream into sessions, and much more, the Retentioneering preprocessing module enables you to dramatically reduce the amount of code, and therefore potential errors. Plus, if you’re dealing with a branchy analysis, which often happens, the preprocessing methods will help you make the calculations structured and reproducible, and organize them as a calculation graph. This is especially helpful for working with a team.
The **path analysis tools** bring behavior-driven segmentation of users to product analysis by providing a powerful set of techniques for performing in-depth analysis of customer journey maps. The tools feature informative and interactive visualizations that make it possible to quickly understand in very high resolution the complex structure of a clickstream.
## Documentation
Complete documentation is available [here](https://doc.retentioneering.com/3.0/doc/index.html).
## Installation
Retentioneering can be installed via pip using [PyPI](https://pypi.org/project/retentioneering/).
```bash
pip install retentioneering
```
Or directly from Jupyter notebook or [google.colab](https://colab.research.google.com/).
```bash
!pip install retentioneering
```
## Quick start
We recommend starting your Retentioneering journey with the [Quick Start document](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html).
## Step-by-step guides
- [Eventstream](https://doc.retentioneering.com/3.0/doc/user_guides/eventstream.html)
### Preprocessing
- [Data processors](https://doc.retentioneering.com/3.0/doc/user_guides/dataprocessors.html)
- [Preprocessing graph](https://doc.retentioneering.com/3.0/doc/user_guides/preprocessing.html)
- [Preprocessing tutorial](https://colab.research.google.com/drive/1WwVI5oQF81xp9DJ6rP5HyM_UjuNPjUk0?usp=sharing)
### Path analysis tools
- [Transition graph](https://doc.retentioneering.com/3.0/doc/user_guides/transition_graph.html)
- [Step matrix](https://doc.retentioneering.com/3.0/doc/user_guides/step_matrix.html)
- [Step Sankey](https://doc.retentioneering.com/3.0/doc/user_guides/step_sankey.html)
- [Clusters](https://doc.retentioneering.com/3.0/doc/user_guides/clusters.html)
- [Funnel](https://doc.retentioneering.com/3.0/doc/user_guides/funnel.html)
- [Cohorts](https://doc.retentioneering.com/3.0/doc/user_guides/cohorts.html)
- [Stattests](https://doc.retentioneering.com/3.0/doc/user_guides/stattests.html)
## Raw data type
Raw data can be downloaded from Google Analytics BigQuery stream, or any other such streams. Just convert that data to the list of triples - user_id, event, and timestamp - and pass it to Retentioneering tools. The package also includes some datasets for a quick start.
## Changelog
This is new major release Retentioneering 3.0. Change log is available [here](https://doc.retentioneering.com/3.0/doc/whatsnew/v3.0.0.html).
The Retentioneering 2.0 (archive) is available [here](https://github.com/retentioneering/retentioneering-tools-2-archive).
## Contributing
This is community-driven open source project in active development. Any contributions,
new ideas, bug reports, bug fixes, documentation improvements are very welcome.
Retentioneering now provides several opensource solutions for data-driven product
analytics and web analytics. Please checkout [this repository](https://github.com/retentioneering/retentioneering-dom-observer) for JS library to track the mutations of the website elements.
Apps are better with math! :)
Retentioneering is a research laboratory, analytics methodology and opensource
tools founded by [Maxim Godzi](https://www.linkedin.com/in/godsie/) and
[Anatoly Zaytsev](https://www.linkedin.com/in/anatoly-zaytsev/) in 2015.
Please feel free to contact us at retentioneering@gmail.com if you have any
questions regarding this repo.
%package help
Summary: Development documents and examples for retentioneering
Provides: python3-retentioneering-doc
%description help
[](https://github.com/retentioneering/retentioneering-tools)
[](https://discord.com/invite/hBnuQABEV2)
[](https://t.me/retentioneering_support)
[](https://pypi.org/project/retentioneering/)
[](https://pypi.org/project/retentioneering/)
[](https://pepy.tech/project/retentioneering)
[](https://pepy.tech/project/retentioneering)
## What is Retentioneering?
Retentioneering is a Python library that makes analyzing clickstreams, user paths (trajectories), and event logs much easier, and yields much broader and deeper insights than funnel analysis.
You can use Retentioneering to explore user behavior, segment users, and form hypotheses about what drives users to desirable actions or to churning away from a product.
Retentioneering uses clickstream data to build behavioral segments, highlighting the events and patterns in user behavior that impact your conversion rates, retention, and revenue. The Retentioneering library is created for data analysts, marketing analysts, product owners, managers, and anyone else whose job is to improve a product’s quality.
[](https://github.com/retentioneering/retentioneering-tools)
As a natural part of the [Jupyter](https://jupyter.org/) environment, Retentioneering extends the abilities of [pandas](https://pandas.pydata.org), [NetworkX](https://networkx.org/), [scikit-learn](https://scikit-learn.org) libraries to process sequential events data more efficiently. Retentioneering tools are interactive and tailored for analytical research, so you do not have to be a Python expert to use it. With just a few lines of code, you can wrangle data, explore customer journey maps, and make visualizations.
### Retentioneering structure
Retentioneering consists of two major parts: [the preprocessing module](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-preprocessing) and [the path analysis tools](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html#quick-start-rete-tools).
The **preprocessing module** provides a wide range of hands-on methods specifically designed for processing clickstream data, which can be called either using code, or via the preprocessing GUI. With separate methods for grouping or filtering events, splitting a clickstream into sessions, and much more, the Retentioneering preprocessing module enables you to dramatically reduce the amount of code, and therefore potential errors. Plus, if you’re dealing with a branchy analysis, which often happens, the preprocessing methods will help you make the calculations structured and reproducible, and organize them as a calculation graph. This is especially helpful for working with a team.
The **path analysis tools** bring behavior-driven segmentation of users to product analysis by providing a powerful set of techniques for performing in-depth analysis of customer journey maps. The tools feature informative and interactive visualizations that make it possible to quickly understand in very high resolution the complex structure of a clickstream.
## Documentation
Complete documentation is available [here](https://doc.retentioneering.com/3.0/doc/index.html).
## Installation
Retentioneering can be installed via pip using [PyPI](https://pypi.org/project/retentioneering/).
```bash
pip install retentioneering
```
Or directly from Jupyter notebook or [google.colab](https://colab.research.google.com/).
```bash
!pip install retentioneering
```
## Quick start
We recommend starting your Retentioneering journey with the [Quick Start document](https://doc.retentioneering.com/3.0/doc/getting_started/quick_start.html).
## Step-by-step guides
- [Eventstream](https://doc.retentioneering.com/3.0/doc/user_guides/eventstream.html)
### Preprocessing
- [Data processors](https://doc.retentioneering.com/3.0/doc/user_guides/dataprocessors.html)
- [Preprocessing graph](https://doc.retentioneering.com/3.0/doc/user_guides/preprocessing.html)
- [Preprocessing tutorial](https://colab.research.google.com/drive/1WwVI5oQF81xp9DJ6rP5HyM_UjuNPjUk0?usp=sharing)
### Path analysis tools
- [Transition graph](https://doc.retentioneering.com/3.0/doc/user_guides/transition_graph.html)
- [Step matrix](https://doc.retentioneering.com/3.0/doc/user_guides/step_matrix.html)
- [Step Sankey](https://doc.retentioneering.com/3.0/doc/user_guides/step_sankey.html)
- [Clusters](https://doc.retentioneering.com/3.0/doc/user_guides/clusters.html)
- [Funnel](https://doc.retentioneering.com/3.0/doc/user_guides/funnel.html)
- [Cohorts](https://doc.retentioneering.com/3.0/doc/user_guides/cohorts.html)
- [Stattests](https://doc.retentioneering.com/3.0/doc/user_guides/stattests.html)
## Raw data type
Raw data can be downloaded from Google Analytics BigQuery stream, or any other such streams. Just convert that data to the list of triples - user_id, event, and timestamp - and pass it to Retentioneering tools. The package also includes some datasets for a quick start.
## Changelog
This is new major release Retentioneering 3.0. Change log is available [here](https://doc.retentioneering.com/3.0/doc/whatsnew/v3.0.0.html).
The Retentioneering 2.0 (archive) is available [here](https://github.com/retentioneering/retentioneering-tools-2-archive).
## Contributing
This is community-driven open source project in active development. Any contributions,
new ideas, bug reports, bug fixes, documentation improvements are very welcome.
Retentioneering now provides several opensource solutions for data-driven product
analytics and web analytics. Please checkout [this repository](https://github.com/retentioneering/retentioneering-dom-observer) for JS library to track the mutations of the website elements.
Apps are better with math! :)
Retentioneering is a research laboratory, analytics methodology and opensource
tools founded by [Maxim Godzi](https://www.linkedin.com/in/godsie/) and
[Anatoly Zaytsev](https://www.linkedin.com/in/anatoly-zaytsev/) in 2015.
Please feel free to contact us at retentioneering@gmail.com if you have any
questions regarding this repo.
%prep
%autosetup -n retentioneering-3.0.1
%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-retentioneering -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.1-1
- Package Spec generated
|
