aops-apollo-tool aarch64 2ad970767bb030f3692964dbb9deec9ca037f2d39643b6c89eacd9d6dde0d86f Small tools for aops-apollo, e.g. updateinfo.xml generater smalltools for aops-apollo, e.g.updateinfo.xml generater https://gitee.com/openeuler/aops-apollo aops-apollo aarch64 6998071543aebe3430b09293a0167ce5a148054d675ae69b775ce328f95f7846 Cve management service, monitor machine vulnerabilities and provide fix functions. Cve management service, monitor machine vulnerabilities and provide fix functions. https://gitee.com/openeuler/aops-apollo aops-apollo src ac8a8d948c3ebe8cdbef272d566524088d04b1b981c47de9b8df305661f2f89c Cve management service, monitor machine vulnerabilities and provide fix functions. Cve management service, monitor machine vulnerabilities and provide fix functions. https://gitee.com/openeuler/aops-apollo aops-ceres aarch64 edd04016058c717b2299abf38fc74337bf797624c0748aac939e625c479341f6 An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. https://gitee.com/openeuler/aops-ceres aops-ceres src e0e775e0f5710ff8dadb6438f5ac46d527c0c7310fe9eac313e15c015747d4d9 An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. https://gitee.com/openeuler/aops-ceres aops-hermes aarch64 6c08e9f990dbf787241fe41f718f321a241c15602132a0e7cd721f4a53178023 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes src 18be70584384ec52706ae13b1814584d923c63056141d2460c2422e24162b266 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-tools aarch64 fcd99a13571694a504d4a46acdf9334eafa39709e787a1b8ea8f5b3c1e7f3c39 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus aarch64 320cd181436ef01767ba852f563fe0f3015a0f1cce868dd160331c51a7f59d08 A basic tool libraries of aops, including logging, configure and response, etc. A basic tool libraries of aops, including logging, configure and response, etc. https://gitee.com/openeuler/aops-vulcanus aops-vulcanus src 2dae8f5836a6b0aea1e3027e6a8f48f0a18ae3ec86bde9eb70448b0e99decb96 A basic tool libraries of aops, including logging, configure and response, etc. A basic tool libraries of aops, including logging, configure and response, etc. https://gitee.com/openeuler/aops-vulcanus aops-zeus aarch64 8a5cf674b94540b1b3d8b0dc68844f25da8d0720dd5e17cde4f5264bca7c690e A service which is the foundation of aops. Provide one-click aops deployment, service start and stop, hot loading of configuration files, and database initialization. Provides: aops-zeus https://gitee.com/openeuler/aops-zeus aops-zeus src b47bb1b75eb6a04fe1f25ea7f8cb49f9e840a44feb9f3aa5ba5f0b138abf8fbf A service which is the foundation of aops. Provide one-click aops deployment, service start and stop, hot loading of configuration files, and database initialization. Provides: aops-zeus https://gitee.com/openeuler/aops-zeus async-task aarch64 ab37e84f627dcb11bb90daafd106e0dfb03e2f74998781b6a62bf6133ede8f11 A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub aarch64 93c0abb23fa33b1297530f5a9cde72ee9a90728b27e2367d11a981d0a88191e5 Authentication authority based on oauth2 authhub is a specialized authentication center built on OAuth2, providing robust authentication and authorization capabilities for secure user access control in your applications.. https://gitee.com/openeuler/authHub authHub src a6444c9855561fdc21c06596413f0e0999e87dfff698189b6523cecf9cdf94b7 Authentication authority based on oauth2 authhub is a specialized authentication center built on OAuth2, providing robust authentication and authorization capabilities for secure user access control in your applications.. https://gitee.com/openeuler/authHub authhub-web aarch64 76ad722cb8126e726801e5fd2809a71b0663e0769799b6338e220ebc1b2a5618 Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin aarch64 0fe84004b0f92253263ae54e8d73499e3704b289cc170f0d026521f57d4cface dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater aarch64 ee4371ed702d66bb9949b23ec8cd64699055b8cf20774b7ed338184f6b757c31 A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-anteater src b22e9f684e28797a509341c5f8d48b6452fd863c8da83af1a77f20a920696f90 A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-gopher aarch64 6cb99197c205430e96816c7ffbabce5ab08ccfb0b48399d69f6972f0b8309b5a Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher src 14db2d3b2926fbf77daa1aae8602be6be9e5bbf2067981587a424ec2b8923954 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher-debuginfo aarch64 3189599967c9e27954966c1bba6d0c6c05be55be91f15f733356a7c0c027064b Debug information for package gala-gopher This package provides debug information for package gala-gopher. Debug information is useful when developing applications that use this package or when debugging this package. https://gitee.com/openeuler/gala-gopher gala-gopher-debugsource aarch64 74953342c4f2c618cc4244df94bd08065c17c91aaf452d2115afe6a3848479c2 Debug sources for package gala-gopher This package provides debug sources for package gala-gopher. Debug sources are useful when developing applications that use this package or when debugging this package. https://gitee.com/openeuler/gala-gopher gala-inference aarch64 b689370b4d021a24e1627161ad62d5f2e0d309889621c58af06663ac455864d7 Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops aarch64 e275002a6ce144c806c8e6d32cca98d888e54956010276c2b1554ea48abf39e7 gala-anteater/spider/inference installation package This package requires gala-anteater/spider/inference, allowing users to install them all at once https://gitee.com/openeuler/gala-spider gala-spider aarch64 42934d5c6b3975cc32afc1f0342f0cc6f12017362ae8245812c00311ae2c2b8f OS topological graph storage service and cause inference service for gala-ops project OS topological graph storage service for gala-ops project https://gitee.com/openeuler/gala-spider gala-spider src f2f21d43abf5c02845ef02407c64084ff5b84566097a12fe251254ad26f6f101 OS topological graph storage service and cause inference service for gala-ops project OS topological graph storage service for gala-ops project https://gitee.com/openeuler/gala-spider python-pandas-flavor src d9b1a123653f6431163cf7a643b47a748bb8b2ccb1a78009e8df9460c2dfa599 The easy way to write your own Pandas flavor. **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python-pandas-flavor-help noarch 677222c0460129a3c8de041d3cef52dda56c7be85cc3c5237356f9a4257b41a7 Development documents and examples for pandas-flavor **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python-pingouin src 1cd4d293883b1d6ccb86b0126e10aed397d58c94afb63d1a2f466f132939a8a0 Pingouin: statistical package for Python **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python-pingouin-help noarch e6d3b25d39eafb5fbf01e26d5f4860049e9a6edbdf82d46a86ffec5767f5b93b Development documents and examples for pingouin **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python-seaborn src 820e7c686cd5a0dd4fe10127b29f3c10e32ff1ccdef7cefa0cc10f103959780e Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 8bbb0d01505ea98466c9a578231e350b6c68a9d3d756bd270a06d20caf5f6f99 Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-gala-anteater aarch64 f94e5f9d62d01b3985c5080791a947dbd46fcf1fbadeb6595a590f3bad6d7626 Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference aarch64 90b3c667d3c809e8683dd8246e088e60e11afe78c11f2d3862aab45a4338b477 Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider aarch64 a599b1494c26845a35110a44a5c743752a3fc007a1753fef1a987dc7c35c2a58 Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 7c56d4bac59e97840267ab58f2702d35744e1900ae7045a8b86b51f75b76bf09 The easy way to write your own Pandas flavor. **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python3-pingouin noarch 27a905a69907a0d257ef1904c3f112241707812dc5e873122e877975258a17f0 Pingouin: statistical package for Python **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python3-seaborn noarch 49c90c9a3c275fb3231cbc6d2dcdd417475641d10ee21810e86a238e649dbbf1 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute aarch64 28b42180f0be70ec1c7373337a16cb0ef9853f671b55acef87c78f93569e184e A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information aarch64 4e4eed21b44d610811424985bf192dfa19751f20d71d222f54442a61cced9f9f A host manager service which is the foundation of aops. A host manager service which is the foundation of aops. https://gitee.com/openeuler/aops-zeus zeus-operation aarch64 10e6ddd61fe89f211933af06e6deabdfddd498338d37a89057679d96a1fc2ff4 A operation manager service which is the foundation of aops. A operation manager of aops. https://gitee.com/openeuler/aops-zeus zeus-user-access aarch64 7194dfd8234011fdf0ce1ffb4e77942385b46a051d4868759229d90a1a1294e2 A user manager service which is the foundation of aops. A user manager service which is the foundation of aops. https://gitee.com/openeuler/aops-zeus