aops-apollo-tool aarch64 8ce7287a9a6aa626ec7680e27fb8795e4c0a72b73a88e056c946e06da81d3fe9 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 14a45a08d2754d31393cabb02e4a377586e04c520024089c28647e32acb8cb93 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 54560a19382f4341441afd3346460d0b9bb8b521bd8348d51da20e6ad4822a89 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 59fc967bed7d88d1028eba735f9d4bc3cd7aa84688fe21e8903980d9168765b7 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 df0d79296484197556c9b9de26ce578c19633c2d02397c991be7e57a0cdcf04b 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 7cf68799d1618bb33802a7a36ada1809b74be8bf7b024df8072bd800ef32607a Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes src 1ac3a09edf54f1534e49168970e4d2585abfd965f3927547e6a012d96c1b77f4 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-tools aarch64 6666fd8e22bc7158bea9914ae0b410ed97be2e434c33544105e528e4496230f9 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus aarch64 48f35ba592cf8c234d7f8b9cac628c0f9cca6992f6be733dcfc83653bf5b7043 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 8921d97179b2cf4e480e368b4b36bc194084c034f24672019aa5cec76c29b3f4 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 dd0443fac969ebf595c977e4eda04c609721ff85b993a3c0ad45ecfbefb973f1 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 9658482292176eaa421702add5c289ff09678045b3c45251ed7101d34af403ed 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 86512682ccc7e5c72541d2d116e38c81def078cea53d0b51c53f6ca21eb38a7c A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub aarch64 39d2ae320cc88a5a2165c15e1db582206962e451120b3c998aa2b9e659d2c8d2 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 56205aff43a9d80c2c4a2498f18428756e04cef4d8c3d273317e338adc23dc87 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 d2008a13eed1fb1b6b3c8cf88c5e6a1e26d04099ab17bf8d91c3424a73025fa9 Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin aarch64 d2f71b68f09d9ecca45d560568031b34848ed779bcd45afb65615a17d3d97491 dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater aarch64 98687038e2d2da429e1001d7f5cd9b64e02ad2128519033bbf70d44128124115 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 9132b93fd35a0c303e8f86d00fa48579fff9d707e207bc206884c98efc37efb7 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 28f023905f66bc73a60ea10bf84987f031be9a52c555a916fd00899e4fe708c0 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher src 1adbc4686105371ca7601ff2abe5535c15327626e9a36f8850d5e69703b226fe 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 0f64f1922fd63ba46f1aad93b667dce0b1c99a184a8d19b9253b512f8c43812e 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 ffa1d02ebecc961e22064a25f225f9f231fe322eb47192e02fceb52a41d72089 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 e6169c5964823408de7aa4eb8eca9181d56f19b03f51e5f1bf586d8e444f2b21 Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops aarch64 08867514dcddc85289426f5e6da1ab73a3ed5f7b3fa2c8b4266e85814ffcc2e8 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 1bcbba363f38d03e734474340048f11c5a8be31db3b3ff96d61245c4e4d11ed5 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 aa0460f31eb59a12bad1871b28d234653b2ffae825c867081f8849491b80327e 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 25d50a087787cace70997572bab21af0fb40f5b7c3b0982d2086d10568fcb176 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 61cbe3ee32e2baaddddd2edf174c9cefc42fd7fe3cb1df20658f681a5877dd93 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 18033504fa61428b87e7226cbe84915f66f853e10a7a11575ec8570a8fa4c089 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 d94344c426a0a9d47528616d9d4b03e91f7431de6a764c53c9e920e6475a5228 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 779c5559a78d7e60575c7391f97532b46645081717c2fde0f3fbbaf17c1c2c3b Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 5758c4ee90835439aa0425b6877809b6f99f4f1de24643ac16f0b429c90d3a6a Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-gala-anteater aarch64 02dded2759ae05d086ba3c39717a6ab69129ddad53c51bde6c0397efe1217087 Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference aarch64 50cd817d6771a44452fd6cb7b1ec4dd7c7c32a3c34fe02770e0120507dc5fa92 Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider aarch64 3ac943248afff551061acafe46cb4ee151959c7401f789a653f8215da07e5140 Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 626521647a6a7047029118780a53198450cf164351d813d8c8325616a9216d06 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 579ccce7064e4e92b33378e9693f2d15b47b7d22aaf81e0f92aa35a8ce590c2f 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 b7ef817eb2a24ea1329b8f5f7bc77622c69ef014f848594429bc5f50bdef9ba8 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute aarch64 b04fe9faa30868576f4d7efa877349e1f34f56eedd0a8fbccf387d08b72a232e A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information aarch64 b41a2fc4ccc6cf6ccfc25739464890c29994d47da64db0ce695590daed81c1ff 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 5415a1b5bcb8db6ced64f2c3d75bb1bf914560014e3541791420146557e66004 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 a2dd339f08e2800da4aaf8dad5a9f088236add44640d29c950a1c6aad9445758 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