diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-23 12:47:06 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-23 12:47:06 +0000 |
| commit | affe5eeda0d243e0de24bb7036b88653a232a7cb (patch) | |
| tree | 93079738888fd6a0a5dbe161403f3f7ce3e47daa | |
| parent | 16979a9bc7f027cc0b975b858421ff87bb816f61 (diff) | |
automatic import of python-autovizopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-autoviz.spec | 262 | ||||
| -rw-r--r-- | sources | 2 |
3 files changed, 148 insertions, 117 deletions
@@ -1 +1,2 @@ /autoviz-0.1.58.tar.gz +/autoviz-0.1.601.tar.gz diff --git a/python-autoviz.spec b/python-autoviz.spec index e7234f0..ae75264 100644 --- a/python-autoviz.spec +++ b/python-autoviz.spec @@ -1,11 +1,11 @@ %global _empty_manifest_terminate_build 0 Name: python-autoviz -Version: 0.1.58 +Version: 0.1.601 Release: 1 Summary: Automatically Visualize any dataset, any size with a single line of code License: Apache License 2.0 URL: https://github.com/AutoViML/AutoViz -Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a4/48/7e29c710c9cb8cf0972d760ae591daced8eaa4ec49a9a626720dfccac459/autoviz-0.1.58.tar.gz +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/25/0d/80411d88a3fb86abcef81a594b37ae2c2749b9efa32c5067f94d6dea0e78/autoviz-0.1.601.tar.gz BuildArch: noarch Requires: python3-bokeh @@ -35,45 +35,6 @@ Requires: python3-xlrd Automatically Visualize any dataset, any size with a single line of code. Now you can save these interactive charts as HTML files automatically with the `"html"` setting. -### Sep-2022 Update: AutoViz now provides data cleansing suggestions! #autoviz #datacleaning -From version 0.1.50 onwards, AutoViz now automatically analyzes your dataset and provides suggestions for how to clean your data set. It detects missing values, identifies rare categories, finds infinite values, detects mixed data types, and so much more. This will help you tremendously speed up your data cleaning activities. If you have suggestions to add more data cleaning steps please file an `Issue` in our GitHub and we will gladly consider it. Here is an example of how data cleaning suggestions look:<br> -<img align="center" src="https://i.ibb.co/NKf1gdg/autoviz-data-cleaning.png"> - -In order to get this latest function, you must upgrade autoviz to the latest version by: -``` -pip install autoviz --upgrade -``` - -In the same version, you can also get data suggestions by using `AV.AutoViz(......, verbose=1)` or by simply importing it:<br> - -``` -from autoviz import data_cleaning_suggestions -data_cleaning_suggestions(df) -``` - -### Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud -AutoViz can now create Wordclouds automatically for your NLP variables in data. It detects NLP variables automatically and creates wordclouds for them. See Colab notebook for example: [AutoViz Demo with HTML setting](https://colab.research.google.com/drive/1r5QqESRZDY98FFfDOgVtMAVA_oaGtqqx?usp=sharing) - -<img align="center" src="https://i.postimg.cc/DyT466xP/wordclouds.png"> - -### Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io -We are excited to announce that AutoViz and Deep_AutoViML are now available as containerized applications on Docker. This means that you can build data pipelines using a fantastic tool like [orchest.io](orchest.io) to build MLOps pipelines visually. Here are two sample pipelines we have created: - -<b>AutoViz pipeline</b>: https://lnkd.in/g5uC-z66 -<b>Deep_AutoViML pipeline</b>: https://lnkd.in/gdnWTqCG - -You can find more examples and a wonderful video on [orchest's web site](https://github.com/orchest/orchest-examples) - - -### Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews -Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see below) either in Jupyter Notebooks or in the browser. Use chart_format as follows: -- `chart_format='bokeh'`: interactive Bokeh dashboards are plotted in Jupyter Notebooks. -- `chart_format='server'`, dashboards will pop up for each kind of chart on your web browser. -- `chart_format='html'`, interactive Bokeh charts will be silently saved as Dynamic HTML files under `AutoViz_Plots` directory -<img align="center" src="https://i.postimg.cc/MTCZ6GzQ/Auto-Viz-HTML-dashboards.png" /> - - - [](https://pepy.tech/project/autoviz) [](https://pepy.tech/project/autoviz) [](https://pepy.tech/project/autoviz) @@ -134,18 +95,19 @@ pip install -r requirements.txt Read this Medium article to know how to use [AutoViz](https://towardsdatascience.com/autoviz-a-new-tool-for-automated-visualization-ec9c1744a6ad). -In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the library +In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the AutoViz_Class.<br> +<b>Alert!</b>: You no longer have to do: `from autoviz.AutoViz_Class import AutoViz_Class`. <br> +Instead, you can simply do<br> ```py -from autoviz.AutoViz_Class import AutoViz_Class - +from autoviz import AutoViz_Class AV = AutoViz_Class() ``` Load a dataset (any CSV or text file) into a Pandas dataframe or give the name of the path and filename you want to visualize. If you don't have a filename, you can simply assign the filename argument `""` (empty string). -Call AutoViz using the filename (or dataframe) along with the separator and the name of the target variable in the input. +Call AutoViz method using the filename (or dataframe) along with the separator and the name of the target variable in the input. ```py filename = "" @@ -206,38 +168,16 @@ AutoViz will do the rest. You will see charts and plots on your screen.  -## Maintainers - -* [@AutoViML](https://github.com/AutoViML) -* [@morenoh149](https://github.com/morenoh149) -* [@hironroy](https://github.com/hironroy) - -## Contributing - -See [the contributing file](contributing.md)! - -PRs accepted. - -## License +### Apr-2023 Update: AutoViz now creates scatter plots for categorical variables when data contains only cat variables +From version 0.1.600 onwards, AutoViz now automatically draws `catscatter` plots for pairs of categorical variables in a data frame. A `catscatter` plot is a type of scatter plot that shows the frequency of each combination of categories in two variables. It can be useful for exploring the relationship between categorical variables and identifying patterns or outliers. It creates these plots only if the data contains no numeric variables. Otherwise, it doesn't create them since it would be unncessary. -Apache License, Version 2.0 - -## DISCLAIMER -This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose. - - - - -%package -n python3-autoviz -Summary: Automatically Visualize any dataset, any size with a single line of code -Provides: python-autoviz -BuildRequires: python3-devel -BuildRequires: python3-setuptools -BuildRequires: python3-pip -%description -n python3-autoviz -# AutoViz - -Automatically Visualize any dataset, any size with a single line of code. Now you can save these interactive charts as HTML files automatically with the `"html"` setting. +``` +AutoViz is grateful to the cascatter implementation of Myr Barnés, 2020. +You can see the original here: https://github.com/myrthings/catscatter/blob/master/catscatter.py +# More info about this function here: +# - https://towardsdatascience.com/visualize-categorical-relationships-with-catscatter-e60cdb164395 +# - https://github.com/myrthings/catscatter/blob/master/README.md +``` ### Sep-2022 Update: AutoViz now provides data cleansing suggestions! #autoviz #datacleaning From version 0.1.50 onwards, AutoViz now automatically analyzes your dataset and provides suggestions for how to clean your data set. It detects missing values, identifies rare categories, finds infinite values, detects mixed data types, and so much more. This will help you tremendously speed up your data cleaning activities. If you have suggestions to add more data cleaning steps please file an `Issue` in our GitHub and we will gladly consider it. Here is an example of how data cleaning suggestions look:<br> @@ -276,7 +216,38 @@ Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see b - `chart_format='html'`, interactive Bokeh charts will be silently saved as Dynamic HTML files under `AutoViz_Plots` directory <img align="center" src="https://i.postimg.cc/MTCZ6GzQ/Auto-Viz-HTML-dashboards.png" /> - +## Maintainers + +* [@AutoViML](https://github.com/AutoViML) +* [@morenoh149](https://github.com/morenoh149) +* [@hironroy](https://github.com/hironroy) + +## Contributing + +See [the contributing file](contributing.md)! + +PRs accepted. + +## License + +Apache License, Version 2.0 + +## DISCLAIMER +This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose. + + + + +%package -n python3-autoviz +Summary: Automatically Visualize any dataset, any size with a single line of code +Provides: python-autoviz +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-autoviz +# AutoViz + +Automatically Visualize any dataset, any size with a single line of code. Now you can save these interactive charts as HTML files automatically with the `"html"` setting. [](https://pepy.tech/project/autoviz) [](https://pepy.tech/project/autoviz) @@ -338,18 +309,19 @@ pip install -r requirements.txt Read this Medium article to know how to use [AutoViz](https://towardsdatascience.com/autoviz-a-new-tool-for-automated-visualization-ec9c1744a6ad). -In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the library +In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the AutoViz_Class.<br> +<b>Alert!</b>: You no longer have to do: `from autoviz.AutoViz_Class import AutoViz_Class`. <br> +Instead, you can simply do<br> ```py -from autoviz.AutoViz_Class import AutoViz_Class - +from autoviz import AutoViz_Class AV = AutoViz_Class() ``` Load a dataset (any CSV or text file) into a Pandas dataframe or give the name of the path and filename you want to visualize. If you don't have a filename, you can simply assign the filename argument `""` (empty string). -Call AutoViz using the filename (or dataframe) along with the separator and the name of the target variable in the input. +Call AutoViz method using the filename (or dataframe) along with the separator and the name of the target variable in the input. ```py filename = "" @@ -410,35 +382,16 @@ AutoViz will do the rest. You will see charts and plots on your screen.  -## Maintainers - -* [@AutoViML](https://github.com/AutoViML) -* [@morenoh149](https://github.com/morenoh149) -* [@hironroy](https://github.com/hironroy) - -## Contributing - -See [the contributing file](contributing.md)! - -PRs accepted. - -## License - -Apache License, Version 2.0 - -## DISCLAIMER -This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose. - - - - -%package help -Summary: Development documents and examples for autoviz -Provides: python3-autoviz-doc -%description help -# AutoViz +### Apr-2023 Update: AutoViz now creates scatter plots for categorical variables when data contains only cat variables +From version 0.1.600 onwards, AutoViz now automatically draws `catscatter` plots for pairs of categorical variables in a data frame. A `catscatter` plot is a type of scatter plot that shows the frequency of each combination of categories in two variables. It can be useful for exploring the relationship between categorical variables and identifying patterns or outliers. It creates these plots only if the data contains no numeric variables. Otherwise, it doesn't create them since it would be unncessary. -Automatically Visualize any dataset, any size with a single line of code. Now you can save these interactive charts as HTML files automatically with the `"html"` setting. +``` +AutoViz is grateful to the cascatter implementation of Myr Barnés, 2020. +You can see the original here: https://github.com/myrthings/catscatter/blob/master/catscatter.py +# More info about this function here: +# - https://towardsdatascience.com/visualize-categorical-relationships-with-catscatter-e60cdb164395 +# - https://github.com/myrthings/catscatter/blob/master/README.md +``` ### Sep-2022 Update: AutoViz now provides data cleansing suggestions! #autoviz #datacleaning From version 0.1.50 onwards, AutoViz now automatically analyzes your dataset and provides suggestions for how to clean your data set. It detects missing values, identifies rare categories, finds infinite values, detects mixed data types, and so much more. This will help you tremendously speed up your data cleaning activities. If you have suggestions to add more data cleaning steps please file an `Issue` in our GitHub and we will gladly consider it. Here is an example of how data cleaning suggestions look:<br> @@ -477,7 +430,35 @@ Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see b - `chart_format='html'`, interactive Bokeh charts will be silently saved as Dynamic HTML files under `AutoViz_Plots` directory <img align="center" src="https://i.postimg.cc/MTCZ6GzQ/Auto-Viz-HTML-dashboards.png" /> - +## Maintainers + +* [@AutoViML](https://github.com/AutoViML) +* [@morenoh149](https://github.com/morenoh149) +* [@hironroy](https://github.com/hironroy) + +## Contributing + +See [the contributing file](contributing.md)! + +PRs accepted. + +## License + +Apache License, Version 2.0 + +## DISCLAIMER +This project is not an official Google project. It is not supported by Google and Google specifically disclaims all warranties as to its quality, merchantability, or fitness for a particular purpose. + + + + +%package help +Summary: Development documents and examples for autoviz +Provides: python3-autoviz-doc +%description help +# AutoViz + +Automatically Visualize any dataset, any size with a single line of code. Now you can save these interactive charts as HTML files automatically with the `"html"` setting. [](https://pepy.tech/project/autoviz) [](https://pepy.tech/project/autoviz) @@ -539,18 +520,19 @@ pip install -r requirements.txt Read this Medium article to know how to use [AutoViz](https://towardsdatascience.com/autoviz-a-new-tool-for-automated-visualization-ec9c1744a6ad). -In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the library +In the AutoViz directory, open a Jupyter Notebook and use this line to instantiate the AutoViz_Class.<br> +<b>Alert!</b>: You no longer have to do: `from autoviz.AutoViz_Class import AutoViz_Class`. <br> +Instead, you can simply do<br> ```py -from autoviz.AutoViz_Class import AutoViz_Class - +from autoviz import AutoViz_Class AV = AutoViz_Class() ``` Load a dataset (any CSV or text file) into a Pandas dataframe or give the name of the path and filename you want to visualize. If you don't have a filename, you can simply assign the filename argument `""` (empty string). -Call AutoViz using the filename (or dataframe) along with the separator and the name of the target variable in the input. +Call AutoViz method using the filename (or dataframe) along with the separator and the name of the target variable in the input. ```py filename = "" @@ -611,6 +593,54 @@ AutoViz will do the rest. You will see charts and plots on your screen.  +### Apr-2023 Update: AutoViz now creates scatter plots for categorical variables when data contains only cat variables +From version 0.1.600 onwards, AutoViz now automatically draws `catscatter` plots for pairs of categorical variables in a data frame. A `catscatter` plot is a type of scatter plot that shows the frequency of each combination of categories in two variables. It can be useful for exploring the relationship between categorical variables and identifying patterns or outliers. It creates these plots only if the data contains no numeric variables. Otherwise, it doesn't create them since it would be unncessary. + +``` +AutoViz is grateful to the cascatter implementation of Myr Barnés, 2020. +You can see the original here: https://github.com/myrthings/catscatter/blob/master/catscatter.py +# More info about this function here: +# - https://towardsdatascience.com/visualize-categorical-relationships-with-catscatter-e60cdb164395 +# - https://github.com/myrthings/catscatter/blob/master/README.md +``` + +### Sep-2022 Update: AutoViz now provides data cleansing suggestions! #autoviz #datacleaning +From version 0.1.50 onwards, AutoViz now automatically analyzes your dataset and provides suggestions for how to clean your data set. It detects missing values, identifies rare categories, finds infinite values, detects mixed data types, and so much more. This will help you tremendously speed up your data cleaning activities. If you have suggestions to add more data cleaning steps please file an `Issue` in our GitHub and we will gladly consider it. Here is an example of how data cleaning suggestions look:<br> +<img align="center" src="https://i.ibb.co/NKf1gdg/autoviz-data-cleaning.png"> + +In order to get this latest function, you must upgrade autoviz to the latest version by: +``` +pip install autoviz --upgrade +``` + +In the same version, you can also get data suggestions by using `AV.AutoViz(......, verbose=1)` or by simply importing it:<br> + +``` +from autoviz import data_cleaning_suggestions +data_cleaning_suggestions(df) +``` + +### Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud +AutoViz can now create Wordclouds automatically for your NLP variables in data. It detects NLP variables automatically and creates wordclouds for them. See Colab notebook for example: [AutoViz Demo with HTML setting](https://colab.research.google.com/drive/1r5QqESRZDY98FFfDOgVtMAVA_oaGtqqx?usp=sharing) + +<img align="center" src="https://i.postimg.cc/DyT466xP/wordclouds.png"> + +### Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io +We are excited to announce that AutoViz and Deep_AutoViML are now available as containerized applications on Docker. This means that you can build data pipelines using a fantastic tool like [orchest.io](orchest.io) to build MLOps pipelines visually. Here are two sample pipelines we have created: + +<b>AutoViz pipeline</b>: https://lnkd.in/g5uC-z66 +<b>Deep_AutoViML pipeline</b>: https://lnkd.in/gdnWTqCG + +You can find more examples and a wonderful video on [orchest's web site](https://github.com/orchest/orchest-examples) + + +### Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews +Now you can use AutoViz to create Interactive Bokeh charts and dashboards (see below) either in Jupyter Notebooks or in the browser. Use chart_format as follows: +- `chart_format='bokeh'`: interactive Bokeh dashboards are plotted in Jupyter Notebooks. +- `chart_format='server'`, dashboards will pop up for each kind of chart on your web browser. +- `chart_format='html'`, interactive Bokeh charts will be silently saved as Dynamic HTML files under `AutoViz_Plots` directory +<img align="center" src="https://i.postimg.cc/MTCZ6GzQ/Auto-Viz-HTML-dashboards.png" /> + ## Maintainers * [@AutoViML](https://github.com/AutoViML) @@ -634,7 +664,7 @@ This project is not an official Google project. It is not supported by Google an %prep -%autosetup -n autoviz-0.1.58 +%autosetup -n autoviz-0.1.601 %build %py3_build @@ -674,5 +704,5 @@ mv %{buildroot}/doclist.lst . %{_docdir}/* %changelog -* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.58-1 +* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.601-1 - Package Spec generated @@ -1 +1 @@ -d281a1ae0eea284bd9f2f5664fd4094d autoviz-0.1.58.tar.gz +902a9332b3b8c3aed0ae70cea5518ef4 autoviz-0.1.601.tar.gz |