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[#]: subject: "Explore data visually with Python tools"
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[#]: via: "https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook"
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[#]: author: "Bill Wang https://opensource.com/users/bill-wang"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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Explore data visually with Python tools
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======
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Open source tools have been instrumental in advancing technology and making it more accessible to everyone. Data analysis is no exception. As data becomes more abundant and complex, [data scientists][1] always look for ways to simplify their workflow and create interactive and engaging visualizations. PyGWalker is designed to solve such problems.
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[PyGWalker][2] (Python binding of Graphic Walker) connects a working environment of Python Jupyter Notebook to [Graphic Walker][3] to create an open source data visualization tool. You can turn your [Pandas dataframe][4] into a beautifully crafted data visualization with simple drag-and-drop operations.
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![Exploring data through a visual interface with Pygwalker][5]
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### Get started with PyGWalker
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Use `pip` to install PyGWalker:
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```
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$ python3 -m pip install pygwalker
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```
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Import `pygwalker` and `pandas` to use it in a project:
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```
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import pandas as pd
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import pygwalker as pyg
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```
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Load data into a Pandas datagram and call PyGWalker:
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```
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df = pd.read\_csv('./bike\_sharing\_dc.csv', parse\_dates=\['date'\])
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gwalker = pyg.walk(df)
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```
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You now have a graphical UI to explore and visualize your Pandas dataframe!
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### Explore data with Graphic Walker
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One of the key features of Graphic Walker is the ability to change mark types to create different kinds of charts. For example, create a line chart by changing the **mark** type to a line.
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![Line charts generated by Pygwalker][6]
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You can also compare different measures by creating a **concat** view, which adds more than one measure into rows and columns.
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![Comparing data in the Graphic Walker interface.][7]
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Put dimensions into rows or columns to create a **facet** view of several subviews divided by the value in a dimension.
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![The facets view in Graphic Walker.][8]
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In the **Data** tab, you can view the data frame in a table and configure the analytic and semantic types.
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![Table data in Graphic Walker.][9]
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### Data exploration with PyGWalker
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You can turn your Pandas data into graphical and highly-customizable charts with PyGWalker. You can also use PyGWalker as a powerful tool for exploring data to uncover underlying patterns, trends, and insights.
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Data exploration options are available in the **Exploration Mode** option (in the toolbar). They can be set to either **Point Mode** or **Brush Mode**.
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- **Point Mode**: Explore data by pointing your mouse cursor at a specific segment of the data.
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- **Brush Mode**: Explore data by drawing a selection box around a range of data and then drag it to see generated insights.
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### Try this to see your data
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You can try PyGWalker on these cloud demos: [Google Colab][10], [Binder][11], or [Graphic Walker Online Demo][12].
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PyGWalker is an excellent tool for simplifying data analysis and visualization workflows, particularly for those who want a visual interface for Pandas. With PyGWalker and Graphic Walker, data scientists can easily create stunning visualizations with simple drag-and-drop operations in [Jupyter Notebook][13]. Check out the PyGWalker Git repository for the source code.
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For data scientists who seek an open source solution to automated data exploration and advanced augmented analytics, the project also works on [RATH][14], an open source auto-EDA, AI-empowered data exploration and visualization tool. You can also check out the [RATH Git repository][15] for the source code and an active community.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook
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作者：[Bill Wang][a]
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选题：[lkxed][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/bill-wang
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[b]: https://github.com/lkxed/
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[1]: https://enterprisersproject.com/article/2022/9/data-scientist-day-life?intcmp=7013a000002qLH8AAM
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[2]: https://github.com/Kanaries/pygwalker
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[3]: https://github.com/Kanaries/graphic-walker
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[4]: https://opensource.com/article/20/6/pandas-python
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[5]: https://opensource.com/sites/default/files/2023-03/pygwalker-exploring-data.gif
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[6]: https://opensource.com/sites/default/files/2023-03/line-chart-with-pygwalker.webp
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[7]: https://opensource.com/sites/default/files/2023-03/concat-view-pygwalker.webp
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[8]: https://opensource.com/sites/default/files/2023-03/table-view-pygwalker.webp
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[9]: https://opensource.com/sites/default/files/2023-03/table-data-pygwalker.webp
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[10]: https://colab.research.google.com/drive/171QUQeq-uTLgSj1u-P9DQig7Md1kpXQ2?usp=sharing
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[11]: https://mybinder.org/v2/gh/Kanaries/pygwalker/main?labpath=tests%2Fmain.ipynb
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[12]: https://graphic-walker.kanaries.net/
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[13]: https://opensource.com/downloads/jupyter-guide
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[14]: https://kanaries.net/
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[15]: https://github.com/Kanaries/Rath
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@ -0,0 +1,105 @@
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[#]: subject: "Explore data visually with Python tools"
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[#]: via: "https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook"
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[#]: author: "Bill Wang https://opensource.com/users/bill-wang"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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使用 Python 工具可视化地探索数据
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======
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开源工具在推动技术进步和让每个人都更容易获得它方面发挥了重要作用。数据分析也不例外。随着数据变得越来越丰富和复杂，[数据科学家][1]一直在寻找方法来简化他们的工作流程并创建交互式和吸引人的可视化效果。PyGWalker 就是为解决此类问题而设计的。
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[PyGWalker][2]（Graphic Walker 的 Python 绑定）将 Python Jupyter Notebook 的工作环境连接到 [Graphic Walker][3]，以创建开源数据可视化工具。你可以通过简单的拖放操作将 [Pandas dataframe][4] 变成制作精美的数据可视化。
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![Exploring data through a visual interface with Pygwalker][5]
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### 开始使用 PyGWalker
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使用 `pip` 安装 PyGWalker：
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```
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$ python3 -m pip install pygwalker
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```
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导入 `pygwalker` 和 `pandas` 以在项目中使用它：
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```
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import pandas as pd
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import pygwalker as pyg
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```
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将数据加载到 Pandas 数据包中并调用 PyGWalker：
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```
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df = pd.read\_csv('./bike\_sharing\_dc.csv', parse\_dates=\['date'\])
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gwalker = pyg.walk(df)
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```
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你现在有一个图形用户界面来探索和可视化你的 Pandas dataframe！
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### 使用 Graphic Walker 探索数据
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Graphic Walker 的主要功能之一是能够更改标记类型以创建不同类型的图表。例如，通过将 **mark** 类型更改为折线来创建折线图。
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![Line charts generated by Pygwalker][6]
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你还可以通过创建 **concat** 视图来比较不同的度量，该视图将多个度量添加到行和列中。
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![Comparing data in the Graphic Walker interface.][7]
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将维度放入行或列中以创建几个子视图除以维度中的值的 **facet** 视图。
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![The facets view in Graphic Walker.][8]
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在 **Data** 选项卡中，你可以在表格中查看 dataframe 并配置分析和语义类型。
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![Table data in Graphic Walker.][9]
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### 使用 PyGWalker 进行数据探索
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你可以使用 PyGWalker 将 Pandas 数据转换为高度可定制的图形图表。你也可以使用 PyGWalker 作为探索数据的强大工具，以发现潜在的模式、趋势和洞察力。
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**探索模式** 选项（在工具栏中）提供了数据探索选项。它们可以设置为**点模式**或**刷子模式**。
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- **点模式**：通过将你的鼠标光标指向数据的一个特定部分来探索数据。
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- **刷子模式**：通过在数据范围周围画一个选择框来探索数据，然后拖动它来查看生成的报告。
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### 试试看你的数据
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你可以在这些云演示中试用 PyGWalker：[Google Colab][10]、[Binder][11] 或 [Graphic Walker Online Demo][12]。
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PyGWalker 是一个用于简化数据分析和可视化工作流程的优秀工具，特别是对于那些想要 Pandas 可视化界面的人。借助 PyGWalker 和 Graphic Walker，数据科学家可以在 [Jupyter Notebook][13] 中通过简单的拖放操作轻松创建令人惊叹的可视化效果。查看 PyGWalker Git 仓库获取源代码。
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对于寻求自动化数据探索和高级增强分析的开源解决方案的数据科学家，该项目还适用于 [RATH][14]，这是一种开源自动 EDA、人工智能支持的数据探索和可视化工具。你还可以查看 [RATH Git 仓库][15]获取源代码和活跃的社区。
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook
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作者：[Bill Wang][a]
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选题：[lkxed][b]
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译者：[geekpi](https://github.com/geekpi)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/bill-wang
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[b]: https://github.com/lkxed/
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[1]: https://enterprisersproject.com/article/2022/9/data-scientist-day-life?intcmp=7013a000002qLH8AAM
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[2]: https://github.com/Kanaries/pygwalker
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[3]: https://github.com/Kanaries/graphic-walker
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[4]: https://opensource.com/article/20/6/pandas-python
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[5]: https://opensource.com/sites/default/files/2023-03/pygwalker-exploring-data.gif
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[6]: https://opensource.com/sites/default/files/2023-03/line-chart-with-pygwalker.webp
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[7]: https://opensource.com/sites/default/files/2023-03/concat-view-pygwalker.webp
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[8]: https://opensource.com/sites/default/files/2023-03/table-view-pygwalker.webp
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[9]: https://opensource.com/sites/default/files/2023-03/table-data-pygwalker.webp
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[10]: https://colab.research.google.com/drive/171QUQeq-uTLgSj1u-P9DQig7Md1kpXQ2?usp=sharing
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[11]: https://mybinder.org/v2/gh/Kanaries/pygwalker/main?labpath=tests%2Fmain.ipynb
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[12]: https://graphic-walker.kanaries.net/
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[13]: https://opensource.com/downloads/jupyter-guide
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[14]: https://kanaries.net/
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[15]: https://github.com/Kanaries/Rath
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