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+[#]: subject: "Explore data visually with Python tools"
+[#]: via: "https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook"
+[#]: author: "Bill Wang https://opensource.com/users/bill-wang"
+[#]: collector: "lkxed"
+[#]: translator: " "
+[#]: reviewer: " "
+[#]: publisher: " "
+[#]: url: " "
+
+Explore data visually with Python tools
+======
+
+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.
+
+[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.
+
+![Exploring data through a visual interface with Pygwalker][5]
+
+### Get started with PyGWalker
+
+Use `pip` to install PyGWalker:
+
+```
+$ python3 -m pip install pygwalker
+```
+
+Import `pygwalker` and `pandas` to use it in a project:
+
+```
+import pandas as pd
+import pygwalker as pyg
+```
+
+Load data into a Pandas datagram and call PyGWalker:
+
+```
+df = pd.read\_csv('./bike\_sharing\_dc.csv', parse\_dates=\['date'\])
+gwalker = pyg.walk(df)
+```
+
+You now have a graphical UI to explore and visualize your Pandas dataframe!
+
+### Explore data with Graphic Walker
+
+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.
+
+![Line charts generated by Pygwalker][6]
+
+You can also compare different measures by creating a **concat** view, which adds more than one measure into rows and columns.
+
+![Comparing data in the Graphic Walker interface.][7]
+
+Put dimensions into rows or columns to create a **facet** view of several subviews divided by the value in a dimension.
+
+![The facets view in Graphic Walker.][8]
+
+In the **Data** tab, you can view the data frame in a table and configure the analytic and semantic types.
+
+![Table data in Graphic Walker.][9]
+
+### Data exploration with PyGWalker
+
+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.
+
+Data exploration options are available in the **Exploration Mode** option (in the toolbar). They can be set to either **Point Mode** or **Brush Mode**.
+
+- **Point Mode**: Explore data by pointing your mouse cursor at a specific segment of the data.
+- **Brush Mode**: Explore data by drawing a selection box around a range of data and then drag it to see generated insights.
+
+### Try this to see your data
+
+You can try PyGWalker on these cloud demos: [Google Colab][10], [Binder][11], or [Graphic Walker Online Demo][12].
+
+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.
+
+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.
+
+--------------------------------------------------------------------------------
+
+via: https://opensource.com/article/23/4/data-visualization-pygwalker-jupyter-notebook
+
+作者：[Bill Wang][a]
+选题：[lkxed][b]
+译者：[译者ID](https://github.com/译者ID)
+校对：[校对者ID](https://github.com/校对者ID)
+
+本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
+
+[a]: https://opensource.com/users/bill-wang
+[b]: https://github.com/lkxed/
+[1]: https://enterprisersproject.com/article/2022/9/data-scientist-day-life?intcmp=7013a000002qLH8AAM
+[2]: https://github.com/Kanaries/pygwalker
+[3]: https://github.com/Kanaries/graphic-walker
+[4]: https://opensource.com/article/20/6/pandas-python
+[5]: https://opensource.com/sites/default/files/2023-03/pygwalker-exploring-data.gif
+[6]: https://opensource.com/sites/default/files/2023-03/line-chart-with-pygwalker.webp
+[7]: https://opensource.com/sites/default/files/2023-03/concat-view-pygwalker.webp
+[8]: https://opensource.com/sites/default/files/2023-03/table-view-pygwalker.webp
+[9]: https://opensource.com/sites/default/files/2023-03/table-data-pygwalker.webp
+[10]: https://colab.research.google.com/drive/171QUQeq-uTLgSj1u-P9DQig7Md1kpXQ2?usp=sharing
+[11]: https://mybinder.org/v2/gh/Kanaries/pygwalker/main?labpath=tests%2Fmain.ipynb
+[12]: https://graphic-walker.kanaries.net/
+[13]: https://opensource.com/downloads/jupyter-guide
+[14]: https://kanaries.net/
+[15]: https://github.com/Kanaries/Rath
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