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105 lines
5.2 KiB
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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 |