document/TranslateProject
2
0
Fork 0
mirror of https://github.com/LCTT/TranslateProject.git synced 2024-12-26 21:30:55 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #29183 from lkxed/20230419-3-Explore-data-visually-with-Python-tools

Browse Source 
[手动选题][tech]: 20230419.3 ⭐️⭐️ Explore data visually with Python tools.md
This commit is contained in:
Xingyu.Wang 2023-04-21 08:26:57 +08:00 committed by GitHub
commit a0b6a0cf8f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 105 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

105
sources/tech/20230419.3 ⭐️⭐️ Explore data visually with Python tools.md Normal file
View File

@ -0,0 +1,105 @@
[#]: 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