Plotly/Dash tutorial at PyCon TW 2020
Slide of the tutorial
https://docs.google.com/presentation/d/1QBCLbgaJKH27CqP-6NlwrnCjGk1J3YuLk4GOvOe8lpI/edit?usp=sharing
PyCon TW webpage
https://tw.pycon.org/2020/zh-hant/conference/tutorial/1159454351291318296/
This tutorial target on the data analyst, scientist, or science students, who want to start to show interactive plots or online plots. For example, building DAQ or device monitoring are essential for experiments, but science students have poor UI techniques such as JavaScript. Dash is pure Python API for users, so they can easily build a high quality web-interface.
The participant need basic knowledge of data graphing and html, and some basic Python. After this tutorial, participants can improve their presentations by interactive plots.
Basic plotly:
https://github.com/kunxianhuang/plotly_tutorial/blob/master/Plotly_introduction--Basketball_stat.ipynb
Exercise of titanic dataset:
https://github.com/kunxianhuang/plotly_tutorial/blob/master/Exercise_Titanic_dataset.ipynb
3D scatter plot and animation of SuperKamoikande open data:
https://github.com/kunxianhuang/plotly_tutorial/blob/master/SuperK_Data_Display.ipynb
Exercise of Dash:
https://github.com/kunxianhuang/plotly_tutorial/blob/master/Dash/Dash_exercise_basketball_player_stat.ipynb
License of this tutorial notebook is CC BY-NC-SA
- Basketball Players Stats per Season - 49 Leagues
License by CC BY-NC-SA - Titanic: Machine Learning from Disaster
- COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University
This data set is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020.
You should have installed the following python pacakges to run this tutorial: plotly, dash, jupyter_dash, Pandas, numpy, cufflinks, json.
If not, you can install them with pip3 install -r requirements.txt