ready-made python-Plotly applications for data visualization
Python Interactive Graphing
using Plotly library and Dash web app framework
within the web-based Jupyter Lab interface
Python's Plotly and Dash libraries can be used to create interactive graphs and web applications for data visualization. The Plotly library is built on top of Plotly.js (JavaScript plotting library), and allows users to create on-the-fly a range of customizable graphs. Dash is a web app framework that allows users to build interactive web apps using Python engine and Dash HTML Components. Together, these tools can be used to create engaging and informative visualizations of data that facilitates in-depth analysis.
Jupyter Lab provides a modern and flexible web-based user interface that makes it easy write and execute Python code, as well as include markdown-rendered text (e.g., documentation, instructions, comments) and visualizations (e.g., interactive graphs, figures), all-in-one document. The Jupyter notebooks (.ipynb) are a popular way to share and collaborate on data analysis and scientific computing projects.
To create interactive graphs in Python, you will need to install some libraries. One way to do this is to install the libraries globally on your system. However, a better option is to use a virtual environment specifically for efficient graphing. This approach uses Conda environment manager to separate the installation of the required libraries from your generic settings in the operating system. This helps keep your system clean and organized.
These libraries contains predefined functions that you can call on your own dataset to create customized graphs.
python=3.9 # a programming language that provides the environment for the following libraries
pandas=1.4.0 # to import data from file or url, then manage data structure via DataFrame object
plotly=5.6.0 # a library for creating customized interactive graphs
plotly_express=0.4.1 # a high-level wrapper for Plotly (an alternative approach to Graph Objects)
dash=2.1.0 # (optional) to add on-the-fly customization of the graphs via widgets
dash_bio=1.0.1 # (optional) to have predefined types of traces for specific biology-related tasks
kaleido # to export static images to any format
jupyter # to have Jupyter Notebook (file menagement in the separate tab)
jupyterlab>=3 # to have Jupyter Lab (next-generation user interface)
ipywidgets>=7.6 # to display interactive graphs directly in the notebook
jupyter-dash # to add widgets (sliders, buttons) to Plotly charts in JupyterLab
If you don't have a virtual environment manager for your Python programming, consider using Conda. Simply follow the installation instructions in the Macbook Pro Installation → Install Developer Libraries tutorial to get started. Once you have Conda installed, create a new virtual environment for interactive graphing.
- Create virtual environment
conda create -n data_graphing python==3.9
- Install Requirements
pip install pandas==1.4.0
pip install plotly==5.6.0
pip install plotly_express==0.4.1
pip install dash==2.1.0
pip install dash_bio==1.0.1
pip install -U kaleido
pip install jupyter
pip install "jupyterlab>=3" "ipywidgets>=7.6"
pip install jupyter-dash
- Activate
data_graphingenvironment
conda activate data_graphing
jupyter lab
This will open a browser on a localhost, by default http://127.0.0.1:8888.
On the left-hand side you can browse your local file system to enter the desired working directory. Then, click twice on the selected notebook (any .ipynb file) to open it in the right-hand side panel.
Master your data visualization skills with the comprehensive tutorial, Plotly Graphing - Interactive Examples in the JupyterLab ⤴, offered in the Data Science Workbook ⤴. This step-by-step guide will show you how to use and create interactive graphs available in this repository.