dash
plotly
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dash | plotly | |
---|---|---|
56 | 64 | |
20,339 | 15,067 | |
1.7% | 1.9% | |
9.6 | 9.4 | |
8 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dash
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dash VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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[Python] NiceGUI: Lassen Sie jeden Browser das Frontend für Ihren Python-Code sein
Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.
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Little guidance of a python newbie
You could use something like Streamlit or Dash. In any case you will be accessing your app through the browser.
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Launch HN: Pynecone (YC W23) – Web Apps in Pure Python
Useful list. Dash & bokeh as two more in the space
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Python projects with best practices on Github?
I also heard of Dash which serves the same purpose I guess, but I think it has more to offer.
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4 Streamlit Alternatives for Building Python Data Apps
Plotly is a plotting library, and Dash is their open-source framework for building data apps with Python, R or Julia. (Dash also has an Enterprise version, but we'll focus on the open-source library here.)
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NiceGUI: Let any browser be the frontend for your Python code
Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.
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Containerizing Shiny for Python and Shinylive Applications
Shiny is a framework that makes it easy to build interactive web applications. Shiny was introduced 10 years ago as an R package. In his 10th anniversary keynote speech, Joe Cheng announced Shiny for Python at the 2022 RStudio Conference. Python programmers can now try out Shiny to create interactive data-driven web applications. Shiny comes as an alternative to other frameworks, like Dash, or Streamlit.
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How can I build interactive plots using Matplotlib/Numpy?
Take a look at streamlit and dash. Matplotlib widgets might also be of interest.
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Looking for web app generator from JSON data
Dash
plotly
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/plotly/plotly.py
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How to Create a Pareto Chart 📐
First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.
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Other programing options?
Plotly documentation (https://plotly.com/python/)
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Advice on upgrading my Presentation template
I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.
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[OC] Clustering Images with OpenAI CLIP, T-SNE, UMAP & Plotly
Plotly GitHub repository: https://github.com/plotly/plotly.py
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Anyone else feel ‘trapped’ in power bi?
Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.
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FiftyOne Computer Vision Model Evaluation Tips and Tricks – Feb 03, 2023
Because the confusion matrix is implemented in plotly, it is interactive! To interact visually with your data via the confusion matrix, attach the plot to a session launched with the dataset:
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Create interactive plots with Python and Plotly
I've created a notebook in this Github repo to demo some of Plotly basic capabilities and I highly recommend checking out the official documentations for examples of each plot type and to discover lots of cool stuff that you can put in your notebook/site 🙂.
- GUI for a Dynamically Created Dataframe
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Инструменты Python. Библиотеки для анализа данных
- plotly (https://plotly.com/python/);
What are some alternatives?
Altair - Declarative statistical visualization library for Python
bokeh - Interactive Data Visualization in the browser, from Python
matplotlib - matplotlib: plotting with Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
folium - Python Data. Leaflet.js Maps.
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
seaborn - Statistical data visualization in Python
streamlit - Streamlit — A faster way to build and share data apps.
bqplot - Plotting library for IPython/Jupyter notebooks
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
pygal - PYthon svg GrAph plotting Library
Graphviz - Simple Python interface for Graphviz