Streamlit is a Python-based library that allows data scientists to create and share machine learning applications. Here are some of its key features:
Simplicity: Streamlit's API is designed to be intuitive, which makes it easy to build data apps without needing any web development experience.
Interactivity: Streamlit allows you to add widgets for user interaction. Adding a widget is as simple as declaring a variable.
Compatibility: Streamlit is compatible with most Python libraries, including pandas, matplotlib, seaborn, plotly, Keras, PyTorch, and SymPy.
Deployment: Streamlit provides a platform to deploy, manage, and share your apps.
Data caching: Streamlit simplifies and speeds up computation pipelines by caching data.
What's the difference between this Python project and similar ones?
Streamlit is often compared with other Python libraries for building data apps, such as Gradio, Dash, Panel, Flask, and Jupyter. Here are some key differences:
Gradio: Like Streamlit, Gradio is a Python library for creating interactive web UIs. However, Gradio is more focused on machine learning demos, while Streamlit is designed for creating data dashboards.
Dash: Dash is a low-code framework for building data apps with the Plotly plotting library. It's a good choice for building production-ready data dashboards for larger companies.
Panel: Panel is a Python library for creating flexible dashboards and web apps. It's a good choice if you already have Jupyter Notebooks and need more flexibility.
Flask: Flask is a more general framework for web application development. It's a good choice if you want to build your own solution from the ground up.
Jupyter: Jupyter is a notebook that data scientists use for data analysis and manipulation. It's a good choice if your team is very technical.
--
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/2551
**Author:** [@yasserotiefy](https://github.com/yasserotiefy)
**Created:** 1/17/2024
**Status:** ❌ Closed
**Base:** `master` ← **Head:** `master`
---
### 📝 Commits (1)
- [`448898e`](https://github.com/vinta/awesome-python/commit/448898e46c15bcaf993c04dd00824caf95ca2573) Update README.md by adding Streamlit
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## What is this Python project?
Streamlit is a Python-based library that allows data scientists to create and share machine learning applications. Here are some of its key features:
- **Simplicity**: Streamlit's API is designed to be intuitive, which makes it easy to build data apps without needing any web development experience.
- **Interactivity**: Streamlit allows you to add widgets for user interaction. Adding a widget is as simple as declaring a variable.
- **Compatibility**: Streamlit is compatible with most Python libraries, including pandas, matplotlib, seaborn, plotly, Keras, PyTorch, and SymPy.
- **Deployment**: Streamlit provides a platform to deploy, manage, and share your apps.
- **Data caching**: Streamlit simplifies and speeds up computation pipelines by caching data.
## What's the difference between this Python project and similar ones?
Streamlit is often compared with other Python libraries for building data apps, such as Gradio, Dash, Panel, Flask, and Jupyter. Here are some key differences:
- **Gradio**: Like Streamlit, Gradio is a Python library for creating interactive web UIs. However, Gradio is more focused on machine learning demos, while Streamlit is designed for creating data dashboards.
- **Dash**: Dash is a low-code framework for building data apps with the Plotly plotting library. It's a good choice for building production-ready data dashboards for larger companies.
- **Panel**: Panel is a Python library for creating flexible dashboards and web apps. It's a good choice if you already have Jupyter Notebooks and need more flexibility.
- **Flask**: Flask is a more general framework for web application development. It's a good choice if you want to build your own solution from the ground up.
- **Jupyter**: Jupyter is a notebook that data scientists use for data analysis and manipulation. It's a good choice if your team is very technical.
--
Anyone who agrees with this pull request could submit an *Approve* review to it.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/2551
Author: @yasserotiefy
Created: 1/17/2024
Status: ❌ Closed
Base:
master← Head:master📝 Commits (1)
448898eUpdate README.md by adding Streamlit📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
What is this Python project?
Streamlit is a Python-based library that allows data scientists to create and share machine learning applications. Here are some of its key features:
What's the difference between this Python project and similar ones?
Streamlit is often compared with other Python libraries for building data apps, such as Gradio, Dash, Panel, Flask, and Jupyter. Here are some key differences:
--
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.