What is Streamlit and what are its top alternatives?
Streamlit is a popular open-source framework used for building and sharing data apps in Python effortlessly. Its key features include easy-to-use APIs, interactive widgets, and automatic updates on code changes. However, Streamlit has limitations in terms of scalability for large datasets and lack of built-in support for complex visualizations.
- Dash: Dash is a web application framework that enables the creation of interactive, web-based dashboards in Python. It offers a wide range of interactive components, support for complex visualizations, and easy deployment. However, the learning curve may be steeper compared to Streamlit.
- Gradio: Gradio is a Python library that allows for rapid prototyping of machine learning models with simple UI components for input and output. It excels in simplicity and ease of use, but it may not have the same level of flexibility as Streamlit.
- Panel: Panel is a Python library that allows users to create custom interactive web apps and dashboards from Python objects. It provides a high level of customization and flexibility, but it may require more coding compared to Streamlit.
- Bokeh: Bokeh is a Python library for creating interactive data visualizations in web browsers. It offers powerful tools for data exploration and visualization, but it may require more effort in terms of customization compared to Streamlit.
- Voilà: Voilà turns Jupyter notebooks into standalone web applications, making it easy to share interactive dashboards and applications. It provides seamless integration with Jupyter notebooks, but it may have limitations in terms of flexibility for custom app development.
- R Shiny: R Shiny is an R package that enables the creation of interactive web applications directly from R. It offers a wide range of interactive elements and customization options, but it may not be as user-friendly as Streamlit for Python developers.
- S Streamlit for Julia: Streamlit for Julia brings the Streamlit experience to the Julia programming language, allowing for easy creation of data apps and dashboards. It leverages the simplicity of Streamlit with the power of Julia, but it may have fewer resources and community support compared to Streamlit for Python.
- Anvil: Anvil is a platform that allows for building full-stack web apps with nothing but Python. It provides drag-and-drop components for building web interfaces and database management, but it may not offer the same level of control and customization as Streamlit.
- Plotly Dash: Plotly Dash is a Python framework for building analytical web applications. It offers a wide range of interactive components, support for complex visualizations, and easy deployment, but it may require more coding compared to Streamlit.
- Flexdashboard: Flexdashboard is an R package that enables the creation of interactive dashboards with R Markdown. It provides a simple and flexible way to create dashboards, but it may not offer the same level of interactivity and ease of use as Streamlit.
Top Alternatives to Streamlit
- Dash
Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included. ...
- Jupyter
The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...
- Flask
Flask is intended for getting started very quickly and was developed with best intentions in mind. ...
- Shiny
It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge. ...
- Bokeh
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. ...
- Django
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
- Plotly.js
It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more. ...
- TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...
Streamlit alternatives & related posts
- Dozens of API docs and Cheat-Sheets17
- Great for offline use12
- Works with Alfred8
- Excellent documentation8
- Quick API search8
- Fast5
- Good integration with Xcode and AppCode3
- Great for mobile dev work2
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- In-line code execution using blocks19
- In-line graphing support11
- Can be themed8
- Multiple kernel support7
- LaTex Support3
- Best web-browser IDE for Python3
- Export to python code3
- HTML export capability2
- Multi-user with Kubernetes1
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From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.
I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.
Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
Flask
- Flexibilty14
- For it flexibility10
- Flexibilty and easy to use9
- Flask8
- User friendly7
- Secured6
- Unopinionated5
- Orm3
- Secure2
- Beautiful code1
- Easy to get started1
- Easy to develop and maintain applications1
- Not JS1
- Easy to use1
- Documentation1
- Python1
- Minimal1
- Lightweight1
- Easy to setup and get it going1
- Perfect for small to large projects with superb docs.1
- Easy to integrate1
- Speed1
- Get started quickly1
- Customizable1
- Simple to use1
- Powerful1
- Rapid development1
- Open source0
- Well designed0
- Productive0
- Awesome0
- Expressive0
- Love it0
- Not JS10
- Context7
- Not fast5
- Don't has many module as in spring1
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One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.
Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.
Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.
Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?
- R Compatibility8
- Free3
- Highly customizable and extensible2
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Hi - I am looking to develop an app accessed by a browser that will display interactive networks (including adding or deleting nodes, edges, labels (or changing labels) based on user input. Look to use Django at the backend. Also need to manage graph versions if one person makes a graph change while another person is looking at it. Mainly tree networks for starters anyway. I probably will use the Networkx package. Not sure what the pros and cons are using Bokeh vs Matplotlib. I would be grateful for any comments or suggestions. Thanks.
- Rapid development670
- Open source487
- Great community424
- Easy to learn379
- Mvc276
- Beautiful code232
- Elegant223
- Free206
- Great packages203
- Great libraries194
- Restful79
- Comes with auth and crud admin panel79
- Powerful78
- Great documentation75
- Great for web71
- Python57
- Great orm43
- Great for api41
- All included32
- Fast28
- Web Apps25
- Easy setup23
- Clean23
- Used by top startups21
- Sexy19
- ORM19
- The Django community15
- Allows for very rapid development with great libraries14
- Convention over configuration14
- King of backend world11
- Full stack10
- Great MVC and templating engine10
- Fast prototyping8
- Mvt8
- Easy to develop end to end AI Models7
- Batteries included7
- Its elegant and practical7
- Have not found anything that it can't do6
- Very quick to get something up and running6
- Cross-Platform6
- Easy Structure , useful inbuilt library5
- Great peformance5
- Zero code burden to change databases5
- Python community5
- Map4
- Just the right level of abstraction4
- Easy to change database manager4
- Modular4
- Many libraries4
- Easy to use4
- Easy4
- Full-Text Search4
- Scaffold3
- Fastapi1
- Built in common security1
- Scalable1
- Great default admin panel1
- Node js1
- Gigante ta1
- Rails0
- Underpowered templating26
- Autoreload restarts whole server22
- Underpowered ORM22
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs8
- Configuration hell8
- Admin7
- Not as clean and nice documentation like Laravel5
- Python4
- Not typed3
- Bloated admin panel included3
- Overwhelming folder structure2
- InEffective Multithreading2
- Not type safe1
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Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.
Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.
For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d go with Ember.js.
However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.
All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.
Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?
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- Integrated zoom and filter-out tools in charts and maps10
- Great support for complex and multiple axes9
- Powerful out-of-the-box featureset8
- Beautiful visualizations6
- Active user base4
- Impressive support for webgl 3D charts4
- Charts are easy to share with a cloud account3
- Webgl chart types are extremely performant3
- Interactive charts2
- Easy to use online editor for creating plotly.js charts2
- Publication quality image export2
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We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.
If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.
Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
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Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.
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