#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

Overview

30 Days Of Streamlit 🎈

This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

How to participate

All you need to participate is a computer, a basic understanding of Python, and your curiosity. 🧠

A new challenge is released daily via Streamlit's Twitter and LinkedIn accounts as well as the #30DaysOfStreamlit app.

Streamlit App

Complete the daily challenges, share your solutions with us on Twitter or LinkedIn, and get rewarded with cool Streamlit swag! 😎

What are the daily challenges?

Find out more about the specific challenges by participating! The 30-day challenges are divided by 3 levels of difficulty to appeal to participants of all skill levels:

Beginner (Days 1-7) Intermediate (Days 8-23) Advanced (Days 24-30)
Set up your local and cloud coding environments, install Streamlit, and build your first Streamlit app. Learn about a new Streamlit command each day and use it to create and deploy a simple Streamlit app. Learn about important topics such as session state, efficient data and memory management via caching, complex layouts, and much more.

Prizes

If getting up to speed with the fastest and easiest way to build data apps isn't already the best summer gift, you can also win Streamlit goodies!

Complete the daily challenges, share your solutions with us on Twitter or LinkedIn, and get rewarded with cool Streamlit swag! 🎁

Resources

Translations

Want to help us expand the reach of #30DaysOfStreamlit and English isn't your primary language? Translate the challenges into your preferred language and link to them below!

Comments
  • Found typos in Day25.md file

    Found typos in Day25.md file

    In st.session_state README file, I've found some typos in Line-by-line explanation section. Here they are --->

    1. Here, we use st.numerical_input to accept numerical inputs of the weight values:
    2. Notice how the on_charge option specifies the 2 custom functions...

    Whereas (in sentences) they should be replaced --->

    FROM

    1. st.numerical_input
    2. on_charge

    TO

    1. st.number_input
    2. on_change

    Hope you found this helpful! 😃 P.S.: I know fixing typos isn't a better way to contribute but I feel that at some places where code is included, any parameter name or feature name should not be written with a typo.

    Let me know your thoughts @dataprofessor ! Open for feedback.

    opened by ShruAgarwal 2
  • Small fixes to Day 22

    Small fixes to Day 22

    1. The first example has st.write and it should be st.subheader
    2. Replacing write notation to with notation
    3. adding the comment just to be consistent with the first example
    opened by franciscoed 0
  • Support query params for daily challenges

    Support query params for daily challenges

    This PR adds support for query params so that we can share challenge specific links, for example:

    • https://share.streamlit.io/snehankekre/30days/query-params-challenges?challenge=Day+2
    • https://share.streamlit.io/snehankekre/30days/query-params-challenges?challenge=Day+8

    Changing the selectbox value will update the URL, and accessing a specific day's URL will update the selectbox value. The idea is that participants will be able to share challenges for specific days. We can even use day-specific URLs in our daily social media posts. Here's an app demonstrating this behavior.

    https://user-images.githubusercontent.com/20672874/162945347-039e56e2-bafb-42d8-9825-e800e97dc19d.MOV

    enhancement 
    opened by snehankekre 0
  • Replace 'function' and 'method' with 'command'

    Replace 'function' and 'method' with 'command'

    In addition to formatting Markdown, this PR substitutes instances of function and method with command.

    • Streamlit command (not "function", "method", etc): any of our main API calls. Examples: st.write, st.cache, st.line_chart() , etc.
    • We use the word command because (1) it's more "scripty", and (2) function/method have meanings that in the end depend on implementation details. Some commands are actually implemented as functions while others are methods, but the user shouldn't care.
    opened by snehankekre 0
  • Update README with instructions on how to participate

    Update README with instructions on how to participate

    This PR updates the README with an overview of the 30-day challenge, instructions to participate, information about daily challenges, prizes, and additional resources.

    opened by snehankekre 0
  • Day 14 throws

    Day 14 throws "No module named 'ipywidgets'" error

    May need to include pip install ipywidgets as a step as well. Maybe I didn't have it installed at first, but as this is targeted at someone who is running this from scratch this may need to be specifically stated.

    opened by halfgingerbeard 0
  • Syntax Unification of markdown and Translation of Traditional Chinese

    Syntax Unification of markdown and Translation of Traditional Chinese

    Hello guys,

    I noticed there are some different usages of markdown syntax in the contents. So I try to fix them (before day 7 now, follow the progress of translation). And the link of Translation of Traditional Chinese.

    There are the points of Syntax Unification:

    • Remove redundant (e.g. make h1/h2 title text bold)
    • Add explicit language tags for all the code cell
    • Use the same format for all the sentences (e.g. figure, references, code block)

    If the direction of the syntax unification is okay, I will check the content of the following days. :smile:

    opened by DragonChen-TW 1
Owner
Streamlit
The fastest way to build custom ML tools
Streamlit
A Tools that help Data Scientists and ML engineers train and deploy ML models.

Domino Research This repo contains projects under active development by the Domino R&D team. We build tools that help Data Scientists and ML engineers

Domino Data Lab 73 Oct 17, 2022
Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo

Tangram 1.4k Jan 5, 2023
Exemplary lightweight and ready-to-deploy machine learning project

Exemplary lightweight and ready-to-deploy machine learning project

snapADDY GmbH 6 Dec 20, 2022
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in

Backprop 227 Dec 10, 2022
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 2, 2023
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

AutoViz and Auto_ViML 519 Jan 3, 2023
Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

Naive-Bayes Spam Classificator Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm. Main goal is to code a

Viktoria Maksymiuk 1 Jun 27, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

Jina AI 17k Jan 1, 2023
Model factory is a ML training platform to help engineers to build ML models at scale

Model Factory Machine learning today is powering many businesses today, e.g., search engine, e-commerce, news or feed recommendation. Training high qu

null 16 Sep 23, 2022
This machine-learning algorithm takes in data from the last 60 days and tries to predict tomorrow's price of any crypto you ask it.

Crypto-Currency-Predictor This machine-learning algorithm takes in data from the last 60 days and tries to predict tomorrow's price of any crypto you

Hazim Arafa 6 Dec 4, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 9, 2023
MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine Learning work with thousands of other users.

The collaboration platform for Machine Learning MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine

MLReef 1.4k Dec 27, 2022
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

BDFD 6 Nov 5, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models tabular data.

Robin 55 Dec 27, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

wenqi 2 Jun 26, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 8, 2023
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Jan 3, 2023