Getting-started-with-Streamlit-for-Data-Science - GitLite

KO | EN

gitlite — search

#python #hacktoberfest #react #typescript #android #rust #docker #windows #awesome #kubernetes #awesome-list #ios #mcp #open-source

Getting-started-with-Streamlit-for-Data-Science

(★ 87)

Getting started with Streamlit for Data Science, published by Packt

파일 탐색기

최종 버전 다운로드 (.zip)

- - clt_demo.py
  - example.py
  - hello_world.py
- - plot_demo.py
- - penguins.csv
  - penguins.py
  - requirements.txt
- - trees.csv
  - trees.py
- - feature_importance.png
  - output_penguin.pickle
  - penguins.csv
  - penguins_ml.py
  - penguins_streamlit.py
  - random_forest_penguin.pickle
  - requirements.txt
- - feature_importance.png
  - output_penguin.pickle
  - penguins.csv
  - penguins_ml.py
  - penguins_streamlit.py
  - random_forest_penguin.pickle
  - requirements.txt
- - pretty_trees.py
  - trees.csv
- - gist_example.py
  - penguin_animated.py
  - penguins.csv
  - tree_animated.py
  - trees.csv
- - feature_importance.png
  - output_penguin.pickle
  - penguins.csv
  - penguins_ml.py
  - penguins_streamlit.py
  - random_forest_penguin.pickle
  - requirements.txt
- - airport_location.csv
  - haversine.png
  - job_problems.md
  - job_streamlit.py
  - Screen Shot 2020-11-30 at 1.48.16 PM.png
.DS_Store
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/PacktPublishing/Getting-started-with-Streamlit-for-Data-Science

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Getting-started-with-Streamlit-for-Data-Science

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

⚠️ 이 프로젝트는 규모가 큰 저장소라, 이 방법이 실제 핵심 제품이 아니라 내부 하위 패키지를 가리키는 것일 수 있습니다. README 전체를 함께 확인해보세요.

pip install -r Chapter02/penguin_app/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Getting Started with Streamlit for Data Science

<a href="https://www.packtpub.com/product/getting-started-with-streamlit-for-data-science/9781800565500?utm_source=github&utm_medium=repository&utm_campaign=978-1-80056-550-0"><img src="https://m.media-amazon.com/images/I/41ezAWgOZdS.jpg" alt="Getting Started with Streamlit for Data Science" height="256px" align="right"></a>

This is the code repository for [Getting Started with Streamlit for Data Science](https://www.packtpub.com/product/getting-started-with-streamlit-for-data-science/9781800565500?utm_source=github&utm_medium=repository&utm_campaign=978-1-80056-550-0), published by Packt.

**Create and deploy Streamlit web applications from scratch in Python**

This book's repository has moved to: https://github.com/tylerjrichards/Getting-Started-with-Streamlit-for-Data-Science

## What is this book about?
Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time.

This book covers the following exciting features: 
* Set up your first development environment and create a basic Streamlit app from scratch
* Explore methods for uploading, downloading, and manipulating data in Streamlit apps
* Create dynamic visualizations in Streamlit using built-in and imported Python libraries
* Discover strategies for creating and deploying machine learning models in Streamlit
* Use Streamlit sharing for one-click deployment

If you feel this book is for you, get your [copy](https://www.amazon.com/dp/B095Z1R3BP) today!

<a href="https://www.packtpub.com/?utm_source=github&utm_medium=banner&utm_campaign=GitHubBanner"><img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" 
alt="https://www.packtpub.com/" border="5" /></a>

## Instructions and Navigations
All of the code is organized into folders.

The code will look like the following:
```
import pandas as pd
penguin_df = pd.read_csv('penguins.csv')
print(penguin_df.head())
```

**Following is what you need for this book:**
This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you’re a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.	.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

### Software and Hardware List

| Chapter  | Software required                   | OS required                        |
| -------- | ------------------------------------| -----------------------------------|
| 1 - 11   | Python 3+                           | Windows, Mac OS X, and Linux (Any) |
| 1 - 11   | Streamlit 0.81+                     | Windows, Mac OS X, and Linux (Any) |
| 1 - 11   | GitHub                              | Windows, Mac OS X, and Linux (Any) |

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://static.packt-cdn.com/downloads/9781800565500_ColorImages.pdf).

### Related products <Other books you may enjoy>
* Interactive Dashboards and Data Apps with Plotly and Dash [[Packt]](https://www.packtpub.com/https://www.packtpub.com/product/interactive-dashboards-and-data-apps-with-plotly-and-dash/9781800568914?utm_source=github&utm_medium=repository&utm_campaign=9781800568914product/interactive-dashboards-and-data-apps-with-plotly-and-dash/9781800568914?utm_source=github&utm_medium=repository&utm_campaign=978-1-80056-891-4) [[Amazon]](https://www.amazon.com/dp/1800568916)

* Tableau Prep Cookbook [[Packt]](https://www.packtpub.com/product/tableau-prep-cookbook/9781800563766?utm_source=github&utm_medium=repository&utm_campaign=9781800563766) [[Amazon]](https://www.amazon.com/dp/1800563760)

## Get to Know the Author
**Tyler Richards**
is a data scientist at Facebook, working on community integrity. Before this gig, his focus was on helping bolster the state of US elections for the nonprofit Protect Democracy. He is a data scientist and industrial engineer by training, which he gets to make use of in fun ways such as applying machine learning to local campus elections, creating algorithms to help P&G target Tide Pod users, and finding ways to determine the best ping pong players in friend groups. He is always looking for a new project, a new adventure.