First, we're going to start to learn how to make Streamlit apps by reproducing the plotting demo we saw before in the Streamlit demo, with a Python file that we've made ourselves. In order to do that, we will do the following:

Make a Python file where we will house all our Streamlit code.
Use the plotting code given in the demo.
Make small edits for practice.
Run our file locally.

Our first step is to create a folder called plotting_app, which will house our first example. The following code makes this folder when run in the terminal, changes our working directory to plotting_app, and creates an empty Python file we'll call plot_demo.py:

mkdir plotting_app
cd plotting_app
touch plot_demo.py

Now that we've made a file called plot_demo.py, open it with any text editor (if you don't have one already, I'm partial to VS Code (https://code.visualstudio.com/download). When you open it up, copy and paste the...

Making an app from scratch

Now that we've tried out the apps others have made, let's make our own! This app is going to focus on using the central limit theorem, which is a fundamental theorem of statistics that says that if we randomly sample with replacement enough from any distribution, then the distribution of the mean of our samples will approximate the normal distribution.

We are not going to prove this with our app, but instead, let's try to generate a few graphs that help explain the power of the central limit theorem. First, let's make sure that we're in the correct directory (in this case, the streamlit_apps folder that we created earlier), make a new folder called clt_app, and toss in a new file.

The following code makes a new folder called clt_app, and again creates an empty Python file, this time called clt_demo.py:

mkdir clt_app
cd clt_app
touch clt_demo.py

Whenever we start a new Streamlit app, we want to make sure to...

Flow control in Streamlit

As we talked about just before, there are two solutions to this data upload default situation. We can provide a default file to use until the user interacts with the application, or we can stop the app until a file is uploaded. Let’s start with the first option. The following code uses the st.file_uploader() function from within an if statement. If the user uploads a file, then the app uses that; if they do not, then we default to the file we have used before:

import altair as alt
import pandas as pd
import seaborn as sns
import streamlit as st
 
st.title("Palmer's Penguins")
st.markdown("Use this Streamlit app to make your own scatterplot about penguins!")
 
penguin_file = st.file_uploader("Select Your Local Penguins CSV (default provided)")
if penguin_file is not None:
    penguins_df = pd.read_csv(penguin_file)
else:
    penguins_df = pd.read_csv("penguins.csv")
 
selected_x_var = st.selectbox(
 ...

Data manipulation in Streamlit

Streamlit runs our Python file from the top down as a script, so we can perform data manipulation with powerful libraries such as pandas in the same way that we might in a Jupyter notebook or a regular Python script. As we’ve discussed before, we can do all our regular data manipulation as normal. For our Palmer’s Penguins app, what if we wanted the user to be able to filter out penguins based on their gender? The following code filters our DataFrame using pandas:

import streamlit as st
import pandas as pd
import altair as alt 
import seaborn as sns
st.title("Palmer's Penguins")
st.markdown('Use this Streamlit app to make your own scatterplot about penguins!')
penguin_file = st.file_uploader(
    'Select Your Local Penguins CSV (default provided)')
if penguin_file is not None:
    penguins_df = pd.read_csv(penguin_file)
else:
    penguins_df = pd.read_csv('penguins.csv')
selected_x_var =...

Persistence with Session State

One of the most frustrating parts of the Streamlit operating model for developers starting out is the combination of two facts:

By default, information is not stored across reruns of the app.
On user input, Streamlits are rerun top-to-bottom.

These two facts make it difficult to make certain types of apps! This is best shown in an example. Let’s say that we want to make a to-do app that makes it easy for you to add items to your to-do list. Adding user input in Streamlit is really simple, so we can create one quickly in a new file called session_state_example.py that looks like the following:

import streamlit as st
st.title('My To-Do List Creator')
my_todo_list = ["Buy groceries", "Learn Streamlit", "Learn Python"]
st.write('My current To-Do list is:', my_todo_list)
new_todo = st.text_input("What do you need to do?")
if st.button('Add the new To-Do...

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Key benefits

Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo models
Gain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power users
Discover the full range of Streamlit’s capabilities via hands-on exercises to effortlessly create and deploy well-designed apps

Description

If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days! Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills. You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.

Who is this book for?

This book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you’ll get the most out of this book if you’ve used Python libraries like Pandas and NumPy in the past.

What you will learn

Set up your first development environment and create a basic Streamlit app from scratch
Create dynamic visualizations using built-in and imported Python libraries
Discover strategies for creating and deploying machine learning models in Streamlit
Deploy Streamlit apps with Streamlit Community Cloud, Hugging Face Spaces, and Heroku
Integrate Streamlit with Hugging Face, OpenAI, and Snowflake
Beautify Streamlit apps using themes and components
Implement best practices for prototyping your data science work with Streamlit

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Al Bedo Mar 17, 2024

I don't know if I am being unfair in only giving this 2 stars. It is not a bad book at all, but it really didn't match my hopes and expectations. Anyone looking more for a Streamlit reference and tutorial book will be disappointed. The chapters each revolve around projects showing different aspects of data display (tables, charts etc etc) with Streamlit. Certainly, various elements of Streamlit are introduced along the way, but I found it difficult to locate any content dealing with particular aspects, and this is not helped by the very skimpy index.Anyone who likes learning via projects that are unrelated to whatever personal projects they might have might find this book more useful than I did. Safe to say that it is not an introductory/tutorial for Streamlit, but assumes that you have some working knowledge of st already.

Amazon Verified review

N/A Feb 28, 2024

really accurate, without code sampling problems

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Karan Ambasht Feb 01, 2024

Just finished reading Tyler Richards’s new Streamlit book by Packt and I must say, it's an interesting read!Although I don't work with machine learning on a daily basis, reading this book felt like a throwback to my grad school days when I was first introduced to the world of data science and machine learning.Here are the top 5 things that stood out for me:1. The introduction to the Streamlit framework was easy to understand, even for someone completely new to it. I was able to connect the dots with my experiences working with Jupyter notebooks back in the day.2. All the code files are easily accessible through a single link on Github, which includes every example discussed in the book. This made it easy to follow along and test out the concepts.3. As someone coming from the data visualization world, I was impressed by the wide range of options integrated into the framework for easy visualizations.4. The book covers interaction with OpenAI modules, which has become increasingly relevant. There is a gradual move through chapters into ML, AI, and then web deployment.5. The most important piece that made me happy was the demo on how Streamlit can be utilized to stand out in job interviews.Whether you're looking to brush up on your knowledge or dive into the field, I highly recommend giving this book a read!

David G Jan 10, 2024

Bought it for my cousins, one is a data engineer and one is swe and both love it. Highly recommend it

Alex Syzoniuk Dec 04, 2023

Book not worth money. It's more about otters libraries than actually Streamlit. Disappointed, save your money

Streamlit for Data Science: Create interactive data apps in Python , Second Edition

What do you get with Print?

Streamlit for Data Science

Streamlit plotting demo

Making an app from scratch

Summary

Exploring Palmer’s Penguins

Flow control in Streamlit

Debugging Streamlit apps

Developing in Streamlit

Exploring in Jupyter and then copying to Streamlit

Data manipulation in Streamlit

An introduction to caching

Persistence with Session State

Summary

Learn more on Discord

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Streamlit for Data Science: Create interactive data apps in Python , Second Edition

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