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Web App Development Made Simple with Streamlit

Web App Development Made Simple with Streamlit

By : Moscato
4.3 (10)
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Web App Development Made Simple with Streamlit

Web App Development Made Simple with Streamlit

4.3 (10)
By: Moscato

Overview of this book

This book is a comprehensive guide to the Streamlit open-source Python library and simplifying the process of creating web applications. Through hands-on guidance and realistic examples, you’ll progress from crafting simple to sophisticated web applications from scratch. This book covers everything from understanding Streamlit's central principles, modules, basic features, and widgets to advanced skills such as dealing with databases, hashes, sessions, and multipages. Starting with fundamental concepts like operation systems virtualization, IDEs, development environments, widgets, scripting, and the anatomy of web apps, the initial chapters set the groundwork. You’ll then apply this knowledge to develop some real web apps, gradually advancing to more complex apps, incorporating features like natural language processing (NLP), computer vision, dashboards with interactive charts, file uploading, and much more. The book concludes by delving into the implementation of advanced skills and deployment techniques. By the end of this book, you’ll have transformed into a proficient developer, equipped with advanced skills for handling databases, implementing secure login processes, managing session states, creating multipage applications, and seamlessly deploying them on the cloud.
Table of Contents (23 chapters)
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Free Chapter
1
Part 1: Getting Started with Streamlit
5
Part 2: Building a Basic Web App for Essential Streamlit Skills
10
Part 3: Developing Advanced Skills with a Covid-19 Detection Tool
15
Part 4: Advanced Techniques for Secure and Customizable Web Applications

DataFrames, plots, and visualizations

Now, it’s time to deal with DataFrames, so let’s comment on something and continue exploring widgets.

DataFrame is the name that the pandas library gives to its data. When a file, such as a CSV file, is imported into pandas, the result will be a DataFrame. We can think of a DataFrame as an Excel or Google Sheets table – that is, a piece of data made up of columns and rows. Columns are the features or variables and rows are the records or cases. So, to keep things simple, we can say that a DataFrame is a data structure made up of columns and rows.

First, we need to install pandas, with the following command:

pipenv install pandas

If you followed our suggestion and are using pipenv for virtual environments, run the following command:

pip install pandas

If you are using a plain, standard Python installation, then write the following code:

st.header("Dataframes and Tables")
import pandas as pd
df =...

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