Background Introduction
In the fields of data analysis and machine learning, we “tech geeks” often face a super annoying problem: how to present those complex model results or data visualizations to non-technical friends? The traditional solution is usually to team up with front-end and back-end developers, but this process is not only time-consuming and labor-intensive but often drives people crazy, with development cycles as long as a marathon and costs that can be frighteningly high.
The emergence of Streamlit is simply a timely rain for developers! It acts like a super helpful assistant, allowing Python developers to quickly build interactive web applications with an API that is as simple as it gets and powerful features that are astonishing, all without needing to learn those complex front-end skills. It’s like you originally needed to build a big bridge by yourself, but now you just press a button and the bridge is automatically built. Isn’t that super magical?
Installation Guide
The process of installing Streamlit is simpler than eating melon! Don’t worry, I won’t let you get lost in the sea of technology.
First, make sure you have Python installed on your computer. If not, you need to sort out this “basic project” first! After installing Python, open your terminal (or command prompt, Windows users don’t panic, it’s that black little window).
Next, enter the following command, like sending an invitation to your computer saying “please bring Streamlit to my house”:
pip install streamlitOnce the installation is complete, you can start Streamlit’s development server with the following command to see just how powerful it is:
streamlit helloThis will run a sample application, like a “welcome gift” from Streamlit, allowing you to quickly understand its basic functionalities. Don’t underestimate this little application; it’s a “small expert” that can help you get started quickly!
However, if you plan to showcase your Streamlit application on the “big stage” (i.e., deploy it in a production environment), you will need to install some additional “gear.” For example, if you plan to deploy on Heroku, you need to install <span>streamlit-heroku</span>; if using Docker, then you need to install <span>streamlit-docker</span>. Which one to install depends on where your “battlefield” is!
Basic Usage
Basic usage is the key to quickly getting started with Streamlit, just like learning to ride a bike; you first need to learn how to push it along. Don’t worry, I’ll guide you step by step.
Step One: Write a Simple Code
Let’s look at a super simple code example to feel the “magic” of Streamlit. Imagine you are creating a super cool application, but actually, it can be done with just a few lines of code. Isn’t that amazing?
import streamlit as st
# Set application title
st.title("My First Streamlit App")
# Add text input box
user_input = st.text_input("Please enter your name:")
st.write("Hello, ", user_input, "!")
# Add button
if st.button("Click me"):
st.write("Wow, you really clicked me!")Let’s analyze these few lines of code:
-
•
st.title: This is to give your application a name, just like naming your child is important. -
•
st.text_input: This is a place for users to input text, for example, you can ask users to enter their names, and then you can greet them with that name. -
•
st.write: This function acts like a “universal printer,” allowing you to output anything, such as text, numbers, or even a smiley face. -
•
st.button: This is a button that users can click, and then you can tell them “Hey, you clicked me!” Isn’t that fun?
Step Two: Run Your Application
After writing the code, just enter the following command in the terminal to start your application:
streamlit run your_script.pyReplace <span>your_script.py</span> with your script file name. Then, open your browser and visit <span>http://localhost:8501</span>, and you will see your first Streamlit application!
Advanced Usage
When you start to feel that Streamlit is not “exciting” enough
Once you have mastered the basics and feel like a “Streamlit veteran,” it’s time to try its advanced features! These features are like giving your application “superpowers,” making it even cooler.
Sidebar: Add a “Little Helper” to Your Application
Imagine you are working on a complex dashboard where users need to switch between different options. At this time, st.sidebar comes into play! The sidebar acts like a “little helper” that can hold navigation menus, parameter settings, or even some tips. For example:
import streamlit as st
# Create a sidebar
st.sidebar.title("Settings")
option = st.sidebar.selectbox("Choose an option", ["Option 1", "Option 2", "Option 3"])
st.write(f"You selected: {option}")This code will display a dropdown menu in the sidebar, allowing users to select different options. Isn’t that convenient?
Multi-column Layout: Make the Interface Less “Crowded”
Sometimes, if your application has too much content, the interface can look messy. In this case, st.columns and st.expander can help you “organize” things. For example:
import streamlit as st
# Create a multi-column layout
col1, col2 = st.columns(2)
with col1:
st.write("This is the content of the first column")
st.button("Click me")
with col2:
st.write("This is the content of the second column")
st.text_input("Type something here")This code will split the interface into two columns, each can hold different content. Doesn’t it instantly make the interface feel much cleaner?
Integrating Data Science Libraries: Bring Data to Life
The most powerful aspect of Streamlit is its seamless integration with Python’s data science libraries. For instance, you can use Pandas to handle data, use Matplotlib or Plotly to draw charts, and then display them through Streamlit. For example:
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
# Create a simple data table
data = {
"Date": ["2024-01-01", "2024-01-02", "2024-01-03"],
"Sales": [100, 150, 200]
}
df = pd.DataFrame(data)
# Display data table
st.write("Sales Data Table:")
st.dataframe(df)
# Draw line chart
fig, ax = plt.subplots()
ax.plot(df["Date"], df["Sales"])
st.pyplot(fig)This code will not only display a data table but also draw a line chart. Isn’t that cool?
Practical Use Cases
Imagine you are a data analyst, and your boss asks you to present sales data
Imagine you are a data analyst, and your boss suddenly calls you to the meeting room, saying he wants you to present a sales data analysis report. You might think, “Oh no, how much time will this take!” Don’t worry, with Streamlit, you can not only quickly finish it but also impress your boss.
Build an Interactive Dashboard
You can use Streamlit to build an interactive dashboard, allowing your boss to filter data and view charts himself, instead of you explaining it dryly. For example:
import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load data
data = pd.read_csv("sales_data.csv")
# Create a sidebar
st.sidebar.title("Filter Conditions")
selected_year = st.sidebar.selectbox("Select Year", data["Year"].unique())
filtered_data = data[data["Year"] == selected_year]
# Display data table
st.write("Filtered Sales Data:")
st.dataframe(filtered_data)
# Draw bar chart
st.write("Sales Amount Bar Chart:")
fig, ax = plt.subplots()
sns.barplot(x="Month", y="Sales", data=filtered_data)
st.pyplot(fig)The “Magic” of This Code
-
• Sidebar: The boss can select different years in the sidebar, and the data will automatically update. It’s like giving the boss a “magic wand” to explore the data himself.
-
• Data Table: Using
<span>st.dataframe</span>to display the data table clearly. -
• Charts: Using Seaborn to draw bars.
Summary
All the way, full of gains
Through today’s “exploration journey,” we have unlocked various “hidden skills” of Streamlit. From its background story to installation methods, to basic usage and advanced features, and finally to its “superpowers” in practical work. Streamlit is truly an amazing tool; it is not only easy to use but also helps you quickly build interactive web applications.
How powerful is it?
-
• Easy to get started: Just like sending a text message to the computer, a few lines of code can complete an application.
-
• Powerful features: Sidebar, multi-column layout, data visualization… these features instantly elevate your application.
-
• Saves time: No more “battles of wits” with front-end developers; you can handle everything yourself.
-
• Strong interactivity: Let bosses and clients explore data themselves, and they will think you are a “data wizard.”
Finally, I want to say…
If you are still struggling with how to quickly build interactive applications, Streamlit is definitely worth a try! It’s like a “superhero” that reaches out to help you whenever you need it. Don’t hesitate any longer; give it a try! Who knows, your next project might shine because of it!
If you think this article is good, please like, share, and follow, as this will be the strongest motivation for me to continue producing more high-quality articles!