Hey, friends! Today I want to introduce you to a super cool Python library – Streamlit! This thing is simply a “magic wand” in the world of data application development, waving it can conjure up beautiful web applications, it’s just too awesome! 🎩✨
I still remember the first time I encountered Streamlit; it felt like discovering a new continent. Previously, creating a data visualization application required dealing with front-end, back-end, and databases, which was a hassle. Now with Streamlit, it’s like installing a rocket booster for data application development; it can take off in a flash! 🚀
The most attractive part of Streamlit is its simplicity. Whether you are a Python beginner or a pro, you can quickly get started. It simplifies complex web application development into just a few lines of Python code, it’s just so considerate!
Installing Streamlit is super easy, just one command:
pip install streamlit
Once installed, let’s see how to use it. Create a new file, for example, called <span>app.py</span>, and write these lines of code:
import streamlit as st
st.title("My First Streamlit App")
name = st.text_input("Please enter your name")
st.write(f"Hello, {name}! Welcome to the magical world of Streamlit!")
Save the file and run it in the command line:
streamlit run app.py
Look! A simple web application is born just like that. Doesn’t it feel like magic?
Streamlit also has many cool components, such as charts, maps, file uploads, etc. My favorite is its chart functionality, which works perfectly with Pandas and Matplotlib, making it incredibly convenient. Let’s take a look at this example:
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
data = pd.DataFrame({
'Fruit': ['Apple', 'Banana', 'Orange', 'Strawberry'],
'Sales': [100, 80, 120, 90]
})
st.dataframe(data)
fig, ax = plt.subplots()
ax.bar(data['Fruit'], data['Sales'])
st.pyplot(fig)
This code generates a page containing a data table and a bar chart, isn’t it cool?
Speaking of which, I remember a few days ago at work, I created a data analysis dashboard using Streamlit. My colleague saw it and exclaimed, “Wow, this is amazing!” That’s right, it’s that magical! 😎
One advanced usage of Streamlit that I highly recommend is interactive components. For example, you can add a slider to dynamically adjust the chart:
import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
x = st.slider('Choose a value', 0.0, 10.0, 5.0)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y, 'ro')
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)
st.pyplot(fig)
This way, users can see the sine function values in real-time for different x values, super intuitive!
However, friends, please note that while Streamlit is powerful, it’s not omnipotent. My advice is:
-
First, think clearly about your application logic before starting to code -
Look at Streamlit’s official documentation; there are many useful tips in there -
If you encounter performance issues, consider using the <span>@st.cache</span>decorator for optimization
To be honest, after using Streamlit, I feel like my productivity has doubled! What used to take a week to develop a data application can now be done in a day; it’s just too great!
By the way, I have a little trick to share. If you want your Streamlit application to look more professional, you can try customizing the theme:
st.set_page_config(
page_title="My Super Cool App",
page_icon="🚀",
layout="wide",
initial_sidebar_state="expanded",
)
This way, your application can have a personalized title, icon, and layout, isn’t that awesome?
Finally, I want to say that Streamlit really makes data application development super fun. It’s like giving us a data magic wand, allowing us to easily turn ideas into reality. Whether you are a data scientist, analyst, or developer, Streamlit can make your work more efficient and enjoyable.
If you are also using Streamlit, feel free to share your works in the comments! Let’s exchange ideas and improve together! I’m already looking forward to seeing your amazing creations! 😃
In the next issue, I plan to introduce how to build a stock analysis tool using Streamlit, so remember to follow along! See you next time! 👋