1. What is Streamlit?

Streamlit is an open-source Python library that allows us to create beautiful data applications using only Python. It is like a magical toolbox that makes complex data visualization and interactive features super simple.

2. Installation and Quick Start

First, we need to install Streamlit:

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pip install streamlit

Let’s write the simplest Streamlit application:

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import streamlit as st

st.title('My First Streamlit App')
st.write('Hello, World!')

Tip: Save it as app.py, then enter streamlit run app.py in the command line to see the effect!

3. Core Components of Streamlit

3.1 Text Display

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import streamlit as st

st.title('This is a Title')
st.header('This is a Level 1 Header')
st.subheader('This is a Level 2 Header')
st.text('This is normal text')
st.markdown('**This is Markdown text**')

3.2 Data Display

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import streamlit as st
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame({
    'Name': ['Xiao Ming', 'Xiao Hong', 'Xiao Hua'],
    'Age': [18, 19, 20],
    'Score': [90, 85, 95]
})

st.write('This is a table:')
st.dataframe(df)

3.3 Interactive Components

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import streamlit as st

# Input box
name = st.text_input('Please enter your name')

# Slider
age = st.slider('Please select your age', 0, 100, 25)

# Button
if st.button('Click Me'):
    st.write(f'Hello, {name}! You are {age} years old.')

4. Drawing Charts

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import streamlit as st
import pandas as pd
import plotly.express as px

# Create sample data
data = pd.DataFrame({
    'Month': ['January', 'February', 'March', 'April'],
    'Sales': [100, 120, 80, 150]
})

# Draw a chart
fig = px.bar(data, x='Month', y='Sales', title='Monthly Sales Statistics')
st.plotly_chart(fig)

5. Practical Tips

Layout Tips: Use columns to achieve side-by-side display

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import streamlit as st

col1, col2 = st.columns(2)

with col1:
    st.header('Left Side Content')
    st.write('This is the content on the left.')

with col2:
    st.header('Right Side Content')
    st.write('This is the content on the right.')

Tips:

• Remember to frequently use st.help() to check the component usage instructions
• After saving the file, Streamlit will automatically detect changes and refresh the page
• Using st.cache decorator can cache data to improve application performance

Conclusion

Today we learned the basic usage of Streamlit, it really is a very friendly tool! With just a few lines of Python code, we can create professional data applications. We learned:

• The basic concepts of Streamlit
• The usage of core components
• Data display and chart drawing
• Practical layout tips

Friends, today’s Python learning journey ends here! Remember to code yourself, and feel free to ask me in the comments if you have any questions. I wish you all happy learning and continuous improvement in Python!