Streamlit: A Rapid Development Tool for Visualization in Python!

# Streamlit: A Rapid Development Tool for Visualization in Python!
Hello everyone! Today I want to introduce a super user-friendly Python web application framework: Streamlit. It’s like a magical wand that allows us to quickly create beautiful data applications with simple Python code. While other frameworks require you to master various skills in front-end and back-end, Streamlit is a tool that lets you achieve full-stack development using only Python. Today, I will take you on a journey to explore this interesting tool!
## Getting Started
### 1. First Streamlit Application
```python
# Import necessary libraries
import streamlit as st
import pandas as pd
import numpy as np

# Add title and text
st.title('My First Streamlit Application')
st.write('Hello, Streamlit! 👋')

# Display data
df = pd.DataFrame({
    'Name': ['Zhang San', 'Li Si', 'Wang Wu'],
    'Age': [25, 30, 28]})
st.dataframe(df)  # Display interactive data table

# Add various input controls
name = st.text_input('Please enter your name')
age = st.slider('Select your age', 0, 100, 25)
favorite = st.multiselect(
    'Choose your favorite programming languages',
    ['Python', 'Java', 'JavaScript', 'C++'])

# Display user input
if name:
    st.write(f'Hello, {name}!')
st.write(f'Your selected age is: {age} years old')
st.write('Your favorite programming languages:', favorite)

# Create sample data
import plotly.express as px
chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['A', 'B', 'C'])

# Use different chart types
st.line_chart(chart_data)  # Line chart
st.area_chart(chart_data)  # Area chart
st.bar_chart(chart_data)   # Bar chart

# Use Plotly to draw interactive charts
fig = px.scatter(chart_data, x='A', y='B',
                 color='C', size='C')
st.plotly_chart(fig)

# Use column layout
col1, col2 = st.columns(2)
with col1:
    st.header('Left Side Content')
    st.write('This is the left column')
with col2:
    st.header('Right Side Content')
    st.write('This is the right column')

# Use tabs
tab1, tab2 = st.tabs(['Chart 1', 'Chart 2'])
with tab1:
    st.line_chart(chart_data)
with tab2:
    st.bar_chart(chart_data)

# File upload
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
    data = pd.read_csv(uploaded_file)
    st.write(data)

# File download
if st.button('Download Sample Data'):
    csv = chart_data.to_csv(index=False)
    st.download_button(
        label="Download CSV file",
        data=csv,
        file_name='data.csv',
        mime='text/csv'
    )

# Cache data computation results
@st.cache_data  
def load_large_dataset():
    # Simulate loading a large dataset
    data = pd.DataFrame(
        np.random.randn(1000, 5),
        columns=['A', 'B', 'C', 'D', 'E']
    )
    return data

# Use cached data
data = load_large_dataset()
st.write('Preview of the large dataset:', data.head())