Introduction to Streamlit

<span>Streamlit</span> is an open-source Python library designed to help data scientists and programmers quickly build powerful interactive applications. It simplifies the process of building data applications, allowing developers to focus on core logic without worrying about UI design.

Features

• Easy to Use: Build complex interactive applications easily with a minimalist API.
• Fast Iteration: The application automatically reloads after code changes without needing a manual restart.
• Data Visualization: Integrates multiple chart libraries for easy data visualization.
• Highly Interactive: Supports various interactive controls to enhance user experience.
• Easy Deployment: One-click deployment to servers or cloud platforms.

How to Install Streamlit

Installing<span>Streamlit</span> is very simple; you just need to use the pip command in the command line. Here are the specific steps to install and import<span>Streamlit</span>:

# Use pip to install Streamlit
pip install streamlit

Once installed, you can import<span>Streamlit</span> in your Python script like this:

import streamlit as st

Now you have successfully installed and imported<span>Streamlit</span>, and you can start using it to build your applications.

Features of Streamlit

<span>Streamlit</span> is an open-source Python library for quickly building simple yet powerful data applications.

Features

• Easy to Use: Build a data application in just a few lines of code.
• Highly Interactive: Supports rich user interaction features such as sliders, dropdown menus, etc.
• Hot Reloading: See updates in the browser immediately after code changes.
• Supports Multiple Data Sources: Easily integrate data visualization libraries like<span>matplotlib</span>, <span>Altair</span>, etc.
• Convenient Deployment: Quickly deploy to servers or platforms like<span>Streamlit Sharing</span>.

Basic Features of Streamlit

Text Display

In <span>Streamlit</span>, you can easily display text information. Use the <span>st.write()</span> function to display text, and it automatically handles Markdown syntax.

import streamlit as st

# Display normal text
st.write('Hello, Streamlit!')

# Display Markdown text
st.write('# This is a heading')
st.write('This is a **bold** text')

Data Display

<span>Streamlit</span> supports displaying various data types, such as tables and charts. Use <span>st.dataframe()</span> to display DataFrame types of data.

import pandas as pd
import streamlit as st

# Create a DataFrame
df = pd.DataFrame({
    'first column': [1, 2, 3, 4],
    'second column': [10, 20, 30, 40]
})

# Display DataFrame
st.dataframe(df)

Input Controls

<span>Streamlit</span> provides various input controls, such as text input boxes and selection boxes. Here is an example of a text input box:

import streamlit as st

# Create a text input box
name = st.text_input('Please enter your name')

# Display the entered name
st.write('Your name is:', name)

Buttons and Interactivity

Using the <span>Streamlit</span> button, you can create interactive applications. Here is an example of a button click event:

import streamlit as st

# Create a button
if st.button('Click me'):
    st.write('Button was clicked!')

File Upload

<span>Streamlit</span> supports file upload functionality, making it easy to handle files uploaded by users.

import streamlit as st

# Create a file uploader component
uploaded_file = st.file_uploader("Choose a file to upload", type=['txt', 'csv'])

# Read and display file content
if uploaded_file is not None:
    st.write(uploaded_file.read())

From the introduction of these basic features, you can see the convenience and efficiency of<span>Streamlit</span> in building data applications. In the following chapters, we will delve into more advanced features and application scenarios.

Advanced Features of Streamlit

1. Caching Data

In<span>Streamlit</span>, you can use the <span>@st.cache</span> decorator to cache data or computations to improve application performance.

import streamlit as st

@st.cache
def load_data():
    # Assume there is a time-consuming data loading process here
    return "Loaded data"

data = load_data()
st.write(data)

2. Interactive Widgets

<span>Streamlit</span> offers various interactive widgets, such as sliders and selection boxes, allowing users to interact with data in real-time.

# Slider example
age = st.slider('How old are you?', 0, 130, 25)
st.write("I'm ", age, 'years old')

# Selection box example
option = st.selectbox(
    'Which number do you like best?',
     [1, 2, 3, 4, 5])

st.write('You selected:', option)

3. Data Visualization

<span>Streamlit</span> supports various data visualization libraries, such as matplotlib and seaborn, allowing you to easily display charts in your application.

import matplotlib.pyplot as plt
import numpy as np

# Generate data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create chart
plt.figure()
plt.plot(x, y)

# Display chart
st.pyplot(plt)

4. Generate Reports

Using<span>Streamlit</span>, you can easily generate reports from data analysis and results, supporting downloads in various formats.

# Import report generation library
from streamlit.report_thread import get_report_ctx

# Get current report context
session = get_report_ctx()

# Generate report
st.download_button(
    label="Download report",
    data=session.get_report(),
    file_name='report.md',
    mime='text/plain'
)

5. Multi-page Applications

<span>Streamlit</span> supports creating multi-page applications, allowing page switching through<span>st.sidebar</span>.

# Add page selector in the sidebar
page = st.sidebar.selectbox("Choose a page", ["Page 1", "Page 2"])

# Display corresponding page based on user selection
if page == "Page 1":
    st.write("This is Page 1")
elif page == "Page 2":
    st.write("This is Page 2")

The above are parts of advanced features, providing programmers with more capabilities to extend and optimize applications. Here are the specific content and code examples.

Practical Application Scenarios of Streamlit

Data Visualization Reports

In data analysis and visualization, <span>Streamlit</span> provides a simple way to create interactive reports. Here is an example of creating a data visualization report using<span>Streamlit</span> and<span>pandas</span>:

import streamlit as st
import pandas as pd

# Load data
data = pd.read_csv('data.csv')

# Display data
st.write(data)

# Create interactive chart
st.bar_chart(data['sales'])

Interactive Web Applications

Using<span>Streamlit</span>, you can easily build web applications with interactive features. Here is a simple interactive web application where users can choose different options through a dropdown menu:

import streamlit as st

# Create dropdown menu
option = st.selectbox('Choose an option', ['Option A', 'Option B', 'Option C'])

# Display content based on the option
if option == 'Option A':
    st.write('You selected Option A')
elif option == 'Option B':
    st.write('You selected Option B')
else:
    st.write('You selected Option C')

Machine Learning Model Deployment

<span>Streamlit</span> is very suitable for deploying machine learning models. Here is an example of deploying a simple linear regression model using<span>Streamlit</span>:

import streamlit as st
import numpy as np
from sklearn.linear_model import LinearRegression

# Train model
model = LinearRegression()
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 2, 2.5, 4, 5])
model.fit(X, y)

# Create input box
input_value = st.number_input('Enter a number')

# Make prediction using the model
prediction = model.predict([[input_value]])

# Display prediction result
st.write('Prediction result:', prediction[0])

Real-time Data Analysis

In scenarios requiring real-time data analysis, <span>Streamlit</span> also performs excellently. Here is an example of displaying data changes in real-time:

import streamlit as st
import time

# Create a list to store data
data_list = []

# Add a button to add data
if st.button('Add Data'):
    data_list.append(time.ctime())

# Display data list
st.write(data_list)

These application scenarios demonstrate the versatility and ease of use of<span>Streamlit</span>. Whether for data visualization, web application development, or machine learning model deployment, <span>Streamlit</span> provides strong support.

Conclusion

Through this introduction, I believe everyone has gained a deeper understanding of the powerful features of<span>Streamlit</span>. Whether basic features or advanced capabilities, <span>Streamlit</span> helps us easily build interactive applications. Let’s explore the infinite possibilities of<span>Streamlit</span> together and make our development work more efficient.