Basic Introduction to Streamlit

<span>Streamlit</span> is an open-source Python library for quickly building shareable data applications. It allows data scientists and researchers to easily create interactive web applications without deep knowledge of web development.

Features

• Easy to Use: Quickly start and run applications with a simple API.
• Interactivity: Easily add interactive elements like buttons, sliders, etc.
• Data Visualization: Supports various chart libraries like Matplotlib, Altair, etc.
• Caching: Smart caching features to improve application performance.
• Easy Deployment: One-click deployment to shared servers or your own server.

How to Install Streamlit

Installing<span>Streamlit</span> is very simple; you just need to run the following command in your command line interface:

pip install streamlit

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

import streamlit as st

This completes the installation and import of<span>Streamlit</span>, and you can start using it to build your interactive applications.

Streamlit’s Functional Features

<span>Streamlit</span> is an open-source Python library for quickly building shareable machine learning models and data analysis applications.

Features

• Easy to Use: Build powerful web applications with very little code.
• Strong Interactivity: Easily add interactive elements to enhance user experience.
• Automatic Updates: Application content updates automatically after code changes.
• Supports Various Data Formats: Seamless integration with various data sources and formats.
• Rich Plugins: The community provides a wealth of plugins to extend application functionality.

Basic Functions of Streamlit

Creating a Basic Interactive Interface

Using<span>st.title</span> and <span>st.text</span> functions, we can easily create a basic interactive interface.

import streamlit as st

# Set title
st.title('Welcome to Streamlit!')

# Add text
st.text('This is a simple Streamlit app.')

Input Data

<span>st.text_input</span> function allows users to input text data, which is very useful for creating interactive applications.

# User input
user_input = st.text_input('Please enter your name:')
st.write('Hello, ', user_input)

Display Data

Using<span>st.write</span>, you can easily display text, data, and charts.

# Display data
st.write('Here is a list of items:')
st.write([1, 2, 3, 4, 5])

Using DataFrame

If you are working with a Pandas DataFrame,<span>st.dataframe</span> can help you easily display it.

import pandas as pd

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

# Display DataFrame
st.dataframe(df)

File Upload

Streamlit allows users to upload files, which is very useful for data processing and analysis applications.

# File upload
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    # Read file content
    data = uploaded_file.read()
    st.write(data)

Using Charts

Streamlit supports various chart libraries like Matplotlib and Altair, making it easy to integrate charts.

import matplotlib.pyplot as plt

# Create chart
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 9, 16])

# Display chart
st.pyplot(fig)

The following is the content of the basic functions section, covering some core usages of Streamlit, providing programmers with a quick start guide.

Advanced Features of Streamlit

1. Caching

Utilizing caching can speed up the operation of data-intensive applications.

import streamlit as st
import pandas as pd

@st.cache
def load_data():
    return pd.read_csv('data.csv')

data = load_data()
st.write(data)

2. Interactive Widgets

Enhance user interaction with the application using interactive widgets.

import streamlit as st

option = st.selectbox(
    'Select an option',
    ('Option A', 'Option B', 'Option C')
)

'You selected:', option

3. Data Visualization

Combine with third-party libraries to achieve rich data visualization effects.

import streamlit as st
import matplotlib.pyplot as plt
import numpy as np

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

plt.figure()
plt.plot(x, y)
st.pyplot(plt)

4. Multi-Page Applications

Create multi-page applications for modular management of complex functionalities.

import streamlit as st

def main():
    menu = ["Home", "About"]
    choice = st.sidebar.selectbox("Select a page", menu)

    if choice == "Home":
        st.write("This is the home page")
    elif choice == "About":
        st.write("This is the about page")

if __name__ == "__main__":
    main()

5. Custom Themes

Customize<span>Streamlit</span> themes to meet personalized needs.

import streamlit as st

st.markdown("""
            <style>
                .main {
                    color: blue;
                }
            </style>
            """, unsafe_allow_html=True)

st.markdown('<p class="main">This is custom themed text</p>', unsafe_allow_html=True)

Practical Application Scenarios of Streamlit

Data Visualization Reports

In data analysis and visualization,<span>Streamlit</span> provides a concise interface to easily create interactive reports. Below is an example of using<span>Streamlit</span> and<span>pandas</span> to generate a data report:

import streamlit as st
import pandas as pd

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

# Display data
st.write(data)

# Select column
selected_column = st.selectbox('Select a column for visualization', data.columns.tolist())

# Draw chart
st.line_chart(data[selected_column])

Machine Learning Model Deployment

<span>Streamlit</span> can help developers quickly deploy machine learning models for online predictions. Below is a simple deployment example:

import streamlit as st
from sklearn.externals import joblib

# Load model
model = joblib.load('model.pkl')

# Input features
feature1 = st.number_input('Input feature 1')
feature2 = st.number_input('Input feature 2')

# Prediction
if st.button('Predict'):
    prediction = model.predict([[feature1, feature2]])
    st.write('Prediction result:', prediction)

Interactive Web Applications

Using<span>Streamlit</span>, you can quickly build interactive web applications. Below is a simple online calculator example:

import streamlit as st

# Calculator function
def calculate(operation, num1, num2):
    if operation == '+':
        return num1 + num2
    elif operation == '-':
        return num1 - num2
    elif operation == '*':
        return num1 * num2
    elif operation == '/':
        return num1 / num2

# User input
num1 = st.number_input('Input number 1')
num2 = st.number_input('Input number 2')
operation = st.selectbox('Select an operator', ['+', '-', '*', '/'])

# Calculate and display result
if st.button('Calculate'):
    result = calculate(operation, num1, num2)
    st.write('Result:', result)

The following is a detailed content of these three application scenarios:

Data Visualization Reports

This scenario demonstrates how to use<span>Streamlit</span> to quickly create an interactive data report, which is very useful for data analysts.

Machine Learning Model Deployment

In this scenario, we see how to deploy a trained machine learning model using<span>Streamlit</span> to provide online prediction functionality.

Interactive Web Applications

Finally, this scenario illustrates<span>Streamlit</span>‘s powerful capability in building simple interactive web applications, making it very suitable for rapid prototyping.

Conclusion

<span>Streamlit</span> has become the tool of choice for data scientists and developers due to its simplicity and powerful functionality. Through this article, you should be able to use<span>Streamlit</span> to quickly build interactive applications. Continuously exploring and discovering<span>Streamlit</span>‘s advanced features will help you take your projects to the next level.