Hello everyone, I am Ze’an, here to introduce a super powerful Python library—Streamlit.
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What is Streamlit
Streamlit is a Python library for creating data applications. Its design philosophy is “fast, intuitive, and fun,” aimed at enabling developers to quickly transform data into interactive charts and dashboards. With Streamlit, you can easily create various data visualizations, such as bar charts, line charts, scatter plots, etc., and you can also add text, images, forms, and other elements to make your data applications more colorful.
Another highlight of Streamlit is its “zero configuration” feature. You don’t need to go through cumbersome configurations; with just a few lines of code, you can quickly build a runnable data application. This makes Streamlit very suitable for developers who want to quickly get started with data visualization.
In summary, Streamlit is a powerful and easy-to-use data application framework that can make your data lively and interesting, helping you better understand and analyze data.
How to Install or Import Streamlit
Installing Streamlit
To use Streamlit in Python, you first need to ensure that you have a Python environment installed. Streamlit can be installed via Python’s package manager pip. Open the terminal or command prompt and enter the following command:
pip install streamlit
If you are using Anaconda or Miniconda, you can use the following command:
conda install streamlit
Wait for the installation to complete, which may take a few minutes.
Importing Streamlit
After installing Streamlit, you can use it by importing the Streamlit module in your Python script or Jupyter Notebook. At the top of your code file, add the following line:
import streamlit as st
This completes the import of Streamlit, and you can start writing your Streamlit application.
Running Streamlit Applications
To run a Streamlit application, you need a <span>.py</span> file that contains the logic of the Streamlit application. Run this file from the command line, and Streamlit will automatically start a server and open your default browser to display your application interface.
Run the following command in the command line:
streamlit run your_app.py
Replace <span>your_app.py</span> with the name of your Streamlit application file. After the first run, it will prompt you to open the browser and provide a URL. Open this URL in the browser, and you will see your Streamlit application.
The above steps outline the basic process of installing and importing Streamlit. Next, let’s see how to build a simple application using Streamlit.
Streamlit Usage Example
Streamlit is a Python library for quickly creating data applications. It allows you to build interactive data visualizations, machine learning models, and dashboards with minimal code.
Below we will demonstrate the basic usage of Streamlit through a simple example. This example will create an application that showcases a random number generator.
First, you need to install Streamlit. In your command line tool, run the following command to install it:
pip install streamlit
Once the installation is complete, you can create a new Python file, such as <span>random_number_generator.py</span>, and write the following code in it:
import streamlit as st
import random
# Create a text label
st.title('Random Number Generator')
# Create a slider to select the range of random numbers
min_value = st.slider('Minimum value', min_value=0, max_value=100)
max_value = st.slider('Maximum value', min_value=0, max_value=100)
# Generate a random number and display it on the dashboard
random_number = random.randint(min_value, max_value)
st.write(f'The random number is: {random_number}')
In the code above, <span>st.title()</span> is used to add a title, <span>st.slider()</span> creates a slider that can slide within a specified range, allowing users to select the range for the random number. <span>st.write()</span> is used to output information on the page.
After saving the file, run the following command in the command line to start the Streamlit application:
streamlit run random_number_generator.py
This will automatically open your default browser and display the application you just created. You can generate different random numbers by adjusting the slider, and the results will update in real-time.
The power of Streamlit lies in its simplicity and interactivity, which allows even non-professional front-end developers to quickly create feature-rich data applications. With Streamlit, you can easily combine various functions such as data analysis and machine learning models to build a wide range of dashboards for demonstration, analysis, and decision support.
Streamlit Application Scenarios
Streamlit is a Python library for quickly creating data applications. It enables developers to build interactive data visualization tools and machine learning model interfaces with a very low barrier to entry. Here are some common application scenarios:
Data Visualization
Data analysts and scientists often need to visually present datasets to decision-makers without a technical background. Using Streamlit, they can quickly create interactive charts and dashboards, such as:
import streamlit as st
st.title('Data Visualization Example')
df = st.data_frame({
'Column1': [1, 2, 3],
'Column2': [4, 5, 6]
})
st.bar_chart(df['Column1'])
st.line_chart(df)
Machine Learning Model Demonstration
Machine learning engineers can easily create an interactive model demonstration interface with Streamlit, allowing users to input features through the UI and get the model’s prediction results, such as:
import streamlit as st
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
iris = load_iris()
X = iris.data
y = iris.target
model = RandomForestClassifier()
model.fit(X, y)
st.title('Machine Learning Model Demonstration')
user_input = st.slider('Petal Length', 0, 3)
prediction = model.predict([[user_input]])
st.write(f"Prediction Result: {prediction[0]}")
Real-time Data Stream Processing
Streamlit is also suitable for building applications that require real-time processing and display of data streams, such as stock price tracking, sensor data analysis, etc.:
import streamlit as st
import time
st.title('Real-time Data Stream Processing')
for i in range(5):
# Assume this is data retrieved in real-time from some data source
current_value = i * 2
st.write(f"Current Value (updated every second): {current_value}")
time.sleep(1)
Educational and Training Tools
Educators and students can use Streamlit to create interactive teaching tools and review materials, such as:
import streamlit as st
st.title('Interactive Math Problem')
num1 = st.number_input('Enter the first number')
num2 = st.number_input('Enter the second number')
result = num1 + num2
st.write(f"Result: {result}")
Rapid Prototyping
During the rapid prototyping phase, developers can use Streamlit to quickly combine different functional modules and validate ideas:
import streamlit as st
st.title('Rapid Prototyping')
if st.button('Click Me'):
st.write('Button clicked!')
These scenarios demonstrate the flexibility and powerful features of Streamlit, enabling Python developers to quickly build beautiful and practical data applications.
Conclusion
In summary, Streamlit is a powerful tool in the Python ecosystem that makes data application development more efficient and fun. For developers looking to turn data analysis into interactive experiences, Streamlit is a choice worth exploring.
