1 Integrating with Other Libraries Explained

Key Concepts

• Library Integration: Incorporating external libraries into your Streamlit app.
• Data Processing Libraries: Libraries like Pandas and NumPy for data manipulation.
• Visualization Libraries: Libraries like Matplotlib and Plotly for creating visualizations.
• Machine Learning Libraries: Libraries like Scikit-learn and TensorFlow for building models.
• APIs and Web Scraping: Using libraries like Requests and BeautifulSoup to interact with external data sources.

Library Integration

Integrating external libraries into your Streamlit app allows you to leverage existing tools and functionalities. This can significantly enhance the capabilities of your application.

Data Processing Libraries

Data processing libraries like Pandas and NumPy are essential for manipulating and analyzing data. These libraries provide powerful functions for data cleaning, transformation, and analysis.

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

data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'score': [85, 90, 95]
}

df = pd.DataFrame(data)
st.write(df)
    

Visualization Libraries

Visualization libraries like Matplotlib and Plotly enable you to create interactive and informative visualizations. These visualizations can help users understand complex data more easily.

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

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

fig, ax = plt.subplots()
ax.plot(x, y)
st.pyplot(fig)
    

Machine Learning Libraries

Machine learning libraries like Scikit-learn and TensorFlow allow you to build and deploy predictive models. These libraries provide a wide range of algorithms and tools for model training and evaluation.

import streamlit as st
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

model = RandomForestClassifier()
model.fit(X_train, y_train)

st.write("Model Accuracy:", model.score(X_test, y_test))
    

APIs and Web Scraping

Libraries like Requests and BeautifulSoup enable you to interact with external data sources through APIs or web scraping. This allows your Streamlit app to fetch and display real-time data.

import streamlit as st
import requests
from bs4 import BeautifulSoup

url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

st.write("Web Page Title:", soup.title.string)
    

Analogies

Think of integrating other libraries into your Streamlit app as adding specialized tools to your toolbox. Data processing libraries are like precision screwdrivers for handling data. Visualization libraries are like colorful paintbrushes for creating visual masterpieces. Machine learning libraries are like advanced calculators for predicting the future. APIs and web scraping libraries are like magic wands for fetching data from the internet.

By mastering the integration of other libraries into your Streamlit app, you can create powerful and versatile applications that can handle a wide range of tasks and data sources.