If you’ve ever thought about quickly building a data application or visualization tool, you might be intimidated by those frameworks that look complex and cumbersome, requiring a bunch of complicated configurations and even advanced front-end knowledge. But with Streamlit, all these problems are solved. Streamlit’s slogan is: Build data applications with Python, simple, fast, and … Read more
Streamlit: Visualize Data Easily and Build Your First Web App in 30 Minutes! Today, I want to introduce a particularly useful Python library – Streamlit. Are you still worried about presenting your data analysis results? Are you still feeling overwhelmed by building web applications? With Streamlit, you’ll find that everything becomes super easy! I will … Read more
Hey, friends! Today I want to introduce you to a super cool Python library – Streamlit! This thing is simply a “magic wand” in the world of data application development, waving it can conjure up beautiful web applications, it’s just too awesome! 🎩✨ I still remember the first time I encountered Streamlit; it felt like … Read more
Hello everyone, I am Liang Ge. Today, I will take you into a very interesting and powerful Python library—Streamlit. It helps developers quickly build and deploy data visualization applications, making complex analytical results simple and understandable. Next, I will explain the charm of Streamlit in detail through six parts: background introduction, installation guide, basic usage, … Read more
Streamlit: The Lightning Builder for Data Applications! Hello everyone, I am Yan Yu. Today we are going to explore Streamlit, a powerful tool that allows you to quickly create data science and machine learning applications. Whether you are a beginner in Python or an experienced developer, Streamlit can help you easily turn your data analysis … Read more
# Streamlit: A Python Library for Rapid Application Development Hello everyone! Today we are going to learn about Streamlit, a Python library that makes building data applications fast and fun. Whether you are a data scientist, analyst, or a beginner, Streamlit can help you easily create interactive web applications. With just a few lines of … Read more
Python Algorithm Implementation from scipy import stats from pylab import * def knnClassify(Target_feature, dataSet, p, k): dataSet_x = dataSet[:, 0:-1] dataSet_y = dataSet[:, -1] L_p = [] for i in range(len(dataSet_x)): l_p = 0 for x_i in range(len(dataSet_x[i])): l_p = (abs(dataSet_x[i][x_i] – Target_feature[x_i]))**p+l_p l_p =math.pow(l_p, 1/p) L_p.append(l_p) L_p = np.array(L_p) y_i = L_p.argsort() y = … Read more
KNN Practical Application – Improving Matchmaking Effectiveness Introduction In simple terms, the KNN algorithm classifies data by measuring the distances between different feature values. The working principle is that there exists a sample dataset, namely the training dataset, and each sample in the dataset has a label, which indicates the correspondence between each data point … Read more