This article details how to create a Python‑based weather data analysis and visualization platform using Flask for the backend, PyEcharts for interactive charts, and web scraping to fetch live meteorological data, covering architecture, core features, and deployment steps.
This article walks through a follower's Pyecharts map visualization problem, explains why the original code failed, and provides a corrected Python script that successfully renders COVID‑19 data maps, complete with screenshots and full code examples.
This tutorial demonstrates how to generate a wide range of 2D and 3D charts—including line, bar, scatter, pie, map, word cloud, gauge, funnel, tree, parallel, sankey, geo, timeline, and 3D visualizations—using the Python library pyecharts, providing complete code snippets and rendering instructions for each chart type.
This article presents a Python‑based project that continuously crawls Weibo hot‑search data, stores it with timestamps, and visualizes trends through dynamic bar, line, and word‑cloud charts using libraries such as BeautifulSoup, pandas, schedule, pyecharts, and jieba.
This guide explains why Pyecharts V0.5 and V1 are incompatible, and provides clear step‑by‑step pip commands to upgrade from the older version to V1 or downgrade from V1 to V0.5, including necessary map‑dependency installations to ensure proper chart rendering.
This article explains why two seemingly identical datasets generate different Pyecharts maps, reveals that the discrepancy is caused by NumPy integer types, and shows how converting them to native Python ints restores the correct visual output.
In this post a Python enthusiast shares a real‑world troubleshooting session where a pyecharts chart fails to display in Jupyter, discusses common causes such as missing git or offline resources, and provides a concrete fix by setting the online host URL via CurrentConfig, along with practical tips for future queries.
Learn how to fetch real‑time COVID‑19 data from Baidu, then use Python’s pyecharts library to build province‑level maps and a dynamic timeline carousel, complete with code snippets and visual results for new, existing, cumulative, recovered, and death cases.
This article demonstrates how to scrape a news webpage with Python, extract and segment its Chinese text using jieba, count word frequencies, and visualize the top ten terms as a word cloud and a pie chart with pyecharts.
This tutorial explains how to scrape a news article with Python, segment Chinese text, count word frequencies, and visualize the top ten words using a word cloud and a pie chart, providing complete code and sample results.
This article demonstrates how to collect, clean, and visualize Taobao mooncake sales data using Python libraries such as Pandas, Pyecharts, jieba and collections, revealing top‑selling flavors, regional distribution, price ranges and shop rankings through step‑by‑step data‑processing and charting techniques.
This article walks through a follower's Pyecharts map problem caused by improper province name handling, presents the original and revised Python code, and shows how correcting the data preprocessing resolves the missing visualization, providing a clear solution for Python developers.
This article surveys twelve widely used Python visualization libraries, dividing them into exploratory and interactive categories, and explains each library's strengths, typical use cases, and key features to help developers choose the right tool for their data analysis needs.
This tutorial explains what Sankey diagrams are, shows their main forms, and provides step‑by‑step Python code using Matplotlib and Pyecharts to generate both basic and advanced Sankey visualizations, including tips on data preparation and styling.
This guide explains why Pyecharts V0.5 and V1 are incompatible, shows how to upgrade from V0.5 to V1 or downgrade from V1 to V0.5 with pip commands, and highlights the need for additional map packages to render geographic charts correctly.
This tutorial introduces pyecharts, the Python wrapper for Baidu's Echarts library, explains how it generates a render.html file, and provides complete code examples for drawing various map visualizations—including basic maps, maps without labels, visual‑mapped maps, world maps, and regional maps—while also showing how to replace the dummy data with your own dataset.
This tutorial shows how to fetch real‑time COVID‑19 data from Baidu, then use Python's pyecharts library to build province‑level maps and a dynamic timeline carousel, complete with code snippets and visual results.
This tutorial explains how to use the pyecharts library—a Python wrapper for Baidu's ECharts—to generate a wide range of interactive visualizations, including bar, line, pie, map, heatmap, scatter, 3D, and combined charts, with complete code examples and rendering instructions for Jupyter notebooks.
This tutorial introduces the Python pyecharts library for creating various map visualizations with Echarts, explains how the code generates HTML files, and provides complete example functions for basic, label‑less, continuous and piecewise visual maps, world and regional maps, plus guidance for using real data.
This article demonstrates how to use Python's requests and BeautifulSoup to crawl text from a news site, process it with collections, numpy, and jieba for word‑frequency analysis, and then visualize the top terms using pyecharts, providing complete code snippets and explanations.
This article demonstrates how to scrape cat breed information from a dedicated website, store the data in Excel, and perform comprehensive analysis and visualizations—including relationship graphs, geographic distribution, size ratios, price extremes, and word clouds—using Python libraries such as requests, lxml, pandas, pyecharts, and stylecloud.
This article explains why passing pandas data directly to Pyecharts results in blank charts due to NumPy data types, and shows how converting pandas Series to Python lists with .tolist() resolves the issue, providing corrected example code.
This tutorial shows how to use Python to crawl Douban's Top250 movie list, handle anti‑scraping measures, extract detailed fields, store the data in Excel, and perform data cleaning, statistical analysis, and visualizations such as year distribution, rating trends, and genre word clouds.
This tutorial demonstrates how to scrape Maoyan movie comments for "The Battle at Lake Changjin", clean and store the data, perform comprehensive visual analyses such as likes, city, gender, watch status, rating, user level, and creator mentions, and finally predict next‑day box office using linear regression with sklearn.
Learn step‑by‑step how to crawl 46,000+ rental listings from Ziroom using Python, extract house details with regex, clean and transform the data with pandas, and visualize distribution, pricing and location insights through pyecharts, matplotlib and seaborn, revealing rental market patterns in Beijing.
This tutorial demonstrates how to use the powerful pyecharts library to generate eye‑catching 2D maps, a province‑level map of Guizhou, and a rotating globe, all rendered as interactive HTML pages that can be viewed in a browser.
This article demonstrates how to use the Python pyecharts library to generate impressive visualizations, including a basic 2D map, a detailed map of Guizhou province, and an interactive rotating globe, with step-by-step code examples and screenshots for each visualization.
This tutorial shows how to use Python libraries such as requests, lxml, pandas and pyecharts to crawl the latest COVID‑19 statistics from Baidu, store them in an Excel file, and create interactive province‑level maps that illustrate confirmed, death and recovery numbers.
This tutorial explains how to convert a physical address into latitude‑longitude coordinates using the Baidu Maps Geocoding API and then visualize the point on a map with Python's pyecharts library, including code snippets and step‑by‑step instructions.
This article walks through installing pyecharts, explains its version differences, and demonstrates a series of bar‑chart techniques—including basic charts, axis labeling, multiple series, styling, horizontal orientation, mark lines/points, label rotation, and interactive zoom—complete with code snippets and visual examples.
This tutorial shows how to use Python's pyecharts library to create geographic visualizations of Chinese provincial GDP, covering single‑year maps, piecewise color segmentation, and animated timelines for multiple years, with full code examples and data preparation steps.
In this article we demonstrate how to use Python’s requests and BeautifulSoup to scrape a static webpage listing the world’s top 100 most beautiful women, process the data with pandas and pyecharts for continent, nationality and profession statistics, and apply Baidu’s face‑recognition API to score each celebrity’s attractiveness.
Using publicly available statistics from the National Bureau of Statistics, this article visualizes and analyzes China’s divorce rates over the past decade, comparing national trends, provincial variations, and city-specific patterns such as Beijing versus Shanghai, revealing a steady increase and regional disparities.
This guide walks through generating word clouds from JD product reviews using Python, covering preprocessing, tokenization, frequency analysis, and visual creation with the stylecloud, wordcloud, and pyecharts libraries, including mask image selection, palette customization, and full code snippets for each method.
This article demonstrates how to use Python to crawl 3,032 Chinese recipe entries from Douguo.com, clean the data with Pandas, and create insightful visualizations—including rating distributions, cuisine comparisons, and ingredient word clouds—using pyecharts, providing complete code snippets and analysis of the results.
This article compares five popular Python visualization libraries—Matplotlib, Pyecharts, Seaborn, Plotly, and Bokeh—by drawing identical multi‑series bar charts, evaluating interactivity, aesthetics, code complexity, documentation quality, and assigning a subjective score to each.
This tutorial demonstrates how to convert PDF admission lists into Excel, extract and filter candidate information using Python, compute score segment statistics, and generate bar and pie charts with pyecharts for clear visual insight into graduate enrollment data.
This guide demonstrates how to install PyEcharts, import necessary modules, and use the Liquid chart class to create customizable waterball charts in Python, including setting colors, shapes such as rectangle, diamond, round‑rect, triangle, and arrow, and rendering them to HTML files.
This guide walks you through using Python to crawl proxy server data, extract city names with the cpca library, count occurrences per city, and create an interactive heat‑map of nationwide proxy distribution using the Pyecharts visualization toolkit.
This article walks through the complete process of designing, implementing, and visualizing a Python web crawler that extracts tourism hotspot data from ticketing sites for China's 2019 National Day holiday, covering requirement analysis, URL and element inspection, data collection, cleaning, and geographic heat‑map presentation using Pyecharts.
This tutorial walks you through using Python to collect tourism data from Qunar, extract key fields such as name, price, and rating, store the results in Excel with pandas, and visualize sales and popularity trends using pyecharts, including a simple recommendation algorithm.
This article walks through collecting Python job postings from Lagou by handling anti‑scraping measures, parsing POST requests, storing results in Excel, and then using pyecharts to create bar, map, and pie visualizations that reveal city distribution, salary ranges, and experience requirements.
This tutorial introduces pyecharts, a Python library that integrates with ECharts, covering installation via pip, handling network issues with a Tsinghua mirror, and detailed code examples for creating bar, pie, boxplot, line, radar, scatter, grid, and overlapping charts to visualize monthly precipitation and evaporation data.
The article walks through a full Python workflow that scrapes up to 500 Douban movie reviews for "Dying to Survive" and "Hidden Blade," cleans and stores them in pandas, performs SnowNLP sentiment analysis, and visualizes city distribution, rating trends, and word clouds with pyecharts.
This tutorial demonstrates how to collect Python job postings from Lagou using Python's requests library, process the JSON response with pandas, and create insightful visualizations—including bar charts, word clouds, and geographic heatmaps—while handling anti‑scraping measures and data cleaning steps.
This tutorial shows how to capture hot comments from NetEase Cloud Music using Python web‑scraping techniques, handle the required encryption parameters, and then transform the data into bar charts and word clouds with pyecharts and WordCloud for clear visual insight.
This guide walks you through extracting Python job listings from Lagou.com with Python's requests library, parsing the JSON response, storing the results in CSV, and visualizing key insights such as education requirements, work experience, salary distribution, word clouds, and geographic salary heatmaps using pandas, matplotlib, and pyecharts.
