This article explains how to build animated line, bar, and pie charts in Python using Matplotlib's FuncAnimation library, demonstrating data preparation, key parameters, code examples, and saving the resulting animations as GIF files.
This article provides a comprehensive guide to creating visualizations in Python using Matplotlib and related libraries, covering the three‑step workflow of problem definition, data transformation, and parameter tuning, along with detailed code examples for figures, subplots, legends, annotations, saving files, and Pandas‑based plotting.
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 compiles a comprehensive, subjectively curated collection of Python libraries for data visualization, machine learning, deep learning, AutoML, model interpretability, resource monitoring, and debugging, providing brief descriptions and links to each tool for developers and researchers.
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.
This article describes a Python Flask‑based system that crawls tourism site reviews, processes the data with pandas, stores it in MySQL, and visualizes sentiment analysis, keyword frequencies, and rating trends through interactive ECharts charts on a responsive web dashboard.
This guide demonstrates how to install common Python plotting libraries and provides ready-to-use functions for creating line, scatter, bar, stacked bar, pie, histogram, heatmap, boxplot, interactive Plotly scatter, and pandas DataFrame visualizations with example code snippets.
This article introduces fifteen highly useful Python packages, ranging from data visualization with Dash and game development with Pygame to web requests, JSON handling, progress bars, and home automation, providing concise descriptions and key features to help developers quickly discover valuable tools.
This guide demonstrates how to generate line, bar, scatter, and box charts using Matplotlib, as well as line, bar, scatter, box, heatmap, and joint plots with Seaborn, providing complete Python code snippets for each visualization type.
This article demonstrates how to read Excel data with Python's pandas library and generate a range of visualizations—including line, bar, pie, scatter, box, histogram, subplot, heatmap, jointplot, and violin plots—using matplotlib and seaborn, with step-by-step code examples and required package installations.
This article explains how to use Python's Matplotlib library and its FuncAnimation feature to build dynamic line, pie, and bar charts—demonstrated with COVID‑19 death data—while covering essential parameters, code examples, and methods for saving the animations.
This tutorial presents a comprehensive collection of 25 Matplotlib visualizations—including scatter, bubble, regression, jitter, count, marginal histograms, density, Joy, and many other chart types—each explained with concise descriptions and complete Python code snippets that demonstrate data loading, styling, annotations, and layout customization for effective data analysis.
This article introduces fifteen highly useful Python libraries—ranging from data‑visualisation with Dash to web requests, image handling, progress bars, and home automation—explaining their main features, typical use‑cases, and why they are valuable tools for both beginners and experienced programmers.
This article introduces a collection of widely used Python libraries—including Matplotlib, Pillow, NumPy, scikit-image, OpenCV, Seaborn, Plotly, Bokeh, Pycairo, and SimpleCV—detailing their core functionalities, typical use cases, and providing ready‑to‑run code examples for data visualization and computer‑vision tasks.
This article demonstrates how to create detailed terrain maps in Python by installing and importing rasterio, geopandas, cartopy, and related libraries, configuring map fonts, and executing a comprehensive script that reads vector and raster data, applies custom color maps, and renders the final visualization.
This article explains how to automatically rotate Matplotlib X‑axis labels based on their length, using the rotation property, and provides a concise implementation along with tips for asking clear programming questions in community groups.
This article walks through a common Matplotlib pie‑chart issue—displaying color legends below the chart and showing percentages outside—by presenting the problem, solution screenshots, and concise explanations to help Python users implement the fix.
This article reviews popular Python visualization libraries—Matplotlib, Seaborn, Plotly, Altair, Bokeh, and Folium—by evaluating their interactivity, syntax flexibility, and data‑type support, and provides practical code examples and pros‑cons to help beginners choose the most suitable tool for their data‑visualization needs.
This tutorial explains how to install Python, Pandas, and Matplotlib, prepare an Excel file, and then create ten different chart types—including bar, scatter, pie, line, box, area, heatmap, polar, contour, and 3D charts—using concise code examples to visualize data effectively.
This tutorial explains how to generate animated line, pie, and bar charts in Python using Matplotlib's FuncAnimation, covering required imports, data preparation, chart configuration, animation loops, and saving the resulting GIFs for effective data visualization.
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 introduces the powerful open‑source Python visualization library Plotly, showing how a single line of code can generate interactive, publication‑ready charts—from basic bar and box plots to advanced 3‑D and heatmap visualizations—while integrating seamlessly with pandas and Jupyter notebooks.
This article curates a comprehensive list of popular Python libraries for data handling, mathematics, machine learning, automated machine learning, data visualization, and model interpretation, providing brief descriptions and GitHub statistics such as stars, contributions, and contributor counts.
This article demonstrates how to use Python's requests and BeautifulSoup libraries to scrape current and 14‑day weather data from China Weather Network, store it in CSV files, and then apply pandas, numpy, and matplotlib for comprehensive visual analysis of temperature, humidity, air quality, wind direction, and climate distribution.
This tutorial shows how to fetch historical stock data using Python, compute 5‑day and 20‑day moving averages with pandas, and visualize them with matplotlib to support short‑term and medium‑term trading strategy design.
This tutorial walks through using pandas to import, review, preprocess (including integration, cleaning, transformation, handling missing and duplicate values, outlier detection, and sampling), analyze (descriptive statistics and correlation), and visualize e‑commerce data with Python, providing practical code examples for each step.
This article walks through the technical implementation of high‑performance multidimensional tables—covering data preparation, layout calculation, rendering optimizations, interaction handling, and irregular cell merging—demonstrating how AntV engineers built a flexible Canvas‑based solution for complex data analysis.
This article explains what multidimensional tables are, why they outperform simple charts, and how thoughtful design—using visual encoding, statistical summaries, and interactive components—turns dense data grids into intuitive, actionable analytics tools for diverse users.
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 demonstrates how to fetch historical stock data with pandas_datareader, calculate 5‑day and 20‑day moving averages using pandas, and visualize the price series together with these averages in a Matplotlib chart to illustrate simple trading‑strategy concepts.
This tutorial walks you through creating a fully interactive sales‑data dashboard using Python's Pandas for data handling, Plotly for visualizations, and Streamlit to assemble a sleek web interface, covering data import, sidebar filters, key metrics, charts, and deployment steps.
This report describes a Python‑based web application built with Flask that scrapes hot‑search data from Weibo, Baidu and Zhihu, processes it using jieba and other libraries, and visualizes the results together with domestic COVID‑19 statistics using ECharts on a responsive front‑end page.
This guide shows how to fetch financial time‑series data from Quandl using Python, store it in pandas, explore it with head/tail, and create line, volume, and candlestick visualizations with matplotlib, including code snippets and best practices for API keys and date handling.
This article introduces ten Python visualization libraries—matplotlib, Seaborn, ggplot, Bokeh, pygal, Plotly, geoplotlib, Gleam, missingno, and Leather—detailing their origins, key features, strengths, and typical use cases for creating static and interactive charts across various domains.
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 article introduces several powerful Python data‑visualization libraries—including Plotly, Cufflinks, Folium, Altair, and the JavaScript‑based D3.js—explains their strengths, provides installation commands, and offers practical code examples for creating interactive charts, maps, and 3D visualizations within Jupyter notebooks.
This article provides a step‑by‑step guide to using Matplotlib for creating and customizing plots in Python, covering how to add titles, text, annotations, axis labels, legends, colors, markers, grids, axis limits, dual axes, filled areas, patches, and style themes, complete with runnable code examples and visual results.
This tutorial explains how to create, read, and write JSON files using Python's json module, load them into Pandas dataframes, perform data manipulation, rename columns, save to CSV, fetch JSON from URLs, and visualize the data with Seaborn, providing code examples and visual illustrations.
Discover 15 essential Python packages—from data‑visualization tools like Dash to web frameworks such as Flask, multimedia libraries like Pygame, and utilities for JSON handling, progress bars, and home automation—each explained with key features, typical use‑cases, and illustrative code snippets.
This tutorial explains how to retrieve live aircraft positions from the OpenSky Network via its REST API, process the data with Python libraries such as requests, pandas and NumPy, convert coordinates, and visualise the results on an interactive Bokeh map that updates every few seconds in a web browser.
This article demonstrates how to crawl Douban short reviews for the TV show "Actors Please Take Your Place" Season 2, clean and deduplicate the data, apply Baidu's SKEP sentiment model, and visualize word clouds, rating distributions, posting times, and sentiment scores, providing full Python code for replication.
This article introduces a straightforward method for producing animated line, bar, and pie charts in Python using Matplotlib's FuncAnimation, guiding readers through required imports, data preparation, chart parameterization, animation setup, and saving the resulting GIFs.
The author describes encountering a Python 2.7 UnicodeEncodeError when Plotly attempts to open a browser for each generated time‑series chart, explains why the error occurs with non‑ASCII filenames, and shows how adding exception handling resolves the issue while processing weather data for thousands of cities.
The author records API request durations in a MySQL table, then uses Python, pandas, and Plotly to fetch the data, filter out outliers, and generate a violin chart that clearly visualizes the distribution of response times for multiple endpoints.
This article demonstrates how to retrieve data from a MySQL database using Python, process it, and create an interactive Plotly chart saved as an HTML file, accompanied by code examples and a brief showcase of the resulting visualization.
This article demonstrates how to scrape the full 2018 Chinese variety‑show list from Douban using Python Selenium and BeautifulSoup, compile detailed metadata and actor information into Excel, and then analyze popularity rankings, rating distributions, frequent celebrity appearances, and common negative feedback.
Learn step-by-step how to use Python’s itchat library to extract your WeChat contacts’ province and city information, handle QR code authentication, parse the data, and create visual maps of friend distribution, all illustrated with screenshots and code snippets.
This article introduces seven relatively unknown Python libraries—including Arrow, TensorFlow, Zappa, Peewee, Sanic with uvloop, Bokeh, and Blaze—highlighting their key features and why they attracted developers' attention in 2018, offering useful alternatives to more popular tools.
This tutorial demonstrates how to use Python's itchat library to extract WeChat friends' signatures, save them to a file, and then generate a word‑cloud visualization with custom background and font, guiding readers through each step from data collection to visual output.
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.
This article documents a Python‑based web‑scraping project that harvested over 1.38 million Zhihu followers, filtered high‑impact users, and visualized insights such as follower distribution, gender ratio, top influencers, geographic spread, education, industry, and certification details, highlighting challenges and lessons learned.
