This talk by Baixin Bank's BI leader outlines the bank's business model, multi-dimensional data analysis requirements, and the design of a laddered analysis solution, including indicator and analysis system construction, user‑product‑enterprise scenario modeling, and productization of data insights for operational decision‑making.
This article walks through a real‑world Python project that uses Selenium to crawl the first ten pages of Taobao snack listings, extracts sales, price and location data, visualizes price distribution and geographic concentration, generates a word‑cloud of top user comments, and lists the top‑selling stores, providing full source code for replication.
This article introduces Plotly Express, a high‑level Python visualization library, covering installation, built‑in datasets, color palettes, themes, and step‑by‑step examples for bar, scatter, bubble, matrix, area, line, pie, sunburst, funnel, 3D, map, and polar charts, demonstrating how to generate dynamic, publication‑ready plots with just a few lines of code.
This article presents a comprehensive guide to essential Pandas operations, including creating Series and DataFrames, common methods for data selection, indexing, grouping, reading and writing files, handling missing values, sorting, statistical analysis, and data transformation, with practical code examples for each feature.
This guide walks you through handling textual data with pandas, covering basic and new string dtypes, essential string methods for formatting, alignment, counting, encoding, and advanced operations such as splitting, replacing, concatenating, matching, and extracting patterns, all illustrated with clear code examples.
This article explains why A/B testing is essential, illustrates real-world examples from ByteDance, details the multi-layer architecture of the Volcano Engine A/B testing system, outlines experiment design, implementation, statistical analysis, best practices, and future trends, providing a comprehensive guide for product teams.
This article presents ten diverse Python project concepts—including voice‑controlled GUIs, AI betting and trading bots, a virtual assistant, concert monitoring, automatic SSL renewal, facial recognition, contact tracing, file organization, and YouTube career‑path aggregation—to help developers practice and expand their programming and artificial‑intelligence expertise.
Using Python and pandas, this article collects and analyzes 97,331 danmu comments from the first episode of Mango TV’s “Brother” show, presenting data previews, word clouds, top‑liked remarks, super‑active users, and favorite performers, while also sharing the data‑scraping script.
Technical Support teams boost self‑processing rates by building personal knowledge bases, using diagnostic tools, standardizing issue templates, managing feedback channels, creating automation tools, analyzing data trends, and conducting regular post‑mortems, thereby reducing developer support load and increasing overall efficiency.
The article outlines a systematic framework for detecting abnormal KPI fluctuations in daily dashboards—verifying data accuracy, applying period‑over‑period and 3‑sigma rules, then attributing causes across product, competitor and market scopes using MECE‑based horizontal, vertical funnel, and cross analyses, and quantifying impacts with control‑variable, slot, marginal‑effect, prior‑judgment and difference‑in‑differences methods for rapid analyst response and potential automation.
This article examines the evolution of code review, defines key quality metrics such as LOC, inspection time, defect count, and derived rates, analyzes data from CodeCollaborator and Alibaba’s DEF platform, and offers actionable insights to enhance front‑end code review effectiveness.
This tutorial walks through collecting complaint data from the Black Cat platform during the pandemic, cleaning and merging the datasets with pandas, analyzing temporal trends, and generating insightful word clouds using jieba and WordCloud, all illustrated with Python code and visualizations.
The article explains how analysts should responsibly identify problems and choose analysis frameworks, illustrating typical traps such as unclear metric definitions, Simpson's paradox, and false causation through three practical scenarios and offering structured thinking methods to avoid chaotic analysis.
This article presents Dr. Jin Yaran’s comprehensive overview of causal inference challenges, frameworks, and practical case studies—including DID, double machine learning, causal forests, and meta‑learners—applied to Kuaishou’s live‑streaming product, and discusses complex experimental designs such as bilateral and time‑slice experiments.
The article describes a full‑traffic A/B testing framework for Xianyu that hashes seller IDs to create exclusive experiment and control groups, ensuring each seller sees only one strategy, and demonstrates that a chat‑incentive for new or churned sellers boosted chat exposure by 22 % and modestly improved overall buyer‑seller metrics without harming transaction efficiency.
This article presents a curated collection of 100 frequently used pandas functions, organized into six categories—statistical aggregation, data cleaning, data selection, plotting and element‑wise operations, time‑series utilities, and miscellaneous helpers—providing concise Chinese explanations for each function’s purpose.
This article introduces Python's pandas library as a powerful open‑source alternative to MATLAB for data modeling competitions, covering basic, intermediate, and advanced functions—including data I/O, inspection, logical filtering, visualization, aggregation, and integration with tqdm for progress tracking—complete with code examples.
The article outlines the core abilities algorithm engineers need—strong data analysis, solid coding and engineering practices, problem definition, product mindset, and continuous learning—illustrated with real project cases and practical advice for thriving in today’s increasingly competitive tech environment.
This comprehensive tutorial introduces Pandas—the powerful Python library for data manipulation and analysis—covers installation, data import, inspection, cleaning, indexing, selection, sorting, grouping, transformation, statistical functions, visualization, and exporting, all illustrated with clear code examples and visual outputs.
The paper presents a comprehensive A/B testing framework for online experiments that guides practitioners through four stages—designing objectives and metrics, implementing random traffic allocation with robustness checks, evaluating effects using descriptive statistics and hypothesis testing, and making rollout decisions based on multidimensional significance and attribution analyses.
Using Python, the author collected and visualized Douban and Weibo comments on the film “Chinese Doctor,” showing rating distributions, keyword word clouds, and fan sentiment, and compares the 2021 movie with the 2019 documentary to illustrate audience perception of the pandemic drama.
Spyder, a powerful native-Python scientific IDE, offers integrated editing, interactive consoles, variable browsing, documentation viewing, and development tools, plus extensibility via plugins and APIs, and can be installed easily through Anaconda or other package managers, making it a versatile choice for engineers, scientists, and data analysts.
This article introduces a free Pandas cheat sheet covering data creation, reshaping, selection, exploration, modification, grouping, and merging, provides direct download links, showcases key functions with images, and explains how to obtain high‑resolution versions for quick reference.
This article explains how data has become a critical production resource, outlines the limitations of traditional data‑analysis workflows, defines data products and their components, describes their advantages and key characteristics, and shares practical case studies of data‑product implementations in a large e‑commerce environment.
Spyder is a native‑Python scientific IDE offering advanced editing, debugging, variable exploration, and interactive consoles, with extensible plugins and API support, and can be installed via Anaconda or other package managers, making it ideal for scientists, engineers, and data analysts.
This article provides a concise overview of key Python data‑analysis libraries—including NumPy, pandas, matplotlib, IPython/Jupyter, SciPy, scikit‑learn, and statsmodels—explaining their core features, typical use cases, and how they interoperate to form a powerful scientific computing ecosystem.
This tutorial introduces pandasql, a compact Python library that enables SQL queries on pandas DataFrames within the Rodeo IDE, covering installation, built‑in datasets, basic queries, aggregation, joins, and visualization tips for data‑analysis beginners.
This tutorial shows how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu to fetch WeChat friend data and produce visual analyses of gender distribution, avatar face detection and tags, signature word clouds and sentiment, and geographic location, all illustrated with charts and code examples.
This tutorial presents 25 practical pandas techniques, covering version checking, DataFrame creation, column renaming, row and column reversal, dtype selection, type conversion, memory optimization, reading and concatenating multiple files, handling missing values, string splitting, series expansion, aggregation, pivot tables, categorizing continuous data, DataFrame styling, and profiling, all illustrated with clear code examples.
The article introduces causal inference for ride‑hailing businesses, explaining the difference between causality and correlation, common misconceptions, and how randomized experiments and observational techniques like propensity‑score matching can quantify effects of actions such as coupons, driver assignments, and platform growth decisions.
This article explains how to use Python's NumPy library to translate common mathematical formulas—such as powers, roots, absolute values, vector and matrix operations—into concise, executable code, covering setup, basic operations, and practical examples for data analysis and machine learning.
This guide explains why Python is essential, presents a step‑by‑step learning roadmap covering beginner basics, backend web development, web crawling, data analysis, and machine learning, and provides curated resources and project links to help learners progress efficiently.
The article presents a comprehensive guide to Python's built‑in functions, standard libraries, and third‑party packages across file I/O, web scraping, databases, data cleaning, statistical analysis, machine learning, visualization, and more, rating each with stars and offering a free e‑book collection for readers.
Discover seven powerful Python tools—including Pandas, Selenium, Flask, Scrapy, Requests, Faker, and Pillow—that streamline data analysis, web testing, API calls, web development, web scraping, mock data generation, and image processing, complete with installation steps and code examples to boost your development efficiency.
This tutorial walks data analysts through transitioning from Excel and SQL to Python, covering environment setup, data import with pandas, web scraping, cleaning, renaming, type conversion, filtering, grouping, merging, and visualization using Jupyter Notebook and popular libraries.
This article explains why AB testing enhances user experience, outlines its end‑to‑end workflow, details configuration, split, and data models, describes a decoupled frontend AB testing architecture with Core and Coupler packages, and shares real case studies demonstrating measurable improvements.
This comprehensive guide introduces awk as a powerful Linux text‑analysis tool, explains its underlying record‑field model, demonstrates practical one‑liners and scripts for reporting, filtering, formatting, file splitting, and shows advanced features like built‑in variables, conditionals, arrays, and string functions.
This article explains why simple single‑feature analysis can miss important user groups, introduces decision trees—especially the CHAID algorithm—as a way to uncover multi‑attribute segments, and provides step‑by‑step instructions for building descriptive and predictive trees in SPSS, including how to interpret tree visuals and benefit tables.
This article introduces the fundamentals of user profiling, explains tag types and their business value, and demonstrates a data‑driven e‑commerce case study that analyzes gender, age, region, marital status, education, profession, product preferences, purchase timing, and price sensitivity to guide targeted promotion strategies.
This article demonstrates how to use Python's data‑analysis ecosystem (NumPy, pandas, statsmodels, etc.) together with Flask, MySQL, and ECharts to create a web‑based sales forecasting tool that automates data extraction, modeling, prediction, and visualisation.
This tutorial introduces awk as a versatile Linux text‑analysis tool, explains its execution model (BEGIN, body, END), demonstrates practical commands for reporting, filtering, formatting, and advanced scripting, and provides numerous code snippets and visual examples to help readers quickly apply awk in real‑world scenarios.
The article explains how to perform efficient user retention, funnel, and session analysis on large ClickHouse datasets by replacing costly multi‑table joins with bitmap compression, the built‑in retention function, windowFunnel, and high‑order array functions, providing practical SQL examples and performance insights.
This article analyzes a dataset of over 5,000 Douyin influencers, exploring gender balance, likes, fan counts, comments, shares, content categories, geographic distribution, graduation schools, verification status, and bio word clouds, and provides full Python code for each visualization.
Eight practical Python techniques—including list comprehensions, lambda functions, map/filter, NumPy’s arange and linspace, pandas axis handling, and DataFrame concatenation, merging, joining, applying, and pivot tables—are presented to boost efficiency and elegance in data analysis tasks.
This article presents eight Python data analysis techniques—including list comprehensions, lambda expressions, map/filter, NumPy arange and linspace, pandas axis handling, and DataFrame concatenation, merging, joining, applying, and pivot tables—to improve code efficiency, readability, and analytical capabilities.
This article walks through acquiring Tianjin bus line data via the Gaode Map API, cleaning and converting geographic coordinates, visualizing station distributions with matplotlib and Baidu maps, and then applying complex‑network analysis to reveal degree distributions, clustering coefficients, and small‑world characteristics of the city’s public‑transport system.
The Xianyu pricing guidance system blends new‑product market values with depreciation factors derived from usage, condition and category attributes—extracted via real‑time text mining and image analysis—to recommend dynamic price ranges adjusted for supply‑demand and seller urgency, currently covering 60% of listings with over 65% overall accuracy.
The GitHub 2020 Digital Insight Report, produced by X‑lab and multiple research institutions, analyzes 860 million event logs, 54.21 million active repositories and 14.54 million developers to reveal growth trends, activity metrics, regional distributions, project influence networks (OpenGalaxy), and monthly‑star highlights, offering actionable insights for open‑source governance and community management.
This article walks through a full Python‑based sales analysis for a cosmetics company, covering data loading, cleaning, aggregation, visualization, and business‑focused conclusions such as product demand cycles, regional performance, and customer retention strategies.
The article outlines three developmental stages for engineers—from merely implementing PRD specifications, to understanding business and selecting appropriate technical solutions, and finally proactively contributing business ideas—while describing practical methods for demand exploration, project initiation, management, and data‑driven iteration within a mobile development context.
This article explains the distinct concepts, applications, and techniques of data mining and data analysis, highlighting their definitions, typical tools, and how each contributes to extracting insights from large datasets in various industries.
Using Python, this tutorial demonstrates how to scrape 1,585 cherry product listings from Taobao, extract key fields, clean and transform the data, and visualize regional sales and price distributions, highlighting the most popular provinces, stores, and price ranges, all with reproducible code.
This article introduces five open-source stock trading tools—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match-trade—detailing their authors, star counts, features, and repository links, offering developers practical resources to build or enhance their own trading applications.
This guide walks you through the complete Python data‑analysis workflow—reading and writing data, processing with NumPy and pandas, modeling with statsmodels and scikit‑learn, and visualizing results with Matplotlib—while highlighting the key tools and learning path for beginners and busy professionals alike.
This article describes how to collect commit data from five popular Python GitHub repositories with Pydriller, parse source files into abstract syntax trees, extract standard library imports, compare pre‑ and post‑commit usage, and visualize the ten most frequently used standard libraries.
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 article outlines Ctrip's approach to time series forecasting, covering background, common tools such as factor‑based models, traditional statistical methods like ARIMA, and machine‑learning techniques including tree and neural networks, and shares practical experiences on data splitting, feature engineering, model stability, and evaluation.
This article explains the two forms of SQL CASE expressions, demonstrates how to use CASE for concise data aggregation and conditional updates, and provides practical examples with customer and order tables to simplify queries and avoid pitfalls.
This article explores the concept of data sensitivity for algorithm engineers, defines its meaning, discusses how to measure it, offers practical steps to develop the skill through data analysis and feature engineering, and reveals the hidden pattern in a label‑prediction example that illustrates its importance.
This tutorial walks you through reading Chinese GeoJSON files, converting projections, preparing scatter data, and creating a detailed map with an inset of the South China Sea using Python‑geopandas, Matplotlib, and custom legends, while also covering installation tips and known limitations.
This guide introduces Plotnine, a Python implementation of ggplot2, covering installation, data preparation, quick plotting with qplot, building layered graphics, customizing colors, sizes, gradients, creating bar and line charts, and links to further documentation.
This article explains how Vaex, an open‑source DataFrame library, lets data scientists efficiently explore, visualize, and analyze massive datasets—such as the over‑billion‑row NYC taxi records—using memory‑mapping and virtual columns, all on a standard notebook without costly cloud resources.
This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.
This article walks through five common data‑cleaning scenarios—dropping/renaming columns, handling duplicates and nulls, string manipulation, merging tables, and window‑function ranking—showing practical SQL and Python code that can be applied to large‑scale data warehouses.
This article demonstrates how to use standard shell utilities such as grep, cut, sort, uniq, awk, and join to quickly extract insights—like top companies, common surnames, popular given names, age distribution, and hometown rankings—from a JSON dataset of Beijing points‑based residency applicants.
This article analyzes why the used‑car search conversion is low, reconstructs user search scenarios from query data, categorizes search intents, identifies pain points across the search funnel, and proposes product‑level redesigns and recommendation strategies to educate vague users and deliver more precise results.
The October 2020 national survey of 362,409 Chinese programmers reveals an average salary of ¥14,459, a median of ¥12,500, notable regional variations, a surge in hiring demand, and highlights data cleaning steps taken to ensure accuracy.
This article examines JD.com’s transition from traffic‑driven acquisition to a data‑centric, algorithm‑powered membership model, detailing the construction of a robust data foundation, multi‑level analysis methods, productized dashboards, and growth‑hacking experiments that boosted PLUS member retention and revenue.
This article outlines a comprehensive approach for search product managers to drive system improvements, covering overall architecture, query understanding, recall and ranking optimization, business and presentation rules, content enrichment, frontend design, and methods for uncovering user needs through data and behavior analysis.
This article analyzes a Scrapy‑Redis crawl of Lianjia data from July 2020, cleans and enriches the dataset, and uses pandas profiling and visualizations to uncover Shanghai’s second‑hand housing price distribution, regional hotspots, housing types, and market dynamics during the first half of 2020.
This article explains how to use Python's pandas and numpy libraries to batch‑process dozens of CSV or Excel files, extracting the maximum and minimum values from a specific column efficiently without manual effort.
This article walks through loading the Speed Dating dataset, examining its features and missing values, visualizing match rates by gender and age, performing correlation analysis, and building a logistic regression model with SMOTE oversampling to predict whether a pair will successfully match.
This article explores the classic Speed Dating dataset, performing data cleaning, exploratory analysis of match rates, gender and age effects, correlation studies, and finally building a logistic regression model with SVMSMOTE oversampling to predict matchmaking success, achieving around 83% accuracy.
This guide presents seven practical techniques—from using Pandas Profiling and Cufflinks‑Plotly visualizations to mastering IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple outputs, and live slide conversion with RISE—to accelerate and enrich everyday data analysis workflows.
The article outlines three progressive stages for technical staff—from merely following PRD specifications, to understanding business needs and selecting appropriate solutions, and finally to proactively shaping business strategy—while sharing practical methods for demand exploration, project initiation, management, and data‑driven iteration.
This tutorial shows how to use standard Unix shell tools such as grep, cut, sort, uniq, awk, and join to extract insights—top companies, most common surnames, popular given names, age distribution, and hometown statistics—from a JSON dataset of over 6,000 Beijing residency applicants.
This article explains why large‑scale Elasticsearch deployments need rigorous performance testing, compares available testing tools, walks through installing and configuring the official Rally benchmark suite, details hardware recommendations, shows how to run tests against multiple cloud providers, and teaches you how to interpret the resulting metrics to make informed cluster‑selection decisions.
Python’s versatility makes it essential across fields—from AI and cloud computing to web development, web scraping, game creation, and data analysis—offering high demand, lucrative careers for IT professionals and productivity boosts for non‑IT workers, as illustrated by real‑world examples and industry trends.
This article introduces eight essential Python libraries—Gensim, TensorFlow, SciPy, NumPy, Matplotlib, Pandas, Scikit‑Learn, and Keras—that empower developers to clean, prepare, merge, and accurately analyze data for effective data mining.
This talk guides product managers and designers on breaking down complex goals—like achieving a million‑yuan sales target in three months—through structured problem‑decomposition methods such as MECE and SMART, while outlining a personal development framework that cultivates analytical, communication, and leadership skills across career stages.
This comprehensive guide explains what data analysis is, the types of data, its historical evolution, the relationship between data analysis, data science and business intelligence, essential skills, why analysis matters, and a step‑by‑step framework with models and metric design for effective decision‑making.
This article analyses the unique challenges of stranger‑social platforms such as Tinder and Tantan, exploring business models, user behavior, network effects, gender dynamics, data collection, algorithmic matching, risk control, and system architecture to guide product strategy and optimization.
This article, the final part of the GraphTech ecosystem series, provides a comprehensive overview of the front‑end graph visualization layer, detailing its role, benefits, and a curated list of over 70 open‑source and commercial libraries, toolkits, software, and built‑in visualizers for graph data analysis.
This article presents a complete data‑analysis workflow that scrapes Bilibili video metrics from January 2019 to March 2020, cleans and preprocesses 50,130 records, and extends the classic RFM model into an IFL framework—calculating interaction, frequency and like rates—to score and rank up‑creators across multiple categories, with code and datasets provided for replication.
The article examines TikTok's dramatic increase in US user retention by dissecting supply‑side content growth, operational localization, marketing exposure, algorithmic matching, and external influences, and then proposes data‑driven and algorithmic interventions to sustain and amplify the platform's growth.
This article demonstrates how to scrape BI engineer job listings from Zhaopin.com using Python, clean and enrich the data, and create visual analytics with FineBI to reveal salary trends, city distribution, experience levels, and education requirements in the Chinese job market.
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 article explains how to avoid idle user tags by integrating them into business processes, using a VIP fruit‑plate service example to illustrate tag basics, two promotion strategies, key performance metrics, and deeper implementation steps that turn profiling data into concrete operational value.
The article explores why bad cases occur in e‑commerce recommendation and search pipelines, classifies their types, demonstrates data‑driven analysis methods, and proposes practical online and offline strategies—including rule‑based fixes, model improvements, and iterative feedback loops—to continuously improve user experience and business metrics.
This article demonstrates how to use SQL, including dense_rank and date arithmetic, to detect users who have recorded a specific event for at least seven consecutive days within a month, providing step‑by‑step queries and a complete combined statement.
This article demonstrates how to replace typical Excel operations such as data import/export, VLOOKUP, pivot tables, and chart creation with Python libraries pandas and plotly, providing code examples and explanations for each task.
This article presents an intelligent inventory management solution that predicts product consumption using two time‑series algorithms—Facebook's Prophet and LSTM deep learning—detailing data sources, preprocessing, model configuration, evaluation metrics, and a comparative analysis of their performance and suitability.
This tutorial explains how to load a CSV file of financial time‑series data into pandas, perform basic exploratory analysis, compute absolute and relative changes, generate log returns, visualize results, and apply weekly or monthly resampling while avoiding look‑ahead bias.
This article presents a comprehensive analysis of a global supermarket's four‑year sales dataset, covering data collection, preprocessing, exploratory visualizations, sales, quantity, profit, market segmentation, product performance, customer segmentation, RFM modeling, and actionable recommendations to improve revenue and customer retention.
This tutorial demonstrates how to use pandas for calculating total and average scores, adding column means, removing duplicate records, and transposing Excel data, providing clear code examples and explanations of key functions such as sum, mean, duplicated, drop_duplicates, and transpose.
This article provides a comprehensive, step‑by‑step guide to using pandas for data preprocessing, merging, indexing, sorting, grouping, extraction, filtering, sampling, and statistical analysis, complete with clear code examples and visual results to help readers master essential data manipulation in Python.
This tutorial walks through essential pandas operations—including importing modules, building a sample shopping dataset, reading and writing CSV files, inspecting data structures, and performing thorough data cleaning such as handling missing values, trimming spaces, case conversion, replacements, deletions, duplicate removal, type casting, and column renaming—complete with code snippets and visual results.
The article presents May 2020 programmer salary statistics across China, analyzes data anomalies and corrective rules, highlights regional wage differences, and recommends the book “Oracle High‑Performance System Architecture Practice” for deep understanding of Oracle database performance and optimization techniques.
This tutorial walks through a complete Python workflow for analyzing a month of Taobao mobile app data, covering data import, cleaning, feature engineering, traffic and behavior metrics, funnel conversion, RFM segmentation, and visualizations to reveal user patterns and business performance.
This article explains what an IoT platform is, how it fits into a complete IoT system, the functions it provides such as connectivity, protocol handling, security, data collection and analysis, and offers guidance on when businesses should adopt one despite cost trade‑offs.
