Learn how to efficiently create, open, edit, and save Excel workbooks using Python’s openpyxl library, covering worksheet management, cell operations, merging, formatting, formulas, row/column iteration, and value-only extraction, with practical code examples for both small and large files.
This article describes how QA engineers automated the repetitive Excel‑based mapping and validation process of a valuation system by using Python pandas scripts to filter, compare, and export data, reducing manual effort from ten minutes to under one minute while improving accuracy and coverage.
This article provides a detailed tutorial on using various Python libraries such as xlrd, xlwt, xlutils, xlwings, openpyxl, XlsxWriter, win32com, and pandas for reading, writing, modifying, and visualizing Excel files, complete with code examples and best‑practice tips.
This article walks through diagnosing a hidden Excel parsing exception using Arthas, identifies the root cause in header mapping logic, and then explains Java Agent fundamentals before providing a step‑by‑step demo that injects custom code into a running JVM.
This article details a step‑by‑step optimization of a Java backend Excel import pipeline, covering requirement analysis, performance bottlenecks, caching strategies, batch insertion techniques, parallel processing, and practical code examples to reduce a 10‑minute import to under two minutes.
This article walks through building a front‑end‑only tool that reads student data from Excel, creates customized poster images using SheetJS, html2canvas, and JSZip, and solves out‑of‑memory crashes by analyzing JavaScript heap usage, refactoring recursive code, and optimizing DOM cloning with ignoreElements.
This tutorial explains how to package reusable Python functions into importable modules and provides a step‑by‑step guide, including code, to merge multiple Excel files into a single workbook using pandas, enabling easier reuse and automation of common data‑processing tasks.
This tutorial shows how to read two Excel worksheets with pandas, merge them on matching name columns using a single merge command, and save the combined data back to a new Excel file, all with just a few lines of Python code.
This tutorial introduces Python's openpyxl library for reading, writing, and manipulating Excel files, covering installation, basic methods, and four practical examples including creating multiplication tables, inserting rows, transposing data, and importing text files into spreadsheets.
This tutorial demonstrates how to use Python and pandas to read CSV and Excel files, merge them on a key column, and export the combined result, offering a faster, more reliable alternative to Excel's VLOOKUP for large datasets.
This tutorial demonstrates how to use Python, requests, and openpyxl to analyze a job‑listing website, construct the appropriate Ajax URLs, write a scraper that parses JSON responses, and export the collected position data into an Excel workbook.
This article demonstrates a step‑by‑step Python workflow for reading two Excel sheets, calculating haversine distances between latitude‑longitude pairs, identifying the nearest point for each record, and exporting the results, complete with code snippets, progress display, and performance advantages over traditional GIS tools.
This guide shows how to use Python, openpyxl, and yagmail to automatically read department bonus Excel files and email each manager their report, while also sending individual employees personalized bonus details, streamlining a tedious manual process into a few lines of code.
This article walks through a step‑by‑step optimization of a Java‑based Excel import pipeline, covering the transition from raw POI to EasyPOI and EasyExcel, caching database lookups, batch inserts, optimal batch sizes, parallel streams, and logging reductions to achieve sub‑minute import times for hundreds of thousands of rows.
This article details a step‑by‑step optimization of a Java backend Excel import pipeline, covering migration from POI to EasyExcel, caching database lookups, batch insertion with MySQL VALUES, and parallel processing to reduce a 100k‑row import from minutes to under two minutes.
This tutorial demonstrates how to use Python's requests and BeautifulSoup libraries to scrape the titles and release years of the top 250 movies from Douban, clean the data, export it to Excel, and then create pivot tables and charts to visualize yearly film production trends.
This guide walks through creating PDF templates, inserting dynamic data, and exporting both PDF and Excel files in a Java backend using iText for PDFs and EasyExcel for spreadsheets, complete with code examples and testing screenshots.
This article introduces Apache POI, the Java API for Microsoft Office documents, explains its core components such as Workbook, Sheet, Row, and Cell, and provides detailed code examples for creating, reading, and converting Java object lists to Excel files, including Maven dependencies and annotation usage.
This article provides a step‑by‑step guide on using the PhpSpreadsheet library in PHP to create, style, and populate Excel worksheets, covering object instantiation, cell merging, formatting, data insertion, and exporting the file as an XLSX document.
This article explains how to export massive amounts of data from Python to Excel by installing openpyxl, providing complete code examples for reading and writing both .xlsx and .xls files, comparing xlrd/xlwt with openpyxl, and recommending the best approach for large‑scale data export.
This article details how to dramatically speed up the import of massive Excel files in a Java backend by switching to EasyExcel, caching database lookups, using batch inserts, and applying parallel streams while eliminating excessive logging.
This guide walks you through preparing data with Pandas, saving it to Excel using direct to_excel, ExcelWriter with custom date and number formats, applying styles via Pandas Styler, leveraging both xlsxwriter and openpyxl engines for advanced formatting, auto‑adjusting column widths, and using templates for reusable layouts, all illustrated with code snippets and screenshots.
This article details a step‑by‑step optimization of a Java backend Excel import pipeline, covering data validation caching, batch insertion with MySQL VALUES, switching to EasyExcel, parallel stream insertion, and logging reduction to achieve sub‑minute import times for hundreds of thousands of rows.
This article demonstrates how to create a Laravel export class using Maatwebsite\Excel that supports merging cells, custom column formatting, and handling large numeric values, providing a complete PHP implementation with constructor, collection, event registration, and column format methods for generating Excel files.
This article explains how to prevent frequent full GC and out‑of‑memory errors when using Apache POI for massive Excel imports and exports by leveraging SXSSFWorkbook for XLSX, choosing appropriate processing models for XLS/XLSX, and provides performance test results and code samples.
This article explains how automation testers can use JUnit parameterized testing with Selenium to avoid repetitive test scripts, covering annotation‑based and Excel‑driven data sources, and provides complete Java code examples for both approaches.
This tutorial walks through creating a Python program that scrapes product links, names, and images from a Taobao store, logs the process, exports the data to Excel, and provides a user‑friendly Tkinter GUI for searching and controlling the scraper.
This tutorial walks you through installing openpyxl, understanding Excel's workbook‑sheet‑cell hierarchy, and provides step‑by‑step code examples for reading, writing, styling, and manipulating Excel files using Python, serving as a handy reference for developers.
This article explains how to use EasyPOI for Excel template design, covering version compatibility, Maven configuration, template creation for simple and complex reports, expression syntax, image export pitfalls, and Java code examples to render data into templates without common pitfalls.
This article details how to dramatically speed up Excel‑driven fee‑record imports by replacing naïve POI row‑by‑row processing with EasyExcel, caching database look‑ups, using MySQL batch INSERTs, and finally applying parallel streams for multi‑threaded insertion, while also trimming excessive logging.
This guide walks through adding an Excel import dialog with ElementUI, configuring upload handling, token authentication, progress and success callbacks, and a SpringBoot POI‑based import endpoint, then shows how to create an export button that uses RuoYi’s ExcelUtil and annotation‑driven export logic.
This tutorial shows how to use Python's openpyxl and python-docx libraries to read company data from an Excel sheet, replace placeholder tags in a Word contract template, and automatically generate individualized contracts for each company in a batch process.
This article details a step-by-step optimization of a Java-based Excel import process, covering migration from POI to EasyExcel, caching database lookups, using batch inserts with VALUES, and leveraging parallel streams to dramatically reduce import time for massive datasets.
This tutorial demonstrates how to combine Excel’s interactive features with Python’s Pandas library to perform comprehensive data operations—including reading, generating, filtering, sorting, handling missing values, deduplication, merging, grouping, calculation, statistics, visualization, sampling, pivot tables, and VLOOKUP—showing when each tool excels.
This article demonstrates how to use Python's xlwt, xlrd, and xlutils libraries to create an Excel file with a multiplication table, read its contents, and modify the data by copying the workbook, providing step‑by‑step code examples and screenshots.
This tutorial demonstrates how to import CSV and Excel stock‑listing files using pandas, adjust parsing parameters, view data summaries, and combine multiple sheets into a single DataFrame through concatenation, providing a practical example of data import and merging in Python.
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 tutorial demonstrates how to use Python's xlrd library to read an Excel file containing student records and retrieve a specific student's information by name or ID, covering installation, code walkthrough, and sample output.
This guide walks you through building a Python web‑scraping project that overcomes common anti‑scraping defenses, extracts QQ numbers from a target site, and saves the results into structured Excel files, while highlighting required libraries, request headers, and AJAX handling techniques.
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 guide demonstrates how to programmatically pull date, title, and document number fields from thousands of similarly structured Word tables, normalize the dates, and export the data into a formatted Excel spreadsheet using Python's docx, datetime, and openpyxl libraries.
This article demonstrates how to use Python (with pandas and openpyxl) to efficiently clean Excel worksheets by removing duplicate and empty rows, stripping spaces, reformatting dates, and formatting numeric values with proper precision and currency symbols.
This article introduces a Python-based WeRead assistant that uses xlrd/xlwt for Excel handling, PyQt for a GUI, and WeChat Read APIs to fetch bookshelf data and export notes, providing step‑by‑step code explanations and usage instructions for developers.
This tutorial explains how to use Python's openpyxl library to create, open, modify, iterate over cells and ranges, and finally save Excel workbooks, providing practical code examples for automating data collection and analysis tasks.
This article explains how to combine Python and Excel with the xlwings library, covering installation, add‑in configuration, project creation, VBA‑to‑Python interaction, and several code examples that demonstrate data manipulation and custom functions within Excel.
This tutorial demonstrates how to use Python and pandas to clean Excel data by removing duplicate and empty rows, stripping spaces, reformatting date strings, and standardising numeric values with two‑decimal precision and currency symbols, providing complete code examples for each step.
This guide shows data product managers how to master essential Excel features—including pivot tables, VLOOKUP, and the Text‑to‑Columns tool—so they can turn raw data into actionable insights and implement data‑driven product strategies efficiently.
This article provides a comprehensive guide to Python's file handling capabilities, covering built‑in functions, the csv module, numpy's load and save methods, pandas' extensive read/write utilities, Excel libraries such as xlrd and openpyxl, and popular database connectors like pymysql and sqlalchemy.
This article demonstrates how to use Apache POI in a Maven‑based Java project to read and write both XLS and XLSX Excel files, covering library setup, core APIs, data type handling, and practical import/export implementations with Spring MVC and MyBatis.
This guide walks through using Python's os and pathlib modules to traverse directories, demonstrates how to combine file paths, shows practical examples of merging multiple Excel workbooks and sheets with pandas, xlrd, and xlsxwriter, and explains splitting a workbook by column values, providing complete code snippets and results.
This guide outlines the essential Python knowledge and libraries needed to automate office tasks such as Excel, PowerPoint, Word, email, batch file handling, data analysis, and web scraping, providing practical resources and examples for non‑IT professionals.
This article presents practical Python scripts that automate repetitive workplace tasks such as system data entry, Excel file merging and chart creation, Word resume parsing, operational KPI monitoring, and email dispatch, enabling professionals to boost efficiency and focus on higher‑value work.
This article explains how to build a reusable Java utility for exporting massive Excel reports, covering customizable headers, field formatting, multithreaded data retrieval, asynchronous processing, and provides full source code with detailed analysis and usage instructions.
This article explains how Java backend developers can replace memory‑heavy Apache POI with EasyExcel for fast, low‑memory Excel imports, covering basic reading, custom converters for gender and dates, and multiple Spring Boot integration patterns including controllers, listeners, constructor injection and lambda‑based approaches.
This article explains how to design a reusable Java utility for exporting large volumes of data to Excel using Apache POI, covering header configuration, data transformation, cell formatting, multi‑threaded data retrieval, asynchronous processing, and provides sample code and usage instructions.
This tutorial walks you through using Python and Pandas to import Excel files, explore DataFrames, apply filters, perform statistical calculations, create pivot tables, and emulate Excel functions like VLOOKUP, providing code examples and visual guides for each step.
This article presents a Java utility that reads Excel files (both .xls and .xlsx) using Apache POI, extracts headers and numeric data into a nested List‑of‑Map structure, and includes error handling and type conversion details.
This article presents a Java method that uses Apache POI to write data into XLSX files, detailing the implementation steps, code example, and practical considerations for generating test reports with Excel in automated testing environments.
This article introduces a curated list of widely used Java libraries—including logging, XML, PDF, Excel, JSON, HTTP, Apache Commons, Joda‑Time, and HTML parsing tools—explaining their main features and when to choose each for backend development projects.
Developers can boost efficiency by using Excel to concatenate column values into SQL IN clauses or C# List syntax, locate duplicate entries, split cell contents by delimiters, and permanently disable automatic hyperlink creation, with step‑by‑step visual guides illustrating each technique.
This article details how to build an automated testing framework for algorithm interfaces with TestNG, Maven, Allure Report, Excel data-driven inputs, and Jenkins integration, covering project setup, plugin configuration, test execution, and report generation to streamline repetitive testing tasks.
This article explains how to generate and serve XLSX files from a backend server using the JavaScript libraries js-xlsx and exceljs, covering data preparation, server implementation, and styling options for rows, columns, and cells.
XCEL is a cross‑platform Excel data‑cleaning tool built with Electron and Vue that visualizes filtering, leverages multi‑process architecture, optimizes rendering and memory usage, and provides detailed implementation steps, performance tricks, and code examples for developers.
This guide compares popular Node.js npm packages for Excel file handling, recommends using js‑xlsx for parsing and excel‑export for generation, and provides detailed installation steps, core concepts, code examples, and a complete workflow for reading, converting to JSON, and writing Excel files on both server and browser environments.
