This guide explains how to reliably identify and remove existing watermarks from .xlsx files and then add new, precisely positioned semi‑transparent watermarks using Java's Apache POI library, complete with Maven setup, code examples, and performance tips.
This guide shows how to efficiently extract timestamp‑filtered logs from a 16 GB text file in Go by reading the file in chunks, reusing memory with sync.Pool, and parallelising processing with goroutines, achieving roughly 25 seconds runtime.
This guide explains how to use PHP’s zip_read() function to open a ZIP archive, iterate through its entries, retrieve each entry’s name and size, and properly close the archive, while noting its limitations and related extraction functions.
This article provides a curated set of Python scripts that automate common batch tasks—including merging Excel and CSV files, renaming and moving files, PDF merging, image conversion and processing, QR‑code and word‑cloud generation, holiday marking, and bulk email sending—complete with ready‑to‑run code examples.
This comprehensive guide demonstrates how to read, write, modify, and format Excel, Word, and CSV files in Python using libraries such as xlrd/xlwt/xlutils for Excel, python-docx for Word, and the built‑in csv module, complete with code examples and feature comparison tables.
This article explains how to handle log files that are too large to fit into memory by reading them line‑by‑line with Java streams, using a Counter class and BitSet to track service usage and efficiently generate a top‑10 report.
This article introduces the pdf2docx Python library for converting PDF files to DOCX, detailing its capabilities such as layout, paragraph, image, and table parsing, outlining current limitations, providing installation instructions, and showing a concise code example for practical use.
The article explains why Apache POI loads entire .xlsx zip files into memory causing OOM, demonstrates the issue with code examples, and shows how switching to EasyExcel's streaming read dramatically reduces memory usage while handling large Excel files in Java.
This article shows how to use Python's os and pandas libraries to automatically traverse nested folders, locate all .xlsx Excel files, and add standardized '经度' and '纬度' column headers, providing a clear, step‑by‑step solution for batch Excel header insertion.
This article demonstrates how to use a short Python script with pandas and os modules to automatically locate all .xls and .xlsx files in nested subfolders, add standardized column headers, and save the updated workbooks in a single, streamlined step.
This article walks through generating a 10 GB file of age values, reading it line‑by‑line on a 4 GB RAM, 2‑core machine, measuring single‑thread performance, then redesigning the pipeline with a producer‑consumer model, blocking queues and multithreaded string splitting to dramatically boost CPU utilization and cut processing time while managing memory consumption.
This article explains how to generate a 10 GB file of age data, read it efficiently on a machine with limited memory, and use both single‑threaded and multithreaded Java techniques—including a producer‑consumer model and divide‑and‑conquer—to identify the age that appears most frequently, while analyzing performance, memory usage, and CPU utilization.
This article walks through a practical Python solution for inserting one PDF into another using the PyPDF2 library, covering three possible strategies, selecting the simplest page‑by‑page method, providing installation instructions, and presenting complete example code with explanations.
This tutorial explains how to extract logs from a 16 GB file in about 25 seconds by reading the file in chunks, reusing buffers with sync.Pool, and processing those chunks concurrently with Go goroutines, avoiding full‑memory loads or slow line‑by‑line scans.
This guide shows how to list dataset numbers where algorithm A failed by generating a full sequence with seq, extracting successful runs via ls, grep, cut, and Python, and then using comm to identify missing entries.
This guide presents a collection of concise Unix one‑liners that act as Swiss‑army‑knife tools for merging files, computing unions, intersections and differences, summing columns, listing file metadata, and leveraging xargs for flexible batch processing, all with minimal code.
