This guide explains how to combine a master XLS file with a detailed XLSX file by matching the "Name" column, handling non‑standard headers, skipping index columns, inserting new fields at a specific position, and exporting the merged result using Python, pandas, xlrd, and openpyxl.
This article explains how Python can efficiently read and write both XLS and XLSX Excel files using built‑in modules xlrd/xlwt and the third‑party openpyxl library, provides six reusable functions for common operations, and demonstrates testing procedures to verify correct data handling.
This article provides a comprehensive guide to Python Excel automation, comparing nine Excel libraries, and offering step‑by‑step tutorials with code examples for reading with xlrd, writing with xlwt, and copying/modifying workbooks with xlutils, including installation commands and formatting techniques.
This tutorial introduces nine Python libraries for Excel manipulation, compares xlrd and xlwt, shows how to install them, demonstrates core functions for reading sheets, rows, columns, and cells with xlrd, and provides detailed examples of creating, formatting, merging, and styling Excel files using xlwt.
This tutorial demonstrates how to install and use Python libraries such as xlrd, xlwt, xlutils, XlsxWriter, OpenPyXL, python-docx, and the csv module to read, write, modify, and format Excel, Word, and CSV files, including code examples and feature comparisons.
This article introduces Python's xlwt, xlrd, and openpyxl libraries for Excel data processing, explains how to install them, and provides step‑by‑step code examples for writing and reading .xls and .xlsx files, making it a practical guide for data‑handling tasks.
This article introduces Python's capabilities for handling Excel files, explains the differences between xls and xlsx formats, demonstrates using xlrd/xlwt for xls and openpyxl for xlsx, and provides six utility functions for reading, writing, and appending data, complete with test results.
This tutorial explains how to read and write Excel files in Python using xlrd/xlwt for .xls, openpyxl for .xlsx, and pandas for high‑level data handling, including installation commands, code examples, and a practical case of processing a tab‑separated car‑listing file.
This article provides a detailed, step‑by‑step tutorial on using various Python libraries—including xlrd, xlwt, xlutils, xlwings, openpyxl, XlsxWriter, win32com, and pandas—to read, write, modify, and visualize Excel files, complete with installation commands, code examples, and practical tips for data handling and chart generation.
This tutorial explains how to use Python's xlrd, xlwt, and openpyxl libraries to read, write, and append data in both XLS and XLSX Excel formats, providing complete code examples, performance considerations, and test results for each operation.
This article demonstrates how to use Python 3.7 with the xlrd and xlwt libraries to read data from an Excel workbook, process cell values, write new data, apply styles, and adjust cell dimensions, providing practical code examples for handling Excel files in everyday tasks.
This article demonstrates how to use Python's xlrd and xlwt libraries to read, manipulate, and write large Excel workbooks, replacing tedious manual column operations with a concise script that processes multiple sheets and aggregates data automatically.
This tutorial introduces Python libraries such as xlrd/xlwt/xlutils, python-docx, and the csv module, compares their features, shows how to install them, and provides complete code examples for reading, writing, and modifying Excel, Word, and CSV files.
This tutorial walks through setting up a Python environment, installing libraries, creating and editing Word documents with python-docx, converting files and generating PDFs using win32com, extracting Excel data with xlrd, visualizing scores with matplotlib, and automating batch document generation with docx-mailmerge.
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 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 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 tutorial demonstrates how to automate Excel report generation using Python libraries xlwt, xlrd, and xlutils, covering installation, creating workbooks, writing and merging cells, reading data, applying formulas, and saving files to specific directories with complete code examples.
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 article presents a Python script for validating and cross-referencing data between Excel files, particularly useful for big data testing scenarios where developers need to verify filtered data accuracy.
xlrd is a Python module for reading Excel files, and this guide explains how to install it, locate its documentation, and demonstrates basic operations such as opening a workbook, accessing sheets, retrieving rows, columns, and cell values with example code snippets.
This tutorial demonstrates how to use Python's xlrd and json modules to read an Excel file, extract its rows and columns, and convert the data into a JSON string suitable for API testing, preserving non‑ASCII characters.
This article introduces Python's xlrd and xlwt libraries for reading and writing Excel files, explains why Excel handling is useful in automated testing, and provides step‑by‑step code examples for writing single cells, writing columns, modifying data, and applying cell styles.
