This is a work from Big Data Digest, please contact us for reprints. Compiled by: Zhang Yuanyuan, Traveler, Aileen “Introduction: In this issue of the Beginner’s Guide to Data Science, we continue to help you learn Python. This time, our editors have gathered several useful cheat sheets for common Python libraries online, making it easier … Read more
Produced by Big Data Digest Source: Medium Translated by: Zhu Shuai, Xue Qing, Xia Yawei This is an introductory guide to pandas, where the author uses simple language and straightforward example code to showcase the overview of pandas and some advanced operations. “… It is a library that everyone engaged in data science must master,” … Read more
As a data analyst with eight years of experience in the field, Python has long been my “right-hand man” in my work. It is no exaggeration to say that my daily tasks almost always involve Python, from data cleaning to modeling decisions, it runs through the entire data analysis process. Recently, I have received many … Read more
The Advantages of Combining Python and Excel Everyone knows that Excel is very convenient for handling small amounts of data. However, when faced with tens of thousands of rows or more, traditional manual operations can be very time-consuming and prone to various errors. This is where Python comes into play—Python not only significantly reduces manual … Read more
Project IntroductionProject Name: Batch Filter and Consolidate ‘Wallet’ Sales Records from Excel FilesLibraries Used:os, pandasProject Overview: This example is used to filter rows where the “Product Name” column is “Wallet” from all worksheets of Excel files in a specified folder and its subfolders, and consolidate this data into a new Excel file. Additionally, each qualifying … Read more
From Zero to Automation Expert: My 30-Day Journey in Python for Office Automation That afternoon, as I manually copied Excel data to a Word report for the fifteenth time, I suddenly realized how much of my life I was wasting on repetitive tasks. As an ordinary office worker, I used to think that “automation” was … Read more
Introduction Often, the datasets we need are very large due to high resolution, necessitating storage in chunks (in fact, many high-resolution datasets are provided for download in a chunk-compressed format).The naming convention of these chunk files usually indicates their corresponding latitude and longitude ranges, but finding and verifying them one by one can be cumbersome, … Read more
It was a memorable Friday evening when I attempted to process five million GPS records of taxis in New York City using GeoPandas, and my 16GB RAM MacBook Pro began to roar like a helicopter taking off. This wasn’t my first time encountering issues during geospatial analysis, but watching the Jupyter kernel crash repeatedly made … Read more