Debjeet Bhowmik

In our last couple of posts (Post #147 Post #148), we’ve focused entirely on reading data from files. Now, we’ll learn how to do the opposite: write data to files. This is the key to making our programs save their state, create reports, or generate log files. In this post, we’ll cover the two main … Read more

In Post #147, we explored the .read(), .readline(), and .readlines() methods. We learned that .read() and .readlines() are risky for large files because they load everything into memory, and that using .readline() can be a bit clumsy. So, what is the best practice? How do we read a file line-by-line in a way that is … Read more

In Post #146, we learned how to open a file in “read” mode (‘r’) using the with open() statement. Now that we have an open file object, how do we actually get the content out of it? Python’s file objects provide several methods for reading data. In this post, we’ll explore the three primary methods … Read more

In Post #145, we learned that the Pythonic way to open a file is with the with open() statement. We used the syntax with open(“my_file.txt”, “w”) as file:. That second argument, “w”, is the file mode. The file mode is a crucial single-character string that tells Python what you intend to do with the file. … Read more

In Post #144, we learned how to tell Python where to find a file using absolute and relative paths. Now we’re ready for the next step: actually opening that file to read or write data. While there is a basic open() and close() pattern, the modern, safe, and universally recommended way to work with files … Read more

In Post #143, we established the need for data persistence by saving our program’s data to files. Before we can write the code to open a file, we first have to answer a simple question: where is the file? Your computer’s file system is like a giant tree of folders and files, and we need … Read more

Welcome to Part 6 of our series: File Handling and Error Management! This section marks a huge leap in the power and usefulness of the programs we can build. So far, every program we’ve written has had a major limitation: it has no memory. The moment our script finishes running, all the data we created—every … Read more

Welcome to the final post of Part 5! In this section, we moved beyond just writing code that works and began exploring the philosophy of writing code that is clean, efficient, and “Pythonic.” To wrap up, we’ll first briefly recap the key Pythonic idioms and features we’ve learned. Then, for our project, we will do … Read more

In Post #140, we built a practical @timer decorator to measure a function’s execution time. This demonstrated how decorators can add functionality to a function without modifying its source code. Another extremely common and useful application for decorators is logging. In this post, we’ll build a @logger decorator. This tool will help us with debugging … Read more

In our last few posts (Posts #137 Post #138 Post #139), we’ve built up the theory and syntax of decorators, from understanding functions as objects to using the clean @ syntax. Now it’s time to put that knowledge to practical use and build a decorator that does something genuinely useful. In this post, we will … Read more