Ever had multiple columns in your DataFrame and thought, “Hmm, wouldn’t it be great if I could just mash these into one clean column?” Whether you're cleaning names, constructing addresses, or stitching strings together for a custom key — concatenating values in a DataFrame is a go-to move. Let’s walk through all the nifty ways... Continue Reading →
Creating an Empty Pandas DataFrame
In the world of data wrangling, sometimes you start with nothing—literally. Maybe you’re prepping to collect API results. Or you're waiting for user input. Or building up data from scratch during a loop. Whatever the reason, knowing how to create an empty DataFrame with defined columns is a must-have trick in your Python toolbox. Let’s... Continue Reading →
strftime() vs strptime() – The Dynamic Duo of Python DateTime
Ah, Python dates. You never quite realize how much of a mess they are—until you're knee-deep in some API response with a timestamp like "2025-04-10T14:30:00Z" and someone asks you to make sense of it. Enter the Batman and Robin of Python's datetime module:👉 strftime() — the Formatter👉 strptime() — the Parser Let’s break this dynamic... Continue Reading →
Parsing Dates with strptime() in Python
You’ve seen it before—some CSV or API gives you a weird string like "2025-04-10 15:45:00" and you think, "Cool, but how do I work with this?" Say hello to strptime() — the friendly counterpart to strftime(). While strftime formats datetime objects into strings, strptime does the reverse: it parses strings into datetime objects. strftime() can... Continue Reading →
Mastering strftime() in Python
If you’ve ever pulled a datetime value in Python and thought, “Ugh, what even is this format?” — you’re not alone. Python gives us strftime() for that exact reason. Whether you want to show 2025-04-10 as "10 April, 2025" or "Thursday 10th of April 2025, 03:45 PM" — strftime has your back. Let's decode it... Continue Reading →
*args vs **kwargs in Python – What’s the Difference and When to Use Them?
Python is a language that values simplicity and flexibility, and nowhere is that more evident than in how it handles function arguments. If you've ever come across *args and **kwargs and wondered, "What’s going on here?" — you're in the right place. Let’s break it down with plain English, some real examples, and use cases... Continue Reading →
Understanding **kwargs in Python: A Beginner’s Guide
In Python, the flexibility of functions is one of the things that makes it a favorite language for developers. You can pass arguments in multiple ways, which allows you to create more dynamic and versatile functions. One such way is through kwargs. But what exactly is kwargs, and why is it so useful? Let's dive... Continue Reading →
Function Overloading in Python
In many programming languages like C++ or Java, function overloading is a familiar term — you can define multiple functions with the same name but different arguments. Python, however, has its own way of making things happen. So, what happens if you try this in Python? def greet(name): print("Hello", name) def greet(name, age): print("Hello", name,... Continue Reading →
ExcelWriter Engines in Python (Pandas 2.0+)
Let’s be honest—Excel isn’t going anywhere. Whether you're building a weekly report, automating insights, or sending a “fancy” output to a stakeholder, chances are, you're still saving stuff as .xlsx. And if you're in Python land, pandas.ExcelWriter is your trusty sidekick. But—what powers this tool behind the scenes? Yep, we’re talking about the engines. With... Continue Reading →
Pandas DataFrame vs. Spark DataFrame: Which One Should You Use & When?
Ever felt like your laptop’s about to take off while processing that “innocent” CSV file with 1 million rows? 😂Yep. You’re probably using Pandas, and it’s starting to sweat. That’s where Spark DataFrames come in — but wait, don’t ditch Pandas just yet!Let’s break it down. Think of it like this: Pandas is your reliable... Continue Reading →
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