Data is at the heart of every successful machine learning project. Without it, there can be no insight, no prediction,...
Data can be stored in many formats, tables, texts, images, sounds, videos, and so on. Most of the time, the...
Dreamt of crafting machine learning models but held back by a lack of coding skills? That's exactly why we developed Octai.
Ever noticed how a bouquet is more appealing when flowers are grouped by type or color, rather than being scattered randomly? Similarly, in the vast garden of data, sometimes it's beneficial to group related pieces of information together, so we can understand patterns and trends more clearly. This process of gathering and summarizing data based on specific criteria is known as 'aggregation'.
In the bustling digital world, data is the new gold. But like raw gold, data often needs refining. That's where platforms like ChatGPT and powerful programming languages like Python come into play. Both offer unique avenues for data manipulation. Let's explore these paths.
Remember the times when you'd bookmark a page in your favorite book, ensuring you could pick up right where you left off? In the digital realm, especially when working with data, we also need a method to 'bookmark' or save our progress, so we can return to it later with ease. This concept of preserving our data for future use is what we refer to as 'exporting.'
Imagine you've been handed a jigsaw puzzle. To solve it more efficiently, you might start by sorting and dividing the pieces based on colors or edges. Similarly, in the world of data, there are times when we need to dissect a large dataset into smaller, more manageable chunks. This process is known as 'splitting'.
Have you ever tried merging two different puzzles to create a whole new picture? In the world of data, we often find ourselves needing to join two sets of information to gain deeper insights. This process is like bringing together two puzzle pieces to see a bigger picture. In technical terms, this is known as 'merging' or 'joining' datasets. But no matter what you call it, the essence remains the same: it's about combining information from two separate sources.
Imagine you have a deck of cards. When you play a specific game, it might be beneficial to have the cards in order, whether by number or by suit. Similar to how you'd order that deck, in data analysis, we often need to arrange our data in a particular sequence, either ascending or descending. This process is called 'sorting'.
Imagine visiting a foreign country and coming across street signs in a language you don't understand. Fortunately, you have a translator that converts those unfamiliar terms into something you recognize. In the world of data, column names often serve as these "signs," guiding us through the information. Sometimes, to make data more understandable or to fit a specific format, we need to change these column names. This process is known as "renaming."