Combining Datasets One After Another - Concat

Imagine you've just finished stacking a deck of playing cards. Now, if a friend hands you another deck, you might place it right on top or beneath the first one. The cards from both decks stay in order but are now part of a taller stack. In the world of data, this idea of lining things up one after another is what we call 'concatenation.'

What is Concatenation? 

Concatenation is like creating a taller tower of building blocks by stacking them one after the other. In the context of datasets, it means joining two or more sets of information end-to-end. Unlike merging, where we combine data based on a shared element (like a customer name or product ID), concatenation is about adding more rows to our dataset or more columns, depending on our direction.

Why Concatenate Datasets? 

Suppose you run a monthly newsletter, and for every month, you have a separate list of subscribers. At the end of the year, if you wish to see your entire yearly subscriber list in one place, you'd concatenate each monthly list to form a complete yearly list.

The Process:

  1. Order Matters: Just like stacking those playing cards, the sequence in which you concatenate datasets is important. The first dataset you select will be on top (or to the left), and the next will follow.
  2. Same Structure: For a smooth concatenation, it's essential that the datasets have the same structure. Think of it like aligning blocks of the same size. If we're adding rows, the number of columns and their order should be the same in both datasets.
  3. Executing Concatenation: With our datasets aligned and in order, we simply command them to join end-to-end, and voilà! Our datasets are concatenated.

As you navigate through our platform, you'll discover just how effortless concatenating datasets can be. Whether you're just starting out with data or are a seasoned pro, concatenation is a tool you'll find invaluable for organizing and analyzing information.