First off, compression is important because it saves space on your hard drive (or whatever device you’re using to store all your precious data). And who doesn’t love more free storage? But that’s not all compressed files also take less time to transfer over the internet or through other networks. This means faster download speeds and happier customers!
Now, let me introduce you to Huffman coding a technique for compressing data by assigning shorter codes (or “symbols”) to more frequently occurring characters in your text. For example, if you’re working with English language texts, the letter ‘e’ is the most common character followed closely by ‘t’, ‘a’, and so on. By using shorter codes for these letters, we can save a lot of space (and time) when transmitting or storing our data!
So how does Huffman coding work exactly? Well, let me break it down for you in simple terms:
1. First, we create a frequency table to count the number of times each character appears in our text. This gives us an idea of which characters are more common and which ones aren’t as frequent.
2. Next, we sort these frequencies from highest to lowest (or vice versa) and assign them to “nodes” on a binary tree. Each node represents a symbol or code that will be used to compress our data. The root of the tree is called the “root node”, while the leaves are called “leaf nodes”.
3. We then repeat this process for each level of the tree, creating new nodes and assigning them codes based on their position in relation to other nodes. For example, if we have a left child node with code ‘0’ and a right child node with code ‘1’, we can use these codes to represent our symbols or characters when compressing our data!
4. Finally, we encode our text using the assigned codes (or “symbols”) and transmit/store it in its compressed form. When someone wants to read this compressed file, they simply decode it back into its original format by following the same process in reverse order!
And that’s pretty much all there is to Huffman coding with Python a simple yet powerful algorithm for compressing data using shorter codes (or “symbols”) based on frequency. So if you want to learn more about this technique and how it can benefit your projects, be sure to check out our tutorial series or reach out to us directly!
Later!