PANDAS FOR DATA SCIENCE

Does it matter the way you do it? Perhaps one is quicker than the opposite?

When using Pandas, most data scientists would go for df['x'] or df["x"] — it doesn’t really matter which one you employ so long as you follow whichever you’ve chosen. You possibly can read more about this here:

Hence, any longer, wherever I’ll write df["x"], this can equally confer with df['x']. Nevertheless, there’s an alternative choice. You may also go for df.x. While it’s a less frequent option, it could possibly improve readability, assuming that the column’s name is a legitimate Python identifier.¹

Does it matter which syntax you select? This text goals to handle this issue, from two most significant points of view: readability and performance.

The 2 approaches — df["x"] and df.x — are common methods for accessing the column (here, "x") from an information frame (here, df). In the info science realm, most certainly the previous is more steadily used — at the least my experience from quite a lot of data science projects suggests this.

Readability and ease of use

Let’s consider the methods’ benefits and downsides when it comes to readability and ease:

1. df["x"]: That is the express method. This selection allows for using columns with names which have spaces or special characters, or more generally, which might be invalid Python identifiers. Because of this syntax, you immediately know that "x” is the name of a column. Nevertheless, that is the less readable version for eyes: whenever you see loads of such code, you will have to struggle with visual clutter in front of your eyes.
2. df.x: This method provides a more concise syntax, as each time you employ df.x, you save three characters. You’ll appreciate this especially when concise code is preferred. Using df.x, it’s like…