Removing irrelevant or redundant columns

Large datasets often contain numerous columns, some of which may be irrelevant to the specific analyses or tasks at hand. By eliminating these columns, we can get some significant benefits. Firstly, storage requirements are dramatically reduced, leading to cost savings and more efficient use of resources. Additionally, the streamlined dataset results in faster query performance, optimized memory usage, and expedited processing times for complex analyses. This not only improves the overall efficiency of data processing tasks but also facilitates easier management and maintenance of large datasets. Furthermore, in cloud-based environments, where storage costs are a factor, the removal of unnecessary columns directly contributes to cost efficiency. So, let’s have a look at how we can drop columns in an efficient way.

In the e-commerce dataset we presented earlier, we have collected information about customer purchases. However, as your...