Скачать или смотреть Understanding NumPy Concatenation Functions

In this section, we will explore the core functions used for concatenation in NumPy: np.concatenate, np.vstack, and np.hstack. Each of these functions serves a unique purpose. The np.concatenate function is versatile and allows you to concatenate two or more arrays along a specified axis. It’s the most general form of concatenation in NumPy. On the other hand, np.vstack is specifically for vertical stacking, essentially stacking arrays row-wise, making it ideal for combining datasets of similar features. np.hstack, as the name suggests, is used for horizontal stacking, which is column-wise concatenation. This can be particularly useful when you have datasets with the same number of rows but different features. Understanding the differences between these functions and their specific use cases is crucial for choosing the right approach depending on your data structure and the analysis you are conducting. We will look into examples that highlight the strengths and weaknesses of each function to give you a comprehensive understanding.