To import NumPy in Python, you typically start by acquiring the library if you haven’t already done so. This is usually done via pip, Python’s package installer. Run the command `pip install numpy` in your terminal or command prompt to fetch the latest version from the Python Package Index (PyPI). Once NumPy is installed, you can seamlessly integrate it into your scripts. The conventional import statement is as follows: `import numpy as np`. This allows you to utilize NumPy’s extensive functionalities while keeping your code concise, as it leverages the `np` alias.

Once NumPy is imported, you can access its myriad of functions and classes tailored for numerical operations with impressive performance. For instance, you can create arrays using `np.array()`, perform element-wise operations, and utilize functions like `np.mean()` or `np.linalg.inv()` for mathematical computations. It’s noteworthy to remember that NumPy arrays provide advantages over native Python lists, particularly in terms of speed and efficiency when dealing with large datasets. For iterative operations through multi-dimensional arrays, methods such as `np.where()` and broadcasting features significantly enhance code readability and performance.