Welcome to Half 3 of Introducing NumPy, a primer for these new to this important Python library. Part 1 launched NumPy arrays and the way to create them. Part 2 coated indexing and slicing arrays. Half 3 will present you the way to manipulate current arrays by reshaping them, swapping their axes, and merging and splitting them. These duties are useful for jobs like rotating, enlarging, and translating photographs and becoming machine studying fashions.
NumPy comes with strategies to vary the form of arrays, transpose arrays (invert columns with rows), and swap axes. You’ve already been working with the reshape()
methodology on this sequence.
One factor to concentrate on with reshape()
is that, like all NumPy assignments, it creates a view of an array reasonably than a copy. Within the following instance, reshaping the arr1d
array produces solely a brief change to the array:
In [1]: import numpy as npIn [2]: arr1d = np.array([1, 2, 3, 4])
In [3]: arr1d.reshape(2, 2)
Out[3]:
array([[1, 2],
[3, 4]])
In [4]: arr1d
Out[4]: array([1, 2, 3, 4])
This conduct is helpful if you wish to quickly change the form of the array to be used in a…