Python Tutorial : The power of NumPy arrays

Key Takeaways

numpy or numerical Python is an invaluable Python package for data scientists it is the fundamental package for scientific computing in Python and provides a number of benefits for writing efficient code in this lesson we'll highlight one of the most important advantages of numpy the numpy array numpy arrays provide a fast and memory efficient alternative to Python lists typically we import numpy as NP and use NP array to create a numpy array numpy arrays are homogeneous which means that they must contain elements of the same type we can see the type of each element using the dot d type method suppose we created an array using a mixture of types here we create the array nums and P floats using the integers 1 & 3 and a float 2.5 can you spot the difference in the output the integers now have a proceeding dot in the array that's because numpy converted the integers to floats to retain that arrays homogeneous nature using dot d type we can verify that the elements in the array are floats homogeneity allows numpy arrays to be more efficient and faster than Python lists requiring all elements be the same type eliminates the overhead needed for data type checking when analyzing data you'll often want to perform operations over entire collections of values quickly say for example you'd like to square each number within a list of numbers it'd be nice if we could simply Square the list and get a list of squared values returned unfortunately Python lists don't support these types of calculations we could square the values using a list by writing a for loop or using a list comprehension but neither of these approaches is the most efficient way of doing this here lies the second advantage of numpy arrays they're broadcasting functionality an umpire arrays vectorize operations so that they are performed on all elements of an object at once this allows us to efficiently perform calculations over entire arrays notice that by squaring the array nums and P all elements are squared at once another advantage of numpy arrays is their indexing capabilities when comparing basic indexing between a 1 dimensional array and lists the capabilities are identical when using two dimensional arrays and lists the advantages of arrays are clear to return the second item of the first row in our two-dimensional object the array syntax is square bracket 0 comma 1 square bracket the analogous list syntax is a bit more verbose as you have to surround both the 0 and the 1 with square brackets to return the first column of values in our 2d object the array syntax is square bracket colon comma 0 square bracket lists don't support this type of syntax so we must use a list comprehension to return columns numpy arrays also have a special technique called boolean indexing suppose we wanted to gather only positive numbers from the sequence listed here with an array we can create a boolean mask using a simple inequality indexing the array is as simple as enclosing this inequality in square brackets to do this using a list we need to write a for loop to filter the list or use a list comprehension in either case using an umpire array to index is less verbose and has a faster runtime now that we've covered powerful and efficient numpy arrays let's start putting what we've learned into practice