NumPy for Data Engineers – Dataquest
Python programming is a essential talent for knowledge engineers. When it involves working with knowledge, there is a highly effective library that may improve your code’s effectivity dramatically, particularly once you’re working with massive datasets: NumPy.
At current, that is the ninth of 14 programs in our Data Engineering path — we not too long ago added a course on Algorithm Complexity as effectively.
Completing the Data Engineering path requires a Premium subscription, however you’ll be able to check out the primary mission of this new course, or another course within the path, with a free account — no bank card required!
What will I study on this course?
This course affords a start-from-scratch training on NumPy for knowledge engineers. That means you will not have to have any prior expertise with NumPy, and you will not be losing any time studying issues that are not related to knowledge engineering work.
After taking a tour of the fundamentals, you may shortly begin utilizing NumPy to construct and manipulate two-dimensional and three-dimensional arrays. Mastering arrays will help you carry out calculations throughout massive swaths of information without delay, quite than looping by means of it row by row, saving you time and processing energy.
As the course will get into extra superior functions of NumPy, you may additionally learn to assess your reminiscence utilization, and you may study concerning the limitations of NumPy. This offers an awesome lead-in to the following course in our Data Engineer path: Processing Large Datasets in Pandas.
By the top of those two programs, you’ll use your Python abilities and your new NumPy and Pandas data to work with and course of enormous datasets a lot, rather more effectively than is feasible with inventory Python.
And after all, you may be doing all of this in our interactive, in-your-browser platform. You’ll work with actual knowledge and write and run actual code with out having to fret about downloading datasets, putting in libraries, or another hassles.
Why do knowledge engineers have to study NumPy?
NumPy is without doubt one of the hottest — and highly effective — libraries for knowledge work in Python. In truth, it is so highly effective that pandas, the most well-liked Python knowledge science library, relies upon upon and makes use of some NumPy performance.
From the angle of a knowledge engineer, NumPy’s chief benefit is that it lets you do vectorized math utilizing arrays. This strategy is much extra environment friendly than looping by means of every row of a dataset separately to carry out calculations.
The effectivity that the array operations in NumPy provide when in comparison with “stock” Python is especially essential for knowledge engineers, who’re typically coping with enormous quantities of information and tasked with processing it as shortly as doable.
Ready to begin studying NumPy? Dive in and check out the brand new course for free (no bank card required!)