Classes class Derivative_Calculator: def power_rule(*args): deriv = sympy.diff(*args) return deriv def sum_rule(*args): derive = sympy.diff(*args) return deriv differentiatie = Derivative_Calculator differentiatie.power_rule(Derivative)Ĭlasses are an integral part of object-oriented programming languages. To perform the following task, you can import the numpy library as np and proceed to convert the text data into categorical data as shown in the above code block.ġ6. In most of the developed natural language processing projects, we usually prefer the conversion of the text data into the form of vectors and numbers for increased optimized computation. In a topic such as Computer Vision, we can make use of numpy arrays for the visualization of RGB or grayscale images in the form of a numpy array and converting each of the elements accordingly. The applications of numpy are numerous in every single field. ![]() You can convert the stored lists with integer elements into the numpy framework and start performing various operations on them. There are a wide array of problems and tasks that developers and programmers can solve with the help of this amazing library. Numerical Python is one of the best libraries for the computation of mathematical problems. Numpy import numpy as np X = np.array(X) y = np.array(y) y = to_categorical(y, num_classes=vocab_size) So, without further ado, let us get started with our exploration of these code blocks.ĩ. While the initial focus and the targeted audience for this article are for beginner data scientists, other intermediate and advanced Data Science enthusiasts who are switching from a previous programming language to Python will find this article useful. The seventeen code blocks shown in the article offer great reusability on most tasks with little or no modification. In this article, our primary objective is to cover some code blocks that will be used regularly by data scientists no matter what type of projects, problems, or tasks they are working on at the current time. Hence, every level of coders, including beginners, intermediate-level coders, advanced, or experts, must develop the habit of remembering useful codes for acquiring quicker solutions. It is essential to remember that some lines of code or particular code blocks are always reusable, and they can utilize in multiple programs. Every data scientist must know certain code blocks to get started with their Data Science and machine learning journeys. While other programming languages have more complex annotations for solving a particular task, Python offers an easier solution most of the time. ![]() Python offers some of the simplistic and versatile codes to implement complex problems within minimal code blocks.
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