NumPy

Data PreparationUp to 8000 times faster than standard functionsPhoto by Djim Loic on UnsplashWhen dealing with smaller datasets it is easy to assume that normal Python methods are quick enough to process data. However, with the increase in the volume of data produced, and generally available for analysis, it is becoming more important than ever to optimise code to be as fast as possible.We will therefore look into how using vectorization, and the numpy library, can help you speed up numerical data processing.Python is…

A matrix approach to standardizing your dataImage by authorOne of the first step when dealing with new data in data-science and machine learning is often to standardize — or “scale”- your data. The most common procedure — often called Z-score Normalization — is simply to remove the mean from each column-feature, and divide it by its standard-deviation. You’re left with features that have 0-mean and 1-variance. This make every feature “comparable” in scale. The next step is then to introspect your data to find potentiel…

Get rich in NumPyPhoto by Ashin K Suresh on UnsplashEvery data scientist admires someone. For some, it might be people who create killer data visualizations; for others, it is simply anyone who answers their StackOverflow questions. For me, it was people who used NumPy like a ninja.I don't know. I have always thought that the ability to use a long forsaken function buried deep inside the documentation on rare edge cases spoke a lot about a programmer's skill. Reinventing the wheel for a particular task is challenging, but…

Who said you should know everything?Photo by Austin Distel on UnsplashNumPy (or Numeric Python) sits at the core of every Data Science and Machine Learning project. It is undoubtedly one of the most important libraries ever built in Python. Moreover, the whole data-driven ecosystem is in some way or the other dependent upon NumPy and its core functions.Given that the library holds wide applicability in industry and academia due to its unparalleled potential, acquaintance with its functions and syntax has become an utmost…

JAX is a Python library with NumPy functions used to cut out trivial operations in machine learning modeling Automatic differentiation can make a huge difference in a deep learning model’s success, reducing the development time for iterating over models and experiments. Earlier, programmers had to design their own gradients which make the model vulnerable to bugs apart from consuming a lot of time which in course of time proved to be disastrous. To keep track of gradients over a neural network, libraries like…

Learn about NumPy universal functions (ufuncs) and how to create them. Code your own vectorized functions.Photo by Jeremy Bezanger on UnsplashHave you ever wondered about the origin of NumPy’s magical performance? NumPy powers the performance, under the hood, of many daily drivers of the data scientist, such as pandas, among an extensive list. Of course, you’d be right to think about optimized arrays written in C and Fortran. Half right, at least. The other half is not the arrays but NumPy’s functions themselves. NumPy…

Make Working with NumPy Even BetterPhoto by Tim Mossholder on UnsplashNumPy is one of the quintessential data science libraries available in Python. Being able to work with it effectively can make your day-to-day work much, much easier. The four tips you’ll learn in this tutorial will give you much greater control over how the library works and what you can expect from it! Let’s dive right in.In data science, deep learning, and linear algebra, you’ll often need to generate special arrays like arrays and matrices of zeroes…

This article will show you the challenges, components, and steps you need to make/overcome to create a basic deep learning frameworkPhoto by Vlado Paunovic on UnsplashLet's start by defining what we want to create and figure out what components we need: we want a framework that supports automatic differentiation to compute the gradient for several operations, a standardized way to build neural network layers using the aforementioned operations with a modular approach to combine them in a larger neural network model, and…

The ins and outs of how broadcasting works under the hoodPhoto by Donald Giannatti on UnsplashWhat is broadcasting? Broadcasting is a mechanism that allows Numpy to handle arrays of different shapes during arithmetic operations. In broadcasting, we can think of it as a smaller array being “broadcasted” into the same shape as the larger array, before doing certain operations. In general, the smaller array will be copied multiple times, until it reaches the same shape as the larger array. Note that the array we say here…

Showcasing how to dump NumPy arrays into .csv filesPhoto by Kelly Sikkema on UnsplashIntroductionWhen working with arrays in NumPy and Python, we usually need to dump them into output files of various forms, including comma-separated values (CSV).In today’s short tutorial we will be discussing about a few different approaches one can use in order to write NumPy arrays into such files. Finally, we will discuss about one approach that you should typically avoid when it comes to dumping NumPy arrays into files.First, let’s…