Discover the treasures of Python!

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Image by annca from Pixabay

Python is a beautiful language. Simple to use yet powerfully expressive. But are you using everything that it has to offer?

Every well-experienced developer knows that knowing the hidden treasures of their programming language of choice helps them get around many common bugs and everyday coding hassles.

Here are some of those treasures that come straight from Python’s built-in libraries!

(1) Convenient OS functions

It makes interaction with your file-system so much easier.

The basics of NLP

Modern organizations work with huge amounts of data. That data can come in a variety of different forms including documents, spreadsheets, audio recordings, emails, JSON, and so many, many more. One of the most common ways that such data is recorded is via text. That text is usually quite similar to the natural language that we use from day-to-day.

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Natural Language Processing (NLP) is the study of programming computers to process and analyze large amounts of natural textual data. …

2019 was a big year for all of Data Science.

Companies all over the world across a wide variety of industries have been going through what people are calling a digital transformation. That is, businesses are taking traditional business processes such as hiring, marketing, pricing, and strategy, and using digital technologies to make them 10 times better.

Data Science has become an integral part of those transformations. With Data Science, organizations no longer have to make their important decisions based on hunches, best-guesses, or small surveys. Instead, they’re analyzing large amounts of real data to base their decisions on real, data-driven facts. …

Deep Learning is the foundation

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Self-driving cars, also referred to as autonomous cars, are cars which are capable of driving with little to no human input. A fully self-driving car would be able to drive you from Los Angeles to New York City all on its own while you sit back, relax, and enjoy the smooth ride.

Self-driving cars have been receiving tremendous attention as of late, in large part due to the technological boom of Artificial Intelligence (AI). …

Pandas is the go-to library for processing data in Python. It’s easy to use and quite flexible when it comes to handling different types and sizes of data. It has tons of different functions that make manipulating data a breeze.

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But there is one drawback: Pandas is slow for larger datasets.

By default, Pandas executes its functions as a single process using a single CPU core. That works just fine for smaller datasets since you might not notice much of a difference in speed. But with larger datasets, and so many more calculations to make, speed starts to take a major hit when using only a single core. …

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Artificial Intelligence (AI) is taking over every industry. We’ve had electricity, we’ve had the internet, and now, we have AI.

AI in the Modern Age

The goal of artificial intelligence is to simulate human intelligence using computers. Humans (at least so far) are a lot smarter than computers. We can solve complex problems such as building bridges. We can understand each other’s feelings and emotions just by looking at one’s body language. A computer can’t do that, at least, not with programming it with millions of lines of code.

Computers are great at performing calculations very very quickly. A modern PC or Macbook that you can buy at the mall for $1000 will contain a processor which runs at 3.0 GHz speed. …

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Pandas is the gold standard library for all things data. With functionality to load, filter, manipulate, and explore data, it’s no wonder that it’s a favorite among Data Scientists.

Most of us naturally stick to the very basics of Pandas. Load up data from a CSV file, filter a few columns, and then jump right into the data visualizations. Yet Pandas actually comes with many lesser-known but useful functions that can make handling data a whole lot easier and cleaner.

This tutorial will guide you through 5 of those more advanced functions — what they do and how to use them. …

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Look, a Star Wars ship shaped like a Lambda!

In Python, a Lambda Function refers to a small, anonymous function. We call the functions anonymous because technically it has no name — we don’t define it with the standard def keyword that we normally use in Python. Instead, Lambda Functions are defined as one-liners that execute a single expression.

Although they look different, Lambda Functions behave in the same way as regular functions that are declared using the def keyword. They are executed in a similar way as regular Python functions, with the exception that they strictly execute a single expression.

Lambda functions are mainly used for creating small, single-use functions. You’ll often see them in-place of what might otherwise be a fully defined function, but written as a Lambda to save time and space. …

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After much community hype and anticipation, TensorFlow 2.0 was finally released by Google on September 30, 2019.

TensorFlow 2.0 represents a major milestone in the library’s development. Over the past few years, one of TensorFlow’s main weaknesses, and a big reason many people switched over to PyTorch, was its very complicated API.

Defining deep neural networks required far more work than was reasonable. This led to the development of several high-level APIs that sat on-top of TensorFlow including TF Slim and Keras.

Now things have come full circle as Keras will be the official API of TensorFlow 2.0. Loading data, defining models, training, and evaluating are all now much easier to do, with cleaner Keras style code and faster development time. …

A collection of art… Python art!

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Come see Python’s wonderful collections of art!

One of the biggest strengths of Python is its extensive selection of modules and packages. These extend Python’s functionality into a number of popular domains including Machine Learning, Data Science, web development, frontend, and more. One of the best of these is Python’s built-in collections module.

In a general sense, collections in Python are containers that are used to store collections of data such as a list, dict, tuple, and set. These containers are built right into Python and can be used out of the box. …

George Seif

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