When a function is not a function
Lambda functions are very powerful items in the Python world that bridge the gap of whether or not you need to formally define a function or not. I would label the lambda function in Python as a sort of trump card for helping you to implement something quick and easy, without the formality of defining a function and any additional work that that would encompass. Remembering that “With great power comes great responsibility”, knowing when you can and when you shouldn’t use a lambda function is equally important.
Why reinvent the wheel? Python has many built-in functions that do some of the things you are currently wrapping your brains around
Python natively has many built-in functions that everyone who wants to become familiar with the programming language should know. In this article, I’ll highlight the most important ones that you may use to develop the code that will power your machine learning or data science project. How often do you find yourself coding and want to do something that seems simple, but when you start to try and type it out you think ‘There’s got to be a better way’. If this happens to you and the concept is actually really simple, there probably is an easier way, the Python…
Living the past 18 months with the pain of plantar fasciitis has been excruciating, but learning the root causes of it will allow you to conquer it!
I would consider myself to be a very active person over the past year, but in September 2019, I first started to notice pain in my heels when I woke up in the morning. At first, I thought maybe I was overworking myself and didn’t really pay too much attention to it. A couple of months later near the holidays, it grew to be a nearly unbearable pain in the mornings, when taking those first few steps. It slowly grew to be very painful even after spending more than 15 or so minutes off my feet and then standing back…
Getting Started
Knowing how to interpret the errors you receive when running your code enables you to diagnose and troubleshoot issues in no time!
For those learning a programming language, understanding the syntax is really the heart of understanding how the code is to be written and implemented. A crucial next step once you start to understand syntax and code structures is to understand the possible errors that you will run into when your code doesn’t run. A thorough understanding of what the error is and why you’re receiving it makes diagnosing and troubleshooting the error much faster. When I tried to self-teach myself Python initially when I encountered an error I would copy and paste it into the black hole that is stackoverflow.com…
A huge hurdle in starting and developing a solid fitness routine usually lies with not having a plan and success measures. This is where technology can help.
Does this sound familiar?
Set a New Year’s Resolution to get in shape.
Day 1: start a new gym membership and workout!
Day 10: Combination of aches and pains causes you to cut back the time at the gym or even miss a planned gym session.
Day 30: The gym and fitness routine planned is no longer being followed and wait 11 more months to create a “New” New Year’s Resolution about getting in shape.
If this does sound familiar, there are a mountain of reasons why people aren’t able to achieve their fitness goals and in this article, we…
The heart and soul of Python coding… Learning to make clean, simple, and easy to read functions within Python is Priceless.
I think that one could argue that the function programming construct is probably one of the most important concepts in coding. The concept of a function is an abstraction allowing a user to be able to simply know what the function does and the inputs required to generate an output. This allows a small bit of the code block that houses the function to serve as an automatic processor of data that goes into it that is reusable with all inputs that match the required data types of the inputs. Functions can be built to filter input data, perform data…
Beyond having strong technical knowledge, there are other key skills that are crucial to developing and productionalizing successful data science projects (or any engineering project for that fact).
In writing this article, I am coming from a customer perspective as I have served as a product owner for various digital products within my company. I have worked with many different folks with backgrounds in data science, machine learning, as well as the more traditional software engineers and one thing is clear. Many people have strong technical skills, but it’s the intangible non-technical skills that make a world of a difference in starting a project and getting it over the line to deliver value. The ones that had the skills mentioned below were incredible to work with and we…
One data type that is incredibly versatile for data science work and provides tons of utility is the dictionary, allowing a built-in relationship and hierarchy within itself.
A confession I have is that prior to learning about Python and computer science in general, I had no idea that such a data type existed. When I finally was exposed to it and got to see the extreme amounts of potential that it has, dictionaries quickly became one of the data types that I admired for their structure and versatility. This article will aim to provide a quick overview of what dictionaries are, general rules about how they are to be used, practical ways to use them, and then some quick hypothetical examples of where they can be used.
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As it’s been said, code is read way more times than it is written. Developing good formatting habits while writing code is almost as important as making it work.
The primary function for code is to work, the second is to make it interpretable if you have plans to use it more than once. Developing good habits and following the general recommended formatting practices that are presented in PEP-8 (found here) is an easy and quick way to standardizing the appearance of your code. PEP-8 (Python Enhancement Proposal #8) was created as a means to try and create a template of common practices for the purpose of enhancing readability and standardization. …
Before you can start building data science projects, coding is a must and within Python understanding the key properties and differences of data types will make learning to code that much smoother.
Learning to code for data science can be quite daunting, especially if you don’t have any previous experience and/or understanding of coding or programming languages. To make matters worse, if most people are like me, then truly understanding the simple concepts like data types and structures, might be things you brush off and continue to trudge on to the more important things like diving headfirst into TensorFlow, Keras, and Scikit-Learn. The folly in this approach is that whenever you run into a problem and get a Type Error when you execute your code there’s a good chance you’ll just copy…
