Using Python for Data Today, Python is one of the most useful and most widely used programming languages for working with data. Being able to write and read Python programs is a skill highly sought after within data professions. For anyone who wants to launch a career in data, a familiarity with Python is more or less a necessity. At the very least, it’ll be immensely useful.

Though this language may be serpentine in name, in practice it’s relatively straightforward. Python is frequently ranked as one of the easiest to learn programming languages for its high readability, consistency, and brevity. Wanna-be wordsmiths who crave a program that resembles a Leo Tolstoy novel will have to focus their attention elsewhere. Maybe take up Java? For the rest of us who would prefer a workflow that’s as streamlined as possible, Python is more or less perfect.

Data analysis skills are becoming increasingly valuable in today’s technological world. For the person who wants to expand their career options and avoid stagnation in the workplace, improving digital literacy should be a top priority. One of the most versatile ways to do this is to learn how to analyse, manipulate, and extract insights from data using Python. Read on for a general overview of what Python is, why it’s so great for data analysis, and how to start learning.

What is Python Programming Language?

Python’s been around for quite some time. Released back in 1991, Python has since established itself as a strong contender in the general-purpose coding language category. Whereas languages like JavaScript or CSS have more or less specific realms that they operate in, Python can be used for tons of different purposes.

Some of the different activities Python is used for include:

  • Server-side web development
  • Software development
  • Math functions
  • Database systems
  • Machine learning
  • Artificial intelligence
  • Handling Big Data

But what really is Python? One key way it’s described is an object-oriented, high-level,dynamically-typed programming language. That sure is a mouthful! Though to the newbie this sentence may sound like nonsense, if we break it down it’s relatively simple. Let’s look:

  • Python is a programming language, meaning that it’s a set of notations that communicate instructions to a computer or algorithm, which produce specific outputs. It’s a way of speaking to a computer that it can understand.

  • Python is an object-oriented language, meaning that its central focuses are the objects that are manipulated by processes, rather than on the processes that manipulate objects. Working with Python is like adopting the perspective of a rock that’s being rained on, rather than the perspective of the rain hitting the rock.

  • Python is a high-level language, meaning that it’s relatively easy for a human to understand and relatively difficult for a computer to understand. In contrast, low-level languages are easy for machines to understand, difficult for humans to understand.

  • Python is a dynamically-typed language, meaning that the type (i.e., category) of variable doesn’t need to be explicitly specified as it’s written out. The program will check the type at the time of execution, determining on its own whether the variable is a function, string, or whatever else.

Just looking at these fundamental principles that make up Python, it should become more clear why it’s such a popular language. The fact that it’s a high-level language should communicate the fact that it has high readability. In fact, Python is about as close to the English language as possible. Looking at how Python is a dynamically-typed language, we can see how its code will be more concise, saving the programmer time and energy.

Using Python for Data Analysis and Data Science

Beginning to come around to the appeal of Python as a programming language? Thousands of hard-working data analysts from around the globe have been similarly enamoured. But there really are a ton of different programming languages out there, so you might need a little more explanation as to why specifically Python has been nominated as the data analysis language. Here are a few points:

Python is known as a beginner-friendly language

For many who want to improve their data skills, spending hundreds of hours learning to code may be a repellant. With Python, basics like the syntax and vocabulary can be learned relatively quickly. So professionals can quickly get back to modelling weather patterns, tracking virus spreads, or whatever else they’re doing.

Python is an open-source language

In this context, open-source refers to the fact that anyone can collaborate with Python without requiring some kind of license or needing to pay a fee. The free and collaborative nature of Python has spurred a ton of innovation. There are so many libraries, frameworks, and other tools available that make Python incredibly useful and keep it relevant. There are many tools available for data-relevant applications, like creating data visualizations.

There’s a huge community of users

Though there may be hundreds of programming languages in existence, in reality only a few of these see regular use. Popularity doesn’t just make Python seem stylish; it has a real, practical purpose. The large community of Python users means there’s always a range of knowledge sharing and collaboration going on. It also means that it’s easy to find other Python users that can share their expertise with you when you’re still using training wheels.

General purpose means collaborating is easy

That Python can be used for a wide range of activities is useful for the user working alone. It’s also really useful for the user that’s collaborating with a wider team. Even if there are some colleagues working on software development, some on data analysis, and some on machine learning, Python will be mutually intelligible to everyone.

How to Learn Python for Data Analysis

Whether you’re interested in becoming a data analyst or upskilling within your current career, you can’t go wrong with learning Python. Learning any programming language can be a life-long process; technology is constantly evolving and newer, more innovative ways of doing things are always coming to light. No one can ever claim to have completely mastered the art of programming. Nevertheless, don’t let this idea of unending education scare you away from picking up the craft. Learning the basics of Python is relatively simple and straightforward. As we’ve said, it’s one of the most beginner-friendly languages out there.

There are tons of free resources out there that will help you start engaging with the fundamentals of Python. Lighthouse Labs has an online crash course in programming essentials with Python that’ll get you started in style.

To begin learning, there are a few things that will be very helpful to know.

Data Types

You might remember that Python is known as an object-oriented language. The main focuses of this programming language are the objects, or the types of data that we want to manipulate. But what are these data types?

There are a few data types that you’ll commonly encounter when working with Python:

  • Integers, or whole numbers
  • Floating-Point Numbers, or numbers with decimals
  • Complex Numbers, algebraic expressions like 6+3x
  • Strings, sequences of characters like Hello

There are other, more complex data types in Python, like Boolean values, functions, lists, and dictionaries. You’ll learn about these in the Python crash course or the 21-Day Data Challenge.

Manipulating Data

Assembling data types can be fun, but it’s more or less useless in itself. What we want to do in Python isn’t just look at our pretty data. We want to do something to it.

There are many ways to manipulate data within Python. Frequently, the pandas library is used by data analysts and scientists to manipulate datasets. This open-source library is great for organizing, analysing, and extracting insights from data. Some of the things you can do with it include:

  • Finding missing values in a dataset
  • Organizing datasets according to certain constraints
  • Merging datasets
  • Plotting datasets on graphs

Conditional Logic

Conditional logic sounds high-tech, but it’s a very simple idea; and one that’s essential to Python as well as every other programming language. Simply put, conditional logic is the execution of different tasks based on whether a condition is met.

Take a look at the conditions you’ll encounter in Python. If you remember middle school math classes, they’ll probably seem a bit familiar:

  • == , or equal to
  • != , or “not equal to*
  • < , or less than
  • <= , or less than or equal to
  • /> , or greater than
  • >= , or greater than or equal to

Functions & Loops

In Python, a function is a block of code that you can repeatedly call. In data analysis, you can pass data through the function, and it’ll return other data to you. Functions are really useful for manipulating data.

You can create a function in Python by using the keyword def, meaning “define”. This lets the program know that the following code will be defining a new function. You can use functions to do things like print certain strings or integers to the program, organize data into a list, plot data on a graph, and much more.

A loop can be confused with a function, in that it also is a reusable block of code. But it has a specific purpose, which is to execute a statement for a set number of times, or until a condition is met. For example, you can use a for loop to iterate over the number of items in a list or dataset. Or, you can use a while loop to iterate until a certain condition becomes satisfied.

When you’re learning any language, whether it be Arabic or JavaScript, always remember that the best way to do it is to establish a regular practice. Practice will always make you better at any endeavour.

Feel confident and want to begin your journey to a career as a data professional?

This post was originally posted in January 2021. It has since been udpated with more current information.