Whether you take a Clark Griswold approach to your home’s holiday light display, or are more of a Charlie Brown minimalist, ‘tis the season for twinkly lights everywhere you look. With some savvy programming skills and a Raspberry Pi computer, you can code a custom holiday light display — and we’ll teach you how.

If you’re not familiar with Raspberry Pi, “it’s basically a full computer in the size of a credit card,” explains Jace Van Auken, Codecademy Curriculum Developer who worked on the Learn Raspberry Pi course. Raspberry Pi was invented in 2012 by a programmer named Eben Upton, who was inspired to make a low-cost hobby computer similar to the one he grew up using in the ‘80s, called the BBC Micro.

The single-board computer might look bare bones or unassuming, but it’s capable of much more than meets the eye. “Raspberry Pi is super powerful and it continues to get more powerful,” Jace says. What’s so neat about Raspberry Pi is that it’s not only a practical tool for learning programming and computing, but it can also be used for creative pursuits and fun stuff that’s purely for entertainment purposes. In fact, Raspberry Pi is a go-to tool for STEAM (short for Science, Technology, Engineering, Art, and Math) projects.

Given that, we’re going to show you how to use Raspberry Pi to make holiday decor. Jace will explain how you can use your Raspberry Pi to create a light display that animates and changes colors. With a LED string light, your Raspberry Pi kit, coding knowledge, and some imagination, you’ll be decking your halls with code in no time.

The coding skills you need for this project

This project is a fun, hands-on opportunity to learn how to use Raspberry Pi, but you need some background knowledge before you get your hands dirty. Working with hardware and wires might be a little bit intimidating at first, and it’s not as straightforward as other projects where you’re just writing code. You can learn the skills you need for this project in the free Codecademy course Learn Raspberry Pi.

Raspberry Pi runs on the open-source operating system Linux. You can use lots of different programming languages with Raspberry Pi, but Python tends to be the most popular. There’s even an integrated development environment (or IDE) for Raspberry Pi called Thonny that comes with Python built-in. In Learn Raspberry Pi, you’ll get introduced to the command line and Linux operating system, plus you’ll learn the ins and outs of the hardware and software that’s used for Raspberry Pi projects.

If you’re new to coding, we have lots of other beginner-friendly courses that will set you up to complete this Raspberry Pi project — and start dreaming up your own projects. Start by checking out the free Codecademy course Learn Raspberry Pi. For a more in-depth look at Linux, you might want to try Introduction to Linux or Learn the Command Line. We also have lots of Python courses for all levels, like the popular beginner course Learn Python 3 or the free intermediate course Python for Programmers.

Gather these supplies

Here’s what you need in order to make a holiday light display with Raspberry Pi.

• Raspberry Pi: You’ll need a Raspberry Pi computer with power for this project. Any RPi variation will work, and there are lots of options available at different price points that you can browse on the Raspberry Pi website. We recommend the Raspberry Pi 400 kit, which has a Raspberry Pi 4 built into a keyboard and comes with all the accessories you need to get started (a mouse, power supply, and monitor adapter).
• Addressable LED strand: Find flexible strand lights that are “addressable,” which means that each LED light can be individually programmed to create animations or custom displays. We used this inexpensive option on Amazon.
• Protoboard: Also known as a “breadboard,” this is a surface with rows and columns of holes that you can use to prototype circuits.
• 5V power supply, 2 Amps: In order to properly power your Raspberry Pi, you need a 5 Volt power supply around 2 Amps.
• Level-shifting chip: A level-shifter is necessary because the Raspberry Pi data ranges from 0-3.3V, but the LEDs want data from 0-5V. In many cases, the 3.3V data will work for the LEDs but isn’t a guarantee. You can find the integrated circuit we used by searching the product number: 74LS245N.
• Hookup wire: This is the type of wire that you use when you build circuits with a protoboard. You’ll need Female/Male jumper wires for this project.

Start coding your holiday lights with Raspberry Pi

Before you jump in and start this project, spend some time tinkering around with Raspberry Pi. In the free Codecademy course Learn Raspberry Pi, we’ll walk you through how to properly set up a circuit in Raspberry Pi to run an external device like an LED light. It’s a good idea to take the course to get a detailed explanation of how to use Raspberry Pi — this project will make way more sense if you have a clear understanding of the technology first.

Set up your Raspberry Pi and circuit

Got all your supplies handy? ​​Using a breadboard we’re able to supply the LEDs 5V of power, and we can use the level-shifter to pass data from the Raspberry Pi to the LEDs. You’ll want to reference this image below for the wiring:

We can zoom in and see where we placed the wires on our Raspberry Pi. These general-purpose input/ouput (GPIO) pins allow the Raspberry Pi to control external components like lights. The black wire (on the right) is connected to pin 6 on the 40-pin header. The green wire (on the left) is connected to GPIO18, which is pin 12 on the 40-pin header. You can get a quick diagram of the Raspberry Pi’s 40-pin header by opening up the terminal and typing pinout.

Take a closer look at the circuit wiring in the image below. The Raspberry Pi has 2 wires connecting to the breadboard: the green wire supplies data, the black wire is your ground wire. Our breadboard has a 5V power supply, and a level-shifting IC with 3 wires going to the addressable LEDs.

(Expert tip from Jace: It’s important that every component in this project shares ground, because ground is the reference point for the different voltages in this system. In many cases, if something is not working it is because the grounds of each component were disconnected somewhere.)

A note about safety: Be careful when you’re setting up your circuit. While the voltages and currents that come from your device’s general-purpose input/output (or GPIO) are relatively low, it’s possible to accidentally damage your Raspberry Pi and breadboard if you don’t take certain precautions. (We cover how to do this in the course Learn Raspberry Pi.)

Test your circuit

Now it’s time to write some code and test that your circuit works. Open up your terminal and run the following command to install the necessary Python modules. Be sure to use sudo (short for “superuser do”) when you run this command and your file.

sudo pip install rpi_ws281x adafruit-circuitpython-neopixel

With these modules installed, now we can turn on the lights. Run this code as sudo. If everything is set up correctly, you’ll see the first LED on your strand light up!

import board
import neopixel

NUM_PIXELS = 42

pixels = neopixel.NeoPixel(board.D18, NUM_PIXELS)
pixels[0] = (255, 255, 255)

Design your lights

Be creative and decide what shape or design you’d like to use for your lights — we chose a snowflake. Jace printed a snowflake pattern on a piece of paper, drew a dot where the lights will lay, and labeled each light with a number (1-50). He arranged the 50 LED lights so that each branch of the snowflake would contain 7 LED lights. Jace used hot glue to attach the LED strand light to the paper so the wired lights can maintain the shape of the snowflake.

The following code goes further and creates functions to light up all the LEDs or just a single branch. In the main function there is an infinite loop that randomly colors each branch blue over a white snowflake. Try it out!

import time
import random
import board
import neopixel

# adjust these based on your project
NUM_PIXELS = 42
PIXELS_PER_BRANCH = 7

# color variables
RED = (255, 0, 0)
GREEN = (0, 255, 0)
BLUE = (0, 0, 255)
WHITE = (255, 255, 255)

# global neopixel instance
pixels = neopixel.NeoPixel(board.D18, NUM_PIXELS)

# fill a branch (0-6) a specific color
def fill_branch(branch, color):
start = branch * PIXELS_PER_BRANCH
finish = start + PIXELS_PER_BRANCH
pixels[start:finish] = [color] * PIXELS_PER_BRANCH

# fill all the pixels
def fill_all(color):
pixels[:] = [color] * NUM_PIXELS
if __name__ == "__main__":
	while True:
		# create random branch indexes
		branches = [0, 1, 2, 3, 4, 5]
		random.shuffle(branches)
        
		# restart all LEDs to WHITE
		# go through random indexes
		# and light up branches to blue
		fill_all(WHITE)
		time.sleep(0.5)
		for i in branches:
			fill_branch(branches[i], BLUE)
			time.sleep(0.5)

A bit of diffusion paper over the whole thing hides all the wires and showcases the LEDs so we can enjoy the programmed animation!

Show us your creations

We hope this Raspberry Pi project keeps you entertained this holiday season, and inspires you to think of more ways to combine creativity and coding. If you tackle this DIY project, we want to see your finished product! Be sure to share a photo of your own Raspberry Pi holiday lights creation and tag Codecademy on Instagram, Facebook, and Twitter.

There’s a reason why Python is frequently crowned the most popular programming language among professional developers and people learning to code. Python is an easy-to-read, versatile programming language that’s used in many different areas of software development.

For example, Python has a stellar reputation in data science, explains Ada Morse, Codecademy Curriculum Developer in Data Science. “Python’s the standard, so it’s a good one to know,” she says. Want to learn the ins and outs of using Python for data science? Our new free course Getting Started with Python for Data Science will teach you how to use Python to explore real-life datasets and answer questions using data.

The exercises in Getting Started with Python for Data Science are designed to mimic the work that you’d do as a Data Scientist or Data Analyst, so it’s a great way to test the waters and see if you like the field, Ada says. The course is also open to absolute beginners and anyone who wants to learn more about data. We’ll walk you through everything you need to know to use the main data science tools — Python, Pandas, and Jupyter Notebooks — and you’ll see firsthand what makes Python so awesome. Read on to learn more about why you should learn Python if you want to work with data.

Python has a simple, English-like syntax.

Data science can be intimidating for folks who aren’t super comfortable with numbers and math. With Python, rather than having to make sense of a jumble of complicated symbols and equations on a screen, the syntax looks like a natural (or spoken) language. Python is designed to be readable, which is one reason why it’s so approachable for coding beginners, Ada says.

Take a look at this snippet of Python code — you can probably guess what it’s supposed to do just by reading it:

name = 'Codey'
print(name + ' is learning to code')
if name is not "Codey":
print("Welcome " + name)
for letter in name:
print(letter+"!")

Since Python is so easy to learn, you can start learning more complicated concepts with it quickly. Compared to other data science languages (like Julia, for example), you don’t need as much theoretical computer science knowledge to work with Python, Ada says. “Python handles some of the technical details for you,” she says.

There are lots of handy Python add-ons.

The neat thing about Python is that there are tons of libraries and frameworks that handle standard tasks in different areas of software development, from machine learning to data science. These prewritten code packages do a lot of grunt work for you, so you can write Python code faster and build apps that are pre-organized and structured.

For example, in Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation. “Pandas is really helpful because instead of having to reinvent how to work with tables of data, a lot of the basic code has already been written,” Ada explains. “Now your job is just to apply that to the dataset that you want to work with.”

Some go-to Python libraries for data science include NumPy, MatPlotLib, and SciPy. Read this blog to learn more about the various Python libraries and tools that you can take advantage of while learning the language.

You can build other cool things with Python.

Python is not strictly a data science language; you can use it to create websites, test software, and build machine learning models. The course Getting Started with Python for Data Science is a great introduction to common coding principles that will come up again as you work on different coding projects or learn new languages altogether. Take a look at all of Codecademy’s Python courses to get a sense of how versatile the language is — you might be inspired to explore more in-depth Python topics, like the skill path Machine Learning Fundamentals or Build Python Web Apps with Flask.

You really can’t go wrong choosing Python as a first language whether you want to pursue data science or another specialty. And once you know one programming language, it’s typically easier to pick up other ones because there are so many overlapping concepts across languages.

Python is extremely popular.

As a beginner, you’ll probably find yourself searching lots of different coding questions on Stack Overflow or Google. Since so many people use Python, it’s easy to find reputable resources and documentation. “You’ll be able to find tutorials or courses or something in Python, whereas a less popular language might be harder to find those sorts of resources,” Ada says.

Speaking of, Codecademy has lots of resources that you can turn to while learning Python (or any other language), including articles and explainers, our community-driven code documentation called Docs, practice projects, plus courses and tutorials.

Python is the industry-standard programming language for data science. “The popularity of Python means that most Data Scientists ‘speak’ Python to a certain degree,” Ada says. If you’re interested in having a career in data science, knowing Python will help you stand out as a serious candidate — and enable you to jump right in working on projects once you get hired.

Even if you don’t aspire to become a professional Data Scientist, knowing how to work with data is a very important and marketable skill. “It’s hard to think of a job that wouldn’t have any sort of contact with data these days,” Ada says. Becoming the go-to Python and data person at your organization can boost your career potential in any field.

Ready to learn Python for data science?

In our free introductory course Getting Started with Python for Data Science, you’ll get hands-on practice working with real datasets in Python. We’ll teach you how to work with the trifecta of data science tools: Python, Pandas, and Jupyter Notebooks. By the end of the course, you’ll be able to explore and summarize a dataset, filter data to find specific categories, and format raw data so you can answer a data question, Ada says. This course is great for absolute beginners, and will set you up nicely to take another Codecademy’s data science course.

If you’re loving using Python to answer questions about data, maybe this could be the start of a new career for you? Be sure to check out the Codecademy career paths in data science to learn the skills you need to work in this exciting area of tech.

Getting Started with Python for Data Science | Codecademy

Work hands-on with real datasets while learning Python for data science.

Python is kind of like the frozen yogurt of programming languages — it’s extremely popular and versatile on its own, but it’s even better when you add toppings. Of course, by “toppings,” we’re talking about the many Python libraries and tools that level-up what you can do with the language.

With data science, in particular, there are lots of pre-written Python code packages and extra tools that allow you to work with data in more advanced ways, explains Ada Morse, Codecademy Curriculum Developer in Data Science. In the new free course Getting Started with Python for Data Science, you’ll get to use Pandas, a Python module that’s used for data manipulation.

“Pandas is really helpful because instead of having to reinvent how to work with tables of data, a lot of the basic code has already been written,” Ada says. “Now your job is just to apply that to the dataset that you want to work with.” Pandas is just a taste of what you can do with Python, and there are thousands of additional libraries you can choose from. Curious which data science libraries and tools you should try first? Here are the most common, beginner-friendly Python libraries and tools that you can use for data science.

Pandas

This is the standard data science library that’s used for data manipulation in Python. “Anyone who does data science in Python works in Pandas — and actually, a lot of the time the vast majority of the code will be Pandas as opposed to Python,” Ada says. Pandas comes with pre-packaged code for working with tables of data that’s organized into rows and columns.

In Getting Started with Python for Data Science, you’ll start working with Pandas right away to import datasets, summarize the data, identify problems, and explore possible outcomes.

Jupyter Notebooks

In our new course Getting Started with Python for Data Science, you’ll get hands-on practice using Jupyter Notebooks, an interactive workspace for developing data science code and visualizations, Ada says. With Jupyter Notebooks, you can execute Python code, review the output quickly, and record your results just like you would in an analog notebook.

Jupyter Notebooks is an essential tool for data analysis, because you can test a bunch of hypotheses and keep a running log of your results. “Most working data scientists do their work in Jupyter Notebook,” Ada says.

If you’re a beginner who’s just learning how to code, using Jupyter Notebooks to test lines of code one at a time is super helpful. Jupyter Notebooks supports other programming languages besides Python, like R and Java. “If then you want to learn something else, chances are you can do that in Jupyter Notebooks and feel at home,” Ada says.

MatPlotLib

If you want to make compelling data visualization and graphical plots, you’ll want to use MatPlotLib. With this Python package, you can make all kinds of interactive visualizations including pie charts, heat maps, histograms, and 3D bar charts. (Take a look at this gallery to see all the gorgeous MatPlotLib data visualizations you can use in your work.) In the course Learn Data Visualization, you’ll turn data into impactful line, bar, and pie graphs.

Seaborn

Another Python add-on for data visualizations is Seaborn, which enables you to give your charts some style and flair. Using Seaborn you can adjust the background color, grids, borders, and fonts within a chart. Colors and aesthetics might seem superfluous, but when you’re trying to communicate insights with data, style can greatly affect how well your audience perceives your message. In the skill path Visualize Data with Python, you’ll work with Seaborn to style a MatPlotLib graph.

NumPy

NumPy (short for “NumericalPython”), is the standard library for working with numbers in Python, and is frequently used in science and engineering. With NumPy, you can quickly complete numerical operations and create multi-dimensional arrays and matrices. Want to understand how to use this Python library for statistical analysis? Check out the course Learn Statistics with NumPy.

BeautifulSoup

BeautifulSoup is a quirky name for a highly practical package that allows you to scrape data from the web in a format that’s suitable for Python. Once you’ve scraped your data with BeautifulSoup, you can do all kinds of things with Python, like make visualizations with MatPlotLib or analyze it with Pandas. You can learn how to use BeautifulSoup in our course Learn Web Scraping with BeautifulSoup.

Ready to start learning Python? Try our free introductory course Getting Started with Python for Data Science! You’ll get hands-on practice working with real datasets using industry-standard data science tools: Python, Pandas, and Jupyter Notebooks. Once you get familiar with Python, be sure to explore the rest of our Python courses to explore all the other cool things you can make with Python.

Getting Started with Python for Data Science | Codecademy

Work hands-on with real datasets while learning Python for data science.

If your happy place is getting lost inside the pages of a Microsoft Excel workbook, there’s a programming language that you’ll probably get a kick out of: Python. Considered one of the most popular programming languages out there, Python is used for everything from web development to machine learning, and of course, data science.

While there are advantages to using both Excel and Python, “Python is just a little more robust,” says Ada Morse, Codecademy Curriculum Developer in Data Science. The new free Codecademy course Getting Started with Python for Data Science will walk you through how to use Python and Pandas, a library specifically for data manipulation and analysis, to explore, clean, and transform real datasets.

Never coded before? Don’t be intimidated by Python. This course is designed for beginners in mind, and Python has a concise, English-like syntax that reads like a natural (or spoken) language. Here are a few scenarios when you’d want to use Python over a no-code tool like Microsoft Excel, and exactly what you need to start learning the popular programming language.

You’re working with a lot of data.

It might seem like you could add an infinite number of cells to an Excel spreadsheet but there is actually a limit to the number of rows and columns it can hold — 1,048,576 rows and 16,384 columns, to be exact. “Once you’ve got a bigger data set, the advantage of being able to scroll through your data in Excel no longer really makes sense,” Ada says. “The speed of Excel becomes a problem.”

With Python, you can easily work with a very large dataset without sacrificing performance. The Python library PySpark is designed specifically for working with “big data,” which is defined as any data that is too big for a typical modern computer to process and analyze. You can learn more about how to use PySpark in our course Introduction to Big Data with PySpark.

Data scientists often work with lots of different types of data from different sources. While Excel can manage data from multiple sources, Python has libraries that allow you to easily access and process data from lots of other sources. “In a modern data landscape at a company where you’ve got cloud databases, data lakes, and all this sort of stuff, the packages with Python are just a little bit more robust,” Ada says.

The Python library BeautifulSoup, for example, is used to extract data from a website so you can put it into a Python structure called a DataFrame. We’ll show you how to do this in our course Learn Web Scraping with BeautifulSoup.

You’re doing advanced data analysis.

As you move toward more advanced data analytics, you need a tool that can execute sophisticated functions, Ada explains. Excel is a solid entry-level choice for crunching numbers and managing data, but there are hundreds of thousands of Python libraries and packages that can level-up how you analyze, visualize, and understand data. For example, the Python library NumPy can perform numerical operations on large quantities of data. Another library MatPlotLib can be used to generate elegant and interactive data visualizations.

Since Python is so easy to learn and simple to read, you can start mastering more complicated concepts quicker. In the course Getting Started with Python for Data Science, you’ll get to use Pandas and work with real datasets to sort, clean, and analyze data. You can take a closer look at these libraries with the courses Learn Data Analysis with Pandas and Learn Statistics with NumPy. Be sure to explore all of Codecademy’s Python courses — if you already know how to code, you can jump right in with the free course Python for Programmers.

You’d like to incorporate machine learning.

Machine learning is a subset of data science that’s all about teaching a computer to make predictions on its own by picking up on patterns within data. Everything from your social media feed to your smart home appliance relies on machine learning technology.

It’s possible for an Excel super-user to get good enough at using the software to incorporate machine learning and predictions, but it’s much more straightforward with Python. There are a variety of machine learning libraries for Python that you can use to prepare and clean data, choose models to use on the data, and then generate recommendations based on patterns. Some common Python libraries that Machine Learning Engineers use are Tensorflow, sci-kit image, and PyTorch.  

Curious how you can become a Machine Learning Engineer? The Codecademy career path Data Scientist: Machine Learning Specialist will teach you everything you need to know to be job-ready. In this path, you’ll start by learning the basics of Python (you don’t need any experience to get started) and go deep into building neural networks with the language.

When should you use Microsoft Excel?

To be clear: Microsoft Excel is by no means outdated or obsolete, and there are still times when it’s more convenient to use Excel. For example, if you’re working on a very quick project or you need to collaborate on a spreadsheet with several people who may not understand Python or how to code.

The biggest benefit of using Excel is that it’s a “one-stop shop,” Ada says. “All of your data is stored there, and you can create your calculations and visualizations in the same sheet.” If you want to get better at using all of Microsoft Excel’s features, try our free course Analyze Data with Microsoft Excel.

Understanding which data science tools to deploy for a particular project is part of being a Data Analyst. In the new Codecademy career path Business Intelligence Data Analyst you’ll get comfortable using all the tools of the trade, including Excel, Tableau, and SQL.

Start learning how to use Python for data science

These are just some of the reasons why you should learn Python if you want to work with data. By the end of the free course Getting Started with Python for Data Science, you’ll be able to use Python to explore and summarize a dataset, filter data to find specific categories, and format raw data so you can answer a question. And once you get a taste of what you can do with Python, you’ll want to check out all of our Python courses in machine learning, web development, and lots more.

Getting Started with Python for Data Science is also a great way to get introduced to coding. Throughout the course, you’ll pick up fundamental coding principles that will come up again as you learn other programming languages. Once you know one programming language, it’s easier to learn another language because there are so many similarities — and the good news is, whichever language you choose, there’s probably a Codecademy course that will guide you.

Getting Started with Python for Data Science | Codecademy

Work hands-on with real datasets while learning Python for data science.