Data Visualization of Weather with Raspberry Pi

Posted on Sep 5, 2017
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


I am a big fan of the Rapsberry Pi single board computing data platform, and I've used the devices in numerous projects. I recently acquired a Pimoroni Unicorn Phat (Pico Hardware Attached to Top) - a 4 x 8, 255 color LED display and I decided to see how well a small display would work to convey information at a glance.

I will use a Pi Zero W and the Pimoroni Unicorn Phat (I know, its a silly name!) to display local weather conditions in realtime.

To reach this goal, I will use code written in Python to use Pandas to scrape the current hourly weather info based on a given Zip Code. To make full use of the 4 separate LED columns it will display:

  • Temperature
  • Humidity
  • Precipitation,
  • Wind Speed

Before stepping into the code there is the conceptual problem - how do I output a float as a combination of 8 LEDS of various color?

Since the 'screen' will display 4 separate values, we'll need to code them separately.


First is the temperature. As a resident of the NYC area, the annual temperature can range from 0 F to 95 F in a typical year, so I split the temperature into three groups. Cold, Warm, and Hot, represented by blue, green, and red respectively.

Cold is any below 32 F, Warm is 33 - 74 F, and Hot is 75+ F.

According to the display below, it is ~85 degrees, ~80% humidity,  ~20% precipitation, and wind speed of ~16 MPH in NYC

  • Temperature (Blue -> cold [0, 32], Green -> warm [33, 74], Red -> hot[75,100])
  • Humidity reflects % humidity * 8 / 100
  • Precipitation reflects % precipitation * 8 / 100
  • Wind Speed in MPH is a log_2 scale

Data Visualization of Weather with Raspberry Pi

When I took this image it was a bit cooler in Memphis (38101), it is ~75 degrees, ~90% humidity,  ~30% precipitation, and wind speed of ~16 MPH in TN

Data Visualization of Weather with Raspberry Pi

Lastly we have Beaver (25813), ~ 87 degrees, ~65% humidity,  ~0% precipitation, and wind speed of ~8 MPH in WVA.

Data Visualization of Weather with Raspberry Pi


Below is a snipped of code for setting the color and number of active LEDS in a given row.


def set_temp(temp):

    temp = int(temp)

    def linshuffle(linspace,temp):

        ticks = linspace

        tmp = temp



        ticks = pd.Series(ticks)

        return max(1,int(ticks[ticks == temp].index[0]))


    if int(temp) <= 32:

        T = linshuffle(np.linspace(0,32,10),temp)

        R,G,B = (0,0,100)


    elif int(temp) < 75:

        T = linshuffle(np.linspace(32,75,10),temp)

        R,G,B = (0,int(np.ceil(3*int(temp))),0)


    elif int(temp) >= 75:

        T = linshuffle(np.linspace(75,100,10),temp)

        R,G,B = (2*int(temp),0,0)



How do we get weather information?

There are two options, the first is to use a hardware sensor and capture the data. The second method is to scrape the relevant information from the web. In this instance, we can get the hourly weather information containing temperature (F), humidity %, precipitation %, and wind speed (MPH)

The data above is from, their hourly weather information is exactly what we need!

Python & Pandas Code

Here's the Python & Pandas code to quickly scrape the table of data without needing to parse the HTML tree.

def get_weather_df(zip_code):
Zip_Code = zip_code.strip().replace(' ','').split('-')[0]
if len(Zip_Code) == 5 and Zip_Code.isnumeric():
url = '' %(Zip_Code)
url = weather_url()
temp_df = pd.read_html(url)[0]
temp_df = temp_df.iloc[:,1:]
temp_df.columns = ['Time','Description','Temp','Feels','Precip','Humidity','Wind']
temp_df['Time'] = pd.to_datetime(temp_df['Time'])
temp_df['Temp'] = temp_df['Temp'].map(lambda x: int(x[:-1]))
temp_df['Feels'] = temp_df['Feels'].map(lambda x: int(x[:-1]))
temp_df['Precip'] = temp_df['Precip'].map(lambda x: int(x[:-1]))
temp_df['Humidity'] = temp_df['Humidity'].map(lambda x: int(x[:-1]))
temp_df['Wind Direction'] = temp_df['Wind'].map(lambda x: x.split()[0])
temp_df['Wind Speed'] = temp_df['Wind'].map(lambda x: x.split()[1])''
temp_df = temp_df[['Description','Temp','Feels','Precip','Humidity','Wind Direction','Wind Speed']]
return (temp_df)

Lucky for us, the Pandas read_html() method is powerful enough to grab the entire page of data in one go!. The subsequent lines of code are to clean up the content - ie: change strings to ints, remove % symbol and degree sign.

The full code can be found on my github page here

About Author

Scott Edenbaum

Scott Edenbaum is a recent graduate from the NYC Data Science Academy. He was hired by the Academy to assist in buildout of the learning management system and seeks to pursue a career as a Data Scientist. Scott's...
View all posts by Scott Edenbaum >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI