09-09, 14:00–14:20 (Asia/Jerusalem), Hall 7
Language: English
Using simple, “old-school” logging, I recorded my dishwasher’s energy and water use, then leveraged Python and pandas to clean, analyze, and visualize real-world data. A beginner-friendly dive into experiment design and data analysis.
Wash, Dry, Analyze: Turning Dishwasher Logs into Clean Data
Abstract:
I set up a controlled experiment on my dishwasher to uncover what’s really happening with energy and water use—because designing experiments is half the fun, and Python makes the rest a breeze. In this session I’ll show how I:
- Designed test cycles and integrated
power and flowsensors - Used pandas to
import, clean, and flag anomalies in CSV logs - Applied descriptive stats (mean, median, outliers) to evaluate energy, water, and cost
- Created clear, reproducible visualizations with matplotlib
The dishwasher was just an excuse to dive into pandas, and this talk is perfect for beginners eager to start their own data adventures.
Basic
Target audience –Developers, Data Scientists
Other (target audience) –Everybody who owns a dishwasher
I'm a data scientist at SentinelOne, and I'm passionate about the power of data to drive insights and innovation. My work centers around machine and deep learning. I've spent years honing my skills in Python scientific programming.
As an organizing PyData Tel Aviv conference member, I'm dedicated to fostering collaboration and knowledge-sharing in the data science community.
I take pride in building Python packages that make data workflows more streamlined and efficient.
When I'm not working with data, you can find me hiking in the great outdoors
or exploring the vibrant cultural scene of Tel Aviv.
ChatGPT helped with phrasing this paragraph.
Also, the LLM generated the last line entirely, and it is untrue.
I do have two daughters and a partner with whom I love spending time.