[OC] I tracked 10,000+ grocery product prices in Norway for over 10 years. Here's how they changed. Visualization
![[OC] I tracked 10,000+ grocery product prices in Norway for over 10 years. Here's how they changed. Visualization](/api/images/reddit-maps/1rvo5j8_1773741602194.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "[OC] I tracked 10,000+ grocery product prices in Norway for over 10 years. Here's how they changed." and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on **Source:** Real purchase data from Norwegian online grocery store [Oda](https://oda.com), tracked via [Odalytics](https://odalytics.no) — a browser extension I built that analyzes grocery order history across households.
**Tools:** Python, Plotly, PostgreSQL (Supabase)
**Dataset:** 10,416 unique products, 142,827 daily price observations, 4,867 orders from real Norwegian households (2014–2026).
**What you're seeing:**
Each line shows how the price of an everyday grocery staple has changed relative to its starting price, indexed to 100 (3-month rolling average). The white dashed line is Norway's official food CPI from Statistics Norway (SSB), also indexed to 100 at January 2015.
**Key findings:**
- A single **cucumber** went from 13 kr to 34 kr — up **163%** since 2015 - **Butter** (TINE Meierismør 500g, Norway's #1 brand) rose from 27 kr to 62 kr — up **132%** since 2016 - A 12-pack of **eggs** more than doubled: 28 kr → 60 kr (+116% since 2019) - **Salmon fillets** (4pc): 48 kr → 90 kr (+86% since 2019)
Meanwhile, the official SSB food CPI rose **47%** over the same period. Individual staple prices have outpaced official food inflation by 2–3.5x.
Why the gap? CPI is a weighted basket that includes substitution effects (people switching to cheaper brands), quality adjustments, and category reweighting. Individual staple products with no close substitutes — like butter or eggs — can rise much faster than the aggregate index suggests.
For context: 1 NOK ≈ 0.09 USD / 0.085 EUR. Norwegian grocery prices are among the highest in Europe (although relatively low when compared to salaries).
**Other data we have beyond prices:**
- **CO₂ emissions per product** using the Danish Climate Database (10,416 products mapped), so users can track their carbon footprint - **Ultra-processed food (NOVA classification)** — every product classified on the NOVA 1–4 scale, so users can track their spend on ultra-processed food (NOVA 4) - **Country of origin** — where each product is actually produced, so users can track which economies they support
Happy to answer any questions about Norwegian grocery prices!, which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.
The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.
Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...