Today was all about diving deeper into the industry-level sales trends from the Seoul dataset I started cleaning yesterday.

After getting the basic structure set up, I wanted to understand:

💭 “Which industries are consistently dominating the sales charts every quarter?”
💭 “Is 한식음식점 really the MVP? Or is 노량진 수산물판매 stealing the show?”

🔍 What I explored today

1. Grouped total sales by industry and quarter
   • Used groupby on 기준_년분기_코드 and 서비스_업종_코드_명
   • Aggregated national-level total sales per category
2. Ranked the top 5 industries for each quarter in 2024
   • Turns out 한식음식점 (Korean restaurants) was on top more than once
   • But seafood sales in 노량진2동? Absolutely wild numbers.
   • That’s a district-level outlier, not an industry-wide winner
3. Converted sales units to 억 (100M KRW)
   • Because yes, my eyes were bleeding from all those zeros 😅
4. Made a grouped barplot with value labels
   • It actually turned out super readable. Might reuse that template again.

🧠 Insights

• Some industries (like Korean food) are steady earners across all quarters
• Others (like seafood) show extreme spikes in one region only
• Looks like this EDA will split nicely into macro (industry) and micro (district) levels

🔮 What’s next?

Tomorrow’s plan:

• Shift focus to 행정동 (district)-level analysis
• Estimate average sales per store (e.g., 치킨집 in 강남구)
• Possibly join with 사업체 수 data to get “store-level profitability”

That’s a wrap for today!
Feels good to have a clear separation between nationwide industry trends vs. local insights.
We’re building something real here 🚀