An analysis of Ontario retail fuel prices from 1990 to 2023 across 14 cities, built with Python and Pandas, producing time series charts, distributions, and a regional map.
the problem
Retail fuel prices in Ontario fluctuate constantly and vary by region, but the raw historical data is hard to make sense of without visualization.
the approach
Cleaned and analyzed decades of Ontario retail fuel price data in Python and Pandas across 14 cities, then used GeoPandas to map regional differences and Matplotlib to chart price trends and distributions over time.
how it happened
Cleaned and structured over three decades of Ontario retail fuel price records across 14 cities
Built time series charts in Matplotlib to visualize long-term price trends from 1990 to 2023
Used GeoPandas to map regional price differences across the 14 cities
Analyzed price distributions to surface patterns beyond simple year-over-year averages
built with
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