Today I learned how to unpivot a table from a wide format to a long format using the .melt() method.
Why did I even need to learn that? Many datasets are commonly represented in wide format but long formatted data is often more accessible for computers to work with hence the need to learn this important concept
Trust me to always put into practice any new concept I learn. So for my practice pleasure, I worked with unemployment data that originally had one row per year and 12 separate columns for each month. This format is common in raw economic datasets but not ideal for time-series analysis. So I reshaped it using .melt() to convert the wide table into a long format where each row represents a specific year-month pair.
This gives me three clean columns: year, month and unempl_rate. This structure is much better for analysis.
Next, I combined year and month into one date column and converted it into an actual datetime object. This is important because time-series models and visualizations rely on having a real date index.
I sorted the data by date and plotted unemployment rate over time.
This creates a proper chronological unemployment trend line exactly how economists and analysts visualize long-term labor market patterns.
This is a core skill in data work, especially in economics and finance.
Analysts constantly reshape messy datasets into clean time-series form.
This process allows us to:
✔ Track how unemployment moves month-to-month
✔ Identify trends, spikes, and declines
✔ Merge with other economic indicators (inflation, GDP, interest rates)
✔ Understand how policy changes affect the labor market
Learning to reshape and prepare data like this is the foundation of almost every real-world economic analysis.
Stay tuned for tomorrow😊
-SP
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