Using .melt() to reshape government data

The US Bureau of Labor Statistics (BLS) often provides data series in an easy-to-read format - it has a separate column for each month, and each year is a different row. Unfortunately, this wide format makes it difficult to plot this information over time. In this exercise, you will reshape a table of US unemployment rate data from the BLS into a form you can plot using .melt(). You will need to add a date column to the table and sort by it to plot the data correctly.

The unemployment rate data has been loaded for you in a table called ur_wide. You are encouraged to explore this table before beginning the exercise.

This exercise is part of the course

Joining Data with pandas

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Exercise instructions

Use .melt() to unpivot all of the columns of ur_wide except year and ensure that the columns with the months and values are named month and unempl_rate, respectively. Save the result as ur_tall.
Add a column to ur_tall named date which combines the year and month columns as year-month format into a larger string, and converts it to a date data type.
Sort ur_tall by date and save as ur_sorted.
Using ur_sorted, plot unempl_rate on the y-axis and date on the x-axis.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# unpivot everything besides the year column
ur_tall = ____


# Create a date column using the month and year columns of ur_tall
ur_tall['date'] = pd.to_datetime(ur_tall['____'] + '-' + ____)

# Sort ur_tall by date in ascending order
ur_sorted = ____

# Plot the unempl_rate by date
ur_sorted.plot(____)
plt.show()

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