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Exercise

Pivot temperature by city and year

It's interesting to see how temperatures for each city change over time—looking at every month results in a big table, which can be tricky to reason about. Instead, let's look at how temperatures change by year.

You can access the components of a date (year, month and day) using code of the form dataframe["column"].dt.component. For example, the month component is dataframe["column"].dt.month, and the year component is dataframe["column"].dt.year.

Once you have the year column, you can create a pivot table with the data aggregated by city and year, which you'll explore in the coming exercises.

pandas is loaded as pd. temperatures is available.

Instructions

100 XP
  • Add a year column to temperatures, from the year component of the date column.
  • Make a pivot table of the avg_temp_c column, with country and city as rows, and year as columns. Assign to temp_by_country_city_vs_year, and look at the result.