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Exercise

Calculating on a pivot table

Pivot tables are filled with summary statistics, but they are only a first step to finding something insightful. Often you'll need to perform further calculations on them. A common thing to do is to find the rows or columns where the highest or lowest value occurs.

Recall from Chapter 1 that you can easily subset a Series or DataFrame to find rows of interest using a logical condition inside of square brackets. For example: series[series > value].

pandas is loaded as pd and the DataFrame temp_by_country_city_vs_year is available. The .head() for this DataFrame is shown below, with only a few of the year columns displayed:

country city 2000 2001 2002 … 2013
Afghanistan Kabul 15.823 15.848 15.715 … 16.206
Angola Luanda 24.410 24.427 24.791 … 24.554
Australia Melbourne 14.320 14.180 14.076 … 14.742
Sydney 17.567 17.854 17.734 … 18.090
Bangladesh span translate="no">Dhaka 25.905 25.931 26.095 … 26.587

Instructions

100 XP
  • Calculate the mean temperature for each year, assigning to mean_temp_by_year.
  • Filter mean_temp_by_year for the year that had the highest mean temperature.
  • Calculate the mean temperature for each city (across columns), assigning to mean_temp_by_city.
  • Filter mean_temp_by_city for the city that had the lowest mean temperature.