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Group and Compare multiple dataframe columns with conditions in Python

I'm tryng to print out the states with highest population in each region.

Code Sample:

# all unique regions
region_unique = data['Region'].unique()

# highest population
max_pop = data['population'].max()

How can I chain the above lines of code and bring in the 'States' column to achieve my result?

Dataset:

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Solution

  • Considering you haven't mentioned any library...

    You could first create a helper dict, mapping each region to an array of states. Each state is a tuple: (state, pop) (name and population count):

    regions = {}
for state, pop, region in zip(data['States'], data['population'], data['Region']):
    res.setdefault(region, []).append((state, pop))

    Then for each region you can pull out the most inhabited state:

    for region, states in regions.items():
    print(region, max(states, key=lambda _, pop: pop))

    To states under each region with a population less than 100, you can do:

    for region, states in regions.items():
    print(region, list(filter(lambda state: state[1] > 100, states)))