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Python Pandas For Your Grandpa - 3.10 Challenge: Hobbies

2021-02-01 347 words 2 minutes

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Setup

Suppose we’ve polled five couples on their hobbies. For each couple, determine what hobbies the man has that the woman doesn’t have and what hobbies the woman has that the man doesn’t have.

import numpy as np
import pandas as pd

couples = pd.DataFrame({
    'man': [
        ['fishing', 'biking', 'reading'],
        ['hunting', 'mudding', 'fishing'],
        ['reading', 'movies', 'running'],
        ['running', 'reading', 'biking', 'mudding'],
        ['movies', 'reading', 'yodeling']
    ],
    'woman': [
        ['biking', 'reading', 'movies'],
        ['fishing', 'drinking'],
        ['knitting', 'reading'],
        ['running', 'biking', 'fishing', 'movies'],
        ['movies']
    ]
})
print(couples)
##                                    man                               woman
## 0           [fishing, biking, reading]           [biking, reading, movies]
## 1          [hunting, mudding, fishing]                 [fishing, drinking]
## 2           [reading, movies, running]                 [knitting, reading]
## 3  [running, reading, biking, mudding]  [running, biking, fishing, movies]
## 4          [movies, reading, yodeling]                            [movies]

Solution

sets = couples.applymap(set)
woman_not_man = sets.diff(axis=1, periods=1).drop(columns='man')
man_not_woman = sets.diff(periods=-1, axis=1).drop(columns='woman')
hobbies_not_shared = pd.concat((man_not_woman, woman_not_man), axis=1).applymap(list)
print(hobbies_not_shared)
##                    man              woman
## 0            [fishing]           [movies]
## 1   [hunting, mudding]         [drinking]
## 2    [movies, running]         [knitting]
## 3   [mudding, reading]  [fishing, movies]
## 4  [yodeling, reading]                 []

Course Curriculum

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