Contents

Python Pandas For Your Grandpa - 3.10 Challenge: Hobbies

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

  1. Introduction
    1.1 Introduction
  2. Series
    2.1 Series Creation
    2.2 Series Basic Indexing
    2.3 Series Basic Operations
    2.4 Series Boolean Indexing
    2.5 Series Missing Values
    2.6 Series Vectorization
    2.7 Series apply()
    2.8 Series View vs Copy
    2.9 Challenge: Baby Names
    2.10 Challenge: Bees Knees
    2.11 Challenge: Car Shopping
    2.12 Challenge: Price Gouging
    2.13 Challenge: Fair Teams
  3. DataFrame
    3.1 DataFrame Creation
    3.2 DataFrame To And From CSV
    3.3 DataFrame Basic Indexing
    3.4 DataFrame Basic Operations
    3.5 DataFrame apply()
    3.6 DataFrame View vs Copy
    3.7 DataFrame merge()
    3.8 DataFrame Aggregation
    3.9 DataFrame groupby()
    3.10 Challenge: Hobbies
    3.11 Challenge: Party Time
    3.12 Challenge: Vending Machines
    3.13 Challenge: Cradle Robbers
    3.14 Challenge: Pot Holes
  4. Advanced
    4.1 Strings
    4.2 Dates And Times
    4.3 Categoricals
    4.4 MultiIndex
    4.5 DataFrame Reshaping
    4.6 Challenge: Class Transitions
    4.7 Challenge: Rose Thorn
    4.8 Challenge: Product Volumes
    4.9 Challenge: Session Groups
    4.10 Challenge: OB-GYM
  5. Final Boss
    5.1 Challenge: COVID Tracing
    5.2 Challenge: Pickle
    5.3 Challenge: TV Commercials
    5.4 Challenge: Family IQ
    5.5 Challenge: Concerts

Additional Content

  1. Python NumPy For Your Grandma
  2. Neural Networks For Your Dog
  3. Introduction To Google Colab