Method Chaining - Practice Coding Problems

Method Chaining - Problem

Database Easy

You're working as a wildlife researcher analyzing animal data! Given a DataFrame containing information about various animals, your task is to identify the heavyweight champions of the animal kingdom.

Your mission: Find all animals that weigh strictly more than 100 kilograms and return their names sorted by weight in descending order (heaviest first).

DataFrame Schema:

Column Name	Type	Description
`name`	object	Animal's name
`species`	object	Animal species
`age`	int	Animal's age in years
`weight`	int	Animal's weight in kilograms

This problem demonstrates method chaining - a powerful technique where you chain multiple DataFrame operations together for clean, readable data manipulation code.

Input & Output

example_1.py — Basic Case

$ Input: DataFrame: | name | species | age | weight | |----------|---------|-----|--------| | Tatiana | Snake | 98 | 464 | | Khaled | Giraffe | 50 | 41 | | Alex | Leopard | 6 | 328 | | Jonathan | Monkey | 45 | 463 | | Stefan | Bear | 100 | 50 | | Tommy | Panda | 26 | 349 |

› Output: | name | |----------| | Tatiana | | Jonathan | | Tommy | | Alex |

💡 Note: Animals with weight > 100kg: Tatiana(464kg), Alex(328kg), Jonathan(463kg), Tommy(349kg). Sorted by weight descending: Tatiana(464), Jonathan(463), Tommy(349), Alex(328).

example_2.py — All Heavy Animals

$ Input: DataFrame: | name | species | age | weight | |---------|----------|-----|--------| | Jumbo | Elephant | 25 | 5000 | | Rhino | Rhino | 15 | 2300 | | Hippo | Hippo | 8 | 1500 |

› Output: | name | |-------| | Jumbo | | Rhino | | Hippo |

💡 Note: All animals weigh more than 100kg. Sorted by weight: Jumbo(5000kg) > Rhino(2300kg) > Hippo(1500kg).

example_3.py — No Heavy Animals

$ Input: DataFrame: | name | species | age | weight | |--------|---------|-----|--------| | Fluffy | Cat | 2 | 5 | | Buddy | Dog | 5 | 30 | | Tweet | Bird | 1 | 0.5 |

› Output: Empty DataFrame: | name | |------| | |

💡 Note: No animals weigh more than 100kg, so the result is an empty DataFrame with just the 'name' column.

Visualization

Tap to expand

Understanding the Visualization

Input Gate

All animal records enter the processing pipeline

Weight Scanner

Automatic filter removes animals ≤ 100kg

Sorting Scale

Remaining animals sorted by weight (heaviest first)

Name Tag Printer

Only animal names are output for the final list

Key Takeaway

🎯 Key Insight: Method chaining creates an elegant, readable pipeline that leverages pandas' optimized C implementations for maximum performance.

Time & Space Complexity

Time Complexity

⏱️

O(n log n)

O(n) for filtering + O(n log n) for sorting, but optimized at C level

⚡ Linearithmic

Space Complexity

O(k)

O(k) for the filtered result where k ≤ n

✓ Linear Space

Constraints

1 ≤ animals.length ≤ 10⁴
Animal weights are non-negative integers
Weight threshold is strictly greater than 100kg (not inclusive)
Animal names are unique within the dataset

Asked in

G Google 45 a Amazon 38 f Meta 28 N Netflix 22

The optimal solution uses pandas method chaining to elegantly filter animals over 100kg, sort by weight descending, and select only names in one streamlined operation: animals[animals['weight'] > 100].sort_values('weight', ascending=False)[['name']]

Common Approaches

Approach	Time	Space	Notes
✓ Manual Iteration and Sorting	O(n log n)	O(k)	Manually iterate through DataFrame, collect heavy animals, then sort
Pandas Method Chaining (Optimal)	O(n log n)	O(k)	Use pandas method chaining for efficient filtering, sorting, and selection

Manual Iteration and Sorting — Algorithm Steps

Iterate through each row of the DataFrame
Check if animal weight > 100kg
Store qualifying animals with their weights
Sort the collected animals by weight descending
Extract and return just the names

Visualization

Tap to expand

Step-by-Step Walkthrough

Iterate

Go through each animal one by one

Filter

Check weight > 100kg condition

Collect

Store qualifying animals

Sort

Arrange by weight descending

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    char name[50];
    int weight;
} Animal;

int compareWeight(const void *a, const void *b) {
    Animal *animalA = (Animal *)a;
    Animal *animalB = (Animal *)b;
    return animalB->weight - animalA->weight; // Descending order
}

char** selectData(Animal* animals, int size, int* returnSize) {
    Animal* heavyAnimals = malloc(size * sizeof(Animal));
    int count = 0;
    
    // Filter animals over 100kg
    for (int i = 0; i < size; i++) {
        if (animals[i].weight > 100) {
            heavyAnimals[count++] = animals[i];
        }
    }
    
    // Sort by weight descending
    qsort(heavyAnimals, count, sizeof(Animal), compareWeight);
    
    // Extract names
    char** names = malloc(count * sizeof(char*));
    for (int i = 0; i < count; i++) {
        names[i] = malloc(50 * sizeof(char));
        strcpy(names[i], heavyAnimals[i].name);
    }
    
    *returnSize = count;
    free(heavyAnimals);
    return names;
}

Time & Space Complexity

Time Complexity

⏱️

O(n log n)

O(n) for iteration + O(n log n) for sorting

⚡ Linearithmic

Space Complexity

O(k)

O(k) where k is the number of animals over 100kg

✓ Linear Space

Constraints

1 ≤ animals.length ≤ 10⁴
Animal weights are non-negative integers
Weight threshold is strictly greater than 100kg (not inclusive)
Animal names are unique within the dataset

23.4K Views

High Frequency

~8 min Avg. Time

892 Likes

Ln 1, Col 1

Smart Actions

💡 Explanation

AI Ready

💡 Suggestion Tab to accept Esc to dismiss

// Output will appear here after running code

Code Editor Closed

Click the red button to reopen

Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Manual Iteration and Sorting — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

Select Compiler