Omid - Challenge 37

data-challenges
advanced-exercises
🔰 Groups In the question table, the IDs of 20 people who call each other are provided.
Published

March 24, 2026

Illustration for Omid - Challenge 37

Challenge Description

🔰 Groups In the question table, the IDs of 20 people who call each other are provided.

Solutions

library(tidyverse)
library(readxl)
library(igraph)

input = read_excel("files/CH-037 Connected people.xlsx", range = "B2:C25")
test  = read_excel("files/CH-037 Connected people.xlsx", range = "E2:F6")

result = input %>%
  graph_from_data_frame(directed = FALSE) %>%
  components() %>%
  membership()  %>%
  as.data.frame() %>%
  rownames_to_column("name") %>%
  summarise(People = str_c(sort(as.numeric(name)), collapse = ","), .by = "x")

print(result)
print(test)

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Parses the text patterns directly instead of relying on manual cleanup

  • Strengths:

    • The R solution stays close to the workbook rule and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the sheet structure and source ranges remain stable.

  • Gem:

    • The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
import networkx as nx

input = pd.read_excel("CH-037 Connected people.xlsx", sheet_name="Sheet1", usecols="B:C", skiprows=1, nrows=25)
test  = pd.read_excel("CH-037 Connected people.xlsx", sheet_name="Sheet1", usecols="E:F", skiprows=1, nrows=4)

G = nx.Graph()
for _, row in input.iterrows():
    G.add_edge(row['Caller'], row['Respondant'])
subgraphs = list(nx.connected_components(G))
nodes_per_subgraph = [list(subgraph) for subgraph in subgraphs]
result = pd.DataFrame()
result['People'] = nodes_per_subgraph
result['People'] = result['People'].apply(lambda x: sorted(x))

print(result)

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • Strengths:

    • The Python version follows the same rule in a direct dataframe-oriented implementation.

  • Areas for Improvement:

    • The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.

  • Gem:

    • The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • The core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.