Omid - Challenge 180

data-challenges
advanced-exercises
🔰 Result Question Table Parent Child A B C D
Published

March 24, 2026

Illustration for Omid - Challenge 180

Challenge Description

🔰 Result Question Table Parent Child A B C D

Solutions

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

path <- "files/CH-180 Hierarchical Structure.xlsx"
input <- read_excel(path, range = "B2:C13")
test <- read_excel(path, range = "E2:F14") %>% arrange(Code)

in2 <- input %>% mutate(code = row_number(), .by = Parent)
g <- graph_from_data_frame(in2, directed = TRUE)
all_paths <- all_simple_paths(g, from = "A", to = V(g))

df <- map_df(all_paths, ~ data.frame(paths = paste(names(.x), collapse = "-")))

result <- df %>%
  mutate(IDs = str_extract(paths, "\\w$"), rn = row_number()) %>%
  mutate(path = str_replace_all(paths, "\\d", ~ in2$code[as.numeric(.x)])) %>%
  separate_rows(paths, sep = "-") %>%
  left_join(in2 %>% add_row(Parent = NA, Child = "A", code = 1), by = c("paths" = "Child")) %>%
  summarise(Code = paste0(code, collapse = "-"), .by = IDs) %>%
  select(Code, IDs) %>%
  add_row(Code = "1", IDs = "A") %>%
  arrange(Code)

all.equal(result, test, check.attributes = FALSE)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds the intermediate columns that drive the final result

    • 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 
from itertools import groupby
from operator import itemgetter

path = "CH-180 Hierarchical Structure.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=11)
test = pd.read_excel(path, usecols="E:F", skiprows=1, nrows=13)
test['Code'] = test['Code'].astype(str)

def get_hierarchical_code(data, target, root="A"):
    data = sorted([(str(parent), str(child)) for parent, child in data], key=itemgetter(0))
    children_by_parent = {k: list(map(itemgetter(1), g)) for k, g in groupby(data, key=itemgetter(0))}
    path = target
    code = []
    while path != root:
        parent = next((parent for parent, children in data if path in children), None)
        if parent is None:
            raise ValueError(f"Parent for node '{path}' not found. Check the hierarchy.")
        siblings = children_by_parent[parent]
        position = siblings.index(path) + 1
        code.insert(0, str(position))
        path = parent
    code.insert(0, "1")
    return "-".join(code)

unique_values = pd.unique(input.values.ravel())
hierarchical_codes = [get_hierarchical_code(input.values, value) for value in unique_values]
df = pd.DataFrame({ 'Code': hierarchical_codes, 'IDs': unique_values})
print(df.equals(test)) # True

  • 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

    • 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.