Omid - Challenge 262

data-challenges
advanced-exercises
🔰 Question ID A B C Value Data {‘sales’:[{‘region’:‘A’,‘val’:100},{‘region’:‘B’,‘val’:200}]}
Published

March 24, 2026

Illustration for Omid - Challenge 262

Challenge Description

🔰 Question ID A B C Value Data {“sales”:[{“region”:“A”,“val”:100},{“region”:“B”,“val”:200}]}

Solutions

library(tidyverse)
library(readxl)
library(jsonlite)

path = "files/200-299/262/CH-262 JSON Structures.xlsx"
input = read_excel(path, range = "B2:C5")
test  = read_excel(path, range = "E2:G8")

result = input %>%
  mutate(
    sales = map(Data, ~ fromJSON(.x)$sales %>% as_tibble())
  ) %>% 
  unnest(cols = sales) %>%
  select(ID, Region = region, Value = val)

all.equal(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • 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 json

path = "200-299/262/CH-262 JSON Structures.xlsx"

input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=3)
test = pd.read_excel(path, usecols="E:G", skiprows=1, nrows=6).rename(columns=lambda c: c.replace('.1', ''))

def extract_sales(cell):
    if not isinstance(cell, str):
        return []
    try:
        obj = json.loads(cell)
        return obj.get('sales', [])
    except json.JSONDecodeError:
        return []

input['sales_list'] = input['Data'].apply(extract_sales)
result = pd.json_normalize(
    input.explode('sales_list').assign(sales=lambda df: df['sales_list'])[['ID', 'sales']]
    .dropna(subset=['sales'])
    .reset_index(drop=True)['sales']
    .apply(lambda x: x if isinstance(x, dict) else {})
).assign(ID=input.explode('sales_list')['ID'].values)[['ID', 'region', 'val']].rename(columns={'region': 'Region', 'val': 'Value'})


print(result.equals(test))

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.