Omid - Challenge 153

data-challenges
advanced-exercises
🔰 Challenge 153: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 153

Challenge Description

🔰 Challenge 153: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)
library(lubridate)

path = "files/CH-153 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C11")
test  = read_excel(path, range = "G2:H6")

output = input %>%
  mutate(dates = map2(From, To, seq, by = "1 day")) %>%
  unnest(dates) %>%
  distinct(dates) %>%
  group_by(cons = cumsum(c(0, diff(dates)) != 1)) %>%
  summarise(From = min(dates), To = max(dates)) %>%
  ungroup() %>%
  select(-cons)

all.equal(output, test)
#> [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

  • 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

path = "CH-153 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=10)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=4).rename(columns=lambda x: x.split('.')[0])

input['dates'] = input.apply(lambda row: pd.date_range(start=row['From'], end=row['To']), axis=1)
dates = pd.to_datetime(input.explode('dates')['dates'].unique())
grouped_dates = pd.DataFrame({'dates': dates})
grouped_dates['group'] = (grouped_dates['dates'].diff().dt.days > 1).cumsum() + 1
grouped_dates = grouped_dates.groupby('group')['dates'].agg(['min', 'max']).reset_index()
grouped_dates = grouped_dates.rename(columns={'min': 'From', 'max': 'To'})[['From', 'To']]

print(grouped_dates.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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.