Omid - Challenge 294

data-challenges
advanced-exercises
🔰 Group Challenge 294: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 294

Challenge Description

🔰 Group Challenge 294: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/200-299/294/CH-294 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C18")
test  = read_excel(path, range = "G2:H5")

g <- c <- 1
for(i in 1:nrow(input)) {
  input$Group[i] <- g
  c <- ifelse(input$Result[i] == "+", c + 1, 1)
  if(c > 4) { g <- g + 1; c <- 1 }
}

result = input %>%
  group_by(Group) %>%
  summarise(
    Start_Date = format(as.Date(first(Date)), "%d/%b/%Y"),
    End_Date = format(as.Date(last(Date)), "%d/%b/%Y"),
    `number of dates` = n(),
    .groups = 'drop'
  ) %>%
  mutate(
    End_Date = ifelse(`number of dates` < 4, NA, End_Date),
    `number of dates` = ifelse(`number of dates` < 4, "-", `number of dates`)) %>%
  select(-Group) %>%
  unite("Group", Start_Date, End_Date, sep = " - ", remove = TRUE)

all.equal(result, test)
# 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 = "200-299/294/CH-294 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=16)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=3)

g = c = 1
groups = []
for result in input['Result']:
    groups.append(g)
    c = c + 1 if result == '+' else 1
    if c > 4: g, c = g + 1, 1

input['Group'] = groups
input['Date'] = pd.to_datetime(input['Date'], format='%d/%b/%Y').dt.strftime('%d/%b/%Y')

result = input.groupby('Group').agg(
    Start_Date=('Date', 'first'),
    End_Date=('Date', 'last'),
    number_of_dates=('Date', 'count')
).reset_index(drop=True)

result.loc[result['number_of_dates'] < 4, 'End_Date'] = "NA"
result.loc[result['number_of_dates'] < 4, 'number_of_dates'] = "-"
result['Group'] = result['Start_Date'] + ' - ' + result['End_Date']
result = result[['Group', 'number_of_dates']]

print(result)
print(test)

#                        Group number_of_dates
# 0  01/Jan/2024 - 10/Jan/2024              10
# 1  12/Jan/2024 - 16/Jan/2024               4
# 2           17/Jan/2024 - NA               -
#                        Group number of dates
# 0  01/Jan/2024 - 10/Jan/2024              10
# 1  12/Jan/2024 - 16/Jan/2024               4
# 2           17/Jan/2024 - NA               -

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • 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.