Omid - Challenge 203

data-challenges
advanced-exercises
🔰 Group Challenge 203: Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 203

Challenge Description

🔰 Group Challenge 203: Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-203 Custom Grouping.xlsx"
input = read_excel(path, range = "B2:C63")
test  = read_excel(path, range = "G2:H5")

result = input %>%
  complete(Date = seq.Date(min(as.Date(Date)), max(as.Date(Date)), by = "1 day")) %>%
  replace_na(list(Sales = 0)) %>%
  mutate(wday = wday(Date, label = TRUE, locale = "en"),
         Group = month(Date, label = TRUE, abbr = TRUE, locale = "en")) %>%
  filter(!wday %in% c("Sat", "Sun") & Sales == 0) %>%
  summarise(`No missing dates` = n(), .by = Group)

all.equal(result$`No missing dates`, test$`No missing dates`, 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

  • 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 numpy as np

path = "CH-203 Custom Grouping.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=61)
test = pd.read_excel(path, usecols="G:H", skiprows=1, nrows=3)

input['Date'] = pd.to_datetime(input['Date'])
input = input.set_index('Date').asfreq('D', fill_value=0).reset_index()
input['wday'] = input['Date'].dt.day_name()
input['Group'] = input['Date'].dt.strftime('%b')

result_summary = (input.query("wday not in ['Saturday', 'Sunday'] and Sales == 0")
                  .groupby('Group').size().reset_index(name='No missing dates')
                  .sort_values('No missing dates').reset_index(drop=True))

print(test['No missing dates'].equals(result_summary['No missing dates'])) # 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.