Omid - Challenge 34

data-challenges
advanced-exercises
🔰 In the question table, sales data for different customers are provided.
Published

March 24, 2026

Illustration for Omid - Challenge 34

Challenge Description

🔰 In the question table, sales data for different customers are provided.

Solutions

library(tidyverse)
library(readxl)

input = read_excel("files/CH-034 Customer Return Cycle.xlsx", range = "B2:F26")
test  = read_excel("files/CH-034 Customer Return Cycle.xlsx", range = "J2:K6")

result = input %>%
  select(Date, `Customer ID`) %>%
  arrange(`Customer ID`, Date) %>%
  distinct() %>% 
  mutate(lag = lag(Date), .by = `Customer ID`) %>%
  mutate(diff = Date - lag) %>%
  summarise(`Avg Return Cycle` = mean(diff, na.rm = TRUE), .by = `Customer ID`) %>% 
  mutate(`Avg Return Cycle` = as.numeric(`Avg Return Cycle`)) %>%
  select(Customer = `Customer ID`, `Avg Return Cycle`)

identical(result, 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

input = pd.read_excel("CH-034 Customer Return Cycle.xlsx", sheet_name="Sheet1", usecols="B:F", skiprows=1)
test = pd.read_excel("CH-034 Customer Return Cycle.xlsx", sheet_name="Sheet1", usecols="J:K", skiprows=1, nrows = 4)

result = input[['Date', 'Customer ID']].sort_values(by=['Customer ID', 'Date']).drop_duplicates().reset_index(drop=True)
result['lag'] = result.groupby('Customer ID')['Date'].shift(1)
result['diff'] = (result['Date'] - result['lag']).dt.days
result = result.groupby('Customer ID')['diff'].mean().astype(int).reset_index()
result.columns = ['Customer', 'Avg Return Cycle']

print(result == 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.