Omid - Challenge 114

data-challenges
advanced-exercises
🔰 In Question Table 1, the dates and product IDs are provided.
Published

March 24, 2026

Illustration for Omid - Challenge 114

Challenge Description

🔰 In Question Table 1, the dates and product IDs are provided.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-114 Merge.xlsx"
input1 = read_excel(path, range = "B2:C9")
input2 = read_excel(path, range = "B13:C18")
test  = read_excel(path, range = "H2:J9")

r1 = input1 %>%
  separate_rows(`Product ID`, sep = ",") %>%
  inner_join(input2, by = c("Product ID" = "product id")) %>%
  summarise(price = first(price) %>% as.character(), .by = Date) 
r2 = input1 %>%
  left_join(r1, by = "Date") %>%
  replace_na(list(price = "-"))

identical(r2, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • 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-114 Merge.xlsx"

input1 = pd.read_excel(path, usecols = "B:C", nrows = 7, skiprows = 1)
input2 = pd.read_excel(path, usecols = "B:C", nrows = 5, skiprows = 12)
test = pd.read_excel(path, usecols="H:J", nrows=7, skiprows=1).rename(columns=lambda x: x.replace('.1', ''))
test["price"] = test["price"].astype(str)

input1['Product ID'] = input1['Product ID'].str.split(',')
input1 = input1.explode('Product ID')
r1 = input1.merge(input2, left_on='Product ID', right_on='product id') \
           .groupby('Date').agg({'price': lambda x: x.iloc[0].astype(str)}).reset_index()
r2 = input1.merge(r1, on='Date', how='left').fillna({'price': '-'}).groupby('Date').agg({'Product ID': ','.join, 'price': 'first'}).reset_index()

print(r2.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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.