library(tidyverse)
library(readxl)
path = "files/CH-095 Last Inventory.xlsx"
input = read_excel(path, range = "B2:G7")
test = read_excel(path, range = "I2:J7")
result = input %>%
rowwise() %>%
mutate(`Last Inventory` = last(na.omit(c_across(-Product)))) %>%
ungroup() %>%
select(c(1,7))
identical(result, test)
# [1] TRUEOmid - Challenge 95
data-challenges
advanced-exercises
🔰 In the Question table, monthly inventory levels of products are provided.

Challenge Description
🔰 In the Question table, monthly inventory levels of products are provided.
Solutions
Logic:
Reads the workbook ranges needed for the challenge
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-095 Last Inventory.xlsx"
input = pd.read_excel(path, usecols = "B:G", skiprows = 1)
test = pd.read_excel(path, usecols = "i:j", skiprows = 1)
test.columns = test.columns.str.replace(".1", "")
result = input.copy()
result['Last Inventory'] = input.iloc[:, 1:]\
.apply(lambda x: x[x.last_valid_index()], axis = 1)\
.astype("int64")
result = result[['Product', 'Last Inventory']]
print(result.equals(test)) # TrueLogic:
- Reads the workbook ranges needed for the challenge
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.