Omid - Challenge 54

data-challenges
advanced-exercises
🔰 Question Tables Result Date Project A B C Actual Progress
Published

March 24, 2026

Illustration for Omid - Challenge 54

Challenge Description

🔰 Question Tables Result Date Project A B C Actual Progress

Solutions

library(tidyverse)
library(readxl)
library(padr)

input = read_excel("files/CH-054 Missing Values.xlsx", range = "B2:D21")
test  = read_excel("files/CH-054 Missing Values.xlsx", range = "H2:J38")

result = input %>%
  group_by(Project) %>%
  mutate(Date = floor_date(Date, "month")) %>%
  pad() %>%
  fill(`Actual Progress`, .direction = "down") %>%
  mutate(Date = Date + months(1) - days(1)) 

all.equal(result, test, 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
from pandas.tseries.offsets import MonthEnd

input = pd.read_excel("CH-054 Missing Values.xlsx",  usecols="B:D", skiprows=1, nrows = 19)
test = pd.read_excel("CH-054 Missing Values.xlsx",  usecols="H:J", skiprows=1)
test.columns = test.columns.str.replace(".1", "")

input["Date"] = pd.to_datetime(input["Date"])
test["Date"] = pd.to_datetime(test["Date"])
input['Date'] = input['Date'] - MonthEnd(0) 
input = input.set_index('Date').groupby('Project').apply(lambda group: group.asfreq('M')).reset_index(level=0, drop=True).reset_index()
input['Actual Progress'] = input.groupby('Project')['Actual Progress'].fillna(method='ffill')
input['Date'] = input['Date'] + MonthEnd(0) 
input['Project'] = input['Project'].fillna(method='ffill')
input = input.fillna(method='ffill')

print(input.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 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.