Omid - Challenge 62

data-challenges
advanced-exercises
🔰 The progress for month 8 is missing, so the average of the progress values for months 7 and 9 is used.
Published

March 24, 2026

Illustration for Omid - Challenge 62

Challenge Description

🔰 The progress for month 8 is missing, so the average of the progress values for months 7 and 9 is used.

Solutions

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

input = read_excel("files/CH-062 Missing Values.xlsx", range = "B2:D21")
test  = read_excel("files/CH-062 Missing Values.xlsx", range = "H2:J38")

result = input %>%
  mutate(Date = Date + days(1)) %>%
  group_by(Project) %>%
  pad() %>%
  mutate(`Actual Progress` = na.approx(`Actual Progress`)) %>%
  ungroup() %>%
  mutate(Date = Date - days(1))

all.equal(test,result)
#> [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 datetime import timedelta
from scipy.interpolate import interpolate

input = pd.read_excel("CH-062 Missing Values.xlsx", usecols="B:D", skiprows=1, nrows=19)
test = pd.read_excel("CH-062 Missing Values.xlsx", usecols="H:J", skiprows=1)
test.columns = test.columns.str.replace('.1', '')

input["Date"] = pd.to_datetime(input["Date"]) + timedelta(days=1)
all_dates = pd.date_range(start=input["Date"].min(), end=input["Date"].max(), freq='MS')
all_dates = pd.DataFrame(all_dates, columns=["Date"])
all_dates["Date"] = pd.to_datetime(all_dates["Date"]) - timedelta(days=1)
input["Date"] = pd.to_datetime(input["Date"]) - timedelta(days=1)

all_projects = pd.DataFrame(input["Project"].unique(), columns=["Project"])

all_dates["key"] = 0
all_projects["key"] = 0
all_dates = all_dates.merge(all_projects, on="key").drop(columns=["key"]).sort_values(["Project","Date"]).reset_index().drop(columns="index")

all_dates = all_dates.merge(input, on=["Project","Date"], how="left")

all_dates["Actual Progress"] = all_dates.groupby("Project")["Actual Progress"].transform(lambda x: x.interpolate())

print(all_dates["Actual Progress"].round(4).equals(test["Actual Progress"].round(4)))

  • 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.