Omid - Challenge 225

data-challenges
advanced-exercises
🔰 Create a dynamic date column based on the information provided in the question table.
Published

March 24, 2026

Illustration for Omid - Challenge 225

Challenge Description

🔰 Create a dynamic date column based on the information provided in the question table.

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-225 Date Range.xlsx"
start = read_excel(path, range = "C3", col_names = F) %>% pull()
end = read_excel(path, range = "C4", col_names = F) %>% pull()
step = read_excel(path, range = "C5", col_names = F) %>% pull()
test = read_excel(path, range = "E2:E12") %>% mutate(Dates = as.Date(Dates))

result = data.frame(Dates = seq.Date(as.Date(start), as.Date(end), by = step))

all.equal(result, test, check.attributes = FALSE)
#> [1] TRUE

  • 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
from pandas import to_datetime

path = "CH-225 Date Range.xlsx"

start = pd.read_excel(path, usecols="C", skiprows=2, nrows=1, header=None).iloc[0, 0]
end = pd.read_excel(path, usecols="C", skiprows=3, nrows=1, header=None).iloc[0, 0]
step = pd.read_excel(path, usecols="C", skiprows=4, nrows=1, header=None).iloc[0, 0]

test = pd.read_excel(path, usecols="E", skiprows=1, nrows=11).assign(Dates=lambda df: pd.to_datetime(df['Dates']))

result = pd.DataFrame({'Dates': pd.date_range(start=start, end=end, freq=f"{step}D")})

<<<<<<< HEAD
print(result.equals(test)) # True

=======
print(result.equals(test)) # True if the result matches the test data
>>>>>>> b27df38c727c140b11ddbfe6289bd12c4d6cac85

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

  • 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.