library(tidyverse)
library(readxl)
path = "files/CH-206 Column Splitting.xlsx"
input = read_excel(path, range = "B2:B7")
test = read_excel(path, range = "D2:E7")
result = input %>%
mutate(rn = row_number()) %>%
separate_rows(ID, sep = "") %>%
filter(ID != "") %>%
mutate(pos = ifelse(row_number() %% 2 == 0, "Even Positions", "Odd Positions"), .by = rn) %>%
pivot_wider(names_from = pos, values_from = ID, values_fn = ~ paste0(.x, collapse = "")) %>%
select(-rn)
all.equal(result, test, check.attributes = FALSE)
# There is one mistake in result provided.Omid - Challenge 206
data-challenges
advanced-exercises
🔰 Question Result ID ABC A1B2C3 MNXLP QRW32 X1Y2Z3

Challenge Description
🔰 Question Result ID ABC A1B2C3 MNXLP QRW32 X1Y2Z3
Solutions
Logic:
Reads the workbook ranges needed for the challenge
Reshapes the data into the grain required by the task
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-206 Column Splitting.xlsx"
input = pd.read_excel(path, usecols="B", skiprows=1, nrows=6)
test = pd.read_excel(path, usecols="D:E", skiprows=1, nrows=6)
def split_even_odd_chars(series):
return series.apply(lambda x: pd.Series([''.join(str(x)[::2]), ''.join(str(x)[1::2])], index=['Odd Positions', 'Even Positions']))
result = split_even_odd_chars(input.iloc[:, 0])
print(test)
print(result)Logic:
- 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 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.