Omid - Challenge 323

data-challenges
advanced-exercises
🔰 Question Table Result Table Column 1 Column 2 A X C B
Published

March 24, 2026

Illustration for Omid - Challenge 323

Challenge Description

🔰 Question Table Result Table Column 1 Column 2 A X C B

Solutions

library(tidyverse)
library(readxl)

path = "300-399/323/CH-323 Pattern Combinations.xlsx"
input = read_excel(path, range = "B2:C12")
test  = read_excel(path, range = "E2:E4")

n = nrow(input)
result <- input %>%
  mutate(group = map2(1:n, 1:n, ~ rep(.x, .y)) %>% unlist() %>% head(n)) %>%
  mutate(f_word = ifelse(group %% 2 == 1, `Column 1`, `Column 2`),
         s_word = ifelse(group %% 2 == 0, `Column 1`, `Column 2`)) %>%
  summarise(f_word = paste(f_word, collapse = ""),
            s_word = paste(s_word, collapse = "")) %>%
  pivot_longer(everything(), values_to = "Combinations") %>%
  select(-name)

all.equal(result, test)

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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
import polars as pl
import numpy as np

path = "300-399/323/CH-323 Pattern Combinations.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=10)
test = pd.read_excel(path, usecols="E", skiprows=1, nrows=2).values.flatten()

input_pl = pl.from_pandas(input)
n = input_pl.height

group = np.concatenate([np.repeat(i+1, i+1) for i in range(n)])[:n]
input_pl = input_pl.with_columns(pl.Series("group", group))

input_pl = input_pl.with_columns([
    pl.when(pl.col("group") % 2 == 1).then(pl.col("Column 1")).otherwise(pl.col("Column 2")).alias("f_word"),
    pl.when(pl.col("group") % 2 == 0).then(pl.col("Column 1")).otherwise(pl.col("Column 2")).alias("s_word"),
])

f_word = "".join(input_pl["f_word"].to_list())
s_word = "".join(input_pl["s_word"].to_list())

result = np.array([f_word, s_word])

print((result == test).all())  # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Applies the rule iteratively until the output stabilizes

  • 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.