Omid - Challenge 127

data-challenges
advanced-exercises
🔰 Result Stock Price A B Question Table index Challenge 127:
Published

March 24, 2026

Illustration for Omid - Challenge 127

Challenge Description

🔰 Result Stock Price A B Question Table index Challenge 127:

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-127 Add Index Column.xlsx"
input = read_excel(path, range = "B2:C13")
test  = read_excel(path, range = "E2:G13")

compute_index <- function(price_vector) {
  idx <- rep(1, length(price_vector))
  for (i in 2:length(price_vector)) {
    if (price_vector[i] > price_vector[i - 1]) {
      idx[i] <- idx[i - 1] + 1
    } else {
      idx[i] <- 1
    }
  }
  return(idx)
}

result <- input %>%
  group_by(Stock) %>%
  mutate(index = compute_index(Price))

all.equal(result$index, test$index, 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

    • Applies the rule iteratively until the output stabilizes

  • 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 itertools

path = "CH-127 Add Index Column.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=1, nrows=12)
test = pd.read_excel(path, usecols="E:G", skiprows=1, nrows=12).rename(columns=lambda x: x.replace('.1', ''))

input = input.sort_values(by='Stock')
input["index"] = input.groupby(['Stock', (input.groupby('Stock')['Price'].diff() < 0).cumsum()]).cumcount() + 1

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.