Omid - Challenge 270

data-challenges
advanced-exercises
🔰 The Question table contains a list of equations.
Published

March 24, 2026

Illustration for Omid - Challenge 270

Challenge Description

🔰 The Question table contains a list of equations.

Solutions

library(tidyverse)
library(readxl)
library(Ryacas)
library(rootSolve)

path = "files/200-299/270/CH-270 Solving equations.xlsx"
input = read_excel(path, range = "B2:C7")
test  = read_excel(path, range = "E2:F7")

eval_lag = function(eq) yac_str(paste0("Solve(", eq, ", X)")) %>%
  y_rmvars() %>% yac_str() %>% yac_expr() %>% eval()

is_transcendental = function(eq) str_detect(eq, "\\^X")

result = input %>%
  mutate(Equation = str_replace_all(Equation, "=", "==")) %>%
  rowwise() %>%
  mutate(
    Solution = list(
      if (is_transcendental(Equation))
        uniroot.all(function(X) eval(parse(text = str_remove(Equation, "==0"))), c(-10, 10))
      else
        eval_laq(Equation)
    )
  ) %>%
  ungroup()

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Builds the intermediate columns that drive the final result

    • Parses the text patterns directly instead of relying on manual cleanup

  • 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.
from sympy import symbols, Eq, solve, sympify
import pandas as pd

path = "200-299/270/CH-270 Solving equations.xlsx"
input = pd.read_excel(path, usecols="B:C", nrows=5, skiprows=1)
test = pd.read_excel(path, usecols="E:F", nrows=5, skiprows=1)

X = symbols('X')
eqs = input.iloc[:, 1]
solutions = [
    [round(float(sol.evalf()), 4) for sol in solve(sympify(eq.replace('=', '-(') + ')'), X)]
    for eq in eqs
]

input['Solution'] = solutions
print(input)

  • 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.