Omid - Challenge 58

data-challenges
advanced-exercises
🔰 In this challenge, we aim to find an efficient way to calculate the stepped tax based on the tax rates presented in the question table.
Published

March 24, 2026

Illustration for Omid - Challenge 58

Challenge Description

🔰 In this challenge, we aim to find an efficient way to calculate the stepped tax based on the tax rates presented in the question table.

Solutions

library(tidyverse)
library(readxl)

input1 = read_excel("files/CH-058 Stepped Tax.xlsx", range = "B2:D7")
input2 = read_excel("files/CH-058 Stepped Tax.xlsx", range = "F2:G7")
test   = read_excel("files/CH-058 Stepped Tax.xlsx", range = "H2:H7")

input1$To = ifelse(input1$To == "Over", Inf, input1$To) %>% as.numeric()

result = input1 %>% 
  mutate(key = 1) %>%
  full_join(input2 %>% mutate(key = 1), by = "key") %>%
  select(-key) %>%
  filter(From <= To) %>%
  mutate(income_over_threshold = Income - From,
         income_in_threshold = ifelse(Income >= From & Income <= To  , T, F)) %>%
  filter(income_over_threshold >= 0) %>%
  arrange(`Person ID`) %>%
  mutate(tax = ifelse(income_in_threshold, 
                      income_over_threshold * `Tax Rate`,
                      (To - From) * `Tax Rate`)) %>%
  summarise(Tax = sum(tax), .by = c(`Person ID`, Income)) %>%
  select(Tax)

all(round(result$Tax, 1) == round(test$Tax, 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

  • 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 numpy as np
input1 = pd.read_excel("CH-058 Stepped Tax.xlsx", usecols="B:D", skiprows=1, nrows = 6)
input2 = pd.read_excel("CH-058 Stepped Tax.xlsx", usecols="F:G", skiprows=1, nrows = 6)
test   = pd.read_excel("CH-058 Stepped Tax.xlsx", usecols="H", skiprows=1, nrows = 6)

input1.loc[4, 'To'] = float('inf')
input1['key'] = 1
input2['key'] = 1
output = pd.merge(input1, input2, on='key')
output['income_over_threshold'] = output["Income"] - output["From"]
output['income_in_threshold'] = np.where((output["Income"] >= output["From"]) & (output["Income"] <= output["To"]), True, False)
output = output[output['income_over_threshold'] > 0].sort_values(by = ["Person ID"]).reset_index(drop = True)
output['tax'] = np.where(output['income_in_threshold'], 
                         output['income_over_threshold'] * output['Tax Rate'], 
                         (output['To'] - output['From']) * output['Tax Rate'])
output = output.groupby('Person ID').agg({'tax': 'sum'}).astype("float64").reset_index()

output['tax'] = output['tax'].round(2)
test['tax'] = test['Tax'].round(2)

print(all(output['tax'] == test['tax'])) # 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 business rule is readable, but the workbook still requires careful implementation to reach the expected layout.