Omid - Challenge 330

data-challenges
advanced-exercises
🔰 : Custom Grouping!
Published

March 24, 2026

Illustration for Omid - Challenge 330

Challenge Description

🔰 : Custom Grouping!

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/330/CH-330 Custom Grouping.xlsx"
input <- read_excel(path, range = "B2:D13")
test  <- read_excel(path, range = "H2:I8")

result <- input %>%
  mutate(
    p = as.numeric(`Start Date Time`) / 86400,
    q = as.numeric(`End Date Time`) / 86400,
    d = floor(q) - floor(p),
    rep_num = case_when(
      d > 2 ~ NA_real_,
      d == 2 ~ floor(p) + 1,
      (floor(p) + 1 - p) > (q %% 1) ~ floor(p),
      TRUE ~ floor(q)
    ),
    group = if_else(
      is.na(rep_num),
      "Uncategorzied",
      as.character(as.Date(rep_num, origin = "1970-01-01"))
    )
  ) %>%
  summarise(
    `AVG Polution Rate` = round(mean(Polution)),
    .by = group
  ) %>%
  arrange(group)

all.equal(result$`AVG Polution Rate`, test$`AVG Polution Rate`)
# [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

path = "300-399/330/CH-330 Custom Grouping.xlsx"
df = pd.read_excel(path,  usecols="B:D", skiprows=1, nrows=12)
test = pd.read_excel(path, usecols="H:I", skiprows=1, nrows=6)

p = df["Start Date Time"].astype("int64") / (24*60*60*1_000_000_000)
q = df["End Date Time"].astype("int64")   / (24*60*60*1_000_000_000)
b, c = np.floor(p), np.floor(q)
rep = np.where(
    (c - b) > 2, np.nan,
    np.where(
        (c - b) == 2, b + 1,
        np.where((b + 1 - p) > (q % 1), b, c)
    )
)

df["group"] = np.where(
    np.isnan(rep),
    "Uncategorzied",
    pd.to_datetime(rep, unit="D", origin="1970-01-01").strftime("%-m/%-d/%Y")
)

result = (
    df.groupby("group")["Polution"]
      .mean().round()
      .reset_index(name="AVG Polution Rate")
      .sort_values("group")
)

print((result['AVG Polution Rate'] == test['AVG Polution Rate']).all())
# 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.