Omid - Challenge 142

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 142

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)
library(hms)

path = "files/CH-142 Table Transformation.xlsx"
input = read_excel(path, range = "B2:E18") 
test  = read_excel(path, range = "G2:J12") %>% 
  mutate(across(c(In, Out), as_hms))

input = input %>% mutate(Time = as_hms(Time))
ins = input %>% filter(Type == "In") %>% rename(In = Time)
outs = input %>% filter(Type == "Out") %>% rename(Out = Time)

result_df = ins %>%
  left_join(outs, by = c("Date", "ID")) %>%
  filter(In < Out) %>%
  group_by(Date, ID, In) %>%
  slice_min(Out) %>%
  ungroup()

unmatched_ins = anti_join(ins, result_df, by = c("Date", "ID", "In"))
unmatched_outs = anti_join(outs, result_df, by = c("Date", "ID", "Out"))

result = bind_rows(result_df, unmatched_outs, unmatched_ins) %>%
  arrange(Date, ID, coalesce(In,Out)) %>%
  select(Date, ID, In, Out)

all.equal(result, test)
#> [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

path = "CH-142 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:E", skiprows=1, nrows=16)
test = pd.read_excel(path, usecols="G:J", skiprows=1, nrows=10).rename(columns=lambda x: x.split('.')[0])

ins = input[input['Type'] == 'In'].rename(columns={'Time': 'In'})
outs = input[input['Type'] == 'Out'].rename(columns={'Time': 'Out'})

result = ins.merge(outs, on=['Date', 'ID']).query('In < Out').sort_values(by=['Date', 'ID', 'In'])\
    .drop_duplicates(subset=['Date', 'ID', 'In']).reset_index(drop=True)

unmatched_ins = pd.merge(ins, result, on=['Date', 'ID', 'In'], how='left', indicator=True)
unmatched_ins = unmatched_ins[unmatched_ins['_merge'] == 'left_only'].drop(columns=['_merge'])

unmatched_outs = pd.merge(outs, result, on=['Date', 'ID', 'Out'], how='left', indicator=True)
unmatched_outs = unmatched_outs[unmatched_outs['_merge'] == 'left_only'].drop(columns=['_merge'])

result['In_Out'] = result[['In', 'Out']].max(axis=1)

result = pd.concat([result, unmatched_outs, unmatched_ins])\
    .sort_values(by=['Date', 'ID', 'In_Out']).reset_index(drop=True)
result = result[['Date', 'ID', 'In', 'Out']]

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

  • 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.