Omid - Challenge 358

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 358

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path <- "300-399/358/CH-358 Table Transformation.xlsx"
input <- read_excel(path, range = "B3:E17")
test <- read_excel(path, range = "G3:K8") %>%
  mutate(Date = as.Date(Date, origin = "1899-12-30"))

result = input |>
  mutate(across(1, ~ replace_na(.x, "Date"))) %>%
  mutate(Group = cumsum(Column1 == "Date")) %>%
  nest_by(Group) %>%
  mutate(data = list(data |> select(where(~ !all(is.na(.x)))))) %>%
  mutate(data = list(row_to_names(data, row_number = 1))) %>%
  mutate(
    data = list(
      data |> pivot_longer(-Date, names_to = "Variable", values_to = "Value")
    )
  ) %>%
  unnest(data) %>%
  ungroup() %>%
  mutate(
    Date = excel_numeric_to_date(round(as.numeric(Date), 0)),
    Value = as.numeric(Value)
  ) %>%
  summarise(Value = sum(Value, na.rm = T), .by = c(Date, Variable)) %>%
  pivot_wider(names_from = Variable, values_from = Value, values_fn = sum)

all.equal(result, test)
# [1] TRUE

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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 = "300-399\\358\\CH-358 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:E", nrows = 14, skiprows = 2)
test = pd.read_excel(path, usecols="G:K", nrows = 5, skiprows = 2)

split_dfs, cur_df = [], []
input.iloc[:, 0] = input.iloc[:, 0].fillna("Date")

for _, row in input.iterrows():
    if row.iloc[0] == "Date" and cur_df:
        split_dfs.append(pd.DataFrame(cur_df, columns=input.columns))
        cur_df = []
    cur_df.append(row)

if cur_df: 
    split_dfs.append(pd.DataFrame(cur_df, columns=input.columns))

for i, df in enumerate(split_dfs):
    df.columns = df.iloc[0]
    split_dfs[i] = df[1:].reset_index(drop=True)
    split_dfs[i] = split_dfs[i].dropna(axis=1, how='all')

final_dfs = []
for df in split_dfs:
    df_long = df.melt(id_vars=[df.columns[0]], var_name="ABC", value_name="Value")
    final_dfs.append(df_long)

result = pd.concat(final_dfs, ignore_index=True)
grouped_result = result.groupby([result.columns[0], "ABC"], as_index=False).agg({"Value": "sum"})
pivoted_result = grouped_result.pivot(index=grouped_result.columns[0], columns="ABC", values="Value").reset_index()

print(all(pivoted_result == test)) #

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • Aggregates or ranks values at the relevant grouping level

    • 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.