Omid - Challenge 376

data-challenges
advanced-exercises
🔰 Table Transformation!
Published

March 24, 2026

Illustration for Omid - Challenge 376

Challenge Description

🔰 Table Transformation!

Solutions

library(tidyverse)
library(readxl)

path <- "300-399/376/CH-376 Table Transformation.xlsx"
input <- read_excel(path, range = "B4:C11", col_names = F) %>% as.matrix()
test <- read_excel(path, range = "E3:G9")

arr = as.vector(t(input))
arr = arr[!is.na(arr)] %>%
  as.data.frame() %>%
  set_names("value") %>%
  mutate(id = row_number())

result = arr %>%
  mutate(
    type = case_when(
      str_length(value) > 3 ~ "Date",
      str_detect(value, "^[A-Za-z]+$") ~ "Name",
      TRUE ~ "Number"
    )
  ) %>%
  pivot_wider(names_from = type, values_from = value) %>%
  fill(Date, Name, .direction = "down") %>%
  filter(!if_any(everything(), is.na))

print(result)
# Different dates

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Reshapes the data into the grain required by the task

    • 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.
import pandas as pd


path = "300-399/376/CH-376 Table Transformation.xlsx"
input = pd.read_excel(path, usecols="B:C", skiprows=3, nrows=9, header=None)
test = pd.read_excel(path, usecols="E:G", skiprows=2, nrows=6)

v = pd.Series(input.to_numpy().ravel()).dropna()
v = v[v != ""]

df = pd.DataFrame({"val": v})

df["Date"] = df["val"].where(
    df["val"].apply(lambda x: isinstance(x, str) and bool(__import__('re').match(r"\d{1,2}/\d{1,2}/\d{4}", x)))
    | df["val"].apply(lambda x: isinstance(x, pd.Timestamp) or hasattr(x, 'date'))
)
df["Product"] = df["val"].where(df["val"].str.match(r"^[A-Z]$"))
df["Sale"] = pd.to_numeric(df["val"], errors="coerce")

df["Date"] = pd.to_datetime(df["Date"], dayfirst=True).ffill()
df["Product"] = df["Product"].ffill()

result = df[df["Sale"].notna()][["Date", "Product", "Sale"]]
print(result)
# Different dates in some rows

  • 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.