Excel BI - Excel Challenge 743

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excel-formulas

🔰 Answer Expected Name Months Amount Jan Feb Mar Apr May Jun

Published

March 24, 2026

Illustration for Excel BI - Excel Challenge 743

Challenge Description

🔰 Answer Expected Name Months Amount Jan Feb Mar Apr May Jun

Solutions

library(tidyverse)
library(readxl)

path = "Excel/700-799/743/743 Amount Distribution.xlsx"
input = read_excel(path, range = "A2:C7")
test  = read_excel(path, range = "E2:Q7") %>%
  arrange(Name)

calendar = expand.grid(
  Name = input$Name,
  Month = factor(month.abb, levels = month.abb, ordered = TRUE, labels = month.abb)
)

result = input %>%
  separate_longer_delim(Months, delim = ", ") %>%
  mutate(Month= month(as.numeric(Months), label = TRUE, abbr = TRUE, locale = "en_US.UTF-8")) 

r2 = result %>%
  full_join(calendar, by = c("Name", "Month")) %>%
  arrange(Name, Month) %>%
  mutate(non_zero_months = sum(ifelse(is.na(Amount), 0, 1)), 
         per_month_amount = ifelse(is.na(Amount), 0, Amount / non_zero_months),
         .by = Name) %>%
  select(Name, per_month_amount, Month) %>%
  pivot_wider(names_from = Month, values_from = per_month_amount)

all.equal(r2, test, check.attributes = FALSE)
# [1] TRUE

Logic: Read the workbook ranges needed for the challenge; Derive the required intermediate columns; Parse the packed text or string structure; Aggregate or rank the data at the required grouping level.
Strengths: The reshaping step mirrors the workbook output closely instead of forcing extra post-processing.
Areas for Improvement: The solution assumes the workbook layout and selected ranges remain stable, so any structural change in the sheet would require small adjustments.
Gem: The last reshape turns a raw transformation into something that already looks like a report.

import pandas as pd
import numpy as np
from calendar import month_abbr

path = "700-799/743/743 Amount Distribution.xlsx"
input = pd.read_excel(path, usecols="A:C", skiprows=1, nrows=5)
test = (pd.read_excel(path, usecols="E:Q", skiprows=1, nrows=5)
    .rename(columns=lambda col: str(col).replace('.1', ''))
    .sort_values(by="Name")
    .reset_index(drop=True))

month_abbrs = list(month_abbr)[1:]

result = (input.assign(Months=input["Months"].str.split(", "))
          .explode("Months")
          .assign(Month=lambda x: pd.to_datetime(x["Months"].astype(int), format="%m").dt.strftime("%b")))

names_months = pd.DataFrame([(name, month) for name in input["Name"] for month in month_abbrs], 
                          columns=["Name", "Month"])

r2 = (names_months.merge(result[["Name", "Month", "Amount"]], on=["Name", "Month"], how="left")
      .assign(
          non_zero_months=lambda x: x.groupby("Name")["Amount"].transform("count"),
          per_month_amount=lambda x: x["Amount"].fillna(0).div(x["non_zero_months"]).astype(int)
      )
      .pivot(index="Name", columns="Month", values="per_month_amount")
      .reindex(columns=month_abbrs)
      .reset_index())

print(r2.equals(test)) # True

The Python version follows the same grouped logic and keeps the transformation explicit in a dataframe pipeline.

Difficulty Level

Medium

The individual steps are manageable, but the correct transformation pattern is not obvious from the raw data.