Excel BI - PowerQuery Challenge 359

excel-challenges
power-query
Data Month Category Count Customers Date,Category,Cust_ID,Amount
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 359

Challenge Description

Data Month Category Count Customers Date,Category,Cust_ID,Amount

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/359/PQ_Challenge_359.xlsx"
input <- read_excel(path, range = "A1:A52")
test <- read_excel(path, range = "C1:F13")

df <- input %>%
  separate_wider_delim(Data, ',', names_sep = "_") %>%
  janitor::row_to_names(1)
df <- df %>% mutate(Date = as.Date(Date), Month = floor_date(Date, "month"))

result <- df %>%
  distinct(Month, Category, Cust_ID) %>%
  arrange(Cust_ID, Month) %>%
  group_by(Cust_ID, Category) %>%
  mutate(
    ok = Month == lag(Month, 1) %m+% months(1) &
      lag(Month, 1) == lag(Month, 2) %m+% months(1)
  ) %>%
  filter(ok) %>%
  ungroup() %>%
  right_join(
    tidyr::expand_grid(
      Month = sort(unique(df$Month)),
      Category = unique(df$Category)
    ),
    by = c("Month", "Category")
  ) %>%
  group_by(Month, Category) %>%
  summarise(
    Count = as.double(n_distinct(Cust_ID[!is.na(Cust_ID)])),
    Customers = ifelse(
      all(is.na(Cust_ID)),
      NA,
      str_c(sort(unique(Cust_ID[!is.na(Cust_ID)])), collapse = ", ")
    ),
    .groups = "drop"
  ) %>%
  mutate(Month = format(Month, "%Y-%m"))

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

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Builds helper columns that drive the final output

    • Uses direct pattern parsing where the workbook encodes logic in text

  • Strengths:

    • The R solution stays close to the workbook logic and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the workbook layout and selected ranges remain stable.

  • Gem:

    • The best part of the solution is choosing the right intermediate shape before formatting the final output.
import pandas as pd
import numpy as np

path = "Power Query/300-399/359/PQ_Challenge_359.xlsx"
input = pd.read_excel(path, usecols=[0], skiprows=1, nrows=51, header=None, names=['Data'])
test = pd.read_excel(path, usecols="C:F", nrows=12)

df = input["Data"].str.split(",", expand=True)
df.columns = ["Date","Category","Cust_ID","Amount"]
df = df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
df = df[df["Date"] != "Date"].reset_index(drop=True)
df["Date"] = pd.to_datetime(df["Date"])
df["Month"] = df["Date"].dt.to_period("M").dt.to_timestamp()
base = df[["Month","Category","Cust_ID"]].drop_duplicates().sort_values(["Cust_ID","Month"])
g = base.groupby(["Cust_ID","Category"])["Month"]
base["ok"] = (base["Month"] == g.shift(1) + pd.offsets.MonthBegin(1)) & (g.shift(1) == g.shift(2) + pd.offsets.MonthBegin(1))
good = base[base["ok"]]
grid = pd.MultiIndex.from_product([sorted(df["Month"].unique()), df["Category"].unique()], names=["Month","Category"]).to_frame(index=False)
result = grid.merge(good, on=["Month","Category"], how="left") \
    .groupby(["Month","Category"], as_index=False) \
    .agg(Count=("Cust_ID","nunique"), Customers=("Cust_ID", lambda x: np.nan if x.isna().all() else ", ".join(sorted(x.dropna().unique()))))
result["Month"] = result["Month"].dt.strftime("%Y-%m")


print(result.equals(test))
# True

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

  • Strengths:

    • The Python version follows the same workbook rule in a direct pandas-oriented implementation.

  • Areas for Improvement:

    • As with the R version, any workbook layout change would require small adjustments.

  • Gem:

    • The implementation stays close to the source challenge instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • It combines reshaping, grouping, or parsing steps that are common in Power Query style problems.

  • The main challenge is reproducing the workbook output structure exactly.