Excel BI - PowerQuery Challenge 356

excel-challenges
power-query
A group is defined as same Region, Type Specific Peer Avg - The average price of all properties in that specific Peer Group seen so far (including current row).
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 356

Challenge Description

A group is defined as same Region, Type Specific Peer Avg - The average price of all properties in that specific Peer Group seen so far (including current row).

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/356/PQ_Challenge_356.xlsx"
input <- read_excel(path, range = "A1:F51")
test <- read_excel(path, range = "I1:L51")

result = input %>%
  mutate(peer_avg = cummean(Price), .by = c(Region, Type, Beds)) %>%
  mutate(reg_avg = cummean(Price), .by = Region) %>%
  mutate(reg_hist_avg = lag(cummean(Price), default = 0), .by = Region) %>%
  mutate(
    prem_disc = ((Price - reg_hist_avg) * 100 / reg_hist_avg) %>% round(2),
    .by = Region
  ) %>%
  mutate(prem_disc = ifelse(is.infinite(prem_disc), 0, prem_disc)) %>%
  group_by(Type) %>%
  mutate(
    q25 = map_dbl(
      seq_along(Price),
      ~ if (.x == 1) {
        NA_real_
      } else {
        quantile(Price[1:(.x - 1)], .25, names = FALSE)
      }
    ),
    q75 = map_dbl(
      seq_along(Price),
      ~ if (.x == 1) {
        NA_real_
      } else {
        quantile(Price[1:(.x - 1)], .75, names = FALSE)
      }
    )
  ) %>%
  ungroup() %>%
  mutate(
    Tier = case_when(
      Price < q25 ~ "Entry",
      Price > q75 ~ "Luxury",
      TRUE ~ "Mid-Market"
    )
  ) %>%
  select(
    ID,
    `Specific Peer Avg` = peer_avg,
    `Premium / Discount %` = prem_disc,
    `Tier Status` = Tier
  )

all.equal(result, test)
# Not all cases correct.

  • 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

  • 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/300-399/356/PQ_Challenge_356.xlsx"
input = pd.read_excel(path, usecols="A:F", nrows=50)
test = pd.read_excel(path, usecols="I:L", nrows=50)

df = input.copy()
df["peer_avg"] = (
    df.groupby(["Region", "Type", "Beds"])["Price"]
      .expanding()
      .mean()
      .reset_index(level=[0,1,2], drop=True)
)
df["reg_avg"] = (
    df.groupby("Region")["Price"]
      .expanding()
      .mean()
      .reset_index(level=0, drop=True)
)
df["reg_hist_avg"] = (
    df.groupby("Region")["reg_avg"].shift().fillna(0)
)
df["prem_disc"] = np.where(
    df["reg_hist_avg"] == 0,
    0,
    ((df["Price"] - df["reg_hist_avg"]) * 100 / df["reg_hist_avg"]).round(2)
)
def expanding_q(s, q):
    return pd.Series(
        [np.nan if i == 0 else s.iloc[:i].quantile(q)
         for i in range(len(s))],
        index=s.index
    )
df["q25"] = (
    df.groupby("Type")["Price"]
      .apply(lambda s: expanding_q(s, .25))
      .reset_index(level=0, drop=True)
)
df["q75"] = (
    df.groupby("Type")["Price"]
      .apply(lambda s: expanding_q(s, .75))
      .reset_index(level=0, drop=True)
)
df["Tier"] = np.select(
    [df["Price"] < df["q25"], df["Price"] > df["q75"]],
    ["Entry", "Luxury"],
    default="Mid-Market"
)
result = df[["ID", "peer_avg", "prem_disc", "Tier"]].rename(columns={
    "peer_avg": "Specific Peer Avg",
    "prem_disc": "Premium / Discount %",
    "Tier": "Tier Status"
})

# Not all cases correct.

  • Logic:

    • Reads the workbook range needed for the challenge

    • Aggregates or ranks values at the relevant grouping level

    • Applies the rule iteratively until the output is complete

  • 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.