Excel BI - PowerQuery Challenge 141

excel-challenges
power-query
& 5 year moving averages for each month group.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 141

Challenge Description

& 5 year moving averages for each month group.

Solutions

library(tidyverse)
library(slider)
library(readxl)

input = read_excel("Power Query/PQ_Challenge_141.xlsx", range = "A1:C35")
test  = read_excel("Power Query/PQ_Challenge_141.xlsx", range = "E1:I35")

result = input %>%
  group_by(Month) %>%
  mutate(
    `3 Year MV` = slide_dbl(Defects, mean,  .after = -1, .before = 3, .complete = TRUE) %>% round(0),
    `5 Year MV` = slide_dbl(Defects, mean, .after = -1, .before = 5, .complete = TRUE) %>% round(0)
  ) %>% 
  ungroup()

identical(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

  • 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

input_data = pd.read_excel("PQ_Challenge_141.xlsx", usecols="A:C", nrows=35)
test = pd.read_excel("PQ_Challenge_141.xlsx", usecols="E:I", nrows=35)

result = input_data.copy()
result["3 Year MV"] = (
    result.groupby("Month")["Defects"]
    .transform(lambda s: s.rolling(window=4, min_periods=4).mean().round(0).shift(-1))
)
result["5 Year MV"] = (
    result.groupby("Month")["Defects"]
    .transform(lambda s: s.rolling(window=6, min_periods=6).mean().round(0).shift(-1))
)

print(result.equals(test))

  • 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.