Excel BI - PowerQuery Challenge 314

excel-challenges
power-query
Process Step Previous Step Steps Chain Process1 A
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 314

Challenge Description

Process Step Previous Step Steps Chain Process1 A

Solutions

library(tidyverse)
library(readxl)
library(igraph)

path = "Power Query/300-399/314/PQ_Challenge_314.xlsx"
input = read_excel(path, range = "A1:C12")
test  = read_excel(path, range = "F1:G12")

result = input %>%
  mutate(`Previous Step` = na_if(`Previous Step`, ""), across(c(Step, `Previous Step`), as.character)) %>%
  group_by(Process) %>%
  group_modify(~{
    prev = deframe(filter(.x, !is.na(`Previous Step`)) %>% select(Step, `Previous Step`))
    chain = function(s) {
      out = s
      while (!is.na(prev[s]) && !(prev[s] %in% out)) {
        s = prev[s]
        out = c(out, s)
      }
      paste(out, collapse = "-")
    }
    mutate(.x, `Steps Chain` = if_else(is.na(`Previous Step`), Step, map_chr(Step, chain)))
  }) %>%
  ungroup() %>%
  select(Process, `Steps Chain`)

all.equal(result, test, check.attributes = FALSE)
# > [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

    • Applies the rule iteratively until the output is complete

  • 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 = "300-399/314/PQ_Challenge_314.xlsx"
input = pd.read_excel(path, usecols="A:C", nrows=12)
test = pd.read_excel(path, usecols="F:G", nrows=12).rename(columns=lambda col: col.replace('.1', ''))

input['Previous Step'].replace("", np.nan, inplace=True)
def chain(row, prev):
    s, out = row['Step'], [row['Step']]
    while pd.notna(prev.get(s)) and prev[s] not in out:
        s = prev[s]
        out.append(s)
    return "-".join(out)
def build_chain(g):
    prev = dict(zip(g['Step'], g['Previous Step']))
    return g.assign(**{'Steps Chain': g.apply(lambda r: r['Step'] if pd.isna(r['Previous Step']) else chain(r, prev), axis=1)})
result = input.groupby('Process', group_keys=False).apply(build_chain)[['Process', 'Steps Chain']].reset_index(drop=True)

print(result.equals(test))

  • 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

    • 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.