Excel BI - PowerQuery Challenge 374

excel-challenges
power-query
ResourceID Project StartDate EndDate R-101 Alpha
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 374

Challenge Description

ResourceID Project StartDate EndDate R-101 Alpha

Solutions

library(tidyr)
library(readxl)
library(lubridate)

path <- "300-399/374/PQ_Challenge_374.xlsx"
input <- read_excel(path, range = "A1:D21")
test <- read_excel(path, range = "G1:J13")

result = input %>%
  arrange(Project, ResourceID) %>%
  group_by(Project, ResourceID) %>%
  mutate(interval = interval(StartDate, EndDate)) %>%
  mutate(
    grp = cumsum(
      int_overlaps(interval, lag(interval, default = first(interval))) == FALSE
    )
  ) %>%
  ungroup() %>%
  summarise(
    ResourceID = first(ResourceID),
    Project = first(Project),
    StartDate = min(StartDate),
    EndDate = max(EndDate),
    .by = c(Project, ResourceID, grp)
  ) %>%
  select(-grp)

# Provided structute not correct. For Theta should be 2 intevals.

  • 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

path = "300-399/374/PQ_Challenge_374.xlsx"
input_df = pd.read_excel(path, usecols="A:D", nrows=20)
test = pd.read_excel(path, usecols="G:J", nrows=12)


def merge_intervals(df):
    df = df.sort_values("StartDate").reset_index(drop=True)
    merged = []
    for _, row in df.iterrows():
        if not merged or row["StartDate"] > merged[-1]["EndDate"]:
            merged.append({"StartDate": row["StartDate"], "EndDate": row["EndDate"]})
        else:
            merged[-1]["EndDate"] = max(merged[-1]["EndDate"], row["EndDate"])
    return pd.DataFrame(merged)


result = (
    input_df
    .groupby(["ResourceID", "Project"], sort=False)
    .apply(merge_intervals, include_groups=False)
    .reset_index(level=[0, 1])
    .reset_index(drop=True)
    [["ResourceID", "Project", "StartDate", "EndDate"]]
    .sort_values(["ResourceID", "Project", "StartDate"])
    .reset_index(drop=True)
)

print(result.equals(test))
# Provided structute not correct. For Theta should be 2 intevals.

  • 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.