Excel BI - PowerQuery Challenge 151

excel-challenges
power-query
Employee Start Date Start Time End Date End Time Total Hours
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 151

Challenge Description

Employee Start Date Start Time End Date End Time Total Hours

Solutions

library(tidyverse)
library(readxl)
library(hms)

test  = read_excel("Power Query/PQ_Challenge_151.xlsx", range = "G1:H6") %>%
  janitor::clean_names()


read_excel_range <- function(file, range) {
  read_excel(file, range = range) %>%
    mutate(across(c(starts_with("Start Time"), starts_with("End Time")), as_hms),
           across(c(starts_with("Start Date"), starts_with("End Date")), as_date)) %>%
    janitor::clean_names()
}

input1 <- read_excel_range("Power Query/PQ_Challenge_151.xlsx", "A1:E6")
input2 <- read_excel_range("Power Query/PQ_Challenge_151.xlsx", "A9:D14")

result <- input1 %>%
  mutate(
    start = as_datetime(start_date) + start_time,
    end = as_datetime(end_date) + end_time,
    datetime = map2(start, end, seq, by = "hour")
  ) %>%
  unnest(datetime) %>%
  mutate(
    weekday = wday(datetime, week_start = 1),
    time = as_hms(datetime)
  ) %>%
  left_join(input2, by = "weekday") %>%
  filter(datetime >= start & datetime <= end, 
         time >= start_time.y & time < end_time.y) %>%
  group_by(employee) %>%
  summarise(total_hours = n() %>% as.numeric())

identical(result, test)
#> [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

    • Reshapes the data into the structure required by the result table

    • 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

test = pd.read_excel("PQ_Challenge_151.xlsx", usecols="G:H", nrows=6)
test.columns = [c.strip().lower() for c in test.columns]

def read_range(path, usecols, skiprows, nrows):
    df = pd.read_excel(path, usecols=usecols, skiprows=skiprows, nrows=nrows)
    df.columns = [c.strip().lower().replace(" ", "_") for c in df.columns]
    return df

input1 = read_range("PQ_Challenge_151.xlsx", "A:E", 0, 6)
input2 = read_range("PQ_Challenge_151.xlsx", "A:D", 8, 6)

input1["start"] = pd.to_datetime(input1["start_date"]) + pd.to_timedelta(input1["start_time"].astype(str))
input1["end"] = pd.to_datetime(input1["end_date"]) + pd.to_timedelta(input1["end_time"].astype(str))

rows = []
for _, row in input1.iterrows():
    rng = pd.date_range(row["start"], row["end"], freq="H")
    for dt in rng:
        rows.append({"employee": row["employee"], "start": row["start"], "end": row["end"], "datetime": dt})
result = pd.DataFrame(rows)
result["weekday"] = result["datetime"].dt.weekday + 1
result["time"] = result["datetime"].dt.time
input2["start_time"] = pd.to_timedelta(input2["start_time"].astype(str))
input2["end_time"] = pd.to_timedelta(input2["end_time"].astype(str))
result["time_td"] = pd.to_timedelta(result["time"].astype(str))
result = result.merge(input2, on="weekday", how="left")
result = result[(result["datetime"] >= result["start"]) & (result["datetime"] <= result["end"]) & (result["time_td"] >= result["start_time"]) & (result["time_td"] < result["end_time"])]
result = result.groupby("employee", as_index=False).size().rename(columns={"size": "total_hours"})

print(result.equals(test))

  • 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.