Excel BI - PowerQuery Challenge 354

excel-challenges
power-query
Sum Trade Sum Trade: Total Trade: Sum of Sum Trade for a date group Rank: Descending (1 is the highest rank) on the basis of Total Trade for a date group. For same Total Trade, later date will have higher rank.
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 354

Challenge Description

Sum Trade Sum Trade: Total Trade: Sum of Sum Trade for a date group Rank: Descending (1 is the highest rank) on the basis of Total Trade for a date group. For same Total Trade, later date will have higher rank.

Solutions

library(tidyverse)
library(readxl)

path <- "Power Query/300-399/354/PQ_Challenge_354.xlsx"
input <- read_excel(path, range = "A1:C51")
test <- read_excel(path, range = "E1:J51")

result = input %>%
  mutate(`Sum Trade` = n(), .by = c(Date, Profession, Type)) %>%
  mutate(`Total Trade` = sum(`Sum Trade`), .by = Date) %>%
  nest_by(`Total Trade`, Date) %>%
  arrange(desc(`Total Trade`), desc(Date)) %>%
  ungroup() %>%
  mutate(Rank = row_number(), .after = everything()) %>%
  unnest(cols = c(data)) %>%
  select(Date, Profession, Type, `Sum Trade`, `Total Trade`, Rank)

all.equal(result, test, check.attributes = FALSE)
# [1] TRUE

  • Logic:

    • Reads the workbook range needed for the challenge

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

    • 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 = "Power Query/300-399/354/PQ_Challenge_354.xlsx"
input_data = pd.read_excel(path, usecols="A:C", nrows=51)
test = pd.read_excel(path, usecols="E:J", nrows=51).rename(columns=lambda col: col.replace(".1", ""))

result = (
    input_data
    .assign(Date=lambda x: pd.to_datetime(x["Date"], dayfirst=True))
    .assign(
        **{
            "Sum Trade": lambda x:
                x.groupby(["Date", "Profession", "Type"])["Date"]
                 .transform("count")
        }
    )
    .assign(
        **{
            "Total Trade": lambda x:
                x.groupby("Date")["Sum Trade"]
                 .transform("sum")
        }
    )
    .sort_values(["Total Trade", "Date"], ascending=[False, False])
    .assign(
        Rank=lambda x:
            x.groupby(["Total Trade", "Date"], sort=False)
             .ngroup()
             .add(1)
    )
    .reset_index(drop=True)
    [["Date", "Profession", "Type", "Sum Trade", "Total Trade", "Rank"]]
)

print(result.equals(test))  # 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 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 easy to moderate:

  • The transformation rule is readable, but the final layout still requires a careful implementation.