Excel BI - PowerQuery Challenge 312

excel-challenges
power-query
Column1 Column2 Column3 Column4 Column5 Column6
Published

March 24, 2026

Illustration for Excel BI - PowerQuery Challenge 312

Challenge Description

Column1 Column2 Column3 Column4 Column5 Column6

Solutions

library(tidyverse)
library(readxl)
library(janitor)

path = "Power Query/300-399/312/PQ_Challenge_312.xlsx"
input = read_excel(path, range = "A1:M11")
test  = read_excel(path, range = "A15:H21")

result = input %>%
  mutate(
    Category = ifelse(Column1 == "Category", Column2, NA),
    State = ifelse(Column3 == "State", Column4, NA),
    Year = ifelse(Column5 == "Year", Column6, NA)) %>%
  fill(Category, State, Year) %>%
  filter(Column1 != "Category") %>%
  row_to_names(row_number = 1) %>%
  select(Category = `Home Loan`, State = Alabama, Year = `2023`, Customer = Months, everything()) %>%
  filter(Customer != "Months") %>%
  pivot_longer(
    cols = -c(Category, State, Year, Customer),
    names_to = "Month",
    values_to = "Amount"
  ) %>%
  mutate(Amount = as.numeric(Amount),
         Year = as.numeric(Year),
         quarter = recode(Month,
           Jan = "Q1", Feb = "Q1", Mar = "Q1",
           Apr = "Q2", May = "Q2", Jun = "Q2",
           Jul = "Q3", Aug = "Q3", Sep = "Q3",
           Oct = "Q4", Nov = "Q4", Dec = "Q4")) %>%
  select(-Month) %>%
  pivot_wider(names_from = quarter,
              values_from = Amount,
              values_fn = sum) 

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
import numpy as np

path = "300-399/312/PQ_Challenge_312.xlsx"
input = pd.read_excel(path, skiprows=0, nrows=11, usecols="A:M")
test = pd.read_excel(path, skiprows=14, nrows=7, usecols="A:H").sort_values(['State', 'Customer']).reset_index(drop=True)

cols = {'Category': ('Column1', 'Column2'), 'State': ('Column3', 'Column4'), 'Year': ('Column5', 'Column6')}
for k, (col, val) in cols.items():
    input[k] = np.where(input[col] == k, input[val], np.nan)
input[['Category', 'State', 'Year']] = input[['Category', 'State', 'Year']].ffill()

input = input[input['Column1'] != "Category"].copy()

input.columns = input.iloc[0]
input = input[1:].reset_index(drop=True)
input.columns.name = None
input = input.rename(columns={
    'Months': 'Customer',
    'Home Loan': 'Category',
    'Alabama': 'State',
    2023: 'Year'
})

df_long = input.melt(
    id_vars=['Category', 'State', 'Year', 'Customer'],
    var_name='Month',
    value_name='Amount'
)

df_long['Amount'] = pd.to_numeric(df_long['Amount'], errors='coerce').astype('Int64')
df_long['Year'] = pd.to_numeric(df_long['Year'], errors='coerce')
df_long = df_long[df_long['Customer'] != 'Months']

df_long['quarter'] = df_long['Month'].map({
    m: f"Q{(i//3)+1}" for i, m in enumerate(
        ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
    )
})

result = df_long.pivot_table(
    index=['Category', 'State', 'Year', 'Customer'],
    columns='quarter',
    values='Amount',
    aggfunc='sum'
).reset_index()

result.columns.name = None
result = result.sort_values(by=['State', 'Customer'], ascending=[True, True]).reset_index(drop=True)

print((test==result).all().all()) # True

  • Logic:

    • Reads the workbook range needed for the challenge

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

    • 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.