library(tidyverse)
library(readxl)
library(rebus)
path = "Power Query/PQ_Challenge_191.xlsx"
input = read_excel(path, range = "A1:A11")
test = read_excel(path, range = "A1:B11")
pattern1 = "\\b[A-Z]+(?:[!@#$%^&*_+=]*[A-Z]*)*[!@#$%^&*_+=]*[0-9]+(?:[!@#$%^&*_+=]*[0-9]*)*\\b"
pattern2 = "\\b[0-9]+(?:[!@#$%^&*_+=]*[0-9]*)*[!@#$%^&*_+=]*[A-Z]+(?:[!@#$%^&*_+=]*[A-Z]*)*\\b"
order_chars = function(text) {
text = str_replace_all(text, "[^[:alnum:]]", "")
letters = str_extract_all(text, "[A-Z]")[[1]] %>% paste0(collapse = "")
numbers = str_extract_all(text, "[0-9]")[[1]] %>% paste0(collapse = "")
result = paste0(letters, "_", numbers)
return(result)
}
result = input %>%
mutate(pat1 = str_extract_all(Text, pattern1),
pat2 = str_extract_all(Text, pattern2)) %>%
mutate(ext = map2(pat1, pat2, ~c(.x, .y))) %>%
select(-c(pat1, pat2)) %>%
unnest(ext, keep_empty = T) %>%
mutate(result = map_chr(ext, order_chars)) %>%
group_by(Text) %>%
summarise(`Answer Expected` = paste0(result, collapse = ", ")) %>%
mutate(`Answer Expected` = if_else(`Answer Expected` == "NA_NA", NA_character_, `Answer Expected`))
res = left_join(test, result, by = c("Text" = "Text"))Excel BI - PowerQuery Challenge 191
excel-challenges
power-query
Extract the words which are either numbers followed by all uppercase English letters or vice versa. There may be special characters in the words. The output need to be shown in uppercase English letters_numbers
Challenge Description
Extract the words which are either numbers followed by all uppercase English letters or vice versa. There may be special characters in the words. The output need to be shown in uppercase English letters_numbers
Solutions
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
import re
path = "PQ_Challenge_191.xlsx"
input = pd.read_excel(path, usecols="A", nrows=10)
test = pd.read_excel(path, usecols="A:B", nrows=10)
pattern1 = r"\b[A-Z]+(?:[!@#$%^&*_+=]*[A-Z]*)*[!@#$%^&*_+=]*[0-9]+(?:[!@#$%^&*_+=]*[0-9]*)*\b"
pattern2 = r"\b[0-9]+(?:[!@#$%^&*_+=]*[0-9]*)*[!@#$%^&*_+=]*[A-Z]+(?:[!@#$%^&*_+=]*[A-Z]*)*\b"
def order_chars(text):
text = str(text) # Convert to string
text = re.sub(r"[^a-zA-Z0-9]", "", text)
letters = re.findall(r"[A-Z]", text)
numbers = re.findall(r"[0-9]", text)
result = "".join(letters) + "_" + "".join(numbers)
return result
result = input.copy()
result["pat1"] = result["Text"].str.findall(pattern1)
result["pat2"] = result["Text"].str.findall(pattern2)
result["ext"] = result.apply(lambda row: row["pat1"] + row["pat2"], axis=1)
result = result.explode("ext")
result["result"] = result["ext"].apply(order_chars)
result = result.groupby("Text").agg({"result": lambda x: ", ".join(x)}).reset_index()
result["Answer Expected"] = result["result"].apply(lambda x: x if x != "_" else None)
result = result.drop(columns=["result"])
result = pd.merge(test, result, on="Text", how="left")
print(result)Logic:
Reads the workbook range needed for the challenge
Aggregates or ranks values at the relevant grouping level
Uses direct pattern parsing where the workbook encodes logic in text
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.