Resolviendo la edición Tuenti Challenge 10 con Python: Reto 3
Toda la información de este reto fue borrada, ya que la página de Tuenti ha echado el cierre.
Reto 3 - Fortunata y Jacinta
El nivel consistía en un homenaje al novelista Pérez Galdós. Para ello, nos daban una versión en .txt de su novela “Fortunata y Jacinta” y debíamos convertir todo el texto a minúsculas, además de identificar como palabras cada conjunto de tres o más letras con símbolos del alfabeto español. Por último, debíamos ordenarlas por frecuencia, siguiendo el orden unicode en caso de llegar a empate.
Por tanto, es una prueba muy relacionada con el uso de regex, y mi propuesta de resolución fue la siguiente:
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| import re | |
| #For this challenge we have two input Galdo's text (pg17013.txt) and some queries of words or ranks to obtain from the Galdo's text | |
| #First we have to clean the text to leave it as a kind of standars, following the steps in the web. | |
| #If the input is a word (W), we have to show its rank and the number of instances of that word are in the text | |
| #If the input is a number(R), we have to get the word and the number of instances according to the rank number received. | |
| #To be fast enough I will choose a dictionary to keep the words and its number of instances. | |
| #I will need to reorder the dictionary at the end, to keep first the words with more instances in the text | |
| #In case of draw in two or more words the ordew will be by alfabet. | |
| import operator | |
| terms = {} #dictionary that will content the words and its ranks | |
| results="" | |
| with open('pg17013.txt','r') as f: | |
| text = f.read() | |
| results = text.lower() #First step of the standard, all the words should be in lower case. | |
| results = re.sub(r'[^abcdefghijklmnñopqrstuvwxyzáéíóúü]',' ',results) #second step of the standard, consider only a, b , c, d, e, f, g, h, i, j, k, l, m, n, ñ, o, p, q, r, s, t, u, v, w, x, y, z, á, é, í, ó, ú, ü. replacing with spaces. | |
| matches = re.finditer(r"[abcdefghijklmnñopqrstuvwxyzáéíóúü]{3,} *\n*",results) #third step, only words with 3 or more letters | |
| results = "" #Variable that will contain at the end the results of the output.txt | |
| for match in matches: | |
| results += match.group() #code neccesary to get the words with 3 or more cases | |
| for term in results.split(" "): #counting terms and its instances | |
| frecuency = terms.get(term,0) | |
| terms.update({term:frecuency+1}) | |
| #A dictionary and a list with the same information, the terms oredered by instances and alfabet | |
| termsDictOrdered = {k: v for k, v in sorted(terms.items(), key=lambda item: (-item[1],item[0]))} | |
| termsListOrdered = sorted(terms.items(), key=lambda item: (-item[1],item[0])) #list of tuples first element in tuple is the word the second the instances | |
| results="" | |
| with open('Input.txt','r') as test: | |
| C = int(test.readline()) #number of cases | |
| for i in range(1,C+1): | |
| line = test.readline().rstrip() #line with whitespace remove it | |
| if line.isnumeric(): #If the line in te input is a number then do this else do that | |
| R = int(line) | |
| word = termsListOrdered[R][0] | |
| instance = termsListOrdered[R][1] | |
| results += f"Case #{i}: {word} {instance}\n" | |
| else: | |
| W = str(line) | |
| instance = termsDictOrdered[W] | |
| indexW = termsListOrdered.index((W,instance)) | |
| results += f"Case #{i}: {instance} #{indexW}\n" | |
| text_file = open("Output.txt", "w") | |
| text_file.write(results) | |
| text_file.close() |