change encoding

data/test.json

@@ -1,9 +1,9 @@
 [
     {
         "id": 1,
-        "title": "Python ðàçðàáîò÷èê",
+        "title": "Python разработчик",
-        "company": "ßíäåêñ",
+        "company": "Яндекс",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 150000,
         "salary_to": 200000,
         "skills": ["Python", "SQL", "Django", "PostgreSQL"],
@@ -12,8 +12,8 @@
     {
         "id": 2,
         "title": "Data Engineer",
-        "company": "Òèíüêîôô",
+        "company": "Тинькофф",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 180000,
         "salary_to": 250000,
         "skills": ["Python", "SQL", "Spark", "Airflow"],
@@ -23,7 +23,7 @@
         "id": 3,
         "title": "Backend Developer",
         "company": "Ozon",
-        "city": "Ñàíêò-Ïåòåðáóðã",
+        "city": "Санкт-Петербург",
         "salary_from": 130000,
         "salary_to": 170000,
         "skills": ["Python", "FastAPI", "MongoDB"],
@@ -32,8 +32,8 @@
     {
         "id": 4,
         "title": "Data Analyst",
-        "company": "ßíäåêñ",
+        "company": "Яндекс",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 140000,
         "salary_to": 180000,
         "skills": ["Python", "SQL", "Tableau"],
@@ -42,8 +42,8 @@
     {
         "id": 5,
         "title": "Junior Python Developer",
-        "company": "Ñáåð",
+        "company": "Сбер",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 100000,
         "salary_to": 130000,
         "skills": ["Python", "SQL", "Git"],
@@ -53,7 +53,7 @@
         "id": 6,
         "title": "Senior Python Developer",
         "company": "Wildberries",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 200000,
         "salary_to": 300000,
         "skills": ["Python", "SQL", "Kafka", "Kubernetes"],
@@ -63,7 +63,7 @@
         "id": 7,
         "title": "Full Stack Developer",
         "company": "Avito",
-        "city": "Ìîñêâà",
+        "city": "Москва",
         "salary_from": 170000,
         "salary_to": 220000,
         "skills": ["Python", "JavaScript", "React", "SQL"],

vacancy_analyzer.py

@@ -1,6 +1,6 @@
 import json
 from collections import Counter
-def load_vacancies(filepath: str) -> list[dict]:
+def  load_vacancies(filepath: str) -> list[dict]:
     try:
         with open(filepath,"r") as file:
             return json.load(file)
@@ -12,9 +12,9 @@ def filter_by_city(vacancies: list[dict], city: str) -> list[dict]:
 def filter_by_skills(vacancies: list[dict], required_skills: list[str]) -> list[dict]:
     return [i for i in vacancies if all(skill.lower() in set(k.lower() for k in i.get('skills')) for skill in required_skills ) ]
 def filter_by_salary(vacancies: list[dict], min_salary: int) -> list[dict]:
-    return [v for v in vacancies if v.get('salary_to') and v.get('salary_from') and v.get('salary_to')>= min_salary and v.get('salary_form')> min_salary]
+    return [v for v in vacancies if v.get('salary_to') and v.get('salary_from') and v.get('salary_to')>= min_salary and v.get('salary_from')> min_salary]
 def extract_unique_companies(vacancies: list[dict]) -> list[str]:
-    return {v.get('company') for v in vacancies if v.get('company')}
+    return list({v.get('company') for v in vacancies if v.get('company')})
 def extract_top_skills(vacancies: list[dict], top_n: int) -> list[tuple[str, int]]:
     all_skills = []
     for v in vacancies:
@@ -72,8 +72,11 @@ def main():
         skils_list.append(input(f"Write №{i + 1} skill: "))
     filered_vacancies=filter_by_skills(filered_vacancies,skils_list)
     filered_vacancies = filter_by_salary(filered_vacancies,int(input("Write salary: ")))
-    print(format_vacancy_short(x) for x in filered_vacancies)
+    print("Уникальные компании: ", ", ".join(extract_unique_companies(filered_vacancies)))
-
+        
+    
+    
+