diff --git a/.vscode/settings.json b/.vscode/settings.json
index 5f3108a..1a12a8a 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,3 +1,5 @@
 {
-    "markdown.extension.toc.updateOnSave": false
+    "markdown.extension.toc.updateOnSave": false,
+    "python.linting.enabled": true,
+    "python.linting.flake8Enabled": true
 }
\ No newline at end of file
diff --git a/hospital-affiliations/aff_hosp.py b/hospital-affiliations/aff_hosp.py
index c45f3e6..2667c57 100755
--- a/hospital-affiliations/aff_hosp.py
+++ b/hospital-affiliations/aff_hosp.py
@@ -1,3 +1,5 @@
+# flake8: noqa E501 (line length)
+
 import pandas as pd
 from fuzzywuzzy import fuzz
 import sys
@@ -5,40 +7,50 @@
 import unicodedata
 import re
 import config
-pd.options.mode.chained_assignment = None  
+pd.options.mode.chained_assignment = None
+
 
 # Check if an element of the list is in affiliation
-def is_hospital_affiliation(affiliation) :
+def is_hospital_affiliation(affiliation):
     affiliation_lower = affiliation.lower()
-    for aff in config.acronyms :
-        if aff in affiliation_lower :
+    for aff in config.acronyms:
+        if aff in affiliation_lower:
             return True
     return False
 
-#correction of the cities : remove the department numbers and convert acronyms
-def remove_department_numbers_and_convert_acronyms(city_name) :
-    # department numbers: Grenoble - 38 => Grenoble
+
+# correction of the cities : remove the department numbers
+def remove_department_numbers(city_name):
+    # Grenoble - 38 => Grenoble
     sep = " -"
     stripped = city_name.split(sep, 1)[0]
-    stripped = stripped.replace("-"," ")
-
-    # acronyms
-    stripped = stripped.replace("Saint","St")
-    stripped = stripped.replace("Mont","Mt")
+    stripped = stripped.replace("-", " ")
 
     return stripped
 
+
+# correction of the cities: convert acronyms
+def convert_acronyms(city_name):
+    stripped = city_name.replace("Saint", "St")
+    stripped = stripped.replace("Mont", "Mt")
+
+    return stripped
+
+
 # correction of the cities : remove accents
-def remove_accents(city_name) :
+def remove_accents(city_name):
     normalized_text = unicodedata.normalize("NFD", city_name)
     text_with_no_accent = re.sub("[\u0300-\u036f]", "", normalized_text)
     return text_with_no_accent
 
-def is_city_in_affiliation(city,affiliation):
+
+def is_city_in_affiliation(city, affiliation):
     return city.lower() in affiliation.lower()
 
-def affiliations_match_ratio(first_affiliation,second_affiliation):
-    return fuzz.ratio(first_affiliation,second_affiliation)
+
+def affiliations_match_ratio(first_affiliation, second_affiliation):
+    return fuzz.ratio(first_affiliation, second_affiliation)
+
 
 def get_corresponding_hospital_from_affiliation(affiliation):
     affiliations_dataframe = pd.read_csv("hospital_affiliations.csv", sep=";")
@@ -47,39 +59,40 @@
     hospital = "N.C"
     affiliation = affiliation.lower()
     for acronym in acronyms:
-        if acronym not in affiliation :
+        if acronym not in affiliation:
             continue
 
         # standarize original dataframe
         affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(is_hospital_affiliation)
-        affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers_and_convert_acronyms).apply(remove_accents)
-        
-        if len(affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]) == 0:
+        affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(remove_department_numbers).apply(convert_acronyms).apply(remove_accents)
+
+        acronyms_dataframe = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]  # noqa: E712
+        if len(acronyms_dataframe) == 0:  # noqa: E712
             continue
 
         # create new dataframe only with affiliations that contain acronyms
-        dataframe_with_acronyms = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]
-        dataframe_with_acronyms["city_in_affiliations"] = dataframe_with_acronyms["standardized_city"].apply(is_city_in_affiliation,affiliation=affiliation)
-        
-        if len(dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]) == 0:
+        acronyms_dataframe["city_in_affiliations"] = acronyms_dataframe["standardized_city"].apply(is_city_in_affiliation, affiliation=affiliation)
+
+        ancronyms_cities_dataframe = acronyms_dataframe[acronyms_dataframe["city_in_affiliations"] == True]  # noqa: E712
+        if len(ancronyms_cities_dataframe) == 0:
             continue
 
         # create new dataframe only with affiliations that contain acronyms and cities
-        dataframe_with_acronyms_and_cities = dataframe_with_acronyms[dataframe_with_acronyms["city_in_affiliations"] == True]
-        dataframe_with_acronyms_and_cities["ratio"] = dataframe_with_acronyms_and_cities["Affiliation"].apply(affiliations_match_ratio,second_affiliation=affiliation)
-        hospital = dataframe_with_acronyms_and_cities["Orga NonCnrs Acorriger"][dataframe_with_acronyms_and_cities["ratio"].idxmax()]             
-        
+        ancronyms_cities_dataframe["ratio"] = ancronyms_cities_dataframe["Affiliation"].apply(affiliations_match_ratio, second_affiliation=affiliation)
+        hospital = ancronyms_cities_dataframe["Orga NonCnrs Acorriger"][ancronyms_cities_dataframe["ratio"].idxmax()]
         break
 
     return hospital
 
+
 def main():
     for line in sys.stdin:
         data = json.loads(line)
-        texte=data["value"]
-        data["value"]=get_corresponding_hospital_from_affiliation(texte)
+        texte = data["value"]
+        data["value"] = get_corresponding_hospital_from_affiliation(texte)
         sys.stdout.write(json.dumps(data))
         sys.stdout.write("\n")
 
+
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()