detaild comments added for clarification

filtro.py

@@ -7,6 +7,8 @@ import csv
 
 MIN_MOVIES = 42  # Only keep relations for actors that have made more than this many movies
 
+#-----------------DOWNLOAD .GZ FILES FROM IMDB DATABASE-----------------#
+
 def download_url(url):
   print("Downloading:", url)
   file_name_start_pos = url.rfind("/") + 1
@@ -29,32 +31,33 @@ urls = ["https://datasets.imdbws.com/name.basics.tsv.gz",
 for url in urls:
   download_url(url)
 
-os.makedirs("data", exist_ok=True)
+os.makedirs("data", exist_ok=True) # Generate (recursively) folders, ignores the comand if they already exists
 
 print("Filtering actors...")
 df_attori = pd.read_csv(
   'name.basics.tsv.gz', sep='\t', compression='gzip',
-  usecols=['nconst', 'primaryName', 'primaryProfession'],
+  usecols=['nconst', 'primaryName', 'primaryProfession'], # Considering only this columns
-  dtype={'primaryName': 'U', 'primaryProfession': 'U'},
+  dtype={'primaryName': 'U', 'primaryProfession': 'U'}, # Both are unsigned integers
-  converters={'nconst': lambda x: int(x.lstrip("nm0"))})
+  converters={'nconst': lambda x: int(x.lstrip("nm0"))}) # All actors starts with nm0, we are just cleaning the output
 df_attori.query('primaryProfession.str.contains("actor") or primaryProfession.str.contains("actress")', inplace=True)
+# A lot of actors/actresses do more than one job (director etc..), with this comand I take all the names that have the string "actor" or "actress" in their profession. In this way both someone who is classified as "actor" or as "actor, director" are taken into consideration
 
 print("Filtering films...")
 df_film = pd.read_csv(
   'title.basics.tsv.gz', sep='\t', compression='gzip',
-  usecols=['tconst', 'primaryTitle', 'isAdult', 'titleType'],
+  usecols=['tconst', 'primaryTitle', 'isAdult', 'titleType'], # Considering only this columns
-  dtype={'primaryTitle': 'U', 'titleType': 'U'},
+  dtype={'primaryTitle': 'U', 'titleType': 'U'}, # Both are unsigned integers
-  converters={'tconst': lambda x: int(x.lstrip("t0")), 'isAdult': lambda x: x != "0"})
+  converters={'tconst': lambda x: int(x.lstrip("t0")), 'isAdult': lambda x: x != "0"}) # # All movies starts with t0, we are just cleaning the output. Then remove all adult movies
 df_film.query('not isAdult and titleType in ["movie", "tvSeries", "tvMovie", "tvMiniSeries"]',
-              inplace=True)
+              inplace=True) # There are a lot of junk categories considered in IMDb, we are considering all the non Adult movies in this whitelist
 filtered_tconsts = df_film["tconst"].to_list()
 
 print("Filtering relations...")
 df_relazioni = pd.read_csv(
   'title.principals.tsv.gz', sep='\t', compression='gzip',
-  usecols=['tconst', 'nconst','category'],
+  usecols=['tconst', 'nconst','category'],  # Considering only this columns
-  dtype={'category': 'U'},
+  dtype={'category': 'U'}, # Unsigned integer
-  converters={'nconst': lambda x: int(x.lstrip("nm0")), 'tconst': lambda x: int(x.lstrip("t0"))})
+  converters={'nconst': lambda x: int(x.lstrip("nm0")), 'tconst': lambda x: int(x.lstrip("t0"))}) # Cleaning
 df_relazioni.query('(category == "actor" or category == "actress") and tconst in @filtered_tconsts', inplace=True)
 # Returns an array of unique actor ids (nconsts) and an array of how many times they appear (counts) => the number of movies they appear in
 nconsts, counts = np.unique(df_relazioni["nconst"].to_numpy(), return_counts=True)
@@ -74,4 +77,4 @@ df_attori.to_csv('data/Attori.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar
 df_film.to_csv('data/FilmFiltrati.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\', columns=['tconst', 'primaryTitle'], header=False, index=False)
 df_relazioni.to_csv('data/Relazioni.txt', sep='\t', quoting=csv.QUOTE_NONE, escapechar='\\', columns=['tconst', 'nconst'], header=False, index=False)
 
-# Takes about 1 min 30 s
+# Takes about 1 min 30 s with MIN_MOVIES = 42