'Refactored by Sourcery'

nlp_profiler/granular_features/numbers.py

@@ -8,8 +8,7 @@ def gather_whole_numbers(text: str) -> list:
     if not isinstance(text, str):
         return []
 
-    line = re.findall(r'[0-9]+', text)
-    return line
+    return re.findall(r'[0-9]+', text)
 
 
 def count_whole_numbers(text: str) -> int:

nlp_profiler/granular_features/stop_words.py

@@ -15,9 +15,8 @@ def gather_stop_words(text: str) -> list:
         return []
 
     word_tokens = word_tokenize(text)
-    found_stop_words = [word for _, word in enumerate(word_tokens)
+    return [word for _, word in enumerate(word_tokens)
                         if word in STOP_WORDS]
-    return found_stop_words
 
 
 def count_stop_words(text: str) -> int:

nlp_profiler/sentiment_polarity.py

@@ -38,7 +38,7 @@ def sentiment_polarity(score: float) -> str:
 
     score = float(score)
     score = (score + 1) / 2  # see https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range
-    score = score * 100
+    score *= 100
     for _, each_slab in enumerate(sentiment_polarity_to_words_mapping):  # pragma: no cover
         # pragma: no cover => early termination leads to loss of test coverage info
         if (score >= each_slab[1]) and (score <= each_slab[2]):

slow-tests/performance_tests/common_functions.py

@@ -23,6 +23,6 @@ def generate_data() -> list:
             text_with_punctuations, text_with_a_date, text_with_dates, text_with_duplicates]
 
     new_data = []
-    for index in range(1):
+    for _ in range(1):
         new_data.extend(data)
     return new_data