thecourseforum · ayung0 · Oct 21, 2024 · Oct 21, 2024 · Oct 22, 2024 · Oct 23, 2024
diff --git a/db/latest.sql b/db/latest.sql
diff --git a/tcf_website/management/commands/average_sentiments.py b/tcf_website/management/commands/average_sentiments.py
@@ -0,0 +1,38 @@
+import os
+import re
+import pandas as pd
+
+def extract_professor_name(professor_full):
+    return re.match(r'^[^()]+', professor_full).group().strip()
+
+def extract_course_mnemonic(course_full):
+    return course_full.split(' |')[0].strip()
-def extract_professor_name(professor_full):
-    return re.match(r'^[^()]+', professor_full).group().strip()
-
-def extract_course_mnemonic(course_full):
-    return course_full.split(' |')[0].strip()
+def extract_professor_name(professor_full):
+    if not isinstance(professor_full, str) or not professor_full.strip():
+        return ""
+    match = re.match(r"^[^()]+", professor_full)
+    return match.group().strip() if match else professor_full.strip()
+
+def extract_course_mnemonic(course_full):
+    if not isinstance(course_full, str) or not course_full.strip():
+        return ""
+    return course_full.split(" |", 1)[0].strip()
-def extract_professor_name(professor_full):
-    return re.match(r'^[^()]+', professor_full).group().strip()
-
-def extract_course_mnemonic(course_full):
-    return course_full.split(' |')[0].strip()
+def extract_professor_name(professor_full):
+    if not isinstance(professor_full, str) or not professor_full.strip():
+        return ""
+    match = re.match(r"^[^()]+", professor_full)
+    return match.group().strip() if match else professor_full.strip()
+
+def extract_course_mnemonic(course_full):
+    if not isinstance(course_full, str) or not course_full.strip():
+        return ""
+    return course_full.split(" |", 1)[0].strip()
+
+def avg_sentiment_df_creator():
+    current_directory = os.path.dirname(os.path.abspath(__file__))
+    reviews_data_path = os.path.join(current_directory, "reviews_data", "reviews_data_with_sentiment.csv")
+    df = pd.read_csv(reviews_data_path)
+    df["instructor_name_only"] = df["instructor"].apply(extract_professor_name)
+    df["course_code_only"] = df["course"].apply(extract_course_mnemonic)
+    avg_sentiment_df = df.groupby(["instructor_name_only", "course_code_only"])["sentiment_score"].mean().reset_index()
+    return avg_sentiment_df
+
+def query_average(df, professor, course):
+    result = df[(df["instructor_name_only"] == professor) & (df["course_code_only"] == course)]
+    if result.empty:
+        print("No data found for the given professor and course.")
+    else:
+        print(f"Average sentiment score for {professor} in {course}: {result['sentiment_score'].values[0]:.2f}")
+
+if __name__ == "__main__":
+    avg_sentiment_df = avg_sentiment_df_creator()
+    print(avg_sentiment_df)
+
+    while True:
+        professor = input("Enter professor name (or type 'exit' to quit): ")
+        if professor.lower() == 'exit':
+            break
+        course = input("Enter course name (or type 'exit' to quit): ")
+        if course.lower() == 'exit':
+            break
+        query_average(avg_sentiment_df, professor, course)