[+] Output statistics

src/process/twitter_visualization.py

@@ -1,6 +1,8 @@
 """
 TODO: Module Docstring
 """
+import statistics
+
 from matplotlib import pyplot as plt
 from tabulate import tabulate
 
@@ -81,8 +83,8 @@ def view_covid_tweets_pop(users: list[ProcessedUser],
         if len(covid) == 0 or len(tweets) == 0:
             continue
         # Get the average popularity for COVID-related tweets
-        covid_avg = sum(t.popularity for t in covid) / len(covid)
+        covid_avg = statistics.mean(t.popularity for t in covid)
-        global_avg = sum(t.popularity for t in tweets) / len(tweets)
+        global_avg = statistics.mean(t.popularity for t in tweets)
         # Get the relative popularity
         user_popularity.append((u.username, covid_avg / global_avg))
 
@@ -99,10 +101,25 @@ def view_covid_tweets_pop(users: list[ProcessedUser],
     print(f"20 Users of whose COVID-related posts are the most popular:")
     print(tabulate([[u[0], f'{u[1]:.2f}'] for u in user_popularity[:20]],
                    ['Username', 'Popularity Ratio']))
+    print()
+
+    # Calculate statistics
+    x_list = [f[1] for f in user_popularity]
+    s = get_statistics(x_list)
+    print(f'With outliers, ')
+    print(f'- mean: {s.mean:.2f}, median: {s.median:.2f}, stddev: {s.stddev:.2f}')
+    print()
 
     # Remove outliers
     print('As there are many outliers in the popularity ratio, they are removed in graphing.')
-    x_list = remove_outliers([f[1] for f in user_popularity])
+    print()
+    x_list = remove_outliers(x_list)
+
+    # Calculate statistics without outliers
+    s = get_statistics(x_list)
+    print(f'Without outliers, ')
+    print(f'- mean: {s.mean:.2f}, median: {s.median:.2f}, stddev: {s.stddev:.2f}')
+    print()
 
     # Graph histogram
     plt.title(f'COVID-related popularity ratios for {sample_name}')

src/utils.py

@@ -2,10 +2,11 @@ import dataclasses
 import inspect
 import json
 import os
+import statistics
 from dataclasses import dataclass
 from datetime import datetime, date
 from pathlib import Path
-from typing import Union
+from typing import Union, NamedTuple
 
 import json5
 import numpy as np
@@ -124,6 +125,22 @@ def remove_outliers(points: list[float], z_threshold: float = 3.5) -> list[float
     return [points[v] for v in range(len(x)) if not is_outlier[v]]
 
 
+class Stats(NamedTuple):
+    mean: float
+    median: float
+    stddev: float
+
+
+def get_statistics(points: list[float]) -> Stats:
+    """
+    Calculate statistics for a set of points
+
+    :param points: Input points
+    :return: Statistics
+    """
+    return Stats(statistics.mean(points), statistics.median(points), statistics.stdev(points))
+
+
 class EnhancedJSONEncoder(json.JSONEncoder):
     def default(self, o):