Create traffic_analysis.py

traffic_analysis.py

@@ -0,0 +1,30 @@
+import pandas as pd
+from scipy.stats import pearsonr
+
+df = pd.read_csv("traffic.csv")
+
+total_pageviews = df['pageviews'].sum()
+average_daily_pageviews = df['pageviews'].mean()
+
+print("Total Pageviews:", total_pageviews)
+print("Average Daily Pageviews:", average_daily_pageviews)
+
+total_events = df['events'].sum()
+event_distribution = df['events'].value_counts()
+
+print("Total Events:", total_events)
+print("Event Distribution:\n", event_distribution)
+
+country_distribution = df.groupby('country')['pageviews'].sum()
+
+print("Geographical Distribution:\n", country_distribution)
+
+df['CTR'] = df['clicks'] / df['pageviews']  # Calculate CTR
+overall_ctr = df['CTR'].mean()
+
+print("Overall CTR:", overall_ctr)
+print("CTR by Link:\n", df.groupby('link')['CTR'].mean())
+
+correlation, _ = pearsonr(df['pageviews'], df['clicks'])
+
+print("Correlation between Pageviews and Clicks:", correlation)