Latest updates column

app.py

@@ -20,6 +20,7 @@ def fetch_data():
                 'State': issue.state, 
                 'Created at': issue.created_at, 
                 'Closed at': issue.closed_at,
+                'Last update': issue.updated_at,
                 'Labels': [label.name for label in issue.labels],
                 'Reactions': issue.reactions['total_count'],
                 'Comments': issue.comments,
@@ -44,22 +45,31 @@ except:
 # Section 1: Issue activity metrics
 st.header("Issue activity metrics")
 
-col1, col2 = st.columns(2)
+col1, col2, col3 = st.columns(3)
 
 state_counts = df['State'].value_counts()
 open_issues = df.loc[df['State'] == 'open']
+closed_issues = df.loc[df['State'] == 'closed']
+# closed_issues['Created at'] = pd.to_datetime(df['Created at'])
+# # closed_issues['Closed at'] = pd.to_datetime(df['Closed at'])
+# closed_issues['Time to Close'] = closed_issues['Closed at'] - closed_issues['Created at']
+# closed_issues['Time to Close'] = closed_issues['Time to Close'].apply(lambda x: x if pd.isnull(x) else x.days)
 
 with col1:
     st.metric(label="Open Issues", value=state_counts['open'])
 
 with col2:
     st.metric(label="Closed Issues", value=state_counts['closed'])
-   
 
-# # TODO Plot: number of open vs closed issues by date
+# with col3:
+#     average_time_to_close = closed_issues['Time to Close'].mean()
+#     st.metric(label="Avg. Days to Close", value=average_time_to_close)
+
+
+# TODO Plot: number of open vs closed issues by date
    
-# # TODO Dataframe: Unresolved conversations
-# ## Issues with new comments. Sorted by number of new comments (based on timeframe above) and/or date of last comment.
+# TODO Dataframe: Unresolved conversations
+## Issues with new comments (or updates?). Sorted by number of new comments (based on timeframe above) and/or date of last comment.
 
 st.subheader("Latest bugs 🐞")
 bug_issues = open_issues[open_issues["Labels"].apply(lambda labels: "type: bug" in labels)]
@@ -75,6 +85,17 @@ st.dataframe(
     }
 )
 
+st.subheader("Latest updates 📝")
+st.dataframe(
+    open_issues[["Issue","Last update","URL"]].sort_values(by="Last update", ascending=False).head(10),
+    hide_index=True,
+    column_config={
+        "Issue": st.column_config.TextColumn("Issue", width=400),
+        "Last update": st.column_config.DatetimeColumn("Last update"),
+        "URL": st.column_config.LinkColumn("🔗", display_text="🔗")
+    }
+)
+
 # Section 2: Issue classification
 st.header("Issue classification")
 col1, col2 = st.columns(2)
@@ -132,7 +153,7 @@ st.dataframe(
 # st.header("Issue dependencies")
 # ## Map: dependencies between issues. Network of issue mentions.x
 
-status.update(label="Checking for updated data...", state="running")
+# status.update(label="Checking for updated data...", state="running")
 updated_data = fetch_data()
 if df.equals(updated_data):
     status.update(label="Data is up to date!", state="complete")