Update app.py

app.py

@@ -1,10 +1,7 @@
 import datetime
-
 import pandas as pd
 import streamlit as st
 import timeago
-import plotly.graph_objects as go
-
 
 st.set_page_config(layout="wide")
 st.markdown(
@@ -36,18 +33,11 @@ def fetch_match_history():
     return df
 
 
-def days_left():
-    end_date = datetime.date(2023, 4, 30)
-    today = datetime.date.today()
-    time_until_date = end_date - today
-    return time_until_date.days
-
+match_df = fetch_match_history()
 
 def num_matches_played():
     return match_df.shape[0]
 
-
-match_df = fetch_match_history()
 teams = sorted(
     list(pd.concat([match_df["home"], match_df["away"]]).unique()), key=str.casefold
 )
@@ -61,147 +51,37 @@ for i, row in match_df.iterrows():
     result = row["result"]
 
     if home_team not in team_results:
-        team_results[home_team] = [0, 0, 0]
+        team_results[home_team] = [0, 0, 0, 0]
 
     if away_team not in team_results:
-        team_results[away_team] = [0, 0, 0]
+        team_results[away_team] = [0, 0, 0, 0]
 
     if result == 0:
         team_results[home_team][2] += 1
         team_results[away_team][0] += 1
+        team_results[home_team][3] += 3
     elif result == 1:
         team_results[home_team][0] += 1
         team_results[away_team][2] += 1
+        team_results[home_team][3] += 3
     else:
         team_results[home_team][1] += 1
         team_results[away_team][1] += 1
+        team_results[home_team][3] += 1
+        team_results[away_team][3] += 1
 
 
 df = pd.DataFrame.from_dict(
-    team_results, orient="index", columns=["wins", "draws", "losses"]
+    team_results, orient="index", columns=["wins", "draws", "losses", "points"]
 ).sort_index()
 df[["owner", "team"]] = df.index.to_series().str.split("/", expand=True)
-df = df[["owner", "team", "wins", "draws", "losses"]]
+df = df[["owner", "team", "wins", "draws", "losses", "points"]]
 df["win_pct"] = (df["wins"] / (df["wins"] + df["draws"] + df["losses"])) * 100
 
-stats = df
-
-tab_team, tab_competition = st.tabs(["Results", "Competition stats"])
-
-
-def get_text_result(row, team_name):
-    if row["home"] == team_name:
-        if row["result"] == 1:
-            return "Win"
-        elif row["result"] == 0.5:
-            return "Draw"
-        else:
-            return "Loss"
-    elif row["away"] == team_name:
-        if row["result"] == 0:
-            return "Win"
-        elif row["result"] == 0.5:
-            return "Draw"
-        else:
-            return "Loss"
-
-
-with tab_team:
-    team = st.selectbox("Team", teams)
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        c1, c2, c3 = st.columns(3)
-        with c1:
-            st.metric("Wins", f"{stats.loc[[team]]['wins'][0]}")
-        with c2:
-            st.metric("Draws", f"{stats.loc[[team]]['draws'][0]}")
-        with c3:
-            st.metric("Losses", f"{stats.loc[[team]]['losses'][0]}")
-
-        st.write("Results")
-        res_df = match_df[(match_df["home"] == team) | (match_df["away"] == team)]
-        res_df["result"] = res_df.apply(lambda row: get_text_result(row, team), axis=1)
-        opponent_column = res_df.apply(
-            lambda row: row["away"] if row["home"] == team else row["home"], axis=1
-        )
-        res_df["vs"] = opponent_column
-        result_column = res_df["result"]
-        new_df = pd.concat([opponent_column, result_column], axis=1)
-        new_df.columns = ["vs", "result"]
-        res_df[["owner", "team"]] = res_df["vs"].str.split("/", expand=True)
-        res_df["played"] = res_df["timestamp"].apply(lambda x: timeago.format(x, now))
-        res_df.sort_values(by=["timestamp"], ascending=True, inplace=True)
-        disp_res_df = res_df.drop(["home", "away", "vs", "timestamp"], axis=1)
-
-        def highlight_results(s):
-            colour = {
-                "Win": "LightGreen",
-                "Draw": "LightYellow",
-                "Loss": "LightSalmon",
-            }
-            return [f"background-color: {colour[s.result]}"] * len(s)
-
-        # Create a friendly index.
-        disp_res_df.reset_index(inplace=True, drop=True)
-        disp_res_df.index += 1
-        disp_res_df = disp_res_df.iloc[::-1]
-
-        # Display the table.
-        st.dataframe(disp_res_df.style.apply(highlight_results, axis=1))
-
-    with col2:
-        c1, c2 = st.columns(2)
-        with c1:
-            st.metric("Win rate", f"{stats.loc[[team]]['win_pct'][0]:.2f}%")
-
-            joined = res_df["timestamp"].min()
-        with c2:
-            st.metric("Competing since", f"{timeago.format(joined, now)}")
-
-        grouped = (
-            res_df.groupby([res_df["timestamp"].dt.date, "result"])
-            .size()
-            .reset_index(name="count")
-        )
-
-        loss_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Loss", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Loss", "count"],
-            name="Losses",
-            marker=dict(color="red"),
-        )
-        draw_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Draw", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Draw", "count"],
-            name="Draws",
-            marker=dict(color="orange"),
-        )
-        win_trace = go.Bar(
-            x=grouped.loc[grouped["result"] == "Win", "timestamp"],
-            y=grouped.loc[grouped["result"] == "Win", "count"],
-            name="Wins",
-            marker=dict(color="green"),
-        )
-
-        fig = go.Figure(data=[loss_trace, draw_trace, win_trace])
-        fig.update_layout(barmode="stack")
-        st.plotly_chart(fig)
-
-
-with tab_competition:
-    col1, col2, col3 = st.columns(3)
-
-    col1.metric("Matches played", f"{num_matches_played():,d}")
-    col2.metric("Live models", f"{len(teams)}")
-    col3.metric("Season ends in", f"{days_left()} days")
-
-    match_counts = (
-        match_df.groupby(match_df["timestamp"].dt.date).size().reset_index(name="count")
-    )
-    match_counts["matches_played"] = match_counts["count"].cumsum()
-
-    st.title("Matches played")
-    st.area_chart(match_counts.set_index("timestamp")["matches_played"])
+stats = df.sort_values(by='points', ascending=False)
+
+st.header("League Table")
+st.dataframe(stats)
 
+st.subheader("Match Results")
+st.write("Note: Match results are not displayed in this version of the app.")