filter very old markets from the mean graph

tabs/dist_gap.py

@@ -2,7 +2,7 @@ import pandas as pd
 import gradio as gr
 import matplotlib.pyplot as plt
 import seaborn as sns
-from datetime import datetime, UTC
+from datetime import datetime, UTC, date
 import plotly.express as px
 
 HEIGHT = 300
@@ -60,6 +60,11 @@ def get_avg_gap_time_evolution_grouped_markets(all_markets: pd.DataFrame) -> gr.
     recent_markets["creation_date"] = pd.to_datetime(
         recent_markets["creation_datetime"]
     ).dt.date
+    # Define the cutoff date
+    cutoff_date = date(2024, 1, 1)
+
+    # Filter the DataFrame with very old markets
+    recent_markets = recent_markets[recent_markets["creation_date"] > cutoff_date]
     avg_dist_gap_perc = (
         recent_markets.groupby(["sample_date", "creation_date"])["dist_gap_perc"]
         .mean()