fixing error in agents graph

tabs/agent_graphs.py

@@ -24,58 +24,6 @@ def plot_rolling_average_dune(
     )
 
 
-def plot_rolling_average(
-    daa_df: pd.DataFrame,
-    market_creator: str = None,
-) -> gr.Plot:
-    """Function to plot the rolling average of daily active agents"""
-    if market_creator is not None:
-        filtered_traders_df = daa_df.loc[daa_df["market_creator"] == market_creator]
-        rolling_avg_df = get_sevenday_rolling_average(filtered_traders_df)
-    else:
-        rolling_avg_df = get_sevenday_rolling_average(daa_df)
-    print(rolling_avg_df.head())
-
-    # Ensure 'creation_date' is a column, not an index
-    if "tx_date" not in rolling_avg_df.columns:
-        rolling_avg_df = rolling_avg_df.reset_index()
-
-    fig = px.bar(
-        rolling_avg_df,
-        x="tx_date",
-        y="rolling_avg_traders",
-    )
-    fig.update_layout(
-        xaxis_title="Date",
-        yaxis_title="7-day rolling average of DAA",
-    )
-
-    return gr.Plot(
-        value=fig,
-    )
-
-
-def get_sevenday_rolling_average(daa_df: pd.DataFrame) -> pd.DataFrame:
-    """Function to get the 7-day rolling average of the number of unique
-    trader_address"""
-    # Create a local copy of the dataframe
-    local_df = daa_df.copy()
-
-    # Sort the dataframe by date
-    local_df = local_df.sort_values(by="tx_date").set_index("tx_date")
-
-    # Group by market_creator and calculate rolling average of unique trader_address
-    rolling_avg = (
-        local_df.resample("D")["trader_address"]
-        .nunique()
-        .rolling(window=7)
-        .mean()
-        .reset_index()
-    )
-    rolling_avg.rename(columns={"trader_address": "rolling_avg_traders"}, inplace=True)
-    return rolling_avg
-
-
 def plot_rolling_average_roi(
     traders_data: pd.DataFrame, pearl_agents: pd.DataFrame
 ) -> gr.Plot:
@@ -96,20 +44,21 @@ def plot_rolling_average_roi(
 
     # Get the 2-week rolling average of ROI
     rolling_avg_roi_df = get_twoweeks_rolling_average_roi(filtered_traders_data)
+    print("Rolling average ROI DataFrame:")
     print(rolling_avg_roi_df.head())
 
-    fig = px.bar(
+    fig2 = px.bar(
         rolling_avg_roi_df,
         x="creation_date",
         y="rolling_avg_roi",
     )
-    fig.update_layout(
+    fig2.update_layout(
         xaxis_title="Week",
         yaxis_title="2-week rolling average of ROI at the trader level",
     )
 
     return gr.Plot(
-        value=fig,
+        value=fig2,
     )
 
 
@@ -124,20 +73,14 @@ def get_twoweeks_rolling_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame
     local_df["creation_date"] = pd.to_datetime(local_df["creation_date"])
 
     # Aggregate ROI at the date level
-    daily_avg = local_df.groupby("creation_date")["roi"].mean().reset_index()
+    daily_avg2 = local_df.groupby("creation_date")["roi"].mean().reset_index()
 
     # Set the datetime index
-    daily_avg = daily_avg.set_index("creation_date")
+    daily_avg2 = daily_avg2.set_index("creation_date")
 
     # Now resample and rolling average
-    weekly_avg = daily_avg.resample("W").mean()
-    rolling_avg = weekly_avg.rolling(window=2).mean().reset_index()
-
-    # Rename columns
-    rolling_avg.rename(
-        columns={"roi": "rolling_avg_roi", "date": "creation_date"},
-        inplace=True,
-    )
+    weekly_avg2 = daily_avg2.resample("W").mean()
+    rolling_avg = weekly_avg2.rolling(window=2).mean().reset_index()
 
     return rolling_avg
 
@@ -164,9 +107,6 @@ def get_weekly_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame:
     # Remove NaN values
     weekly_avg = weekly_avg.dropna(subset=["roi"])
 
-    # Rename columns for clarity
-    weekly_avg = weekly_avg.rename(columns={"roi": "weekly_avg_roi"})
-
     return weekly_avg
 
 
@@ -176,12 +116,12 @@ def plot_weekly_average_roi(
     """Function to plot the weekly average of ROI for pearl agents"""
     # Get the list of unique addresses from the daa_pearl_df
     unique_addresses = pearl_agents["safe_address"].unique()
-    
+
     # Filter the weekly_roi_df to include only those addresses
     filtered_traders_data = traders_data[
         traders_data["trader_address"].isin(unique_addresses)
     ].copy()
-    
+
     # create the date column
     filtered_traders_data["creation_timestamp"] = pd.to_datetime(
         filtered_traders_data["creation_timestamp"]
@@ -192,6 +132,7 @@ def plot_weekly_average_roi(
 
     # Get the weekly average ROI
     weekly_avg_roi_df = get_weekly_average_roi(filtered_traders_data)
+    print("Weekly average ROI DataFrame:")
     print(weekly_avg_roi_df.head())
 
     # Update the plot to use the correct column name 'weekly_avg_roi'