updating the metrics plot

tabs/metrics.py

@@ -93,15 +93,26 @@ def get_metrics(
     return trades_filtered
 
 
-def get_boxplot_metrics(
-    metric_name: str, column_name: str, trades_df: pd.DataFrame
-) -> pd.DataFrame:
+def get_boxplot_metrics(column_name: str, trades_df: pd.DataFrame) -> pd.DataFrame:
     # this is to filter out the data before 2023-09-01
     trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
     trades_filtered = trades_filtered[
-        ["month_year_week", "market_creator", column_name]
+        ["creation_timestamp", "month_year_week", "market_creator", column_name]
     ]
 
+    # adding the total
+    trades_filtered_all = trades_filtered.copy(deep=True)
+    trades_filtered_all["market_creator"] = "all"
+
+    # merging both dataframes
+    all_filtered_trades = pd.concat(
+        [trades_filtered, trades_filtered_all], ignore_index=True
+    )
+    all_filtered_trades = all_filtered_trades.sort_values(
+        by="creation_timestamp", ascending=True
+    )
+    return all_filtered_trades
+
 
 def plot2_trade_details(
     metric_name: str, market_creator: str, trades_df: pd.DataFrame
@@ -164,16 +175,13 @@ def plot_trade_metrics(metric_name: str, trades_df: pd.DataFrame) -> gr.Plot:
         column_name = metric_name
         yaxis_title = "Gross profit per trade (xDAI)"
 
-    trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
-    trades_filtered = trades_filtered[
-        ["month_year_week", "market_creator", column_name]
-    ]
+    trades_filtered = get_boxplot_metrics(column_name, trades_df)
     fig = px.box(
         trades_filtered,
         x="month_year_week",
         y=column_name,
         color="market_creator",
-        color_discrete_sequence=["goldenrod", "purple"],
+        color_discrete_sequence=["goldenrod", "darkgreen", "purple"],
     )
     fig.update_traces(boxmean=True)
     fig.update_layout(