[PdrDashboard] Use polars syntax for dataframes in pdr dashboard (#1619)

pdr_backend/pdr_dashboard/callbacks/callbacks_feeds.py

@@ -30,7 +30,7 @@ def update_page_data(_start_date):
             for key, value in app.data.feeds_metrics_data.items()
         ]
 
-        return app.data.feeds_table_data.to_dict("records"), metrics_children_data
+        return app.data.feeds_table_data.to_dicts(), metrics_children_data
 
     @app.callback(
         Output("feeds_page_table", "data"),
@@ -97,12 +97,10 @@ def filter_table(
 
         if sort_by:
             sort_col = sort_by[0]["column_id"]
-            ascending = sort_by[0]["direction"] == "asc"
-            new_table_data = new_table_data.sort_values(
-                by=sort_col, ascending=ascending
-            )
+            descending = sort_by[0]["direction"] == "desc"
+            new_table_data = new_table_data.sort(by=sort_col, descending=descending)
 
-        return format_df(new_table_data).to_dict("records")
+        return format_df(new_table_data).to_dicts()
 
     @app.callback(
         Output("sales_dropdown", "label"),

pdr_backend/pdr_dashboard/callbacks/callbacks_home.py

@@ -1,5 +1,5 @@
 import dash
-import pandas
+import polars as pl
 from dash import Input, Output, State
 
 from pdr_backend.cli.arg_feeds import ArgFeeds
@@ -53,7 +53,7 @@ def get_display_data_from_db(
         ]
 
         if len(selected_feeds) == 0 or len(selected_predictoors_addrs) == 0:
-            payouts = pandas.DataFrame()
+            payouts = pl.DataFrame()
         else:
             payouts = app.data.payouts_from_bronze_predictions(
                 selected_feeds_addrs,
@@ -113,18 +113,18 @@ def update_predictoors_table_on_search(
             app.data.refresh_feeds_data()
 
         formatted_predictoors_data = (
-            app.data.formatted_predictoors_home_page_table_data.copy()
+            app.data.formatted_predictoors_home_page_table_data.clone()
         )
         selected_predictoors_addrs = [
             predictoors_table[i]["full_addr"] for i in selected_rows
         ]
 
         if "show-favourite-addresses.value" in dash.callback_context.triggered_prop_ids:
-            custom_predictoors = formatted_predictoors_data[
-                formatted_predictoors_data["full_addr"].isin(
+            custom_predictoors = formatted_predictoors_data.filter(
+                formatted_predictoors_data["full_addr"].is_in(
                     app.data.favourite_addresses
                 )
-            ]
+            )
             custom_predictoors_addrs = list(custom_predictoors["full_addr"])
 
             if show_favourite_addresses:
@@ -136,23 +136,23 @@ def update_predictoors_table_on_search(
                     if predictoor_addr not in custom_predictoors_addrs
                 ]
 
-        filtered_data = formatted_predictoors_data.copy()
+        filtered_data = formatted_predictoors_data.clone()
         if search_value:
-            filtered_data = filtered_data[
+            filtered_data = filtered_data.filter(
                 filtered_data["full_addr"].str.contains(search_value)
-            ]
+            )
 
-        filtered_data = filtered_data[
-            ~filtered_data["full_addr"].isin(selected_predictoors_addrs)
-        ]
-        selected_predictoors = formatted_predictoors_data[
-            formatted_predictoors_data["full_addr"].isin(selected_predictoors_addrs)
-        ]
+        filtered_data = filtered_data.filter(
+            ~filtered_data["full_addr"].is_in(selected_predictoors_addrs)
+        )
+        selected_predictoors = formatted_predictoors_data.filter(
+            formatted_predictoors_data["full_addr"].is_in(selected_predictoors_addrs)
+        )
 
-        filtered_data = pandas.concat([selected_predictoors, filtered_data])
+        filtered_data = pl.concat([selected_predictoors, filtered_data])
         selected_predictoor_indices = list(range(len(selected_predictoors_addrs)))
 
-        return (filtered_data.to_dict("records"), selected_predictoor_indices)
+        return (filtered_data.to_dicts(), selected_predictoor_indices)
 
     @app.callback(
         Output("feeds_table", "data"),

pdr_backend/pdr_dashboard/callbacks/callbacks_predictoors.py

@@ -31,7 +31,7 @@ def update_page_data(_start_date):
             for key, value in stats.items()
         ]
 
-        return app.data.predictoors_table_data.to_dict("records"), metrics_children_data
+        return app.data.predictoors_table_data.to_dicts(), metrics_children_data
 
     @app.callback(
         Output("predictoors_page_table", "data"),
@@ -115,12 +115,12 @@ def filter_table(
         if sort_by:
             # Extract sort criteria
             sort_col = sort_by[0]["column_id"]
-            ascending = sort_by[0]["direction"] == "asc"
-            new_table_data = new_table_data.sort_values(
-                by=[sort_col, "full_addr"], ascending=ascending
+            descending = sort_by[0]["direction"] == "desc"
+            new_table_data = new_table_data.sort(
+                by=[sort_col, "full_addr"], descending=descending
             )
 
-        return format_df(new_table_data).to_dict("records")
+        return format_df(new_table_data).to_dicts()
 
     @app.callback(
         Output("apr_dropdown", "label"),

pdr_backend/pdr_dashboard/dash_components/modal.py

@@ -1,7 +1,7 @@
 from typing import List, Optional
 
 import dash_bootstrap_components as dbc
-import pandas
+import polars as pl
 from dash import html
 
 from pdr_backend.cli.arg_feeds import ArgFeed
@@ -85,9 +85,9 @@ def create_figures(self):
 
         if not selected_row:
             figures_args: List[List] = (
-                [pandas.DataFrame(), pandas.DataFrame()]
+                [pl.DataFrame(), pl.DataFrame()]
                 if self.modal_id == "feeds_modal"
-                else [pandas.DataFrame()]
+                else [pl.DataFrame()]
             )
             self.figures = figures_func(*figures_args).get_figures()
             return