⚡️ Increase Resample speed

tide/processing.py

@@ -1,3 +1,5 @@
+from collections import defaultdict
+
 import pandas as pd
 import numpy as np
 import datetime as dt
@@ -1273,19 +1275,30 @@ def _fit_implementation(self, X: pd.Series | pd.DataFrame, y=None):
             for req, method in self.tide_format_methods.items():
                 self.columns_methods.append((tide_request(X.columns, req), method))
 
-        return self
-
-    def _transform_implementation(self, X: pd.Series | pd.DataFrame):
+    def _transform_implementation(self, X: pd.DataFrame):
         check_is_fitted(self, attributes=["feature_names_in_"])
+
         if not self.columns_methods:
-            agg_dict = {col: self.method for col in X.columns}
+            col_to_method = {col: self.method for col in X.columns}
         else:
-            agg_dict = {col: agg for cols, agg in self.columns_methods for col in cols}
+            col_to_method = {}
+            for cols, method in self.columns_methods:
+                for col in cols:
+                    col_to_method[col] = method
             for col in X.columns:
-                if col not in agg_dict.keys():
-                    agg_dict[col] = self.method
+                col_to_method.setdefault(col, self.method)
+
+        method_to_cols = defaultdict(list)
+        for col, method in col_to_method.items():
+            method_to_cols[method].append(col)
+
+        results = []
+        for method, cols in method_to_cols.items():
+            res = X[cols].resample(self.rule).agg(method)
+            results.append(res)
 
-        return X.resample(rule=self.rule).agg(agg_dict)[X.columns]
+        out = pd.concat(results, axis=1)
+        return out[X.columns]
 
 
 class AddTimeLag(BaseProcessing):