Merge dev into samukweku/refactor_expand_grid

CHANGELOG.md

@@ -14,6 +14,7 @@
 - [ENH] Added a `clean_names` method for polars - it can be used to clean the column names, or clean column values . Issue #1343 @samukweku
 - [ENH] Improved performance for non-equi joins when using numba - @samukweku PR #1341
 - [ENH] pandas Index,Series, DataFrame now supported in the `complete` method. - PR #1369 @samukweku
+- [ENH] Improve performance for `first/last` in `conditional_join, when the join columns in the right dataframe are sorted. - PR #1382 @samukweku
 
 ## [v0.27.0] - 2024-03-21
 

janitor/functions/conditional_join.py

@@ -508,7 +508,10 @@ def _conditional_join_compute(
     for condition in conditions:
         left_on, right_on, op = condition
         _conditional_join_type_check(
-            df[left_on], right[right_on], op, use_numba
+            left_column=df[left_on],
+            right_column=right[right_on],
+            op=op,
+            use_numba=use_numba,
         )
         if op == _JoinOperator.STRICTLY_EQUAL.value:
             eq_check = True
@@ -520,19 +523,25 @@ def _conditional_join_compute(
     if (len(conditions) > 1) or eq_check:
         if eq_check:
             result = _multiple_conditional_join_eq(
-                df,
-                right,
-                conditions,
-                keep,
-                use_numba,
-                force,
+                df=df,
+                right=right,
+                conditions=conditions,
+                keep=keep,
+                use_numba=use_numba,
+                force=force,
             )
         elif le_lt_check:
             result = _multiple_conditional_join_le_lt(
-                df, right, conditions, keep, use_numba
+                df=df,
+                right=right,
+                conditions=conditions,
+                keep=keep,
+                use_numba=use_numba,
             )
         else:
-            result = _multiple_conditional_join_ne(df, right, conditions, keep)
+            result = _multiple_conditional_join_ne(
+                df=df, right=right, conditions=conditions, keep=keep
+            )
     else:
         left_on, right_on, op = conditions[0]
         if use_numba:
@@ -558,14 +567,16 @@ def _conditional_join_compute(
     if return_matching_indices:
         return result
 
+    left_index, right_index = result
     return _create_frame(
-        df,
-        right,
-        *result,
-        how,
-        df_columns,
-        right_columns,
-        indicator,
+        df=df,
+        right=right,
+        left_index=left_index,
+        right_index=right_index,
+        how=how,
+        df_columns=df_columns,
+        right_columns=right_columns,
+        indicator=indicator,
     )
 
 
@@ -630,9 +641,9 @@ def _multiple_conditional_join_ne(
     left_on, right_on, op = first
 
     indices = _generic_func_cond_join(
-        df[left_on],
-        right[right_on],
-        op,
+        left=df[left_on],
+        right=right[right_on],
+        op=op,
         multiple_conditions=False,
         keep="all",
     )
@@ -755,7 +766,9 @@ def _multiple_conditional_join_eq(
             )
         if not right_is_sorted:
             right_df = right_df.sort_values(right_columns)
-        indices = _numba_equi_join(left_df, right_df, eqs, ge_gt, le_lt)
+        indices = _numba_equi_join(
+            df=left_df, right=right_df, eqs=eqs, ge_gt=ge_gt, le_lt=le_lt
+        )
         if not rest or (indices is None):
             return indices
 
@@ -909,7 +922,25 @@ def _multiple_conditional_join_le_lt(
                 if condition not in (ge_gt, le_lt)
             ]
 
-            indices = _range_indices(df, right, ge_gt, le_lt)
+            if conditions:
+                _keep = None
+            else:
+                first = ge_gt[1]
+                second = le_lt[1]
+                right_is_sorted = (
+                    right[first].is_monotonic_increasing
+                    & right[second].is_monotonic_increasing
+                )
+                if right_is_sorted:
+                    _keep = keep
+                else:
+                    _keep = None
+
+            indices = _range_indices(
+                df=df, right=right, first=ge_gt, second=le_lt, keep=_keep
+            )
+            if _keep:
+                return indices
 
         # no optimised path
         # blow up the rows and prune
@@ -926,9 +957,9 @@ def _multiple_conditional_join_le_lt(
                 left_on, right_on, op = ge_gt
 
             indices = _generic_func_cond_join(
-                df[left_on],
-                right[right_on],
-                op,
+                left=df[left_on],
+                right=right[right_on],
+                op=op,
                 multiple_conditions=False,
                 keep="all",
             )
@@ -951,6 +982,7 @@ def _range_indices(
     right: pd.DataFrame,
     first: tuple,
     second: tuple,
+    keep: str,
 ) -> Union[tuple[np.ndarray, np.ndarray], None]:
     """
     Retrieve index positions for range/interval joins.
@@ -1019,8 +1051,6 @@ def _range_indices(
         # this is solved by getting the cumulative max
         # thus ensuring that the first match is obtained
         # via a binary search
-        # this allows us to avoid the less efficient linear search
-        # of using a for loop with a break to get the first match
         outcome = _generic_func_cond_join(
             left=left_c,
             right=right_c.cummax(),
@@ -1055,7 +1085,10 @@ def _range_indices(
         # this also implies that the intervals
         # do not overlap on the right side
         return left_index, right_index[starts]
-
+    if keep == "first":
+        return left_index, right_index[starts]
+    if keep == "last":
+        return left_index, right_index[ends - 1]
     right_index = [right_index[start:end] for start, end in zip(starts, ends)]
     right_index = np.concatenate(right_index)
     left_index = left_index.repeat(repeater)

janitor/functions/utils.py

@@ -813,6 +813,10 @@ def _less_than_indices(
 
     if multiple_conditions:
         return left_index, right_index, search_indices
+    if right_is_sorted and (keep == "last"):
+        indexer = np.empty_like(search_indices)
+        indexer[:] = len_right - 1
+        return left_index, right_index[indexer]
     if right_is_sorted and (keep == "first"):
         if any_nulls:
             return left_index, right_index[search_indices]
@@ -902,6 +906,9 @@ def _greater_than_indices(
 
     if multiple_conditions:
         return left_index, right_index, search_indices
+    if right_is_sorted and (keep == "first"):
+        indexer = np.zeros_like(search_indices)
+        return left_index, right_index[indexer]
     if right_is_sorted and (keep == "last"):
         if any_nulls:
             return left_index, right_index[search_indices - 1]
@@ -1043,9 +1050,9 @@ def _keep_output(keep: str, left: np.ndarray, right: np.ndarray):
     grouped = pd.Series(right).groupby(left)
     if keep == "first":
         grouped = grouped.min()
-        return grouped.index, grouped.array
+        return grouped.index, grouped._values
     grouped = grouped.max()
-    return grouped.index, grouped.array
+    return grouped.index, grouped._values
 
 
 class col: