polars version update remaining

examples/articles/ADMExplained.ipynb

@@ -641,12 +641,12 @@
                 "        pl.col(\"Contents\").cast(pl.Utf8)\n",
                 "    ).with_columns(\n",
                 "        pl.when(pl.col(\"Type\") == \"numeric\")\n",
-                "        .then(pl.col(\"Contents\").apply(lambda col: extract_numbers_in_contents(col, 0)))\n",
+                "        .then(pl.col(\"Contents\").map_elements(lambda col: extract_numbers_in_contents(col, 0)))\n",
                 "        .otherwise(pl.lit(-9999))\n",
                 "        .alias(\"BinLowerBound\")\n",
                 "        .cast(pl.Float32),\n",
                 "        pl.when(pl.col(\"Type\") == \"numeric\")\n",
-                "        .then(pl.col(\"Contents\").apply(lambda col: extract_numbers_in_contents(col, 1)))\n",
+                "        .then(pl.col(\"Contents\").map_elements(lambda col: extract_numbers_in_contents(col, 1)))\n",
                 "        .otherwise(pl.lit(-9999))\n",
                 "        .alias(\"BinUpperBound\")\n",
                 "        .cast(pl.Float32),\n",

examples/articles/thompsonsampling.ipynb

@@ -49,7 +49,7 @@
                 ").explode(\"p\").with_columns(evidence=pl.col(\"positives\") / pl.col(\"p\"))\n",
                 "\n",
                 "def betaDistribution(structcol):\n",
-                "    return structcol.apply(\n",
+                "    return structcol.map_elements(\n",
                 "        lambda x: np.random.beta(\n",
                 "            x[\"p\"] * x[\"evidence\"], (1 - x[\"p\"]) * x[\"evidence\"], x[\"n\"]\n",
                 "        ).tolist()\n",

examples/graph_gallery/graph_gallery.ipynb

@@ -256,7 +256,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.11.0"
+            "version": "3.11.4"
         },
         "orig_nbformat": 4
     },

python/pdstools/adm/ADMTrees.py

@@ -473,7 +473,7 @@ def getGainsPerSplit(self) -> Tuple[Dict, pl.DataFrame, dict]:
             list(zip(total_split_list, total_gains_list)), schema=["split", "gains"]
         )
         gainsPerSplit = gainsPerSplit.with_columns(
-            predictor=pl.col("split").apply(lambda x: self.parseSplitValues(x)[0])
+            predictor=pl.col("split").map_elements(lambda x: self.parseSplitValues(x)[0])
         )
         return splitsPerTree, gainsPerTree, gainsPerSplit
 
@@ -491,10 +491,10 @@ def getGroupedGainsPerSplit(self) -> pl.DataFrame:
                     pl.col("gains").implode(),
                     pl.col("gains").mean().alias("mean"),
                     pl.first("split")
-                    .apply(lambda x: self.parseSplitValues(x)[1])
+                    .map_elements(lambda x: self.parseSplitValues(x)[1])
                     .alias("sign"),
                     pl.first("split")
-                    .apply(lambda x: self.parseSplitValues(x)[2])
+                    .map_elements(lambda x: self.parseSplitValues(x)[2])
                     .alias("values"),
                 ]
             )

python/pdstools/utils/hds_utils.py

@@ -300,7 +300,7 @@ def sample_it(s: pl.Series) -> pl.Series:
 
         df_ = (
             df.lazy()
-            .with_columns(pl.first().map(sample_it).alias("_sample"))
+            .with_columns(pl.first().map_batches(sample_it).alias("_sample"))
             .filter(pl.col("_sample"))
             .drop("_sample")
             .collect()
@@ -461,7 +461,7 @@ def getHasher(
             )
 
         else:
-            return pl.col(cols).apply(algorithm)
+            return pl.col(cols).map_elements(algorithm)
 
     def process(
         self,

python/pdstools/utils/hds_utils_experimental.py

@@ -493,7 +493,7 @@ def getHasher(
             return pl.col(cols).hash(**self.seeds)
 
         else:
-            return pl.col(cols).apply(algorithm)
+            return pl.col(cols).map_elements(algorithm)
 
     def to_normalize(self, cols, verbose=False):
         self.normalizationFailures, exprs = [], []