diff --git a/scarf/readers.py b/scarf/readers.py
index 69ef4f9..148059f 100644
--- a/scarf/readers.py
+++ b/scarf/readers.py
@@ -424,15 +424,15 @@ def process_batch(self, dfs: List[pd.DataFrame], filtering_cutoff: int) -> np.ar
         """
         pl_dfs = [pl.DataFrame(df) for df in dfs]
         pl_dfs = pl.concat(pl_dfs)
-        dfs_ = pl_dfs.group_by('barcode').agg(pl.sum('count'))
-        dfs_ = dfs_.filter(pl.col('count') > filtering_cutoff)
-        return np.sort(dfs_['barcode'])
+        dfs_ = pl_dfs.group_by("barcode").agg(pl.sum("count"))
+        dfs_ = dfs_.filter(pl.col("count") > filtering_cutoff)
+        return np.sort(dfs_["barcode"])
 
     def _get_valid_barcodes(
         self,
         filtering_cutoff: int,
         batch_size: int = int(10e3),
-        lines_in_mem: int = int(10e6),
+        lines_in_mem: int = int(10e5),
     ) -> np.ndarray:
         """Returns a list of valid barcodes after filtering out background barcodes.
 
@@ -524,7 +524,8 @@ def cell_names(self) -> List[str]:
             vals = vals[(self.validBarcodeIdx + self.indexOffset)]
         return list(vals)
 
-    def rename_batches(self, collect: List[pl.DataFrame], batch_size: int) -> List:
+    def rename_batches(self, collect: List[pd.DataFrame]) -> List:
+        collect = [pl.DataFrame(df) for df in collect]
         df = pl.concat(collect)
         barcodes = np.array(df["barcode"])
         count_hash = {}
@@ -548,44 +549,38 @@ def consume(
             lines_in_mem: The number of lines to read into memory.
             dtype: The data type of the matrix.
         """
-        matrixIO = pl.read_csv_batched(
+        matrixIO = pd.read_csv(
             self.matFn,
-            has_header=False,
-            separator=self.sep,
-            comment_prefix="%",
-            skip_rows_after_header=1,
-            new_columns=["gene", "barcode", "count"],
-            schema_overrides={"gene": pl.Int64, "barcode": pl.Int64, "count": pl.Int64},
-            batch_size=lines_in_mem,
+            comment="%",
+            sep=self.sep,
+            header=0,
+            chunksize=lines_in_mem,
+            names=["gene", "barcode", "count"],
         )
         unique_list = []
         collect = []
-        while True:
-            chunk = matrixIO.next_batches(1)
-            if chunk is None:
-                break
-            chunk = chunk[0]
-            chunk = chunk.filter(pl.col("barcode").is_in(self.validBarcodeIdx))
-            in_uniques = np.unique(chunk["barcode"])
+        for chunk in matrixIO:
+            chunk = chunk[chunk["barcode"].isin(self.validBarcodeIdx)]
+            in_uniques = np.unique(chunk["barcode"].values)
             unique_list.extend(in_uniques)
             unique_list = list(set(unique_list))
             if len(unique_list) > batch_size:
                 diff = batch_size - (len(unique_list) - len(in_uniques))
                 mask_pos = in_uniques[:diff]
                 mask_neg = in_uniques[diff:]
-                extra = chunk.filter(pl.col("barcode").is_in(mask_pos))
+                extra = chunk[chunk["barcode"].isin(mask_pos)]
                 collect.append(extra)
-                collect = self.rename_batches(collect, batch_size)
+                collect = self.rename_batches(collect)
                 mtx = self.to_sparse(np.array(collect), dtype=dtype)
                 yield mtx
-                left_out = chunk.filter(pl.col("barcode").is_in(mask_neg))
+                left_out = chunk[chunk["barcode"].isin(mask_neg)]
                 collect = []
                 unique_list = list(mask_neg)
                 collect.append(left_out)
             else:
                 collect.append(chunk)
         if len(collect) > 0:
-            collect = self.rename_batches(collect, batch_size)
+            collect = self.rename_batches(collect)
             mtx = self.to_sparse(np.array(collect), dtype=dtype)
             yield mtx