diff --git a/examples/loom2parquetchunks.py b/examples/loom2parquetchunks.py
index 1c1c035..711ef7a 100644
--- a/examples/loom2parquetchunks.py
+++ b/examples/loom2parquetchunks.py
@@ -5,6 +5,8 @@
 # import libraries
 import pandas as pd
 import loompy
+import pyarrow.parquet as pq
+import pyarrow as pa
 
 # define the path to the loom file
 loom_file = "GSE156793_S3_gene_count.loom"
@@ -32,8 +34,7 @@
 development_day = ds.ca["Development_day"]
 
 # make a dataframe with the sample metadata, define the columns types
-sample_df = pd.DataFrame(
-    {
+sample_df = pd.DataFrame({
         "sample_id": sample_id,
         "cell_cluster": cell_cluster,
         "assay": assay,
@@ -68,9 +69,11 @@
 
 # transpose dataset and convert to parquet.
 # process the data per chunks.
-chunk_size = 50000
-number_chunks = 50  # Number of chunks to process, if None, all chunks are processed
+chunk_size = 10000
+writer = None
 count = 0
+number_chunks = 10 # number of chunks to process
+
 for ix, selection, view in ds.scan(axis=1, batch_size=chunk_size):
     # retrieve the chunk
     matrix_chunk = view[:, :]
@@ -102,17 +105,32 @@
     # rename the index column
     df_chunk = df_chunk.rename(columns={"index": "sample_id"})
 
-    # save the chunk to parquet
-    df_chunk.to_parquet(
-        f"gene_count_chunk_{ix}.parquet",
-        index=False,
-        engine="pyarrow",
-        compression="gzip",
-    )
+    if writer is None:
+        # define the schema
+        schema = pa.schema(
+            [
+                pa.field("sample_id", pa.string()),
+                pa.field("cell_cluster_id", pa.int8()),
+                pa.field("development_day", pa.int64()),
+                pa.field("assay_id", pa.int8()),
+            ]
+            + [pa.field(gene_id, pa.float32()) for gene_id in gene_ids]
+        )
+
+        print(len(list(df_chunk.columns)))
+        print(len(schema))
+
+        # create the parquet writer
+        writer = pq.ParquetWriter("GSE156793.parquet", schema, compression="snappy")
+
+    writer.write_table(pa.Table.from_pandas(df_chunk, preserve_index=False))
 
     print(f"Chunk {ix} saved")
-    count = count + 1
 
-    # break the loop if the number of chunks is reached
-    if number_chunks is not None and count >= number_chunks:
+    count += 1
+    if count >= number_chunks:
         break
+
+if writer is not None:
+    writer.close()
+    print(f"Concatenated parquet file written to GSE156793.parquet")