diff --git a/docs/source/python.rst b/docs/source/python.rst
index e4a399d5..f9d89b20 100644
--- a/docs/source/python.rst
+++ b/docs/source/python.rst
@@ -80,6 +80,55 @@ This image can be viewed in `napari` using the
     $ napari test_ngff_image.zarr
 
 
+Writing HCS OME-NGFF images
+===========================
+
+This sample code shows how to write a high-content screening dataset (i.e. culture plate with multiple wells) to a OME-NGFF file::
+
+    import numpy as np
+    import zarr
+
+    from ome_zarr.io import parse_url
+    from ome_zarr.writer import write_image, write_plate_metadata, write_well_metadata
+
+    path = "test_ngff_image.zarr"
+    row_names = ["A", "B"]
+    col_names = ["1", "2", "3"]
+    well_paths = ["A/2", "B/3"]
+    field_paths = ["0", "1", "2"]
+
+    # generate data
+    mean_val=10
+    num_wells = len(well_paths)
+    num_fields = len(field_paths)
+    size_xy = 128
+    size_z = 10
+    rng = np.random.default_rng(0)
+    data = rng.poisson(mean_val, size=(num_wells, num_fields, size_z, size_xy, size_xy)).astype(np.uint8)
+
+    # write the plate of images and corresponding metadata
+    store = parse_url(str(file_path), mode="w").store
+    root = zarr.group(store=store)
+    write_plate_metadata(root, row_names, col_names, well_paths)
+    for wi, wp in enumerate(well_paths):
+        row, col = wp.split("/")
+        row_group = root.require_group(row)
+        well_group = row_group.require_group(col)
+        write_well_metadata(well_group, field_paths)
+        for fi, field in enumerate(field_paths):
+            image_group = well_group.require_group(str(field))
+            write_image(image=data[wi, fi], group=image_group, axes="zyx", storage_options=dict(chunks=(1, size_xy, size_xy)))
+
+
+This image can be viewed in `napari` using the
+`napari-ome-zarr <https://github.com/ome/napari-ome-zarr>`_ plugin::
+
+    import napari
+
+    viewer = napari.Viewer()
+    viewer.open(path, plugin="napari-ome-zarr")
+
+
 Reading OME-NGFF images
 -----------------------