diff --git a/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
index af541666520a..25d186fdadd1 100644
--- a/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
@@ -41,7 +41,7 @@ class DirichletPartitioner(Partitioner):
     The notion of balancing is explicitly introduced here (not mentioned in paper but
     implemented in the code). It is a mechanism that excludes the partition from
     assigning new samples to it if the current number of samples on that partition
-    exceeds the average number that the partition would get in case of even data 
+    exceeds the average number that the partition would get in case of even data
     distribution. It is controlled by`self_balancing` parameter.
 
     Parameters
@@ -205,7 +205,9 @@ def _determine_partition_id_to_indices_if_needed(
         self._unique_classes = self.dataset.unique(self._partition_by)
         assert self._unique_classes is not None
         # This is needed only if self._self_balancing is True (the default option)
-        self._avg_num_of_samples_per_partition = self.dataset.num_rows / self._num_partitions
+        self._avg_num_of_samples_per_partition = (
+            self.dataset.num_rows / self._num_partitions
+        )
 
         # Change targets list data type to numpy
         targets = np.array(self.dataset[self._partition_by])
@@ -232,10 +234,10 @@ def _determine_partition_id_to_indices_if_needed(
                         nid
                     ]
                 # Balancing (not mentioned in the paper but implemented)
-                # Do not assign additional samples to the partition if it already has more
-                # than the average numbers of samples per partition. Note that it might
-                # especially affect classes that are later in the order. This is the
-                # reason for more sparse division that the alpha might suggest.
+                # Do not assign additional samples to the partition if it already has
+                # more than the average numbers of samples per partition. Note that it
+                # might especially affect classes that are later in the order. This is
+                # the reason for more sparse division that the alpha might suggest.
                 if self._self_balancing:
                     assert self._avg_num_of_samples_per_partition is not None
                     for nid in nid_to_proportion_of_k_samples.copy():
diff --git a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
index 210c354a9b28..e3e46813dfc8 100644
--- a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
@@ -221,7 +221,7 @@ def _determine_partition_id_to_indices_if_needed(
             current_partition_id = self._rng.choice(not_full_partition_ids)
             # If current partition is full resample a client
             if partition_id_to_left_to_allocate[current_partition_id] == 0:
-                # When the partition is full, exclude it from the sampling partitions list
+                # When the partition is full, exclude it from the sampling list
                 not_full_partition_ids.pop(
                     not_full_partition_ids.index(current_partition_id)
                 )
diff --git a/datasets/flwr_datasets/partitioner/shard_partitioner.py b/datasets/flwr_datasets/partitioner/shard_partitioner.py
index 797b68add29c..22a6abb7f47c 100644
--- a/datasets/flwr_datasets/partitioner/shard_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/shard_partitioner.py
@@ -50,10 +50,10 @@ class ShardPartitioner(Partitioner):  # pylint: disable=R0902
     a) `num_shards_per_partitions`, `shard_size`; or b) `num_shards_per_partition`)
     In case of b the `shard_size` is calculated as floor(len(dataset) /
     (`num_shards_per_partitions` * `num_partitions`))
-    2) possibly different number of shards per partition (use nearly all data) + the same
-    shard size (specify: `shard_size` + `keep_incomplete_shard=False`)
-    3) possibly different number of shards per partition (use all data) + possibly different
-    shard size (specify: `shard_size` + `keep_incomplete_shard=True`)
+    2) possibly different number of shards per partition (use nearly all data) + the
+    same shard size (specify: `shard_size` + `keep_incomplete_shard=False`)
+    3) possibly different number of shards per partition (use all data) + possibly
+    different shard size (specify: `shard_size` + `keep_incomplete_shard=True`)
 
 
     Algorithm based on the description in Communication-Efficient Learning of Deep
@@ -88,8 +88,8 @@ class ShardPartitioner(Partitioner):  # pylint: disable=R0902
 
     Examples
     --------
-    1) If you need same number of shards per partitions + the same shard size (and you know
-    both of these values)
+    1) If you need same number of shards per partitions + the same shard size (and you
+    know both of these values)
     >>> from flwr_datasets import FederatedDataset
     >>> from flwr_datasets.partitioner import ShardPartitioner
     >>>
@@ -149,7 +149,9 @@ def __init__(  # pylint: disable=R0913
         _check_if_natual_number(num_partitions, "num_partitions")
         self._num_partitions = num_partitions
         self._partition_by = partition_by
-        _check_if_natual_number(num_shards_per_partition, "num_shards_per_partition", True)
+        _check_if_natual_number(
+            num_shards_per_partition, "num_shards_per_partition", True
+        )
         self._num_shards_per_partition = num_shards_per_partition
         self._num_shards_used: Optional[int] = None
         _check_if_natual_number(shard_size, "shard_size", True)