diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 79ba0128f7a4..85b0c7d881c6 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -1507,8 +1507,6 @@ def __init__(
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
-        position : numpy 1-D array, pandas Series or None, optional (default=None)
-            Position of items used in unbiased learning-to-rank task.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
         feature_name : list of str, or 'auto', optional (default="auto")
@@ -1528,6 +1526,8 @@ def __init__(
             Other parameters for Dataset.
         free_raw_data : bool, optional (default=True)
             If True, raw data is freed after constructing inner Dataset.
+        position : numpy 1-D array, pandas Series or None, optional (default=None)
+            Position of items used in unbiased learning-to-rank task.
         """
         self._handle: Optional[_DatasetHandle] = None
         self.data = data
@@ -2263,12 +2263,12 @@ def create_valid(
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
-        position : numpy 1-D array, pandas Series or None, optional (default=None)
-            Position of items used in unbiased learning-to-rank task.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
         params : dict or None, optional (default=None)
             Other parameters for validation Dataset.
+        position : numpy 1-D array, pandas Series or None, optional (default=None)
+            Position of items used in unbiased learning-to-rank task.
 
         Returns
         -------
diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp
index 85adfcd805ef..dad64125bbf8 100644
--- a/src/io/metadata.cpp
+++ b/src/io/metadata.cpp
@@ -540,19 +540,21 @@ void Metadata::SetPosition(const data_size_t* positions, data_size_t len) {
 
   position_load_from_file_ = false;
 
-  #pragma omp parallel for schedule(static, 512) if (num_positions_ >= 1024)
+  position_ids_.clear();
+  std::unordered_map<data_size_t, int> map_id2pos;
   for (data_size_t i = 0; i < num_positions_; ++i) {
-    positions_[i] = positions[i];
+    if (map_id2pos.count(positions[i]) == 0) {
+      int pos = static_cast<int>(map_id2pos.size());
+      map_id2pos[positions[i]] = pos;
+      position_ids_.push_back(std::to_string(positions[i]));
+    }
   }
 
-  position_ids_.clear();
-  std::set<int> position_set;
-  for (int position : positions_) {
-    position_set.insert(position);
-  }
-  Log::Debug("number of unique positions found = %ld", position_set.size());
-  for (int position : position_set) {
-    position_ids_.push_back(std::to_string(position));
+  Log::Debug("number of unique positions found = %ld", position_ids_.size());
+
+  #pragma omp parallel for schedule(static, 512) if (num_positions_ >= 1024)
+  for (data_size_t i = 0; i < num_positions_; ++i) {
+    positions_[i] = map_id2pos.at(positions[i]);
   }
 }
 
diff --git a/tests/c_api_test/preb.txt b/tests/c_api_test/preb.txt
new file mode 100644
index 000000000000..f35c133463e6
--- /dev/null
+++ b/tests/c_api_test/preb.txt
@@ -0,0 +1,500 @@
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diff --git a/tests/python_package_test/data.csv b/tests/python_package_test/data.csv
new file mode 100644
index 000000000000..db7d1d702d9c
--- /dev/null
+++ b/tests/python_package_test/data.csv
@@ -0,0 +1,100 @@
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diff --git a/tests/python_package_test/data_dask.csv b/tests/python_package_test/data_dask.csv
new file mode 100644
index 000000000000..83e2eb959c32
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+1.142709,0.216776,-0.837262,-0.588867,-1.097303
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index 818c863df0da..91dcb827292e 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -747,6 +747,7 @@ def test_ranking_prediction_early_stopping():
     with pytest.raises(AssertionError):
         np.testing.assert_allclose(ret_early, ret_early_more_strict)
 
+
 def test_ranking_with_position_information(tmp_path):
     rank_example_dir = Path(__file__).absolute().parents[2] / 'examples' / 'lambdarank'
     params = {
@@ -771,7 +772,9 @@ def test_ranking_with_position_information(tmp_path):
             position = np.random.randint(28)
             positions.append(position)
             out_file.write(f"pos_{position}\n")
+    positions = np.array(positions)
 
+    # test setting positions through Dataset constructor
     lgb_train = lgb.Dataset(str(tmp_path / 'rank.train'), params=params, position=positions)
     lgb_valid = [lgb_train.create_valid(str(tmp_path / 'rank.test'))]
     gbm_unbiased = lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)
@@ -785,6 +788,17 @@ def test_ranking_with_position_information(tmp_path):
     gbm_unbiased_with_file = lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)
     assert gbm_unbiased.best_score['valid_0']['ndcg@3'] == gbm_unbiased_with_file.best_score['valid_0']['ndcg@3']
 
+    # test setting positions through set_position
+    lgb_train = lgb.Dataset(str(tmp_path / 'rank.train'), params=params)
+    lgb_valid = [lgb_train.create_valid(str(tmp_path / 'rank.test'))]
+    lgb_train.set_position(positions)
+    gbm_unbiased_set_position = lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)
+    assert gbm_unbiased.best_score['valid_0']['ndcg@3'] == gbm_unbiased_set_position.best_score['valid_0']['ndcg@3']
+
+    # test get_position works
+    positions_from_get = lgb_train.get_position()
+    np.testing.assert_array_equal(positions_from_get, positions)
+
     # add extra row to position file
     with open(str(tmp_path / 'rank.train.position'), 'a') as file:
         file.write('pos_1000')
@@ -794,6 +808,7 @@ def test_ranking_with_position_information(tmp_path):
     with pytest.raises(lgb.basic.LightGBMError, match="Positions size \(3006\) doesn't match data size"):
         lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)
 
+
 def test_early_stopping():
     X, y = load_breast_cancer(return_X_y=True)
     params = {