diff --git a/pyproject.toml b/pyproject.toml
index f0e87e4d..0e4603b0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,9 +20,9 @@ license = { text = 'MIT license' }
 requires-python = ">=3.8,<3.13"
 readme = 'README.md'
 dependencies = [
-    "numpy>=1.21.0,<2.0.0;python_version<'3.10'",
-    "numpy>=1.23.3,<2.0.0;python_version>='3.10' and python_version<'3.12'",
-    "numpy>=1.26.0,<2.0.0;python_version>='3.12'",
+    "numpy>=1.21.0;python_version<'3.10'",
+    "numpy>=1.23.3;python_version>='3.10' and python_version<'3.12'",
+    "numpy>=1.26.0;python_version>='3.12'",
     "pandas>=1.4.0;python_version<'3.11'",
     "pandas>=1.5.0;python_version>='3.11' and python_version<'3.12'",
     "pandas>=2.1.1;python_version>='3.12'",
diff --git a/tests/integration/reports/multi_table/_properties/test_column_pair_trends.py b/tests/integration/reports/multi_table/_properties/test_column_pair_trends.py
index eda415e7..65438403 100644
--- a/tests/integration/reports/multi_table/_properties/test_column_pair_trends.py
+++ b/tests/integration/reports/multi_table/_properties/test_column_pair_trends.py
@@ -1,5 +1,6 @@
 from unittest.mock import Mock
 
+import numpy as np
 from tqdm import tqdm
 
 from sdmetrics.demos import load_demo
@@ -17,7 +18,7 @@ def test_end_to_end(self):
         result = column_pair_trends.get_score(real_data, synthetic_data, metadata)
 
         # Assert
-        assert result == 0.45654629583521095
+        assert np.isclose(result, 0.45654629583521095, atol=1e-8)
 
     def test_with_progress_bar(self):
         """Test that the progress bar is correctly updated."""
@@ -37,5 +38,5 @@ def test_with_progress_bar(self):
         result = column_pair_trends.get_score(real_data, synthetic_data, metadata, progress_bar)
 
         # Assert
-        assert result == 0.45654629583521095
+        assert np.isclose(result, 0.45654629583521095, atol=1e-8)
         assert mock_update.call_count == num_iter
diff --git a/tests/unit/multi_table/statistical/test_cardinality_statistic_similarity.py b/tests/unit/multi_table/statistical/test_cardinality_statistic_similarity.py
index 101e6f67..74804500 100644
--- a/tests/unit/multi_table/statistical/test_cardinality_statistic_similarity.py
+++ b/tests/unit/multi_table/statistical/test_cardinality_statistic_similarity.py
@@ -2,6 +2,7 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 
 from sdmetrics.multi_table.statistical import CardinalityStatisticSimilarity
 from sdmetrics.warnings import ConstantInputWarning
@@ -57,7 +58,7 @@ def test__compute_statistic_constant_input(self):
         )
 
         # Run
-        with np.testing.assert_warns(ConstantInputWarning, match=expected_warn_msg):
+        with pytest.warns(ConstantInputWarning, match=expected_warn_msg):
             result = CardinalityStatisticSimilarity._compute_statistic(
                 real_distribution, synthetic_distribution, 'mean'
             )
diff --git a/tests/unit/single_column/statistical/test_statistic_similarity.py b/tests/unit/single_column/statistical/test_statistic_similarity.py
index b6c98f40..09650ecc 100644
--- a/tests/unit/single_column/statistical/test_statistic_similarity.py
+++ b/tests/unit/single_column/statistical/test_statistic_similarity.py
@@ -2,6 +2,7 @@
 
 import numpy as np
 import pandas as pd
+import pytest
 
 from sdmetrics.single_column.statistical import StatisticSimilarity
 from sdmetrics.warnings import ConstantInputWarning
@@ -54,13 +55,13 @@ def test_compute_breakdown_constant_input(self):
             'score': np.nan,
         }
         expected_warn_msg = (
-            'The real data input array is constant. '
-            'The StatisticSimilarity metric is either undefined or infinte.'
+            'The real data input array is constant. The StatisticSimilarity '
+            'metric is either undefined or infinite.'
         )
 
         # Run
         metric = StatisticSimilarity()
-        with np.testing.assert_warns(ConstantInputWarning, match=expected_warn_msg):
+        with pytest.warns(ConstantInputWarning, match=expected_warn_msg):
             result = metric.compute_breakdown(real_data, synthetic_data, statistic='mean')
 
         # Assert