diff --git a/KDEpy/BaseKDE.py b/KDEpy/BaseKDE.py
index 0ed0e78..6b842ef 100644
--- a/KDEpy/BaseKDE.py
+++ b/KDEpy/BaseKDE.py
@@ -195,7 +195,7 @@ def _process_sequence(sequence_array_like):
         """
         # Must convert to float to avoid possible interger overflow
         if isinstance(sequence_array_like, Sequence):
-            out = np.asfarray(sequence_array_like).reshape(-1, 1)
+            out = np.asarray(sequence_array_like, dtype=float).reshape(-1, 1)
         elif isinstance(sequence_array_like, np.ndarray):
             if len(sequence_array_like.shape) == 1:
                 out = sequence_array_like.reshape(-1, 1)
diff --git a/KDEpy/NaiveKDE.py b/KDEpy/NaiveKDE.py
index 487060b..eb51d9d 100644
--- a/KDEpy/NaiveKDE.py
+++ b/KDEpy/NaiveKDE.py
@@ -123,7 +123,7 @@ def evaluate(self, grid_points=None):
         # For every data point, compute the kernel and add to the grid
         bw = self.bw
         if isinstance(bw, numbers.Number):
-            bw = np.asfarray(np.ones(self.data.shape[0]) * bw)
+            bw = np.asarray(np.ones(self.data.shape[0]) * bw, dtype=float)
 
         # TODO: Implementation w.r.t grid points for faster evaluation
         # See the SciPy evaluation for how this can be done
diff --git a/KDEpy/TreeKDE.py b/KDEpy/TreeKDE.py
index 167997d..197c231 100644
--- a/KDEpy/TreeKDE.py
+++ b/KDEpy/TreeKDE.py
@@ -136,7 +136,7 @@ def evaluate(self, grid_points=None, eps=10e-4):
         obs, dims = self.data.shape
         bw = self.bw
         if isinstance(bw, numbers.Number):
-            bw = np.asfarray(np.ones(obs) * bw)
+            bw = np.asarray(np.ones(obs) * bw, dtype=float)
         else:
             bw = np.asarray_chkfinite(bw, dtype=float)
 
diff --git a/KDEpy/binning.py b/KDEpy/binning.py
index 7bc1604..396b164 100644
--- a/KDEpy/binning.py
+++ b/KDEpy/binning.py
@@ -104,16 +104,16 @@ def linbin_cython(data, grid_points, weights=None):
     dx = (max_grid - min_grid) / num_intervals
     transformed_data = (data - min_grid) / dx
 
-    result = np.asfarray(np.zeros(num_intervals + 2))
+    result = np.asarray(np.zeros(num_intervals + 2), dtype=float)
 
     # Two Cython functions are implemented, one for weighted data and one
     # for unweighted data, since creating equal weights is costly w.r.t time
     if weights is None:
         result = _cutils.iterate_data_1D(transformed_data, result)
-        return np.asfarray(result[:-1]) / transformed_data.shape[0]
+        return np.asarray(result[:-1], dtype=float) / transformed_data.shape[0]
     else:
         res = _cutils.iterate_data_1D_weighted(transformed_data, weights, result)
-        return np.asfarray(res[:-1])  # Remove last, outside of grid
+        return np.asarray(res[:-1], dtype=float)  # Remove last, outside of grid
 
 
 def linbin_numpy(data, grid_points, weights=None):
@@ -197,7 +197,7 @@ def linbin_numpy(data, grid_points, weights=None):
     unique_integrals = np.unique(integral)
     unique_integrals = unique_integrals[(unique_integrals >= 0) & (unique_integrals <= len(grid_points))]
 
-    result = np.asfarray(np.zeros(len(grid_points) + 1))
+    result = np.asarray(np.zeros(len(grid_points) + 1), dtype=float)
     for grid_point in unique_integrals:
         # Use binary search to find indices for the grid point
         # Then sum the data assigned to that grid point
@@ -337,7 +337,7 @@ def linbin_Ndim(data, grid_points, weights=None):
 
     # Compute the number of grid points for each dimension in the grid
     grid_num = (grid_points[:, i] for i in range(dims))
-    grid_num = np.array(list(len(np.unique(g)) for g in grid_num))
+    grid_num = np.array(list(len(np.unique(g)) for g in grid_num), dtype="int32")
 
     # Scale the data to the grid
     min_grid = np.min(grid_points, axis=0)
@@ -356,7 +356,7 @@ def linbin_Ndim(data, grid_points, weights=None):
     # Weighted data has two specific routines
     if weights is not None:
         if data_dims >= 3:
-            binary_flgs = cartesian(([0, 1],) * dims)
+            binary_flgs = cartesian(([0, 1],) * dims).astype("int32")
             result = _cutils.iterate_data_ND_weighted(data, weights, result, grid_num, obs_tot, binary_flgs)
         else:
             result = _cutils.iterate_data_2D_weighted(data, weights, result, grid_num, obs_tot)
@@ -367,7 +367,7 @@ def linbin_Ndim(data, grid_points, weights=None):
     # specialize routine for this case.
     else:
         if data_dims >= 3:
-            binary_flgs = cartesian(([0, 1],) * dims)
+            binary_flgs = cartesian(([0, 1],) * dims).astype("int32")
             result = _cutils.iterate_data_ND(data, result, grid_num, obs_tot, binary_flgs)
         else:
             result = _cutils.iterate_data_2D(data, result, grid_num, obs_tot)
diff --git a/KDEpy/bw_selection.py b/KDEpy/bw_selection.py
index 5cf2659..77a8d81 100644
--- a/KDEpy/bw_selection.py
+++ b/KDEpy/bw_selection.py
@@ -55,8 +55,8 @@ def _fixed_point(t, N, I_sq, a2):
     """
 
     # This is important, as the powers might overflow if not done
-    I_sq = np.asfarray(I_sq, dtype=FLOAT)
-    a2 = np.asfarray(a2, dtype=FLOAT)
+    I_sq = np.asarray(I_sq, dtype=FLOAT)
+    a2 = np.asarray(a2, dtype=FLOAT)
 
     # ell = 7 corresponds to the 5 steps recommended in the paper
     ell = 7
diff --git a/KDEpy/tests/test_api.py b/KDEpy/tests/test_api.py
index 6cb82b0..ac3fa02 100644
--- a/KDEpy/tests/test_api.py
+++ b/KDEpy/tests/test_api.py
@@ -51,7 +51,7 @@ def test_api_models_kernels_bandwidths(kde1, kde2, bw, kernel):
         assert err < 0.002
 
 
-type_functions = [tuple, np.array, np.asfarray, lambda x: np.asfarray(x).reshape(-1, 1)]
+type_functions = [tuple, np.array, np.asarray, lambda x: np.asarray(x, dtype=float).reshape(-1, 1)]
 
 
 @pytest.mark.parametrize(
diff --git a/pyproject.toml b/pyproject.toml
index da39a02..8bdedca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,7 +2,7 @@
 name = "KDEpy"
 version = "1.1.9"
 dependencies = [
-    "numpy>=1.14.2,<2.0",
+    "numpy>=1.14.2",
     "scipy>=1.0.1,<2.0",
 ]
 description = "Kernel Density Estimation in Python."