diff --git a/pygmt/clib/session.py b/pygmt/clib/session.py
index 0640ba6ae20..42d74a815a2 100644
--- a/pygmt/clib/session.py
+++ b/pygmt/clib/session.py
@@ -1747,6 +1747,8 @@ def virtualfile_to_dataset(
         vfname: str,
         output_type: Literal["pandas", "numpy", "file"] = "pandas",
         column_names: list[str] | None = None,
+        dtype: type | dict[str, type] | None = None,
+        index_col: str | int | None = None,
     ) -> pd.DataFrame | np.ndarray | None:
         """
         Output a tabular dataset stored in a virtual file to a different format.
@@ -1766,6 +1768,11 @@ def virtualfile_to_dataset(
             - ``"file"`` means the result was saved to a file and will return ``None``.
         column_names
             The column names for the :class:`pandas.DataFrame` output.
+        dtype
+            Data type for the columns of the :class:`pandas.DataFrame` output. Can be a
+            single type for all columns or a dictionary mapping column names to types.
+        index_col
+            Column to set as the index of the :class:`pandas.DataFrame` output.
 
         Returns
         -------
@@ -1854,13 +1861,13 @@ def virtualfile_to_dataset(
             return None
 
         # Read the virtual file as a GMT dataset and convert to pandas.DataFrame
-        result = self.read_virtualfile(vfname, kind="dataset").contents.to_dataframe()
+        result = self.read_virtualfile(vfname, kind="dataset").contents.to_dataframe(
+            column_names=column_names,
+            dtype=dtype,
+            index_col=index_col,
+        )
         if output_type == "numpy":  # numpy.ndarray output
             return result.to_numpy()
-
-        # Assign column names
-        if column_names is not None:
-            result.columns = column_names
         return result  # pandas.DataFrame output
 
     def extract_region(self):
diff --git a/pygmt/datatypes/dataset.py b/pygmt/datatypes/dataset.py
index a0d0547f3ca..274d2fee97c 100644
--- a/pygmt/datatypes/dataset.py
+++ b/pygmt/datatypes/dataset.py
@@ -143,7 +143,12 @@ class _GMT_DATASEGMENT(ctp.Structure):  # noqa: N801
         ("hidden", ctp.c_void_p),
     ]
 
-    def to_dataframe(self) -> pd.DataFrame:
+    def to_dataframe(
+        self,
+        column_names: list[str] | None = None,
+        dtype: type | dict[str, type] | None = None,
+        index_col: str | int | None = None,
+    ) -> pd.DataFrame:
         """
         Convert a _GMT_DATASET object to a :class:`pandas.DataFrame` object.
 
@@ -151,6 +156,16 @@ def to_dataframe(self) -> pd.DataFrame:
         the same. The same column in all segments of all tables are concatenated. The
         trailing text column is also concatenated as a single string column.
 
+        Parameters
+        ----------
+        column_names
+            A list of column names.
+        dtype
+            Data type. Can be a single type for all columns or a dictionary mapping
+            column names to types.
+        index_col
+            Column to set as index.
+
         Returns
         -------
         df
@@ -211,5 +226,11 @@ def to_dataframe(self) -> pd.DataFrame:
                 pd.Series(data=np.char.decode(textvector), dtype=pd.StringDtype())
             )
 
-        df = pd.concat(objs=vectors, axis=1)
+        df = pd.concat(objs=vectors, axis="columns")
+        if column_names is not None:  # Assign column names
+            df.columns = column_names
+        if dtype is not None:
+            df = df.astype(dtype)
+        if index_col is not None:
+            df = df.set_index(index_col)
         return df
diff --git a/pygmt/src/grdhisteq.py b/pygmt/src/grdhisteq.py
index b0285e4e3d5..44d191a417e 100644
--- a/pygmt/src/grdhisteq.py
+++ b/pygmt/src/grdhisteq.py
@@ -238,18 +238,14 @@ def compute_bins(
                     module="grdhisteq", args=build_arg_string(kwargs, infile=vingrd)
                 )
 
-            result = lib.virtualfile_to_dataset(
+            return lib.virtualfile_to_dataset(
                 vfname=vouttbl,
                 output_type=output_type,
                 column_names=["start", "stop", "bin_id"],
+                dtype={
+                    "start": np.float32,
+                    "stop": np.float32,
+                    "bin_id": np.uint32,
+                },
+                index_col="bin_id" if output_type == "pandas" else None,
             )
-            if output_type == "pandas":
-                result = result.astype(
-                    {
-                        "start": np.float32,
-                        "stop": np.float32,
-                        "bin_id": np.uint32,
-                    }
-                )
-                return result.set_index("bin_id")
-            return result