Merge branch 'main' into clib/return_table

Author: seisman

pygmt/clib/session.py

@@ -1621,7 +1621,7 @@ def virtualfile_out(
         Create a virtual file or an actual file for storing output data.
 
         If ``fname`` is not given, a virtual file will be created to store the output
-        data into a GMT data container and the function yields the name of the virutal
+        data into a GMT data container and the function yields the name of the virtual
         file. Otherwise, the output data will be written into the specified file and the
         function simply yields the actual file name.
 

pygmt/datatypes/dataset.py

@@ -180,6 +180,8 @@ def to_dataframe(self) -> pd.DataFrame:
         1   4.0   5.0   6.0       TEXT4 TEXT567
         2   7.0   8.0   9.0        TEXT8 TEXT90
         3  10.0  11.0  12.0  TEXT123 TEXT456789
+        >>> df.dtypes.to_list()
+        [dtype('float64'), dtype('float64'), dtype('float64'), string[python]]
         """
         # Deal with numeric columns
         vectors = []
@@ -192,7 +194,7 @@ def to_dataframe(self) -> pd.DataFrame:
                     colvector.append(
                         np.ctypeslib.as_array(dseg.data[icol], shape=(dseg.n_rows,))
                     )
-            vectors.append(np.concatenate(colvector))
+            vectors.append(pd.Series(data=np.concatenate(colvector)))
 
         # Deal with trailing text column
         textvector = []
@@ -203,16 +205,9 @@ def to_dataframe(self) -> pd.DataFrame:
                 if dseg.text:
                     textvector.extend(dseg.text[: dseg.n_rows])
         if textvector:
-            vectors.append(np.char.decode(textvector))
-
-        df = pd.concat([pd.Series(v) for v in vectors], axis=1)
-
-        # convert text data from object dtype to string dtype
-        if textvector:
-            df = df.convert_dtypes(
-                convert_string=True,
-                convert_integer=False,
-                convert_floating=False,
-                convert_boolean=False,
+            vectors.append(
+                pd.Series(data=np.char.decode(textvector), dtype=pd.StringDtype())
             )
+
+        df = pd.concat(objs=vectors, axis=1)
         return df