diff --git a/tests/baseline/test_curve_2d_plot.png b/tests/baseline/test_curve_2d_plot.png
index 59d368a8..95662421 100644
Binary files a/tests/baseline/test_curve_2d_plot.png and b/tests/baseline/test_curve_2d_plot.png differ
diff --git a/tests/test_well.py b/tests/test_well.py
index 10888bbf..57968c6c 100644
--- a/tests/test_well.py
+++ b/tests/test_well.py
@@ -5,6 +5,8 @@
 import os
 from pathlib import Path
 
+import pandas as pd
+
 import welly
 from welly import Well
 
@@ -148,3 +150,11 @@ def test_iter_well(well):
     for curve in well:
         assert curve == well.data['CALI']
         break
+
+def test_df_object_cols(df):
+
+    df["Test object"] = "54"
+    df["test_str"] = '1z'
+    well = Well.from_df(df)
+    assert all(well.df()["Test object"] == 54)
+    assert all(well.df()["test_str"] == "1z")
diff --git a/welly/curve.py b/welly/curve.py
index 4e5af516..1205ffb2 100644
--- a/welly/curve.py
+++ b/welly/curve.py
@@ -10,6 +10,7 @@
 import numpy as np
 import pandas as pd
 from pandas.api.types import is_any_real_numeric_dtype
+from pandas.api.types import is_numeric_dtype
 from scipy.interpolate import interp1d
 
 from welly.plot import plot_2d_curve, plot_curve, plot_kde_curve
@@ -970,7 +971,7 @@ def to_basis(self,
             Curve. The current instance in the new basis.
         """
         # category data type or a string in data defaults to 'nearest'
-        if pd.api.types.is_categorical_dtype(self.df.iloc[:, 0]) or pd.api.types.is_string_dtype(self.df.iloc[:, 0]):
+        if not is_numeric_dtype(self.df.iloc[:, 0]):
             interp_kind = 'nearest'
 
         new_curve = copy.deepcopy(self)
diff --git a/welly/well.py b/welly/well.py
index 7af9482f..707b1163 100644
--- a/welly/well.py
+++ b/welly/well.py
@@ -632,17 +632,28 @@ def df(self,
             # swap MultiIndex levels
             df = df.swaplevel()
 
-        # I think this is the wrong place to do this.
-        # Anyway, use i not name just in case there are duplicate names.
-        for i, (_, column) in enumerate(df.iteritems()):
-            if is_object_dtype(column.dtype):
-                try:
-                    df.iloc[:, i] = column.astype(float)
-                except ValueError:
-                    pass
+        df = self._convert_object_cols_to_numeric(df)
 
         return df
 
+    def _convert_object_cols_to_numeric(self, df):
+        """
+        Convert object columns into numeric columns, if possible.
+
+        Args:
+            df (pd.DataFrame): dataframe to work
+        Returns:
+            pd.DataFrame. Whole dataframe with conversions
+        """
+        df_nonobject = df.select_dtypes(exclude="object")
+        df_object = df.select_dtypes(include="object")
+        for col in df_object.columns:
+            try:
+                df_object[col] = pd.to_numeric(df_object[col])
+            except ValueError:
+                pass
+        return pd.concat([df_nonobject, df_object], axis=1)
+
     def add_curves_from_las(self, fname, remap=None, funcs=None):
         """
         Given a LAS file, add curves from it to the current well instance.