diff --git a/src/gemdat/jumps.py b/src/gemdat/jumps.py
index 4247e444..250df150 100644
--- a/src/gemdat/jumps.py
+++ b/src/gemdat/jumps.py
@@ -236,22 +236,18 @@ def collective(self, max_dist: float = 1) -> Collective:
         )
 
     @weak_lru_cache()
-    def activation_energies(
-            self,
-            n_parts: int = 10) -> dict[tuple[str, str], tuple[float, float]]:
+    def activation_energies(self, n_parts: int = 10) -> pd.DataFrame:
         """Calculate activation energies for jumps (UNITS?).
 
         Returns
         -------
-        e_act : dict[tuple[str, str], tuple[float, float]]
-            Dictionary with jump activation energies and standard deviations between site pairs.
-        n_parts : int
-            Number of parts to split transitions/jumps into for statistics
+        df : pd.DataFrame
+            Dataframe with jump activation energies and standard deviations between site pairs.
         """
         trajectory = self.transitions.trajectory
         attempt_freq, _ = SimulationMetrics(trajectory).attempt_frequency()
 
-        e_act = {}
+        dct = {}
 
         temperature = trajectory.metadata['temperature']
 
@@ -285,10 +281,13 @@ def activation_energies(
             e_act_arr = -np.log(eff_rate / attempt_freq) * (
                 Boltzmann * temperature) / elementary_charge
 
-            e_act[site_start,
-                  site_stop] = np.mean(e_act_arr), np.std(e_act_arr, ddof=1)
+            dct[site_start, site_stop] = np.mean(e_act_arr), np.std(e_act_arr,
+                                                                    ddof=1)
 
-        return e_act
+        df = pd.DataFrame(dct).T
+        df.columns = ('energy', 'std')
+
+        return df
 
     def jumps_counter(self) -> Counter:
         """Calculate number of jumps between sites.
@@ -318,18 +317,15 @@ def split(self, n_parts) -> list[Jumps]:
         ]
 
     @weak_lru_cache()
-    def rates(self,
-              n_parts: int = 10) -> dict[tuple[str, str], tuple[float, float]]:
+    def rates(self, n_parts: int = 10) -> pd.DataFrame:
         """Calculate jump rates (total jumps / second).
 
         Returns
         -------
-        rates : dict[tuple[str, str], tuple[float, float]]
-            Dictionary with jump rates and standard deviations between site pairs
-        n_parts : int
-            Number of parts to split jumps into for statistics
+        df : pd.DataFrame
+            Dataframe with jump rates and standard deviations between site pairs
         """
-        rates: dict[tuple[str, str], tuple[float, float]] = {}
+        dct = {}
 
         parts = [part.jumps_counter() for part in self.split(n_parts)]
 
@@ -342,6 +338,9 @@ def rates(self,
             jump_freq_mean = np.mean(n_jumps) / denom
             jump_freq_std = np.std(n_jumps, ddof=1) / denom
 
-            rates[site_pair] = float(jump_freq_mean), float(jump_freq_std)
+            dct[site_pair] = float(jump_freq_mean), float(jump_freq_std)
+
+        df = pd.DataFrame(dct).T
+        df.columns = ('rates', 'std')
 
-        return rates
+        return df
diff --git a/tests/integration/sites_test.py b/tests/integration/sites_test.py
index 913a8391..ccbab87f 100644
--- a/tests/integration/sites_test.py
+++ b/tests/integration/sites_test.py
@@ -8,6 +8,7 @@
 from math import isclose
 
 import numpy as np
+import pandas as pd
 import pytest
 from numpy.testing import assert_allclose
 
@@ -125,23 +126,24 @@ def test_n_jumps(self, vasp_jumps):
 
     def test_rates(self, vasp_jumps):
         rates = vasp_jumps.rates(n_parts=10)
-        assert isinstance(rates, dict)
+        assert isinstance(rates, pd.DataFrame)
         assert len(rates) == 1
 
-        rates, rates_std = rates[('48h', '48h')]
-        assert isclose(rates, 542307692307.69226)
-        assert isclose(rates_std, 41893421993.683655)
+        row = rates.loc[('48h', '48h')]
+
+        assert isclose(row['rates'], 542307692307.69226)
+        assert isclose(row['std'], 41893421993.683655)
 
     def test_activation_energies(self, vasp_jumps, vasp_sites):
         activation_energies = vasp_jumps.activation_energies(n_parts=10)
 
-        assert isinstance(activation_energies, dict)
+        assert isinstance(activation_energies, pd.DataFrame)
         assert len(activation_energies) == 1
 
-        e_act, e_act_std = activation_energies[('48h', '48h')]
+        row = activation_energies.loc[('48h', '48h')]
 
-        assert isclose(e_act, 0.17445, abs_tol=1e-4)
-        assert isclose(e_act_std, 0.004059, abs_tol=1e-6)
+        assert isclose(row['energy'], 0.17445, abs_tol=1e-4)
+        assert isclose(row['std'], 0.004059, abs_tol=1e-6)
 
     def test_jump_diffusivity(self, vasp_jumps):
         assert isclose(vasp_jumps.jump_diffusivity(3),