diff --git a/mdevaluate/__init__.py b/mdevaluate/__init__.py
index dd41f92..ef59f43 100644
--- a/mdevaluate/__init__.py
+++ b/mdevaluate/__init__.py
@@ -73,3 +73,12 @@ def open(directory='', topology='*.tpr', trajectory='*.xtc', cached=False,
         except reader.NojumpError:
             reader.generate_nojump_matrixes(coords)
     return coords
+
+def open_energy(file, energies=None):
+    """Reads an gromacs energy file and output the data in a pandas DataFrame.
+       Args:
+           file: Filename of the energy file
+           energies (opt.): Specify energies to extract from the energy file
+    """
+    df = reader.energy_reader(file, energies=energies)
+    return df
diff --git a/mdevaluate/reader.py b/mdevaluate/reader.py
index 0cea171..e07971a 100755
--- a/mdevaluate/reader.py
+++ b/mdevaluate/reader.py
@@ -181,6 +181,44 @@ def correct_nojump_matrixes_for_whole(trajectory):
     for d in range(3):
         reader.nojump_matrixes[d][0] = cor[:, d]
     save_nojump_matrixes(reader)
+    
+    
+def energy_reader(file, energies=None):
+    read_energy(file, energies=None):
+    """Reads an gromacs energy file and output the data in a pandas DataFrame.
+       Args:
+           file: Filename of the energy file
+           energies (opt.): Specify energies to extract from the energy file
+    """
+    if energies is None:
+        energies = np.arange(1, 100).astype('str')
+    directory = file.rsplit("/", 1)[0]
+    ps = subprocess.Popen(("echo", *energies), stdout=subprocess.PIPE)
+    try:
+        subprocess.run(("gmx", "energy", "-f", file, "-o", f"{directory}/tmp.xvg", "-quiet"), stdin=ps.stdout)
+    except FileNotFoundError:
+        print("No GROMACS found!")
+    ps.wait()
+    labels = []
+    is_legend = False
+    with open(f"{directory}/tmp.xvg") as f:
+        for i, line in enumerate(f):
+            if line.split(" ")[0] == "@":
+                if re.search("s\d+", line.split()[1]):
+                    is_legend = True
+                    labels.append(line.split('"')[1])
+            elif is_legend:
+                header = i
+                break
+
+    data = np.genfromtxt(f"{directory}/tmp.xvg", skip_header=header)
+
+    df = pd.DataFrame({"Time":data[:,0]})
+    for i, label in enumerate(labels):
+        tmp_df = pd.DataFrame({label:data[:,i+1]})
+        df = pd.concat([df, tmp_df], axis=1)
+    subprocess.run(("rm", f"{directory}/tmp.xvg"))
+    return df
 
 
 class BaseReader: