diff --git a/torchy_baselines/common/results_plotter.py b/torchy_baselines/common/results_plotter.py
index 6c2cd6b..d447344 100644
--- a/torchy_baselines/common/results_plotter.py
+++ b/torchy_baselines/common/results_plotter.py
@@ -45,25 +45,25 @@ def window_func(var_1: np.ndarray, var_2: np.ndarray,
     return var_1[window - 1:], function_on_var2
 
 
-def ts2xy(timesteps: pd.DataFrame, x_axis: str) -> Tuple[np.ndarray, np.ndarray]:
+def ts2xy(data_frame: pd.DataFrame, x_axis: str) -> Tuple[np.ndarray, np.ndarray]:
     """
-    Decompose a timesteps variable to x ans ys
+    Decompose a data frame variable to x ans ys
 
-    :param timesteps: (pd.DataFrame) the input data
+    :param data_frame: (pd.DataFrame) the input data
     :param x_axis: (str) the axis for the x and y output
         (can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
     :return: (Tuple[np.ndarray, np.ndarray]) the x and y output
     """
     if x_axis == X_TIMESTEPS:
-        x_var = np.cumsum(timesteps.l.values)
-        y_var = timesteps.r.values
+        x_var = np.cumsum(data_frame.l.values)
+        y_var = data_frame.r.values
     elif x_axis == X_EPISODES:
-        x_var = np.arange(len(timesteps))
-        y_var = timesteps.r.values
+        x_var = np.arange(len(data_frame))
+        y_var = data_frame.r.values
     elif x_axis == X_WALLTIME:
         # Convert to hours
-        x_var = timesteps.t.values / 3600.
-        y_var = timesteps.r.values
+        x_var = data_frame.t.values / 3600.
+        y_var = data_frame.r.values
     else:
         raise NotImplementedError
     return x_var, y_var
@@ -81,7 +81,7 @@ def plot_curves(xy_list: List[Tuple[np.ndarray, np.ndarray]],
     :param figsize: (Tuple[int, int]) Size of the figure (width, height)
     """
 
-    plt.figure(figsize=figsize)
+    plt.figure(title, figsize=figsize)
     max_x = max(xy[0][-1] for xy in xy_list)
     min_x = 0
     for (i, (x, y)) in enumerate(xy_list):
@@ -101,7 +101,7 @@ def plot_curves(xy_list: List[Tuple[np.ndarray, np.ndarray]],
 def plot_results(dirs: List[str], num_timesteps: Optional[int],
                  x_axis: str, task_name: str, figsize: Tuple[int, int] = (8, 2)) -> None:
     """
-    plot the results
+    Plot the results using csv files from ``Monitor`` wrapper.
 
     :param dirs: ([str]) the save location of the results to plot
     :param num_timesteps: (int or None) only plot the points below this value
@@ -111,11 +111,11 @@ def plot_results(dirs: List[str], num_timesteps: Optional[int],
     :param figsize: (Tuple[int, int]) Size of the figure (width, height)
     """
 
-    timesteps_list = []
+    data_frames = []
     for folder in dirs:
-        timesteps = load_results(folder)
+        data_frame = load_results(folder)
         if num_timesteps is not None:
-            timesteps = timesteps[timesteps.l.cumsum() <= num_timesteps]
-        timesteps_list.append(timesteps)
-    xy_list = [ts2xy(timesteps_item, x_axis) for timesteps_item in timesteps_list]
+            data_frame = data_frame[data_frame.l.cumsum() <= num_timesteps]
+        data_frames.append(data_frame)
+    xy_list = [ts2xy(data_frame, x_axis) for data_frame in data_frames]
     plot_curves(xy_list, x_axis, task_name, figsize)