diff --git a/copulas/multivariate/vine.py b/copulas/multivariate/vine.py
index 720c56f..c11b769 100644
--- a/copulas/multivariate/vine.py
+++ b/copulas/multivariate/vine.py
@@ -85,6 +85,12 @@ class VineCopula(Multivariate):
             X: `np.ndarray`: data to be fitted.
             truncated: `int` max level to build the vine.
         """
+        # Store column names if X is a DataFrame
+        if hasattr(X, 'columns'):
+            self.columns = list(X.columns)
+        else:
+            self.columns = [f'X{i}' for i in range(X.shape[1])]
+            
         self.n_sample, self.n_var = X.shape
         self.tau_mat = X.corr(method='kendall').values
         self.u_matrix = np.empty([self.n_sample, self.n_var])
@@ -134,8 +140,8 @@ class VineCopula(Multivariate):
 
         return np.sum(values)
 
-    def sample(self, num_rows=1):
-        """Generating samples from vine model."""
+    def _sample_row(self):
+        """Generate a single sample from the vine model."""
         unis = np.random.uniform(0, 1, self.n_var)
         # randomly select a node to start with
         first_ind = randint(0, self.n_var - 1)
@@ -180,7 +186,7 @@ class VineCopula(Multivariate):
                                 break
 
                     if current_ind != -1:
-                        # the node is not indepedent contional on visited node
+                        # the node is not independent conditional on visited node
                         copula_type = current_tree[current_ind].name
                         copula = Bivariate(CopulaTypes(copula_type))
                         copula.theta = current_tree[current_ind].theta
@@ -211,3 +217,16 @@ class VineCopula(Multivariate):
             visited.insert(0, current)
 
         return sampled
+
+    def sample(self, num_rows=1):
+        """Generating samples from vine model.
+        
+        Args:
+            num_rows: Number of rows to sample.
+            
+        Returns:
+            pandas.DataFrame with sampled data.
+        """
+        import pandas as pd
+        samples = np.vstack([self._sample_row() for _ in range(num_rows)])
+        return pd.DataFrame(samples, columns=self.columns)