diff --git a/kaggle/titanic.ipynb b/kaggle/titanic.ipynb
index 01acad9..c5b5af4 100644
--- a/kaggle/titanic.ipynb
+++ b/kaggle/titanic.ipynb
@@ -976,7 +976,7 @@
     }
    ],
    "source": [
-    "sexes = sort(df_train['Sex'].unique())\n",
+    "sexes = sorted(df_train['Sex'].unique())\n",
     "genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
     "genders_mapping"
    ]
@@ -1220,7 +1220,7 @@
    ],
    "source": [
     "# Get the unique values of Pclass:\n",
-    "passenger_classes = sort(df_train['Pclass'].unique())\n",
+    "passenger_classes = sorted(df_train['Pclass'].unique())\n",
     "\n",
     "for p_class in passenger_classes:\n",
     "    print 'M: ', p_class, len(df_train[(df_train['Sex'] == 'male') & \n",
@@ -1430,7 +1430,7 @@
    ],
    "source": [
     "# Get the unique values of Embarked\n",
-    "embarked_locs = sort(df_train['Embarked'].unique())\n",
+    "embarked_locs = sorted(df_train['Embarked'].unique())\n",
     "\n",
     "embarked_locs_mapping = dict(zip(embarked_locs, \n",
     "                                 range(0, len(embarked_locs) + 1)))\n",
@@ -1682,7 +1682,7 @@
     }
    ],
    "source": [
-    "embarked_locs = sort(df_train['Embarked_Val'].unique())\n",
+    "embarked_locs = sorted(df_train['Embarked_Val'].unique())\n",
     "embarked_locs"
    ]
   },
@@ -2393,7 +2393,7 @@
    ],
    "source": [
     "# Get the unique values of Embarked and its maximum\n",
-    "family_sizes = sort(df_train['FamilySize'].unique())\n",
+    "family_sizes = sorted(df_train['FamilySize'].unique())\n",
     "family_size_max = max(family_sizes)\n",
     "\n",
     "df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
@@ -2581,7 +2581,7 @@
     "def clean_data(df, drop_passenger_id):\n",
     "    \n",
     "    # Get the unique values of Sex\n",
-    "    sexes = sort(df['Sex'].unique())\n",
+    "    sexes = sorted(df['Sex'].unique())\n",
     "    \n",
     "    # Generate a mapping of Sex from a string to a number representation    \n",
     "    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
@@ -2590,7 +2590,7 @@
     "    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
     "    \n",
     "    # Get the unique values of Embarked\n",
-    "    embarked_locs = sort(df['Embarked'].unique())\n",
+    "    embarked_locs = sorted(df['Embarked'].unique())\n",
     "\n",
     "    # Generate a mapping of Embarked from a string to a number representation        \n",
     "    embarked_locs_mapping = dict(zip(embarked_locs, \n",