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",