[kaggle] 타이타닉(titanic) | 10. Feature Engineering - Embarked Feature

3.1.2 Fill Null in Embarked

 

df_train['Embarked'].isnull().sum()

 

  - Embarked의 null data는 현재 2개이다.

  -> 이 null data들을 다른 값으로 채우겠다 !

 

- 현재 S에서 가장 많은 탑승객이 있으므로, nulld data를 S로 채우겠다.

 

 

 

 

 

 

• fillna

df_train['Embarked'.fillna('S', inplace=True)

 

 

 

 

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3.2 Change Age (continuous to categorical)

 

- Age는 현재 continuous feature

- Age를 몇 개의 그룹으로 나누어 category화 시켜주자

 

 

 

 

• 새로운 column 생성

df_train['Age_cat'] = 0

 

 

 

 

 

 

• loc을 사용해 10살 간격으로 나누기

df_train.loc[df_train['Age'] < 10, 'Age_cat'] = 0
df_train.loc[(10 <= df_train['Age']) & (df_train['Age'] < 20), 'Age_cat'] = 1
df_train.loc[(20 <= df_train['Age']) & (df_train['Age'] < 30), 'Age_cat'] = 2
df_train.loc[(30 <= df_train['Age']) & (df_train['Age'] < 40), 'Age_cat'] = 3
df_train.loc[(40 <= df_train['Age']) & (df_train['Age'] < 50), 'Age_cat'] = 4
df_train.loc[(50 <= df_train['Age']) & (df_train['Age'] < 60), 'Age_cat'] = 5
df_train.loc[(60 <= df_train['Age']) & (df_train['Age'] < 70), 'Age_cat'] = 6
df_train.loc[(70 <= df_train['Age']), 'Age_cat'] = 7

 

df_test.loc[df_test['Age'] < 10, 'Age_cat'] = 0
df_test.loc[(10 <= df_test['Age']) & (df_test['Age'] < 20), 'Age_cat'] = 1
df_test.loc[(20 <= df_test['Age']) & (df_test['Age'] < 30), 'Age_cat'] = 2
df_test.loc[(30 <= df_test['Age']) & (df_test['Age'] < 40), 'Age_cat'] = 3
df_test.loc[(40 <= df_test['Age']) & (df_test['Age'] < 50), 'Age_cat'] = 4
df_test.loc[(50 <= df_test['Age']) & (df_test['Age'] < 60), 'Age_cat'] = 5
df_test.loc[(60 <= df_test['Age']) & (df_test['Age'] < 70), 'Age_cat'] = 6
df_test.loc[(70 <= df_test['Age']), 'Age_cat'] = 7

 

 

 

 

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but, 위같은 하드코딩은 어렵다.

-> apply 라는 메소드를 사용해 함수를 생성해 조금 더 쉽게 해보자.

 

 

 

 

def category_age(x):
	if x<10:
    	return 0
    elif x<20:
    	return 1
    elif x<30:
    	return 2
    elif x<40:
        return 3
    elif x<50:
        return 4
    elif x<60:
        return 5
    elif x<70:
        return 6
    else:
        return 7

df_train['Age_cat_2'] = df_train['Age'].apply(category_age)

 

 

 

 

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• 둘을 비교해보자 !

(df_train['Age_cat'] == df_train['Age_cat_2']).all()

 

  - all() : 모든게 true일 때만 T, 하나라도 false이면 F

 

 

 

 

 

 

• 쓸데없는 것은 날리기

df_train.drop(['Age', 'Age_cat_2'], axis=1, inplace=True)
df_test.drop(['Age'], axis=1, inplace=True)

  - axis=1 : 컬럼이 날아감