一、准备&基本概况

import pandas as pd
data = pd.read_csv(r"D:\本科\kaggle数据挖掘\titanic\train.csv", index_col = 0)
 
data.head()

data.info()

 

 由运行结果的第二行知，共有891条数据。由运行结果的表格知每个column有多少条数据

data.loc[:,"Age"]

 

data.loc[:,"Age"].values

        

可见：

1.加了“.values”后才是Age数组（不加时会保留PassengerId）所以千万别忘了加 .values !

2.Age是一维数组

  二、处理缺失值

from sklearn.impute import SimpleImputer

1.Age

1)一维改二维

Age = data.loc[:,"Age"].values.reshape(-1, 1) #把age数组从上面的一维数组改成二维数组
Age[:20] #Age的前20条

2）实例化——处理缺失值

下面介绍3种实例化方法（但最终使用了法3完成本步骤）：

a.用均值填补

imp_mean = SimpleImputer() #()内为空，默认为用均值填补
imp_mean = imp_mean.fit_transform(Age)
imp_mean[:20]

 

array([[22.        ],
       [38.        ],
       [26.        ],
       [35.        ],
       [35.        ],
       [29.69911765],
       [54.        ],
       [ 2.        ],
       [27.        ],
       [14.        ],
       [ 4.        ],
       [58.        ],
       [20.        ],
       [39.        ],
       [14.        ],
       [55.        ],
       [ 2.        ],
       [29.69911765],
       [31.        ],
       [29.69911765]])

b.用0值填补 

imp_0 = SimpleImputer(strategy = "constant", fill_value = 0) #用0填补
imp_0 = imp_0.fit_transform(Age)
imp_0[:20]

 

array([[22.],
       [38.],
       [26.],
       [35.],
       [35.],
       [ 0.],
       [54.],
       [ 2.],
       [27.],
       [14.],
       [ 4.],
       [58.],
       [20.],
       [39.],
       [14.],
       [55.],
       [ 2.],
       [ 0.],
       [31.],
       [ 0.]])

c.用中位数填补 

imp_median = SimpleImputer(strategy = "median") #用中位数填补
imp_median = imp_median.fit_transform(Age)
imp_median[:20]

 

array([[22.],
       [38.],
       [26.],
       [35.],
       [35.],
       [28.],
       [54.],
       [ 2.],
       [27.],
       [14.],
       [ 4.],
       [58.],
       [20.],
       [39.],
       [14.],
       [55.],
       [ 2.],
       [28.],
       [31.],
       [28.]])

接下来用 3）中填补好了的数据来

3）替代含缺失值的数据

ata.loc[:,"Age"] = imp_median
data.info()

Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       891 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     204 non-null    object 
 10  Embarked  889 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 83.5+ KB

现在Age是891个数据，即没有缺失值了

然而，同时发现Embarked是889，说明还缺2个

2.Embarked 

1)一维改二维 

Embarked = data.loc[:, "Embarked"].values.reshape(-1,1)

2）实例化——处理缺失值 & 3）替代含缺失值的数据 

imp_mode = SimpleImputer(strategy = "most_frequent") #most_frequent == 众数
data.loc[:,"Embarked"] = imp_mode.fit_transform(Embarked)
data.info()

 

Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    int64  
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       891 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     204 non-null    object 
 10  Embarked  891 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 83.5+ KB

填补缺失值也可以用pandas的方法 

import pandas as pd
data = pd.read_csv(r"D:\本科\kaggle数据挖掘\titanic\train.csv", index_col = 0)
data.head()

 用中值填补

data.loc[:,"Age"] = data.loc[:,"Age"].fillna(data.loc[:,"Age"].median())
data.loc[:,"Age"]

PassengerId
1      22.0
2      38.0
3      26.0
4      35.0
5      35.0
       ... 
887    27.0
888    19.0
889    28.0
890    26.0
891    32.0
Name: Age, Length: 891, dtype: float64

 

删除列

data.dropna(axis = 0, inplace = True) 
data.head()

axis=0删除所有列，axis=1删除所有列
inplace: =True表示在原数据集修改，=False表示生成一个复制对象，在复制对象上修改。（默认情况是True