编码与哑变量

处理缺失值

import pandas as pd
import numpy as np

df = pd.read_csv("./Narrativedata.csv", index_col=0)
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 714 non-null float64
1 Sex 891 non-null object
2 Embarked 889 non-null object
3 Survived 891 non-null object
dtypes: float64(1), object(3)
memory usage: 34.8+ KB

df["Age"].fillna(df["Age"].mean(), inplace=True)

df.dropna(axis=0, inplace=True)

df.shape

(889, 4)

preprocessing.LabelEncoder：标签专用，能够将分类转换为分类数值

from sklearn.preprocessing import LabelEncoder

label = df.iloc[:,-1]
le = LabelEncoder()
le_data = le.fit_transform(label)
df["Survived"] = le_data
df.shape

(889, 4)

pd.DataFrame(le.inverse_transform(le_data)).head()

	0
0	No
1	Yes
2	Yes
3	Yes
4	No

le.classes_

array(['No', 'Unknown', 'Yes'], dtype=object)

preprocessing.OrdinalEncoder：特征专用，能够将分类特征转换为分类数值

from sklearn.preprocessing import OrdinalEncoder

df_ = df.copy(deep=True)
feature = df_.iloc[:,1:-1]
ordinal = OrdinalEncoder(categories="auto")
result = ordinal.fit_transform(feature)

ordinal.categories_

[array(['female', 'male'], dtype=object), array(['C', 'Q', 'S'], dtype=object)]

ordinal.feature_names_in_

array(['Sex', 'Embarked'], dtype=object)

df.shape

(889, 4)

preprocessing.OneHotEncoder：独热编码，创建哑变量

from sklearn.preprocessing import OneHotEncoder

onehot = OneHotEncoder(categories="auto").fit(df.iloc[:,1:-1])
result = onehot.transform(df.iloc[:,1:-1]).toarray()
result

array([[0., 1., 0., 0., 1.],
[1., 0., 1., 0., 0.],
[1., 0., 0., 0., 1.],
...,
[1., 0., 0., 0., 1.],
[0., 1., 1., 0., 0.],
[0., 1., 0., 1., 0.]])

onehot.get_feature_names_out()

array(['Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S'],
dtype=object)

features = pd.DataFrame(result)
features.shape

(889, 5)

data_full = pd.concat([df,pd.DataFrame(result)],axis=1)
# 搞不懂了

data_full.dropna(axis=0,inplace=True)

data_full.head()

	Age	Sex	Embarked	Survived	0	1	2	3	4
0	22.0	male	S	0.0	0.0	1.0	0.0	0.0	1.0
1	38.0	female	C	2.0	1.0	0.0	1.0	0.0	0.0
2	26.0	female	S	2.0	1.0	0.0	0.0	0.0	1.0
3	35.0	female	S	2.0	1.0	0.0	0.0	0.0	1.0
4	35.0	male	S	0.0	0.0	1.0	0.0	0.0	1.0

data_full.drop(axis=1, columns=["Sex","Embarked"], inplace=True)

data_full.columns = ["Age", "Survived", 'Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S']

data_full.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 887 entries, 0 to 888
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 887 non-null float64
1 Survived 887 non-null float64
2 Sex_female 887 non-null float64
3 Sex_male 887 non-null float64
4 Embarked_C 887 non-null float64
5 Embarked_Q 887 non-null float64
6 Embarked_S 887 non-null float64
dtypes: float64(7)
memory usage: 55.4 KB