파이썬 인코딩 실습

인코딩 실습¶

In [ ]:

import seaborn as sns
import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np

In [ ]:

# 타이타닉 데이터
titanic = pd.read_csv('C:/Users/LOVE/Downloads/vscode/ML/titanic/train.csv')

titanic.head(3)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

In [ ]:

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
le = LabelEncoder()
oe = OneHotEncoder()

1. 레이블 인코딩: 성별(Sex)¶

In [ ]:

le.fit(titanic[['Sex']])

c:\Users\LOVE\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\preprocessing\_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)

Out[ ]:

LabelEncoder()

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In [ ]:

le.classes_

Out[ ]:

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

In [ ]:

titanic['Sex_le'] = le.transform(titanic[['Sex']])

titanic.head(3)

c:\Users\LOVE\AppData\Local\Programs\Python\Python39\lib\site-packages\sklearn\preprocessing\_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Sex_le
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	1
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	0

2. 원핫 인코딩: 항구(Embarked)¶

In [ ]:

oe.fit(titanic[['Embarked']])

Out[ ]:

OneHotEncoder()

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In [ ]:

oe.categories_

Out[ ]:

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

In [ ]:

embarked_csr = oe.transform(titanic[['Embarked']])
embarked_csr_df = pd.DataFrame(embarked_csr.toarray(), columns = oe.get_feature_names_out())

embarked_csr_df.head(3)

Out[ ]:

	Embarked_C	Embarked_Q	Embarked_S	Embarked_nan
0	0.0	0.0	1.0	0.0
1	1.0	0.0	0.0	0.0
2	0.0	0.0	1.0	0.0

In [ ]:

pd.concat([titanic, embarked_csr_df], axis = 1)

Out[ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Sex_le	Embarked_C	Embarked_Q	Embarked_S	Embarked_nan
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	1	0.0	0.0	1.0	0.0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	0	1.0	0.0	0.0	0.0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	0	0.0	0.0	1.0	0.0
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	0	0.0	0.0	1.0	0.0
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	1	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	1	0.0	0.0	1.0	0.0
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	0	0.0	0.0	1.0	0.0
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	0	0.0	0.0	1.0	0.0
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	1	1.0	0.0	0.0	0.0
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	1	0.0	1.0	0.0	0.0

891 rows × 17 columns