Titanic: 타이타닉 데이터셋

import pandas as pd
import matplotlib as mpl
import matplotlib.pylab as plt
import seaborn as sns
import numpy as np

# 상대경로
file_path = 'titanic/train.csv'

titanic_data = pd.read_csv(file_path, encoding='CP949')
titanic_data
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# 백업 파일 생성
titanic_data_copy_backup = titanic_data.copy()
titanic_data.to_csv('titanic/train_backup.csv')

file_path = 'titanic/train_backup.csv'
titanic_data_csv_backup = pd.read_csv(file_path)
titanic_data_csv_backup
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titanic_data.info()
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컬럼별 데이터 구성 및 타입 확인

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
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titanic_data.describe()
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sex_dict = {'male':0, 'female':1}
titanic_data['Sex'] = titanic_data['Sex'].map(sex_dict)
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형변

titanic_data.groupby(['Embarked']).mean()
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평균값

Embarked_dict = {'C':0, 'Q':1, 'S':2}
titanic_data['Embarked'] = titanic_data['Embarked'].map(Embarked_dict)
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형변환

titanic_data
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import missingno as msno

titanic_data.isnull().sum()
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Age, Cabin, Embarked 컬럼에 결측치 확인

msno.matrix(titanic_data)
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titanic_data = titanic_data.drop(labels=['Ticket', 'Cabin'], axis=1)
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titanic_data['Age'] = titanic_data['Age'].fillna(titanic_data['Age'].median())

titanic_data['Embarked'] = titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0])
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Age 컬럼은 중간값, Embarked 컬럼은 최빈값으로 채우기

titanic_data.isnull().sum()
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titanic_data['Age'] = titanic_data['Age'].astype(np.int64)
titanic_data['Embarked'] = titanic_data['Embarked'].astype(np.int64)
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titanic_data.info()
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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 10 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   PassengerId  891 non-null    int64
 1   Survived     891 non-null    int64
 2   Pclass       891 non-null    int64
 3   Name         891 non-null    object
 4   Sex          891 non-null    int64
 5   Age          891 non-null    int64
 6   SibSp        891 non-null    int64
 7   Parch        891 non-null    int64
 8   Fare         891 non-null    float64
 9   Embarked     891 non-null    int64
dtypes: float64(1), int64(8), object(1)
memory usage: 69.7+ KB
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# 표를 2x2로 배열하고, 한 표당 15의 크기로 지정
figure, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(nrows=3, ncols=2)
figure.set_size_inches(15, 15)

sns.countplot('Survived', data=titanic_data, ax=ax1)
sns.countplot('Pclass', data=titanic_data, ax=ax2)
sns.countplot('Sex', data=titanic_data, ax=ax3)
sns.countplot('SibSp', data=titanic_data, ax=ax4)
sns.countplot('Parch', data=titanic_data, ax=ax5)
sns.countplot('Embarked', data=titanic_data, ax=ax6)
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def bar_chart(feature):
    survived = titanic_data[titanic_data['Survived']==1][feature].value_counts()
    dead = titanic_data[titanic_data['Survived']==0][feature].value_counts()
    df = pd.DataFrame([survived,dead])
    df.index = ['Survived','Dead']
    df.plot(kind='bar',stacked=True, figsize=(10,5))
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bar_chart('Sex')
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bar_chart('Pclass')
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bar_chart('SibSp')
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bar_chart('Parch')
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bar_chart('Embarked')
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