[Pandas] 타이타닉 생존자 분석

타이타닉 생존자 분석

 

https://www.kaggle.com/datasets/tedllh/titanic-train에서 csv 파일을 다운한다.

 

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

titanic_df = pd.read_csv('titanic_train.csv')
titanic_df

titanic_df['Survived'].groupby(titanic_df['Sex']).mean()

Sex
female    0.742038
male      0.188908
Name: Survived, dtype: float64

titanic_df.pivot_table(index=['Sex'])['Survived']

Sex
female    0.742038
male      0.188908
Name: Survived, dtype: float64

titanic_df.pivot_table(index=['Pclass'], aggfunc=np.sum)['Survived']

Pclass
1    136
2     87
3    119
Name: Survived, dtype: int64

titanic_df['Survived'].groupby(titanic_df['Pclass']).sum()

Pclass
1    136
2     87
3    119
Name: Survived, dtype: int64

ages = []

for index, row in titanic_df.iterrows():
    ages.append((row['Age']//10) * 10)

titanic_df['ages'] = ages

titanic_df['Survived'].groupby(titanic_df['ages']).sum().sort_values(ascending=False)

ages
20.0    77
30.0    73
10.0    41
0.0     38
40.0    34
50.0    20
60.0     6
80.0     1
70.0     0
Name: Survived, dtype: int64

titanic_df['Survived'].groupby(titanic_df['ages']).sum().plot(kind='bar')

def age_to_ages(df):
    return (df['Age']//10) * 10

titanic_df.apply(age_to_ages, axis=1)

0      20.0
1      30.0
2      20.0
3      30.0
4      30.0
       ... 
886    20.0
887    10.0
888     NaN
889    20.0
890    30.0
Length: 891, dtype: float64