Titanic_Data Analysis

本项目提供了两份数据：train.csv文件作为训练构建与生存相关的模型；另一份test.csv文件则用于测试集，用我们构建出来的模型预测生存情况；

PassengerId --Id,具有唯一标识的作用，即每个人对应一个Id
survived --是否幸存 1表示是 0表示否
pclass --船舱等级 1:一等舱 2:二等舱 3:三等舱
Name --姓名，通常西方人的姓名
Sex --性别，female女性 male 男性
Age --年龄
SibSp --同船配偶以及兄弟姐妹的人数
Parch --同船父母或子女的人数
Ticket --船票
Fare --票价
Cabin --舱位
Embarked --登船港口

#读取数据
import pandas as pd
df_train,df_test = pd.read_csv('train.csv'),pd.read_csv('test.csv')

从训练集开始

#查看前五行数据
df_train.head()

	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
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

#查看后5行数据
df_train.tail()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.00	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.00	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.00	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

#查看数据信息，其中包含数据纬度、数据类型、所占空间等信息
df_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

数据纬度：891行 X 12列
缺失字段：Age，Cabin，Embarked
数据类型：两个64位浮点型，5个64位整型，5个python对象

#描述性统计
df_train.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

1.除了python对象之外的数据类型，均参与了计算
2.38.4%的人幸存，死亡率很高
3.年龄现有数据714，缺失占比714/891 = 20%
4.同船兄弟姐妹与配偶人数最大为8，同船父母或子女最大数则为6，看来有大家庭小家庭之分；
5.票价最小为0，最大为512.3，均值为32.20，中位数为14.45，正偏，贫富差距不小；

#那么python对象对应的数据查看
df_train[['Name','Sex','Ticket','Cabin','Embarked']].describe()

	Name	Sex	Ticket	Cabin	Embarked
count	891	891	891	204	889
unique	891	2	681	147	3
top	Lam, Mr. Ali	male	1601	B96 B98	S
freq	1	577	7	4	644

特征分析

# 1.PassengerId，id仅仅是来标识乘客的唯一性，必然与幸存无关

# 2.Pclass
#船舱等级，一等级是整个船最昂贵奢华的地方，有钱人才能享受，有没有可能一等舱有钱人比三等舱的穷人更容易幸存呢？

import numpy as np
import matplotlib.pyplot as plt
#生成pclass-survive的列联表
Pclass_Survived = pd.crosstab(df_train['Pclass'],df_train['Survived'])

Pclass_Survived

Survived	0	1
Pclass
1	80	136
2	97	87
3	372	119

Pclass_Survived.count()

Survived
0    3
1    3
dtype: int64

Pclass_Survived.index

Int64Index([1, 2, 3], dtype='int64', name='Pclass')

#绘制堆积柱形图
Pclass_Survived.plot(kind = 'bar',stacked = True)
Survived_len = len(Pclass_Survived.count())
print(Survived_len)
Pclass_index = np.arange(len(Pclass_Survived.index))
print(Pclass_index)

plt.xticks(Pclass_Survived.index-1,Pclass_Survived