寻找Jack和Rose——泰坦尼克号(Titanic)获救数据分析

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寻找Jack和Rose——泰坦尼克号获救数据分析

导入数据

import pandas as pd
df = pd.read_csv('Titanic.csv')
df.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: 66.2+ KB

数据分析

数据完整性：

（1） 年龄有174个缺失；
（2） 舱位编号大量缺失；
（3） 登录口岸有2个缺失
返回顶部

df.head(5)
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

符号意义：
#PassengerId：乘客ID编号

#Survived：是否存活，0-未存活，1-存活

#Pclass：船舱号，共1，2，3类舱

#Name：乘客姓名

#Sex：乘客性别，Male,Female

#Age：乘客年龄

#SibSp：兄弟姐妹/配偶数量，0~8

#Parch：父母/子女数量，0~6

#Ticket：船票编号

#Fare：票价

#Cabin：舱位编号

#Embarked：登陆口岸，C、Q、S

男女乘客获救率有无明显差异

返回顶部

surv_sex_result = df.loc[:, ['Sex', 'Survived']].groupby(['Sex', 'Survived']).size()
surv_sex_result

Sex     Survived
female  0            81
        1           233
male    0           468
        1           109
dtype: int64

dct_surv_sex = surv_sex_result.to_dict()
dct_surv_sex

{('female', 0): 81, ('female', 1): 233, ('male', 0): 468, ('male', 1): 109}

female_sum = 0
female_surv = 0
for key in dct_surv_sex:
    if key[0] == 'female':
        female_sum += dct_surv_sex[key]
        if key[1] == 1:
            female_surv = dct_surv_sex[key]

female_surv_rate = round(female_surv / female_sum, 2)
female_surv_rate

0.74

male_sum = 0
male_surv = 0
for key in dct_surv_sex:
    if key[0] == 'male':
        male_sum += dct_surv_sex[key]
        if key[1] == 1:
            male_surv = dct_surv_sex[key]

male_surv_rate = round(male_surv / male_sum, 2)
male_surv_rate

0.19

女性获救率74%远大于男性19%

船上是否有明显的贫富差异（求极差）

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round(df['Fare'].max() - df['Fare'].min(), 2)

512.33

从票价上看，贫富差异很大

有亲戚在船上是否对获救率有影响

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# 备份一份是否有亲戚的数据
df_sib = df.copy(deep=True)
df_sib['sib_or_parch'] = 'N'
df_sib.loc[(df['SibSp'] > 0) | (df['Parch'] > 0),'sib_or_parch'] = 'Y'
df_sib.head(5)
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked sib_or_parch
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S Y
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C Y
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S N
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S Y
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S N

有无亲戚的获救情况：

surv_sib_result = df_sib.loc[:, ['Survived', 'sib_or_parch']].groupby(['sib_or_parch', 'Survived']).size()
dct_surv_sib = surv_sib_result.to_dict()
dct_surv_sib

{('N', 0): 374, ('N', 1): 163, ('Y', 0): 175, ('Y', 1): 179}

sib_sum = 0
sib_surv = 0
for key in dct_surv_sib:
    if key[0] == 'Y':
        sib_sum += dct_surv_sib[key]
        if key[1] == 1:
            sib_surv = dct_surv_sib[key]
            
sib_surv_rate = round(sib_surv / sib_sum, 2)
sib_surv_rate

0.51

nosib_sum = 0
nosib_surv = 0
for key in dct_surv_sib:
    if key[0] == 'N':
        nosib_sum += dct_surv_sib[key]
        if key[1] == 1:
            nosib_surv = dct_surv_sib[key]
            
nosib_surv_rate = round(nosib_surv / nosib_sum, 2)
nosib_surv_rate

0.3

有亲戚在船上的获救率50%高于无亲戚的获救率30%

不同登船地点的男女分布情况

返回顶部

embark_sex = df.loc[:, ['Embarked', 'Sex']].groupby(['Embarked', 'Sex']).size()
embark_sex

Embarked  Sex   
C         female     73
          male       95
Q         female     36
          male       41
S         female    203
          male      441
dtype: int64

不同登船地点是否反应该地区的经济状况

（登船地点的票价中位数）
返回顶部

# 中位数
embark_fare = df.loc[:, ['Embarked', 'Fare']].groupby('Embarked').median()
embark_fare
Fare
Embarked
C 29.70
Q 7.75
S 13.00 
# 最大值
embark_fare_max = df.loc[:, ['Embarked', 'Fare']].groupby('Embarked').max()
embark_fare_max
Fare
Embarked
C 512.3292
Q 90.0000
S 263.0000

C地最富， S地次之， Q地最穷

不同年龄段的获救率

返回顶部
按不同年龄规约
children：小于6岁；
teen：6～16岁；
youth：16～36岁；
middle：36～60岁；
old：大于60岁

df_age = df.copy(deep=True)
df_age.loc[(df_age['Age'] <= 6) & (df_age['Age'] > 0), 'age_scope'] = 'children'
df_age.loc[(df_age['Age'] <= 16) & (df_age['Age'] > 6), 'age_scope'] = 'teen'
df_age.loc[(df_age['Age'] <= 36) & (df_age['Age'] > 16), 'age_scope'] = 'youth'
df_age.loc[(df_age['Age'] <= 60) & (df_age['Age'] > 36), 'age_scope'] = 'middle'
df_age.loc[df_age['Age'] > 60, 'age_scope'] = 'old'
df_age
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked age_scope
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S youth
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C middle
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S youth
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S youth
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S youth
... ... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S youth
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S youth
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S NaN
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C youth
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q youth

891 rows × 13 columns

age_surv = dict(df_age.loc[:, ['age_scope', 'Survived']].groupby(['age_scope', 'Survived']).size())
age_surv

{('children', 0): 14,
 ('children', 1): 33,
 ('middle', 0): 106,
 ('middle', 1): 67,
 ('old', 0): 17,
 ('old', 1): 5,
 ('teen', 0): 31,
 ('teen', 1): 22,
 ('youth', 0): 256,
 ('youth', 1): 163}

age_surv_dct = {}
age_set = set(lt[0] for lt in list(age_surv))

for ast in age_set:
    sum = 0
    surv = 0
    for key in age_surv:
        if key[0] == ast:
            sum += age_surv[key]
            if key[1] == 1:
                surv += age_surv[key]
    age_surv_dct[ast] = round(surv / sum, 2)
    
age_surv_dct

{'teen': 0.42, 'old': 0.23, 'children': 0.7, 'youth': 0.39, 'middle': 0.39}

from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts import options as opts

def bar_border_radius(dct):
    c = (
        Bar()
        .add_xaxis([key for key in dct])
        .add_yaxis("获救率", [dct[key] for key in dct], category_gap="60%")
        .set_series_opts(itemstyle_opts={
            "normal": {
                "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                    offset: 0,
                    color: 'rgba(0, 244, 255, 1)'
                }, {
                    offset: 1,
                    color: 'rgba(0, 77, 167, 1)'
                }], false)"""),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": 'rgb(0, 160, 221)',
            }})
        .set_global_opts(title_opts=opts.TitleOpts(title="不同年龄段获救率"))
    )
    return c

bar_border_radius(age_surv_dct).render_notebook()

不同年龄段获救率

小孩获救率最高，老人获救率最低，其他年龄段获救率在40%左右

不同等级客舱的获救率

返回顶部

pcls_surv = dict(df.loc[:, ['Pclass', 'Survived']].groupby(['Pclass', 'Survived']).size())
pcls_surv

{(1, 0): 80, (1, 1): 136, (2, 0): 97, (2, 1): 87, (3, 0): 372, (3, 1): 119}

pcls_surv_dct = {}
pcls_set = set([lt[0] for lt in list(pcls_surv)])

for pst in pcls_set:
    sum = 0
    surv = 0
    for key in pcls_surv:
        if key[0] == pst:
            sum += pcls_surv[key]
            if key[1] == 1:
                surv += pcls_surv[key]
                
    pcls_surv_dct[pst] = round(surv / sum, 2)
    
pcls_surv_dct

{1: 0.63, 2: 0.47, 3: 0.24}

def bar_reversal_axis(dct) -> Bar:
    c = (
        Bar()
        .add_xaxis([key for key in dct])
        .add_yaxis("获救率", [dct[key] for key in dct])
        .reversal_axis()
        .set_series_opts(label_opts=opts.LabelOpts(position="right"))
        .set_global_opts(title_opts=opts.TitleOpts(title="不同等级客舱获救率"))
    )
    return c

bar_reversal_axis(pcls_surv_dct).render_notebook()

不同等级客舱获救率

不同等级客舱的获救率差别很大，1级客舱更便于逃生

Jack和Rose是否在船上？

返回顶部

# 后两个据说是人物原型
df.loc[df['Name'].str.contains('Jack') | df['Name'].str.contains('Emilio Portaluppi') | df['Name'].str.contains('Dawson')]
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
766 767 0 1 Brewe, Dr. Arthur Jackson male NaN 0 0 112379 39.6 NaN C 
df.loc[df['Name'].str.contains('Rose')]
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
855 856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18.0 0 1 392091 9.35 NaN S

船上没有Jack和Rose，只有他们的故事

使用线性回归预测年龄缺失值

返回顶部
和年龄有关系的参数：Survived、Pclass、Name(Mr、Mrs、Miss、Other)、SibSp、Parch、Fare、Embarked

df_age.loc[df_age['Sex'] == 'male', ['Sex']] = 1
df_age.loc[df_age['Sex'] == 'female', ['Sex']] = 2
df_age.loc[df_age['Name'].str.contains('Mrs.'), ['Name']] = '1'
df_age.loc[df_age['Name'].str.contains('Mr.'), ['Name']] = '2'
df_age.loc[df_age['Name'].str.contains('Miss.'), ['Name']] = '3'
df_age.loc[df_age['Name'].str.contains(','), ['Name']] = '4'
df_age.loc[df_age['Name'] == '1', ['Name']] = 1
df_age.loc[df_age['Name'] == '2', ['Name']] = 2
df_age.loc[df_age['Name'] == '3', ['Name']] = 3
df_age.loc[df_age['Name'] == '4', ['Name']] = 4
df_age.loc[df_age['Embarked'] == 'C', ['Embarked']] = 1
df_age.loc[df_age['Embarked'] == 'S', ['Embarked']] = 2
df_age.loc[df_age['Embarked'] == 'Q', ['Embarked']] = 3
df_age.head(5)
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked age_scope
0 1 0 3 2 1 22.0 1 0 A/5 21171 7.2500 NaN 2 youth
1 2 1 1 1 2 38.0 1 0 PC 17599 71.2833 C85 1 middle
2 3 1 3 3 2 26.0 0 0 STON/O2. 3101282 7.9250 NaN 2 youth
3 4 1 1 1 2 35.0 1 0 113803 53.1000 C123 2 youth
4 5 0 3 2 1 35.0 0 0 373450 8.0500 NaN 2 youth 
df_age.tail(5)
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked age_scope
886 887 0 2 4 1 27.0 0 0 211536 13.00 NaN 2 youth
887 888 1 1 3 2 19.0 0 0 112053 30.00 B42 2 youth
888 889 0 3 3 2 NaN 1 2 W./C. 6607 23.45 NaN 2 NaN
889 890 1 1 2 1 26.0 0 0 111369 30.00 C148 1 youth
890 891 0 3 2 1 32.0 0 0 370376 7.75 NaN 3 youth 
from sklearn.linear_model import LinearRegression
from sklearn.externals import joblib


train_columns = ['Survived', 'Pclass', 'Name', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked']
df_train = df_age[(df_age['Age'] > 0) & (df_age['Embarked'] > 0)]
x_train = df_train.loc[:, train_columns]
y_target = df_train['Age']

lr = LinearRegression()
lr.fit(x_train, y_target)
joblib.dump(lr, './lr.pkl')
lr = joblib.load('./lr.pkl')

df_test = df_age[(df_age['Age'].isnull()) & (df_age['Embarked'] > 0)]
df_test
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked age_scope
5 6 0 3 2 1 NaN 0 0 330877 8.4583 NaN 3 NaN
17 18 1 2 2 1 NaN 0 0 244373 13.0000 NaN 2 NaN
19 20 1 3 1 2 NaN 0 0 2649 7.2250 NaN 1 NaN
26 27 0 3 2 1 NaN 0 0 2631 7.2250 NaN 1 NaN
28 29 1 3 3 2 NaN 0 0 330959 7.8792 NaN 3 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ...
859 860 0 3 2 1 NaN 0 0 2629 7.2292 NaN 1 NaN
863 864 0 3 3 2 NaN 8 2 CA. 2343 69.5500 NaN 2 NaN
868 869 0 3 2 1 NaN 0 0 345777 9.5000 NaN 2 NaN
878 879 0 3 2 1 NaN 0 0 349217 7.8958 NaN 2 NaN
888 889 0 3 3 2 NaN 1 2 W./C. 6607 23.4500 NaN 2 NaN

177 rows × 13 columns

for i in range(len(df_test)):
    x_i = df_test.loc[:, train_columns][i:i + 1]  # 取每一条需要预测年龄的数据
    x_PassId = df_test[i:i + 1]['PassengerId']  # 取对应的乘客Id
    age_pred = lr.predict(x_i) if lr.predict(x_i) >= 0 else 0  # 如果预测年龄小于0， 按0
    age_pred = lr.predict(x_i) if lr.predict(x_i) <= 80 else 80  # 如果预测年龄小于80， 按80
    df.loc[df['PassengerId'] == x_PassId.unique()[0], ['Age']] = round(age_pred[0], 1)

df.head(5)
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 
df.tail(5)
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 18.2 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 
df['Age'].median()

29.0

结论：

（1） Titanic号是一条“绅士船”，遇难后妇女儿童的获救率更高。
（2） Titanic号上贫富差距也很大，1等舱的获救率更高。
（3） Rose和Jack不在船上，船上只有他们的传说。

技术总结：

（1） 对层次索引，求构造指标（如获救率等）。
（2） 线性回归预测缺失值，并填入表格。

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