泰坦尼克分析

1 目的：

• 熟悉数据集
• 熟悉seaborn各种操作作

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
home = r'data'
df = sns.load_dataset('titanic', data_home=home)
df.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

2 数据整理

• 缺省值统计
• 缺省值处理：删除或补齐
• 数据二次处理

2.1 统计缺省值计缺省值

df.isnull().sum()

survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

2.2 删除与填充

• 删除deck列

pdata = df.drop('deck', axis=1)
pdata.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	Southampton	no	True

• 年龄使用均值填充

#填充均值
pdata = pdata.fillna(pdata.mean(numeric_only=True)) #Notes:添加numeric_only=True只对数字做处理
#年龄分类
pdata['age_level'] = pd.cut(pdata.age,bins = [0,18,60,100], labels=['child','mid', 'old'])
pdata.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	embark_town	alive	alone	age_level
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	Southampton	no	False	mid
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	Cherbourg	yes	False	mid
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	Southampton	yes	True	mid
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	Southampton	yes	False	mid
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	Southampton	no	True	mid

3 数据统计

3.1 基础数据统计

• 年龄分布
• 船舱人数分布
• 男女分布
• 团队人数分布

年龄较分散，使用直方图进行展示方图进行展示

sns.distplot(pdata.age)

 UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0.Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751sns.distplot(pdata.age)<AxesSubplot: xlabel='age', ylabel='Density'>

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sns.boxplot(pdata.age)

<AxesSubplot: ylabel='age'>

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船舱人数，男女人数，团队人数(1个人，两个人，三个人对应的数量)使用柱状图进行展示

cols = ['sex', 'pclass', 'sibsp']
lens = len(cols)
plt.figure(figsize=(14,3))
for index, col in enumerate(cols):plt.subplot(1, lens,index+1)ax = sns.countplot(x=col, data=pdata)ax.set_title(col)

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3.2 获救数据

• 获救人数与遇难人数
• 根据性别，统计获救与遇难人数
• 根据年龄段，统计获救与遇难人数
• 根据年龄段，性别，统计获救与遇难人数
• 根据年龄段，性别，船舱，统计获救与遇难人数

sns.countplot(x='survived', data=pdata)

<AxesSubplot: xlabel='survived', ylabel='count'>

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• 根据性别进行分类

sns.countplot(x='sex', data=pdata, hue='survived')

<AxesSubplot: xlabel='sex', ylabel='count'>

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• 年龄与获救关系

sns.countplot(x='age_level', data=pdata, hue='survived')

<AxesSubplot: xlabel='age_level', ylabel='count'>

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• 性别，获救，年龄段，船舱获救统计

sns.catplot(x='sex', hue='survived', data=pdata, kind='count', col='age_level', row='pclass')

<seaborn.axisgrid.FacetGrid at 0x1d4d126dc10>

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