基于Python的Titannic数据可视化

Titannic数据是经典的数据分析和数据挖掘的数据，本文基于Python和相关库进行可视化分析

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets

data = pd.read_csv(r"D:/Python/datalearning/sklearn/day08_data.csv")
data.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

data.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

data.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

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['font.family']='sans-serif' 
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号

fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数

plt.subplot2grid((2,3),(0,0)) # 在一张大图里分列几个小图
data.Survived.value_counts().plot(kind='bar')# 柱状图
plt.title(u"获救情况 (1为获救)") # 标题
plt.ylabel(u"人数") # Y轴标签

plt.subplot2grid((2,3),(0,1))
data.Pclass.value_counts().plot(kind="bar") # 柱状图显示
plt.ylabel(u"人数")
plt.title(u"乘客等级分布")

plt.subplot2grid((2,3),(0,2))
plt.scatter(data.Survived, data.Age) #为散点图传入数据
plt.ylabel(u"年龄") # 设定纵坐标名称
plt.grid(b=True, which='major', axis='y')
plt.title(u"按年龄看获救分布 (1为获救)")

plt.subplot2grid((2,3),(1,0), colspan=2)
data.Age[data.Pclass == 1].plot(kind='kde') # 密度图
data.Age[data.Pclass == 2].plot(kind='kde')
data.Age[data.Pclass == 3].plot(kind='kde')

plt.xlabel(u"年龄")# plots an axis lable
plt.ylabel(u"密度")
plt.title(u"各等级的乘客年龄分布")
plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best') # sets our legend for our graph.
plt.subplot2grid((2,3),(1,2))

data.Embarked.value_counts().plot(kind='bar')
plt.title(u"各登船口岸上船人数")
plt.ylabel(u"人数")
plt.show()

Survived_0 = data.Pclass[data.Survived == 0].value_counts() # 未获救
Survived_1 = data.Pclass[data.Survived == 1].value_counts() # 获救
df = pd.DataFrame({u'获救':Survived_1,u'未获救':Survived_0})
df.plot(kind = 'bar', stacked = True)
plt.title(u'各乘客等级的获救情况')
plt.xlabel(u'乘客等级')
plt.ylabel(u'人数')
plt.show()

Survived_m = data.Survived[data.Sex == 'male'].value_counts()
Survived_f = data.Survived[data.Sex == 'female'].value_counts()
df = pd.DataFrame({u'男性':Survived_m,u'女性':Survived_f})
df.plot(kind = 'bar', stacked = True)
plt.title(u'按性别看获救情况')
plt.xlabel(u'性别')
plt.ylabel(u'人数')
plt.show()

fig = plt.figure()
fig.set(alpha = 0.2)

Survived_cabin = data.Survived[pd.notnull(data.Cabin)].value_counts()
Survived_nocabin = data.Survived[pd.isnull(data.Cabin)].value_counts()
df = pd.DataFrame({u'有':Survived_cabin, u'无':Survived_nocabin}).transpose()
df.plot(kind = 'bar', stacked = True)
plt.title(u'按Cabin有无看获救情况')
plt.xlabel(u'Cabin有无')
plt.ylabel(u'人数')
plt.show()

<Figure size 432x288 with 0 Axes>

g = data.groupby(['SibSp','Survived']) # 数据分组
df = pd.DataFrame(g.count()['PassengerId'])
print (df)
g = data.groupby(['Parch','Survived'])
df = pd.DataFrame(g.count()['PassengerId'])
print (df)

                PassengerId
SibSp Survived             
0     0                 398
      1                 210
1     0                  97
      1                 112
2     0                  15
      1                  13
3     0                  12
      1                   4
4     0                  15
      1                   3
5     0                   5
8     0                   7
                PassengerId
Parch Survived             
0     0                 445
      1                 233
1     0                  53
      1                  65
2     0                  40
      1                  40
3     0                   2
      1                   3
4     0                   4
5     0                   4
      1                   1
6     0                   1