基于Python的Titannic数据可视化

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()

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()

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