Python可视化seaborn练习题
seaborn —— 课后练✋
%matplotlib inline import numpy as np import pandas as pd from scipy import stats, integrate import matplotlib as mpl from matplotlib import pyplot as plt import seaborn as sns1234567
练习1：鸢尾花花型尺寸分析
鸢尾花萼片（sepal）和花瓣（petal）的大小关系（散点图）不同种类（species）鸢尾花萼片和花瓣的分布情况（箱图或者提琴图）鸢尾花萼片和花瓣大小的联合分布情况（六角箱图或者核密度估计）data = sns.load_dataset("iris") data.head() # 萼片长度，萼片宽度，花瓣长度，花瓣宽度，种类123 sepal_lengthsepal_widthpetal_lengthpetal_widthspecies05.13.51.40.2setosa14.93.01.40.2setosa24.73.21.30.2setosa34.63.11.50.2setosa45.03.61.40.2setosa
data['sepal_size']=data['sepal_length']*data['sepal_width'] data['petal_size']=data['petal_length']*data['petal_width']12 萼片与花瓣
sns.lmplot(x='sepal_size',y='petal_size',data=data)1
<seaborn.axisgrid.FacetGrid at 0x7fea04b37550> 1
g = sns.PairGrid(data, x_vars=["species"], y_vars=["sepal_size", "petal_size"], aspect=2, size=4) g.map(sns.violinplot, palette="pastel");12345
# your code sns.jointplot(x='sepal_length',y='petal_length',data=data,kind='kde')12
/opt/ds/local/lib/python2.7/site-packages/numpy/ma/core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning. return self.reduce(a) /opt/ds/local/lib/python2.7/site-packages/numpy/ma/core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning. return self.reduce(a) <seaborn.axisgrid.JointGrid at 0x7fe9fc6fd250> 12345678910
练习2：餐厅小费情况分析
小费和总消费之间的关系（散点图+回归分析）男性顾客和女性顾客，谁更慷慨（箱图或者提琴图）抽烟与否是否会对小费金额产生影响（箱图或者提琴图）工作日和周末，什么时候顾客给的小费更慷慨（箱图或者提琴图）午饭和晚饭，哪一顿顾客更愿意给小费（箱图或者提琴图）就餐人数是否会对慷慨度产生影响（箱图或者提琴图）性别+抽烟的组合因素对慷慨度的影响（统计柱状图）data = sns.load_dataset("tips") data.head() # 总消费，小费，性别，吸烟与否，就餐星期，就餐时间，就餐人数123 total_billtipsexsmokerdaytimesize016.991.01FemaleNoSunDinner2110.341.66MaleNoSunDinner3221.013.50MaleNoSunDinner3323.683.31MaleNoSunDinner2424.593.61FemaleNoSunDinner4 小费与总消费
sns.lmplot(x='total_bill',y='tip',data=data)1
<seaborn.axisgrid.FacetGrid at 0x7fe9ff3afed0> 1
sns.boxplot(y='tip',x='sex',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9ff1651d0> 1
sns.boxplot(y='tip',x='smoker',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fd4dac10> 1
day=data['day'].unique() day12
[Sun, Sat, Thur, Fri] Categories (4, object): [Sun, Sat, Thur, Fri]
data_week=pd.DataFrame(('weekend' if x in ['Sun','Sat'] else 'weekday' for x in data.day),index=data.index,columns=['week']) data_expand=pd.merge(data,data_week,left_index=True,right_index=True) data_expand.head()123 total_billtipsexsmokerdaytimesizeweek016.991.01FemaleNoSunDinner2weekend110.341.66MaleNoSunDinner3weekend221.013.50MaleNoSunDinner3weekend323.683.31MaleNoSunDinner2weekend424.593.61FemaleNoSunDinner4weekend
sns.boxplot(y='tip',x='week',data=data_expand)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9ff533c10> 1
sns.violinplot(x='time',y='tip',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9ffcd35d0> 1
sns.violinplot(x='size',y='tip',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9ffef5650> 1
# your code sns.barplot(x='sex',y='tip',hue='smoker',data=data)12
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9ff4e3d10> 1
练习3：泰坦尼克号海难幸存状况分析
不同仓位等级中幸存和遇难乘客的分布（箱图或者提琴图）幸存和遇难乘客的票价分布（箱图或者提琴图）幸存和遇难乘客的年龄分布（箱图或者提琴图）不同上船港口的乘客仓位等级分布（箱图或者提琴图）幸存和遇难乘客堂兄弟姐妹的数量分布（箱图或者提琴图）幸存和遇难乘客父母子女的数量分布（箱图或者提琴图）单独乘船与否和幸存之间的关系（统计柱状图）乘客年龄和船票价格之间的关系（线性回归模型）乘客性别和仓位等级之间的关系（统计柱状图）乘客年龄和仓位等级之间的关系（带抖动的散点图）data = sns.load_dataset("titanic") data.head() # 幸存与否，仓位等级，性别，年龄，堂兄弟姐妹数，父母子女数，票价，上船港口缩写，仓位等级，人员分类，是否成年男性，所在甲板，上船港口，是否幸存，是否单独乘船123 survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue 幸存or遇难：不同仓位影响？
sns.violinplot(x='class',y='survived',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fcdcea50> 1
sns.violinplot(x='alive',y='fare',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fcd213d0> 1
sns.violinplot(x='alive',y='age',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fcbd83d0> 1
sns.violinplot(x='embark_town',y='pclass',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fcaa7690> 1
sns.violinplot(x='alive',y='sibsp',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fc9f41d0> 1
sns.violinplot(x='alive',y='parch',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fc944d10> 1
# your code sns.barplot(x='alone',y='survived',data=data)12
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fc632ad0> 1
sns.lmplot(x='age',y='fare',data=data)1
<seaborn.axisgrid.FacetGrid at 0x7fe9fc5ba110> 1
sns.barplot(x='sex',y='pclass',data=data)1
<matplotlib.axes._subplots.AxesSubplot at 0x7fe9fc508c10> 1
sns.lmplot(x='pclass',y='age',data=data,x_jitter=0.2)1
<seaborn.axisgrid.FacetGrid at 0x7fe9fc499150> 1
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