目录
"""1.数据获取介绍:
Seaborn库函数中有很多的数据集,只要我们安装之后就可以直接使用.
这样就大大方便了我们进行数据分析.
只要执行sns.get_dataset_names()就可以知道我们可以使用那些数据集了
Ps:seaborn我们一般简写为sns
"""import seaborn as sns
sns.get_dataset_names()
"""数据描述理解:
接下来我们可以查看具体的数据表中有什么类型的数据
直接输出表头就行data.head(50),这样就输出了前50个数据
当然你也可用到pandas中的一些操作也可以！
"""import seaborn as sns
# 导出iris数据集
data = sns.load_dataset('iris')
data.head(50)
#1.sns.scatterplot绘制散点图
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(style="white")
data1=sns.load_dataset('tips')
#x='total_bill'就是列表中的名称,不允许修改
fig=sns.scatterplot(x='total_bill',y='tip',data=data1,color='r',marker='*')
plt.title("Scatterplot Figure",color='black')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Scatterplot.png', dpi=500, bbox_inches='tight')
plt.show()
#2.sns.barplot绘制条形图
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(style="white")
data1=sns.load_dataset('tips')
#x='total_bill'就是列表中的名称,不允许修改
fig=sns.barplot(x='day',y='total_bill',data=data1)
plt.title("Barplot Figure",color='black')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Barplot.png', dpi=500, bbox_inches='tight')
plt.show()
#3.sns.lineplot绘制线性图
#绘制折线图和对应的置信区间
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(style='white')
fmri = sns.load_dataset("fmri")
ax = sns.lineplot(x="timepoint", y="signal", data=fmri,color='r')
plt.title("Lineplot Figure",color='black')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Lineplot.png', dpi=500, bbox_inches='tight')
plt.show() 
#4.sns.heatmap绘制热力图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
test_data=np.random.rand(10,12)
fig=sns.heatmap(test_data, cmap="rainbow")
plt.title("HeayMap_Rain Figure",color='black')
plt.xlabel("x range from 0 to 11")
plt.ylabel("y range from 0 to 9")
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/HeayMap_Rain Figure.png', dpi=500, bbox_inches='tight')
plt.show()
#5.sns.distplot绘制直方图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
x=np.random.randn(10000)
fig=sns.distplot(x, color='green')
plt.title("Distplot Figure",color='black')
plt.xlabel("x range from -inf to inf")
plt.ylabel("y range from 0 to 0.40")
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Distplot Figure.png', dpi=500, bbox_inches='tight')
plt.show()
#6.sns.pairplot绘制散图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
x=sns.load_dataset("iris")
fig=sns.pairplot(x)
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Pairplot Figure.png', dpi=500, bbox_inches='tight')
plt.show()
#8.sns.catplot绘制直方图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
x1=sns.load_dataset("exercise")
fig=sns.catplot(x='time',y='pulse',hue='kind',data=x1)
plt.title("Catplot Figure",color='b')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Catplot Figure.png', dpi=500, bbox_inches='tight')
plt.show()
#9.sns.countplot绘制直方图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
x1=sns.load_dataset("titanic")
fig=sns.countplot(x='class',data=x1)
plt.title("Countplot Figure",color='b')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Countplot Figure.png', dpi=500, bbox_inches='tight')
plt.show()
#10.sns.lmplot绘回归图
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(0)#设置种子
sns.set(style='white')
x1=sns.load_dataset("tips")
fig=sns.lmplot(x='total_bill',y='tip',data=x1,scatter_kws={'color':'green'},line_kws={'color': 'red'})
plt.title("Lmplot Figure",color='b')
plt.savefig('C:/Users/Zeng Zhong Yan/Desktop/Lmplot Figure.png', dpi=500, bbox_inches='tight')
plt.show()