MySQL数据优化-多层索引

这篇文章主要介绍了MySQL数据优化多层索引，文章围绕MySQL数据优化多层索引的相关资料展开详细的内容，具有一定的参考价值，需要的小伙伴可以参考一下

一、多层索引

1.创建

环境：Jupyter

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['上半年','上半年','下半年','下半年'], ['一季度','二季度','三季度','四季度']], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','白菜','牛肉','猪肉']]) display(a)

2.设置索引的名称

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['上半年','上半年','下半年','下半年'], ['一季度','二季度','三季度','四季度']], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','白菜','牛肉','猪肉']]) a.index.names=['年度','季度'] a.columns.names=['大类','小类'] display(a)

3.from_arrays( )-from_tuples()

 import numpy as np import pandas as pd index=pd.MultiIndex.from_arrays([['上半年','上半年','下半年','下半年'],['一季度','二季度','三季度','四季度']]) columns=pd.MultiIndex.from_tuples([('蔬菜','胡萝卜'),('蔬菜','白菜'),('肉类','牛肉'),('肉类','猪肉')]) a=pd.DataFrame(np.random.random(size=(4,4)),index=index,columns=columns) display(a)

4.笛卡儿积方式

from_product() 局限性较大

 import pandas as pd index = pd.MultiIndex.from_product([['上半年','下半年'],['蔬菜','肉类']]) a=pd.DataFrame(np.random.random(size=(4,4)),index=index) display(a)

二、多层索引操作

1.Series

 import pandas as pd a=pd.Series([1,2,3,4],index=[['a','a','b','b'],['c','d','e','f']]) print(a) print('---------------------') print(a.loc['a']) print('---------------------') print(a.loc['a','c'])

 import pandas as pd a=pd.Series([1,2,3,4],index=[['a','a','b','b'],['c','d','e','f']]) print(a) print('---------------------') print(a.iloc[0]) print('---------------------') print(a.loc['a':'b']) print('---------------------') print(a.iloc[0:2])

2.DataFrame

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['上半年','上半年','下半年','下半年'], ['一季度','二季度','三季度','四季度']], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','白菜','牛肉','猪肉']]) print(a) print('--------------------') print(a.loc['上半年','二季度']) print('--------------------') print(a.iloc[0])

3.交换索引

swaplevel( )

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['2021','2021','2022','2022'], ['一季度','二季度','三季度','四季度']], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','白菜','牛肉','猪肉']]) a.index.names=['年度','季度'] print(a) print('--------------------') print(a.swaplevel('年度','季度'))

4.索引排序

sort_index( )

level：指定根据哪一层进行排序，默认为最层
inplace：是否修改原数据。默认为False

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['2021','2021','2022','2022'], [1,3,2,4]], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','白菜','牛肉','猪肉']]) a.index.names=['年度','季度'] print(a) print('--------------------') print(a.sort_index()) print('--------------------') print(a.sort_index(level=1))

5.索引堆叠

stack( )

将指定层级的列转换成行

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['2021','2021','2022','2022'], [1,3,2,4]], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','胡萝卜','牛肉','牛肉']]) print(a) print('--------------------') print(a.stack(0)) print('--------------------') print(a.stack(-1))

6.取消堆叠

unstack( )

将指定层级的行转换成列

fill_value：指定填充值。

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['2021','2021','2022','2022'], [1,3,2,4]], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','胡萝卜','牛肉','牛肉']]) print(a) print('--------------------') a=a.stack(0) print(a) print('--------------------') print(a.unstack(-1))

 import numpy as np import pandas as pd a=pd.DataFrame(np.random.random(size=(4,4)),index=[['2021','2021','2022','2022'], [1,3,2,4]], columns=[['蔬菜','蔬菜','肉类','肉类'],['胡萝卜','胡萝卜','牛肉','牛肉']]) print(a) print('--------------------') a=a.stack(0) print(a) print('--------------------') print(a.unstack(0,fill_value='0'))