MySQL数据优化中的多层索引是怎么样的

这期内容当中小编将会给大家带来有关MySQL数据优化中的多层索引是怎么样的，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。

一、多层索引

1.创建

环境：Jupyter

import numpy as npimport pandas as pda=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 npimport pandas as pda=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 npimport pandas as pdindex=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 pdindex = 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 pda=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 pda=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 npimport pandas as pda=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 npimport pandas as pda=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 npimport pandas as pda=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 npimport pandas as pda=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 npimport pandas as pda=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 npimport pandas as pda=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'))

上述就是小编为大家分享的MySQL数据优化中的多层索引是怎么样的了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注行业资讯频道。