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，欢迎关注编程网行业资讯频道。
