MySQL数据优化-多层索引 MySQL数据优化-多层索引

一、多层索引

1.创建
2.设置索引的名称
3.from_arrays()-from_tuples()
4.笛卡儿积方式

二、多层索引操作

1.Series
2.DataFrame
3.交换索引
4.索引排序
5.索引堆叠
6.取消堆叠

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)

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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)

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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)

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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)

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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'])

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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])

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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])

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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'))

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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))

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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))

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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))

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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='https://www.it610.com/article/0'))