MySQL数据优化-多层索引

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 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'))

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