Pandas_规整数据_转换数据_stack() unstack()1

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本文详细解析了Pandas库中df.stack()和df.unstack()函数的使用方法，包括如何通过level参数指定转换层级，dropna参数控制缺失值处理，以及在多级索引和命名索引上的应用实例。

转换数据

df.stack() 列索引→行索引 df.unstack() 行索引→列索引

参数

类型

说明

level

int

string

list

-1 默认值

默认将最内层的列索引转换为最内层的行索引

dropna

bool

True 默认值

数据转换后，如果一行数据均为缺省值Nan，则删除掉

>>> df1
     weight height
cat       0      1
dog       2      3

#Stacking a dataframe with a single level column axis returns a Series
>>> df1.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
-----------------------------------------------------------------------------------
>>> df2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0

#默认将最内层的列索引转换为最内层行索引
>>> df2.stack()
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

>>> df2.stack(0)
             kg    m
cat height  NaN  2.0
    weight  1.0  NaN
dog height  NaN  4.0
    weight  3.0  NaN

>>> df2.stack(-1) / df2.stack(1)
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

>>> df2.stack([0, -1]) / df2.stack([0, 1])
cat  height  m     2.0
     weight  kg    1.0
dog  height  m     4.0
     weight  kg    3.0

>>> df2.stack([-1, 0]) / df2.stack([1, 0])
cat  kg  weight    1.0
     m   height    2.0
dog  kg  weight    3.0
     m   height    4.0
-----------------------------------------------------------------------------------
>>> df3
    weight height
        kg      m
cat    NaN    1.0
dog    2.0    3.0

#stack之后产生缺省数据，用NaN填充
>>> df3.stack(dropna=False)
        height  weight
cat kg     NaN     NaN
    m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN

#stack之后，如果一行全是NaN则删除数据行（默认操作）
>>> df3.stack() / df3.stack(dropna=True)
        height  weight
cat m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
-----------------------------------------------------------------------------------
#Set df2 columns & index 's Name
>>> df2.columns.names=['Lable','Data']
>>> df2
Lable	weight	height
Data	kg	     m
cat    1.0      2.0
dog    3.0      4.0

>>> df2.stack('Lable')
	Data	kg	m
	Lable		
cat	height	NaN	2.0
	weight	1.0	NaN
dog	height	NaN	4.0
	weight	3.0	NaN

>>> df2.stack('Data')

	Lable	height	weight
	Data		
cat	kg	NaN	1.0
	m	2.0	NaN
dog	kg	NaN	3.0
	m	4.0	NaN

>>> df2.stack(['Lable','Data'])
     Lable   Data
cat  height  m       2.0
     weight  kg      1.0
dog  height  m       4.0
     weight  kg      3.0

>>> df2.stack(['Data','Lable'])
     Data  Lable 
cat  kg    weight    1.0
     m     height    2.0
dog  kg    weight    3.0
     m     height    4.0