Pandas_规整数据_转换数据_stack() unstack()1

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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

[code]>>> 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

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