Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.
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上面说的意思：panel在新版本中被xarray的包取代了。可以使用xarray包下的 to_xarray() 方法。
使用方法，例子：
----------官方文档--------------------
pandas.Panel.to_xarray
Panel.to_xarray()[source]
Return an xarray object from the pandas object.
Returns:
xarray.DataArray or xarray.Dataset
Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.
See also
DataFrame.to_hdf
Write DataFrame to an HDF5 file.
DataFrame.to_parquet
Write a DataFrame to the binary parquet format.
Notes
See the xarray docs
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 4)
Coordinates:
* index (index) int64 0 1 2 3
Data variables:
name (index) object 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 389.0 24.0 80.5 nan
num_legs (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. , 24. , 80.5, nan])
Coordinates:
* index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot', 'falcon',
... 'parrot'],
... 'speed': [350, 18, 361, 15]}).set_index(['date',
... 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions: (animal: 2, date: 2)
Coordinates:
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
speed (date, animal) int64 350 18 361 15
import xarray as xr
a = pd.DataFrame({'a':[1,2,3],'b':[4,5,6],'c':[7,8,9]})
b = pd.DataFrame({'a':[11,12,13],'b':[14,15,16],'c':[17,18,19]})
ds = xr.Dataset({1:a, 2:b})
def f(thing):
#print(thing)
return thing.mean()
>>> q = ds.apply(f)
>>> q
<xarray.Dataset>
Dimensions: ()
Data variables:
1 float64 5.0
2 float64 15.0
>>> q[1]
<xarray.DataArray 1 ()>
array(5.)
>>> q[1].values
array(5.)