Python Pandas – Panel

A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s.

The Panel class is deprecated and has been removed in recent versions of pandas. The recommended way to represent 3-D data is with a MultiIndex on a DataFrame via the to_frame() method or with the xarray package. Pandas provides a to_xarray() method to automate this conversion.

The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data. They are:

• items: axis 0, each item corresponds to a DataFrame contained inside.
• major_axis: axis 1, it is the index (rows) of each of the DataFrames.
• minor_axis: axis 2, it is the columns of each of the DataFrames.

A Panel can be created using the following constructor −

pandas.Panel(data, items, major_axis, minor_axis, dtype, copy)

The parameters of the constructor are as follows −

A Panel can be created using multiple ways like −

import pandas as pd
import numpy as np

data = np.random.rand(2, 4, 5)
p = pd.Panel(data)
print(p)

Output:

<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4

import pandas as pd
import numpy as np

data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), 
   'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
print(p)

Output:

Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 2

import pandas as pd
p = pd.Panel()
print(p)

Output:

<class 'pandas.core.panel.Panel'>
Dimensions: 0 (items) x 0 (major_axis) x 0 (minor_axis)
Items axis: None
Major_axis axis: None
Minor_axis axis: None

Select the data from the panel using −

• Items
• Major_axis
• Minor_axis

import pandas as pd
import numpy as np
data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), 
   'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
print(p['Item1'])

Output:

0          1          2
0    0.488224  -0.128637   0.930817
1    0.417497   0.896681   0.576657
2   -2.775266   0.571668   0.290082
3   -0.400538  -0.144234   1.110535

import pandas as pd
import numpy as np
data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), 
   'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
print(p.major_xs(1))

Output:

Item1       Item2
0   0.417497    0.748412
1   0.896681   -0.557322
2   0.576657       NaN

import pandas as pd
import numpy as np
data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), 
   'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
print(p.minor_xs(1))

Output:

Item1       Item2
0   -0.128637   -1.047032
1    0.896681   -0.557322
2    0.571668    0.431953
3   -0.144234    1.302466