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.

pandas.Panel()

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 −

Create Panel

A Panel can be created using multiple ways like −

• From ndarrays
• From dict of DataFrames

From 3D ndarray

 # creating an empty panel import pandas as pd import numpy as np data = np.random.rand(2,4,5) p = pd.Panel(data) print(p) 

Its output is as follows −

 <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 

Note: Observe the dimensions of the empty panel and the above panel, all the objects are different.

From dict of DataFrame Objects

 #creating an empty panel 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) 

Its output is as follows −

 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 

Create an Empty Panel

An empty panel can be created using the Panel constructor as follows −

 #creating an empty panel import pandas as pd p = pd.Panel() print(p) 

Its output is as follows −

 <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 

Selecting the Data from Panel

Select the data from the panel using −

• Items
• Major_axis
• Minor_axis

Using Items

 # creating an empty panel 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']) 

Its output is as follows −

 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 

We have two items, and we retrieved item1. The result is a DataFrame with 4 rows and 3 columns, which are the Major_axis and Minor_axis dimensions.

Using major_axis

Data can be accessed using the method panel.major_axis(index).

 # creating an empty panel 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)) 

Its output is as follows −

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

Using minor_axis

Data can be accessed using the method panel.minor_axis(index).

 # creating an empty panel 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)) 

Its output is as follows −

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

Note: Observe the changes in the dimensions.