Python Pandas — Panel



Original Source Here

Panel in Python Pandas | Insideaiml

The panel is a 3D data container. The term Panel data is based on econometrics.

The names of the 3 axes are designed to provide a specific semantic meaning in describing an operation that includes panel data. those are –

  • items − It is axis 0, each item resembles the DataFrame contained within.
  • major_axis − It is axis 1, the index (rows) of each of DataFrame.
  • minor_axis −It is axis 2, the columns of each of the DataFrame.

Panel in Pandas

Following constructor is used to create Panel in pandas .

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

Pandas Panel Parameters −

Parameters of the constructor | insideaiml

Create Panel in Pandas

There are different ways to create Panel in pandas −

  • From 3D ndarrays
  • From the dictionary of DataFrames object

Creating an empty panel

import pandas as pd

import numpy as np

emptyPanel = pd.Panel()

emptyPanel

Output –

Dimensions: 0 (items) x 0 (major_axis) x 0 (minor_axis)

Items axis: none

Major_axis axis: none

Minor_axis axis: none

Create Panel From 3D ndarray

import pandas as pd

import numpy as np

dataItems = np.random.rand(3,5,6)

panelData = pd.Panel(dataItems)

print(panelData)

Output

Dimensions: 3 (items) x 5 (major_axis) x 6 (minor_axis)

Items axis: 0 to 2

Major_axis axis: 0 to 4

Minor_axis axis: 0 to 5

Create Panel From dict of DataFrame Objects

import pandas as pd

import numpy as np

insideaiml_dict = {‘key1’ : pd.DataFrame(np.random.randn(5, 6)),

‘key2’ : pd.DataFrame(np.random.randn(5, 3))}

panelData = pd.Panel(insideaiml_dict)

print(panelData)

Its output is as follows −

Dimensions: 2 (items) x 5 (major_axis) x 6 (minor_axis)

Items axis: key1 to key2

Major_axis axis: 0 to 4

Minor_axis axis: 0 to 5

Data Selection From Panel

Data selection from the panel −

  • Items
  • Major_axis
  • Minor_axis

Using Items

import pandas as pd

import numpy as np

insideaiml_data = {‘key1’ : pd.DataFrame(np.random.randn(5, 4)),

‘key2’ : pd.DataFrame(np.random.randn(4, 3))}

panelData = pd.Panel(insideaiml_data )

print (panelData [‘key1’])

Its output is as follows −

0 1 2 3

0 0.166712 -0.829255 -1.487678 0.437197

1 0.135945 -0.452119 -0.832342 1.460357

2 0.688532 -0.704603 -1.204584 1.011151

3 1.455288 0.830276 0.822264 -0.385821

4 0.299957 -0.663821 -0.704605 -0.303356

Using major_axis

Data accessed using panel.major_axis(index) method

import pandas as pd

import numpy as np

insideaiml_data = {‘key1’ : pd.DataFrame(np.random.randn(5, 4)),

‘key2’ : pd.DataFrame(np.random.randn(5, 3))}

panelData = pd.Panel(insideaiml_data)

print(panelData.major_xs(1))

Its output is as follows −

key1 key2

0 1.417978 – 1.391994

1 0.009445 – 0.375496

2 0.834670-0.677597

3 0.121576- NaN

Using minor_axis

Data accessed using panel.minor_axis(index) method.

import pandas as pd

import numpy as np

insideaiml_data = {‘key1’ : pd.DataFrame(np.random.randn(5, 4)),

‘key2’ : pd.DataFrame(np.random.randn(5, 3))}

panelData = pd.Panel(insideaiml_data)

print(panelData.minor_xs(1))

Its output is as follows −

key1 – key2

0 0.325758 -1.526423

1 0.778292 -0.936562

2 0.202640 -0.168996

3 -0.448355-0.210160

4 0.188512 -0.215780

I hope you enjoyed reading this article and finally, you came to know about Python Pandas — Panel.

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