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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 names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data. They are −
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items − axis 0, each item corresponds to a DataFrame contained inside.
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major_axis − axis 1, it is the index (rows) of each of the DataFrames.
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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 −
| Parameter | Description |
|---|---|
| data | Data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame |
| items | axis=0 |
| major_axis | axis=1 |
| minor_axis | axis=2 |
| dtype | Data type of each column |
| copy | Copy data. Default, false |
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.
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