Pandas is a popular library in Python for data analysis and manipulation tasks. One of its earlier features, the "Panel," provided a way to handle 3D data structures.
However, with the advancement of data analysis techniques and the enhanced capabilities of Pandas itself, the Panel has been deprecated in the latest version of Pandas and replaced by flexible alternatives like MultiIndex DataFrames.
In this article, we'll explore the concept of the Panel in Pandas and how to create it and will cover basic operations with the help of examples.
Let's Get Started.
What is the Panel?
A panel is a 3D data structure that is used to accommodate data that requires three dimensions of indexing.
It is similar to a DataFrame, which consists of two dimensions, major_axis and minor_axis, but the panel has an additional dimension called items. The items dimension is used to store multiple DataFrames within the panel, each of which has its own rows and columns.
Let us look at the parameters required in Panels in Pandas:
data: can be a NumPy array with three dimensions (items, major, minor) or a dictionary of DataFrames
items: is the axis 0 of the Panel, and it relates to the DataFrames in the Panel
major_axis: is the axis 1 of the Panel, and it relates to the rows of the DataFrames in the Panel
minor_axis: is the axis 2 of the Panel, and it relates to the columns of the DataFrames in the Panel
dtype: is the data type to force for the Panel. If None, then the data type will be inferred from the data
copy: is a boolean that determines whether to copy the data from the inputs. If False, then the data will not be copied, and any changes made to the Panel will be reflected in the inputs
Creating a Panel in Pandas
There are several ways to create a Panel in Pandas. Below will look at a few examples, but before that, we need to make sure we have a pandas version <0.25 in order to use Panel
Running the panel program on the latest version of pandas may result in an AttributeError, specifically stating that the module 'pandas' lacks the 'Panel' attribute. Therefore, it is essential to install a pandas version prior to 0.25 before executing any Panel program.
Steps to install pandas version less than 0.25 :
1. Panel() constructor
The Panel() constructor takes a list of DataFrames as its argument. The DataFrames in the list must have the same number of columns and the same index.
For example, the following code will create a Panel with three DataFrames using the Panel():
The above code output shows a Panel with three DataFrames, each of which is indexed by the same index, that are 2018, 2019, and 2020 and the same columns A and B. The items axis of the Panel is labelled df1, df2, and df3
2. concat() function
The concat() function in pandas is used to concatenate multiple pandas objects along a specified axis.
Syntax
Below is the syntax of the concat() function in Pandas:
In the above code, the axis=0 argument specifies that the DataFrames will be concatenated along the rows. So, the rows of the two DataFrames will append together in the same manner they were passed to concat() to form a concatenated_df.
3. Using a dictionary of DataFrames
A Panel can also be created using a dictionary of DataFrames. The keys of the dictionary are the labels for the items axis, and the values of the dictionary are the DataFrames that will be stored in the Panel.
For example, the following code creates a Panel with three DataFrames using a dictionary. Here, we will use the np.random.randn(arg1,arg2) method in which arg1 is the number of rows and arg2 is the number of columns and after this panel will be created:
Python
Python
import pandas as pd import numpy as np
# Creating dictionary with different DataFrame shapes
data = {
'Data1': pd.DataFrame(np.random.randn(3, 5)),
'Data2': pd.DataFrame(np.random.randn(4, 3)),
'Data3': pd.DataFrame(np.random.randn(2, 6))
}
# Creating a Panel
df = pd.Panel(data)
print(df)
You can also try this code with Online Python Compiler
The output shows the original array with the random values generated and shows the dimensions and axes of the 3D array.
5. Create an Empty Panel
To create an empty panel in pandas, you can use the pd.Panel() constructor without passing any arguments. This creates a panel with no data, essentially an empty container ready to be populated with data.
import pandas as pd
empty_panel = pd.Panel()
Empty panels are useful when you want to initialize a panel structure before filling it with data from various sources or when you need to dynamically populate a panel based on certain conditions or calculations.
Operations on Panel
Let us see some basic operations on the panel:
1. Getting value from Panel
To fetch data from a Panel in pandas, we can use various indexing techniques. In the following example, we are going to use get_value() method:
The get_value() method can be used to fetch data from a Panel at any location. The location can be specified using a variety of indexing techniques, including item names, major and minor axis labels, and positional indexing.
The above code uses get_value(). The location (item='Item1', major=0, minor=0) refers to the first row, the first column of the Item1 item in the Panel and shows it in the output.
2. Deleting value from Panel
We can perform the delete operation in the Panel using the del keyword. Below is the code example:
In the above code, we created a Panel object with two items, Item1 and Item2 after that used the del keyword to delete the Item1 item from the Panel and showed the modified panel in the output.
3. Transpose a Panel
We can perform the transpose operation in the Panel using the transpose() method.
The transpose() method returns a new Panel object with the items and major_axis dimensions swapped. The minor_axis dimension remains the same. Below is the code example:
We can notice in the above output that the transpose() method has swapped the items and major_axis dimensions and minor_axis dimension remains the same.
Getting Data from the Panel with Pandas
In a pandas Panel, you can access data along three axes: items, major_axis, and minor_axis. Let's explore each axis with code, output, and explanation.
Accessing Data along the items Axis:
import pandas as pd
# Create a sample panel
data = {'item1': pd.DataFrame({'A': [1, 2], 'B': [3, 4]}),
'item2': pd.DataFrame({'A': [5, 6], 'B': [7, 8]})}
panel = pd.Panel(data)
# Access data along the items axis
print(panel['item1'])
Output:
A B
0 1 3
1 2 4
Explanation: Accessing data using panel['item1'] retrieves the DataFrame associated with the item named 'item1' from the panel.
Accessing Data along the major_axis Axis:
print(panel.major_xs(0))
Output:
item1 item2
A 1 5
B 3 7
Explanation: Using panel.major_xs(0) retrieves a DataFrame representing the data across all items for the major axis index 0.
Accessing Data along the minor_axis Axis:
print(panel.minor_xs('A'))
Output:
item1 item2
0 1 5
1 2 6
Explanation: Using panel.minor_xs('A') retrieves a DataFrame representing the data across all items for the minor axis index 'A'.
These methods allow you to access data from different perspectives within a Panel in pandas.
Frequently Asked Questions
What is a Panel in pandas?
A Panel is a 3D data structure in pandas for holding heterogeneous data. It consists of three axes: items, major_axis, and minor_axis.
What is the panel data structure in pandas?
The panel is a three-dimensional data structure in pandas used to store data with axes representing items, a major axis, and minor axis.
Which dimension is the panel in pandas?
The panel is a three-dimensional data structure in pandas, representing data along three axes: items, major axis, and minor axis.
Conclusion
Congratulations, you did a fantastic job!!. This article has covered the Panel in Pandas and how to create it and has covered basic operations with the help of examples. Finally, some frequently asked questions are discussed.