Pandas Create Panel: Your Ticket to Mastering Complex Data Analysis

A “panel” is a multi-dimensional data structure that is similar to a DataFrame, but with an additional “dimension” (also known as a “level” or “axis”) in addition to the row and column dimensions.

In Pandas, the panel data structure was deprecated in version 0.20.0 and removed in version 0.25.0.

If you need to work with multi-dimensional data in Pandas, you can use the “MultiIndex” functionality to create a hierarchical index for your DataFrame. This allows you to create a DataFrame with multiple levels of row and column labels, which can be used to represent multi-dimensional data.

Here’s an example of how you might create a MultiIndex DataFrame using Pandas:

import pandas as pd

rows = [('A', 'B', 1), ('A', 'B', 2), ('A', 'C', 3), ('B', 'C', 4)]
columns = [('X', 'Y'), ('X', 'Z')]
df = pd.DataFrame(rows, columns=pd.MultiIndex.from_tuples(columns))
print(df)

This would create a DataFrame with two levels for the rows and columns, like this:

X Y Z
A B 1 NaN NaN
B 2 NaN NaN
C 3 NaN NaN
B C 4 NaN NaN

You can then use the levels of the MultiIndex to access and manipulate the data in the DataFrame. For example, you could use the “loc” attribute to select a specific level of the index:

# Select all rows with level 0 value 'A' 
df.loc['A']

This would return a sub-DataFrame with only the rows that have a value of ‘A’ in the first level of the index.

Overall, the MultiIndex functionality in Pandas provides a way to work with multi-dimensional data in a DataFrame, although it is not as powerful or flexible as the panel data structure that was available in earlier versions of the library.