🗂️ Pandas Panel (Legacy – For Reference Only)

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🧲 Introduction – What Was a Pandas Panel?

A Pandas Panel was a three-dimensional data structure designed for working with data that has items, major axis (rows), and minor axis (columns)—similar to a collection of DataFrames. It was part of older versions of Pandas, but is now deprecated since version 0.25.0 and fully removed in later versions.

🎯 In this guide, you’ll learn:

• What a Panel is and how it was structured
• How to create and access data in Panels (for legacy understanding)
• Why Panels were deprecated
• Modern alternatives using MultiIndex or xarray

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🏗️ 1. What is a Panel in Pandas?

A Panel was a container for 3D data, like a dictionary of DataFrames.

📐 Structure:

• Items → Axis 0 → DataFrames (like pages)
• Major axis → Axis 1 → Rows
• Minor axis → Axis 2 → Columns

✅ Think of it as: Panel[Item][Row][Column]

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🧪 2. Creating a Panel (Legacy Syntax)

import pandas as pd
import numpy as np

data = {
    'Item1': pd.DataFrame(np.random.randn(3, 4)),
    'Item2': pd.DataFrame(np.random.randn(3, 4))
}

panel = pd.Panel(data)
print(panel)

👉 Output (in older Pandas versions):

<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major axis: 0 to 2
Minor axis: 0 to 3

⚠️ This will raise an error in Pandas ≥ 1.0.

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🔍 3. Accessing Data from Panel

panel['Item1']            # Returns DataFrame
panel['Item1'].iloc[0]    # Returns a row from Item1
panel['Item2'].loc[1, 2]  # Access specific cell from Item2

✅ Access via item labels, then use standard DataFrame methods.

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🔁 4. Why Was Panel Deprecated?

Limitation	Description
Performance	Slower and less memory-efficient than other alternatives
Complexity	Harder to understand and maintain in production
Low usage	Rarely used by most users
Redundancy	Could be emulated with MultiIndex DataFrames or xarray

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🔄 5. Modern Alternatives to Panel

✅ Use MultiIndex DataFrames

arrays = [
    ['Item1', 'Item1', 'Item2', 'Item2'],
    [0, 1, 0, 1]
]

index = pd.MultiIndex.from_tuples(arrays, names=['Item', 'Index'])
df = pd.DataFrame(np.random.randn(4, 3), index=index, columns=['A', 'B', 'C'])
print(df)

✅ Use xarray (Recommended)

import xarray as xr

data = xr.DataArray(np.random.randn(2, 3, 4),
                    coords=[['Item1', 'Item2'], range(3), ['A', 'B', 'C', 'D']],
                    dims=['Item', 'Row', 'Col'])
print(data)

✅ xarray is ideal for N-dimensional labeled arrays.

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📌 Summary – Recap & Next Steps

Pandas Panel was once the go-to tool for 3D labeled data, but due to limited performance and flexibility, it was deprecated in favor of MultiIndex DataFrames and xarray.

🔍 Key Takeaways:

• Panels were 3D containers: item × rows × columns
• Deprecated in Pandas 0.25.0 and removed afterward
• Use MultiIndex or xarray for multi-dimensional data handling
• xarray is designed specifically for labeled n-dimensional arrays

⚙️ Real-world relevance: Use xarray for time series, geospatial, and scientific data that require multi-dimensional indexing.

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❓ FAQs – Pandas Panel

❓ Can I still use Panel in the latest version of Pandas?
❌ No. Panels are fully removed since Pandas 1.0. Use xarray or MultiIndex DataFrames instead.

❓ Why not just use nested dictionaries or lists of DataFrames?
✅ While possible, MultiIndex or xarray offers better performance and labeling.

❓ Is there a direct migration path from Panel to DataFrame?
✅ Yes. Use panel.to_frame() (in older versions) to convert Panels to stacked DataFrames.

❓ What is the best alternative to Panel today?
✅ For labeled multi-dimensional data, xarray is the preferred replacement.

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