Resampling a Pandas Panel with Nominal Data

In this article, we’ll delve into the world of Pandas panels and explore how to resample a panel construct. Specifically, we’ll examine the challenges of resampling the minor axis of a panel when dealing with nominal data.

Introduction to Pandas Panels

Pandas panels are an extension of the standard Panel class in Pandas, allowing for more complex data structures. Unlike DataFrames, which have two axes (rows and columns), panels have three axes: items, major_axis, and minor_axis. This enables panels to represent multidimensional data with varying levels of granularity.

In this article, we’ll use a simple example to illustrate the creation of a Pandas panel with nominal data. We’ll then explore how to resample the minor axis using the resample function.

Creating a Panel Construct

Let’s begin by creating a sample panel construct.

import pandas as pd
import numpy as np

time_rng = pd.date_range('1/1/2000', '31/1/2000', freq='D')
PanelData = pd.Panel(np.random.randn(3, 31, 6),
                     items=['Fish', 'Meat', 'Vegetables'],
                     major_axis=time_rng,
                     minor_axis=['a', 'b', 'c', 'd', 'e', 'f'])

In this example, we create a panel with three items (Fish, Meat, and Vegetables), 31 time points (from January 1st, 2000 to January 31st, 2000), and six minor axis values. The resulting PanelData object has the following structure:

PanelData
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 31 (major_axis) x 6 (minor_axis)
Items axis: Fish to Vegetables
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-31 00:00:00
Minor_axis axis: a to f

Resampling the Minor Axis

Now, let’s examine how to resample the minor axis using the resample function.

PanelData.resample('W', how='sum', axis=1)
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 6 (major_axis) x 6 (minor_axis)
Items axis: Fish to Vegetables
Major_axis axis: 2000-01-02 00:00:00 to 2000-02-06 00:00:00
Minor_axis axis: a to f

As expected, resampling the minor axis by week ('W') results in a new panel with six values, each representing the sum of the original values for that week.

However, we want to resample the minor axis using a custom list of zones. How can we achieve this?

Grouping and Resampling

To group the minor axis by our custom zones and then resample, we’ll use the groupby function.

zones = ['Zone 1', 'Zone 1', 'Zone 2', 'Zone 3', 'Zone 1', 'Zone 2']

PanelData.groupby(zones, axis=2).sum()
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 31 (major_axis) x 3 (minor_axis)
Items axis: Fish to Vegetables
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-31 00:00:00
Minor_axis axis: Zone 1 to Zone 3

By grouping the minor axis by our custom zones, we can then resample using the sum aggregation function.

Why Grouping Works

So, why does grouping work when we don’t explicitly specify the axis? It’s because Pandas panels are designed to be highly flexible and extensible. When you group a panel, Pandas looks for the first dimension that has labels (i.e., axes with names). In our case, the minor_axis is labeled with values (‘a’ to ‘f’), which makes it a natural fit for grouping.

When we group the minor axis by our custom zones, Pandas creates new groups based on these labels. The resulting groups are then assigned to the corresponding minor axis value, allowing us to resample using the sum aggregation function.

Conclusion

In this article, we explored how to resample a Pandas panel with nominal data. We learned that grouping and resampling can be achieved using the groupby function. By understanding the nuances of Pandas panels and how they work, you’ll be better equipped to tackle more complex data analysis tasks.

Additional Tips and Variations

• When working with large datasets, it’s essential to optimize your code for performance. In this case, using the groupby function can lead to significant performance gains.
• For more advanced resampling techniques, explore Pandas’ built-in resampling functions (e.g., resample, rolling, ewm) or consider using specialized libraries like NumPy’s indexing capabilities.
• If you’re working with time series data, be mindful of the frequency and time stamps when grouping and resampling.

Next Steps

Now that you’ve mastered resampling a Pandas panel, it’s time to move on to more advanced topics. Here are some suggestions:

• Explore Pandas’ built-in aggregation functions (e.g., mean, median) and learn how to apply them to different types of data.
• Delve into the world of Pandas’ indexing capabilities and learn how to efficiently manipulate your data.
• Consider using specialized libraries like NumPy, SciPy, or PyAlgoTrade for advanced numerical computations.

By following these tips and exploring additional resources, you’ll become proficient in working with Pandas panels and unlocking their full potential.

Last modified on 2023-12-30