Pandas Rolling Operation on Categorical Column

In this article, we’ll explore the process of applying rolling operations on categorical columns in pandas DataFrames. We’ll dive into the specifics of how the pandas library handles categorical data and how you can work around common issues when using rolling methods.

Introduction to Pandas Rolling Operations

Pandas rolling operations are a powerful tool for analyzing time series data or any other type of data that has an index with equally spaced values. The rolling operation allows you to apply a function over a window of fixed size, which is useful for calculating moving averages, summing values within a window, and more.

The rolling method in pandas supports various parameters such as the window size (the number of observations included in the calculation), the direction of the window (forward or backward), and whether the window should be closed or not.

For categorical columns, however, you might encounter issues with how these operations are performed. This is because categorical values can’t be directly converted to numeric values, which pandas relies on for calculations.

Understanding Pandas Categorical Data

Before we dive into rolling operations on categorical data, let’s briefly discuss what pandas categorical data is and why it behaves differently from other types of data.

Pandas categorical data is a way to represent string data as if they were numeric. This allows you to take advantage of many useful operations in pandas without having to manually convert the data to numbers every time.

Here are some key characteristics of pandas categorical data:

• Ordered: Pandas categorical data is ordered, meaning it maintains an internal ordering based on the values themselves.

• **Indexed**: Each value is uniquely identified by its position within the sequence (i.e., `['a', 'b', 'c']` for a sequence with three unique elements).
  

• Deduplicated: By definition, this data does not contain duplicate values.

Categorical columns can be created using the .cat() function or when converting strings to categorical numbers during data import.

The Problem of Rolling Operations on Categorical Data

When attempting to perform rolling operations on a categorical column in pandas, you will encounter errors such as pandas.core.base.DataError: No numeric types to aggregate or TypeError: cannot handle this type -> category.

This is because the .rolling() function requires that all elements within its window be of a numeric type. Since categorical data isn’t directly convertible to numbers, we can’t simply apply rolling operations without handling it first.

A Possible Solution: Encoding Categorical Data

One way around these issues is by encoding your categorical values into numbers. Here are the steps to achieve this:

1. Get the codes of your categories: Use categories attribute on your categorical column (df['movement_state'].cat.categories) to get a list of all possible unique values in the category.

2. Assign numeric codes: Create an array where each value corresponds to its position within your ordered category list (e.g., 'moving' -> 0, 'standing' -> 1, etc.).

   import numpy as np
   
   # Get the categories of the movement_state column
   cat_codes = df['movement_state'].cat.categories
   
   # Create an array with numeric codes for each category
   cat_dict = {category: index for index, category in enumerate(cat_codes)}
   
   # Apply this mapping to your categorical values using `map()`
   df['numeric_movement_state'] = df['movement_state'].map(cat_dict)
   

3. Apply rolling operation: Now you can use .rolling() on the numeric-encoded data.

   for cat_name in df['numeric_movement_state'].cat.categories:
       transformed_df[f'{cat_name} Count'] = grouped_df['numeric_movement_state'].rolling(rolling_window_size, closed='both').apply(lambda s, cat=cat_name: s.value_counts()[cat])
       transformed_df[f'{cat_name} Ratio'] = grouped_df['numeric_movement_state'].rolling(rolling_window_size, closed='both').apply(lambda s, cat=cat_name: s.value_counts(normalize=True)[cat])
   

A More Elegant Solution: Use pd.Grouper with Categorical Data

Another way to approach this problem involves using pandas’ built-in Grouper class along with the .cat() method for categorical data.

Here’s how you can use it:

# Create a grouper that will include all values in 'movement_state'
grouper = pd.Grouper(key='movement_state', axis=1, freq='D') # frequency is important here

# Apply rolling operation on the grouped DataFrame
transformed_df = (grouped_df.groupby(grouper)
                  .apply(lambda group: group['movement_state'].cat.codes)
                  .map(lambda codes: np.bincount(codes)))

In this approach, we’re first converting our categorical data into a numerical representation using .cat.codes. This gives us an array of integers where each integer corresponds to the index within the category list.

We then use np.bincount() to calculate the counts for each category within the window defined by grouper, effectively applying the rolling operation we need.

Conclusion

Pandas rolling operations can be tricky when working with categorical data. By converting your categorical values into a numeric representation and using techniques such as encoding or leveraging pandas’ built-in Grouper class, you can successfully apply these operations to your data.

This approach not only provides an elegant solution but also ensures that you take full advantage of pandas’ capabilities for efficient and accurate analysis.

Last modified on 2023-11-10