Understanding Low Frequency Categories in Pandas Series

In data analysis and machine learning, it’s often necessary to handle low-frequency categories or outliers in datasets. This can be particularly challenging when working with categorical variables. In this article, we’ll explore how to combine low frequency factors or category counts in a pandas series using Python.

Overview of the Problem

Suppose you have a pandas series df.column containing various categories, such as operating systems (Windows, iOS, Android, Macintosh) and devices (Chrome OS, Windows Phone). You want to replace low-frequency categories with ‘Other’ to reduce the number of factors in your regression model. This means you need to identify the rare categories and rename them accordingly.

Understanding Pandas Value Counts

To begin with, let’s understand how pandas value counts work. When you run pd.value_counts(df.column), it returns a series showing the frequency of each unique value in the column. The resulting series is sorted by the values, with the most frequent values first.

For example, consider the following series:

Windows          26083
iOS              19711
Android          13077
Macintosh         5799
Chrome OS          347
Linux              285
Windows Phone      167
(not set)           22
BlackBerry          11

Masking Low Frequency Categories

One approach to handle low-frequency categories is by masking them. You can calculate the percentage of occupancy for each category and use this information to mask the values.

Let’s assume you want to find the categories with a frequency less than 1%. In Python, you can do this using:

series = pd.value_counts(df.column)
mask = (series/series.sum() * 100).lt(1)

This will create a boolean mask where True indicates a category is low-frequency and False otherwise.

Renaming Low Frequency Categories

To rename the low-frequency categories, you can use np.where. This function allows you to perform different operations based on conditions. In this case, we’ll use it to replace the values in the original series with ‘Other’ when they’re masked by the boolean mask.

df['column'] = np.where(df['column'].isin(series[mask].index),'Other',df['column'])

This will rename the categories with a frequency less than 1% to ‘Other’.

Changing the Index

If you want to change the index of the series instead, you can do it by creating a new series that excludes the masked values and then assigning the sum of the original frequencies to the new category.

new = series[~mask]
new['Other'] = series[mask].sum()

This will create a new series with ‘Other’ as one of its categories, where the value is the sum of the frequencies of the masked categories.

Replacing Index Values

Alternatively, you can replace the values in the original index by using np.where.

series.index = np.where(series.index.isin(series[mask].index),'Other',series.index)

This will change the index of the series to ‘Other’ for all masked categories.

Explanation and Example Use Cases

The code snippets above can be used in various scenarios, such as:

• Data Cleaning: When dealing with categorical data, it’s often necessary to handle low-frequency categories. By masking these categories and renaming them, you can improve the accuracy of your models.
• Feature Engineering: You might want to create new features by combining existing ones. For example, creating a binary feature for each category, where 1 indicates presence and 0 absence.
• Data Visualization: When visualizing categorical data, it’s often helpful to group low-frequency categories together.

By understanding how to handle low frequency categories in pandas series, you can improve the quality of your data and make more informed decisions about your models.

Last modified on 2023-09-10