Grouping by `relativedelta` Column in Pandas: How to Fix TypeError

Grouping data is a core functionality in Pandas that makes data aggregation and analysis much easier. However, when working with date-related data, specifically relativedelta objects from dateutil.relativedelta, you might run into a TypeError when trying to group by this column.

Understanding why this happens and how to fix it can save you a lot of frustration. Let’s look at what’s going wrong and explore solutions to seamlessly group data with relativedelta in Pandas.

Understanding the TypeError

If you try to group by a column containing relativedelta objects, you might see an error like this:


TypeError: unhashable type: 'relativedelta'

This error occurs because relativedelta objects are not hashable, meaning they cannot be used as dictionary keys or as group labels in Pandas’ groupby function. Hashable types (like strings, integers, and tuples) are required for grouping data efficiently.

So why is relativedelta unhashable? Unlike regular integers or strings, relativedelta is a mutable object, making it unsuitable as a dictionary key or a grouping label.

How GroupBy Works in Pandas

Pandas’ groupby function is designed to split a dataset into groups, apply a function to each group, and combine the results. This is useful for operations like calculating statistics, summarizing data, or aggregating information.

Common use cases:

Grouping sales data by month or year
Analyzing user behavior by age groups
Aggregating financial transactions by category

However, for any of these to work, the grouping key (the column used for groupby) must be hashable.

Checking What Pandas Documentation Says

The official Pandas documentation is a great resource when debugging issues like this. If we look into it, it states that grouping keys must be hashable to ensure efficient data access and retrieval.

Since relativedelta is a mutable object, it cannot be used directly. This restriction is what causes the TypeError when attempting to group by a relativedelta column.

Fixing the Issue: Alternative Approaches

Now that we understand why the error happens, let’s explore some solutions.

Convert `relativedelta` to a Hashable Type

One easy way to make relativedelta hashable is to convert it into a tuple or a string.

For example, if you have a column storing relativedelta objects, you can transform it like this:


df["relativedelta_str"] = df["relativedelta"].astype(str)
grouped = df.groupby("relativedelta_str").sum()

Alternatively, you can convert it into a tuple:


df["relativedelta_tuple"] = df["relativedelta"].apply(lambda x: (x.years, x.months, x.days))
grouped = df.groupby("relativedelta_tuple").sum()

This method ensures the values are immutable and can be used effectively as group labels.

Using a Custom Grouping Function

Another approach is to define a custom function that classifies the relativedelta objects into meaningful categories:


def categorize_relativedelta(rd):
    if rd.years > 1:
        return "More than a Year"
    elif rd.months > 6:
        return "More than 6 Months"
    return "Less than 6 Months"

df["category"] = df["relativedelta"].apply(categorize_relativedelta)
grouped = df.groupby("category").sum()

This method works well if you need to classify the data into broader categories instead of using exact values.

Practical Example

Let’s work through a full example of loading some data, adding a relativedelta column, and applying one of the solutions.


from dateutil.relativedelta import relativedelta
import pandas as pd

# Sample Data
data = {
    "name": ["Alice", "Bob", "Charlie"],
    "birth_date": ["2000-01-01", "1995-06-15", "1988-09-10"]
}

df = pd.DataFrame(data)
df["birth_date"] = pd.to_datetime(df["birth_date"])

# Adding a relativedelta column (difference from today)
df["age_difference"] = df["birth_date"].apply(lambda x: relativedelta(pd.Timestamp.today(), x))

# Converting relativedelta to a tuple for grouping
df["age_tuple"] = df["age_difference"].apply(lambda x: (x.years, x.months, x.days))

# Group by the new column
grouped = df.groupby("age_tuple").count()

This method allows relativedelta data to be grouped effectively without type errors.

Best Practices for Grouping with `relativedelta`

To avoid issues, keep these best practices in mind:

Convert relativedelta to a hashable type before grouping.
If exact values are not needed, categorize data into bins.
Check object types before applying functions that require hashable keys.
Refer to the Pandas documentation if encountering unexpected behaviors.

Summary

Grouping by a relativedelta column in Pandas throws a TypeError because relativedelta is unhashable. The best way to solve this problem is to convert it to a string or tuple before grouping.

Using custom classification functions is another great way to structure and analyze time-based data more effectively.

By applying these techniques, you can work with relative time differences without encountering errors, leading to a smoother data analysis process.