Which is better, a Python dictionary or a custom python class from a performance perspective?

From a performance perspective, choosing between a Python dictionary and a custom class depends on several factors, such as memory efficiency, lookup speed, and ease of use. Here’s a breakdown:

1. Using a Dictionary (dict)

Pros:

Fast lookups: Dictionary lookups are O(1) on average due to hash table implementation.

More dynamic: Can easily add/remove keys without modifying code structure.

Memory-efficient for small datasets: Since it only holds keys and values, it can be efficient in some cases.

Cons:

Consumes more memory than simple lists or tuples due to hashing overhead.

Less structured: No strict schema, which can lead to errors when accessing non-existent keys.

Example:

filtered_data = {

    "ids": df["id"].tolist(),

    "names": df["name"].tolist(),

    "values": df["value"].tolist(),

}

2. Using a Custom Class

Pros:

Provides better data encapsulation and type safety.

Improves readability and maintainability when dealing with complex data structures.

Can have methods for data processing, reducing redundant code.

Cons:

Slightly slower lookups compared to dicts (attribute access is O(1), but may involve extra function calls).

Uses more memory due to object overhead.

Example:

class FilteredData:

    def __init__(self, df):

        self.ids = df["id"].tolist()

        self.names = df["name"].tolist()

        self.values = df["value"].tolist()

    

    def get_summary(self):

        return f"Total records: {len(self.ids)}"

filtered_data = FilteredData(df)

print(filtered_data.get_summary())  # Example method call

Performance Considerations

Conclusion

Use a dict if you need fast, dynamic key-value storage without strict structure.

Use a class if you need structured data representation with encapsulated logic.

If performance is critical, and you're dealing with large datasets, consider using NumPy arrays or Pandas itself, since they are more optimized than Python lists and dictionaries.