How to filter a dictionary according to an arbitrary condition function?

python

dict-comprehensions

functional-programming

performance-tuning

byAlex Kataev·Sep 5, 2024

# Embrace dictionary comprehension for direct filtering:

filtered_dict = {k: v for k, v in original_dict.items() if condition(v)}

# Need to keep items with values >= 10? Here's an example:

original_dict = {'a': 5, 'b': 15, 'c': 10}
filtered_dict = {k: v for k, v in original_dict.items() if v >= 10}

# The result: {'b': 15, 'c': 10}. Now that's nifty!

Unpacking dictionary filtering

Hold on to your hats! We're about to embark on a dictionary filtering adventure where we'll process key-value pairs, ensure they meet certain conditions, and even flag performance and style considerations along the way.

The showdown: dict comprehensions vs. for loops

Let's cut straight to the chase: a dict comprehension is your fastest draw in the west when filtering dictionaries. Why saddle up with a traditional for loop, which comes with more code and less readability, when you can step out in style with a dict comprehension?

# Old-fashion for loop way- More lines, less style
filtered_dict = {}
for k, v in original_dict.items():
    if condition(v):  # If the condition fits, wear it!
        filtered_dict[k] = v

Tackling multiple conditions: no sweat!

Dict comprehensions don't break a sweat when facing multiple conditions. They just roll up their sleeves and get the job done:

# Multiple conditions? Not an issue!
filtered_dict = {k: v for k, v in original_dict.items() if condition1(v) and condition2(k)}

The Functional approach: filter and lambda

For those inclined towards functional programming, imagine a cool duo: filter function and lambda expression. They might be less readable and slightly slower than dict comprehensions, but hey, variety is the spice of life!

# Functional programming approach: filter with a suit of lambda
filtered_dict = dict(filter(lambda item: condition(item[1]), original_dict.items()))

Performance tuning: Get it right!

Python 2 users, rejoice! Replacing .items() with .iteritems() can give you a performance edge by creating an iterator rather than a list copy of the items. Remember, every nanosecond counts!

# Zipping through large data in Python 2 like a road runner
filtered_dict = {k: v for k, v in original_dict.iteritems() if condition(v)}

For Python 3 users, you're already on the fast lane since .items() produces a view object without consuming extra memory.

Understanding the dictionary filtering spectrum

Ensure consistent data structures: A stitch in time saves nine!

Maximise efficiency by ensuring your dictionaries have consistent data structures. This is especially true if you're working with multi-dimensional data structures, like points on a grid:

# Ensuring consistent data structure: Measure twice, cut once!
original_dict = {'a': (2, 3), 'b': (4, 9), 'c': (5, 6)}
filtered_dict = {k: v for k, v in original_dict.items() if v[0] < 5 and v[1] < 5}

Multiple conditions filtering: Don't trip over your own feet!

Applying several conditions makes you think of using all(). Here's some sage advice: use it judiciously to avoid an overtly complex comprehension:

# Using all() with caution: Knowing is half the battle!
filtered_dict = {k: v for k, v in original_dict.items() if all(condition(x) for x in v)}

Key-value filtering: Dict comprehensions can swing both ways!

Dict comprehensions' beauty lies not just in targeting values, but keys too! And if you're feeling adventurous, you could filter using both key and value. Now that's versatile!

# Filtering by keys and values: Both barrels blazing!
filtered_dict = {k: v for k, v in original_dict.items() if k.startswith('a') and sum(v) > 10}

Level up your filtering game

Tuple unpacking: Clarity on steroids!

Looking to enhance readability? Tuple unpacking to the rescue! It allows direct access to attributes for filtering:

# Tuple unpacking: clarity levels up!
filtered_dict = {(k, v): d for (k, v), d in original_dict.items() if d['score'] > 50}

Conditional dictionary updates: Not just for filtering!

You can use comprehensions not just for filtering, but to conditionally update existing keys. Smoother code, happy coder!

# Not just filtering, but conditional updates too!
original_dict.update({k: modify_value(v) for k, v in original_dict.items() if should_modify(k

Nested dictionaries: A spoonful of caution

Nested dictionaries offer a slightly higher intensity game. But fear not, you just need to traverse each layer attentively:

# Nested dictionaries: Proceed with caution, but proceed nonetheless!
filtered_dict = {k: {ik: iv for ik, iv in v.items() if condition(iv)} for k, v in original_dict.items()}