📋 Dictionaries

One-liners for operations you may commonly perform on dictionaries.

Combination

sum_dicts

Consider an example with two coffee shop locations, each reporting sales as a dictionary, with the goal of producing a dictionary of total sales:

before.py

from collections import defaultdict

# Sales from two locations
north_sales = {"coffee": 120, "cookie": 5, "juice": 40}
south_sales = {"coffee": 80, "cookie": 45, "banana": 10}

# Manual summation using defaultdict
combined_sales = defaultdict(int)
for location_sales in (north_sales, south_sales):
    for product, quantity in location_sales.items():
        combined_sales[product] += quantity

print(combined_sales)
# {"coffee": 200, "cookie": 50, "juice": 40, "banana": 10}

Why avoid this approach?

Note that the dictionary summation logic takes more than half of the code: 5 lines out of 8. But nothing in it relates specifically to business logic - combining sales.

Use UtilsX to drastically simplify the preceding code:

after.py

from utilsx import sum_dicts

# Sales from two locations
north_sales = {"coffee": 120, "cookie": 5, "juice": 40}
south_sales = {"coffee": 80, "cookie": 45, "banana": 10}

# Summation using UtilsX function
combined_sales = sum_dicts(north_sales, south_sales)

print(combined_sales)
# {"coffee": 200, "cookie": 50, "juice": 40, "banana": 10}

Filtering

remove_items_with_zero_values

Consider a case where a coffee shop tracks product sales, but some items haven’t sold at all. The sales report includes all items, even those with zero sales - which clutters the output and may cause confusion in downstream reporting.

before.py

# Sales including items with zero quantity
sales_report = {
    "coffee": 200,
    "cookie": 0,
    "juice": 40,
    "banana": 0,
    "sandwich": 25,
}

# Filtering using a dictionary comprehension
cleaned_report = {item: qty for item, qty in sales_report.items() if qty}

print(cleaned_report)
# {"coffee": 200, "juice": 40, "sandwich": 25}

Why avoid this approach?

The filtering logic still mixes with business logic — it obscures the intent to simply "clean up the report." Repeating this pattern across different places increases maintenance cost.

Use UtilsX to cleanly filter the sales report:

after.py

from utilsx import remove_items_with_zero_values

# Sales including items with zero quantity
sales_report = {
    "coffee": 200,
    "cookie": 0,
    "juice": 40,
    "banana": 0,
    "sandwich": 25,
}

# Remove zero-value entries using UtilsX
cleaned_report = remove_items_with_zero_values(sales_report)

print(cleaned_report)
# {"coffee": 200, "juice": 40, "sandwich": 25}

Modification

multiply_dict_values

Consider a scenario where an inventory system stores product weights in kilograms, but a downstream system - for example, a shipping provider - requires the same data in pounds.

before.py

# Product weights in kilograms
product_weights_kg = {
    "coffee_beans": 12.5,
    "cookies": 3.0,
    "juice_boxes": 8.75,
}

# Manual conversion to pounds
product_weights_lb = {
    weight_kg * 2.20462
    for item, weight_kg in product_weights_kg.items()
}

print(product_weights_lb)
# {"coffee_beans": 27.558, "cookies": 6.614, "juice_boxes": 19.290}

Why avoid this approach?

The logic for applying a constant multiplier gets mixed in with the business need — unit conversion. It adds noise and reduces reuse in other places requiring the same transformation.

Another issue with the code above is a hard-coded 2.20462 ratio.

Use UtilsX to cleanly express unit conversion:

after.py

from utilsx import KG_TO_LBS, multiply_dict_values

# Product weights in kilograms
product_weights_kg = {
    "coffee_beans": 12.5,
    "cookies": 3.0,
    "juice_boxes": 8.75,
}

# Convert to pounds
product_weights_lb = multiply_dict_values(product_weights_kg, KG_TO_LBS)

print(product_weights_lb)
# {"coffee_beans": 27.558, "cookies": 6.614, "juice_boxes": 19.290}

rename_keys_in_nested_dict

Consider a scenario where you’re processing a legacy configuration file in a deeply nested dictionary format. Teams authored this file over many years, and some keys have outdated or inconsistent names.

You want to rename "usr" and "pwd" key names across all levels of nesting to "user" and "password" respectively.

config_example.py

legacy_config = {
    "db_config": {
        "usr": "admin",
        "pwd": "secret",
        "host": "localhost",
        "port": 5432,
    },
    "log_settings": {
        "lvl": "INFO",
        "dir": "/var/log/app",
        "otel_credentials": {
            "usr": "otel_admin",
            "pwd": "otel_password",
        },
    },
}

Use UtilsX to rename those keys at all levels in one line:

utilsx_workflow.py

from utilsx import rename_keys_in_nested_dict

legacy_config = {...}

relevant_config = rename_keys_in_nested_dict(
    dictionary=legacy_config,
    renaming={
        "usr": "user",
        "pwd": "password",
    },
)

print(relevant_config)
# {
#     "db_config": {
#         "user": "admin",
#         "password": "secret",
#         "host": "localhost",
#         "port": 5432,
#     },
#     "log_settings": {
#         "lvl": "INFO",
#         "dir": "/var/log/app",
#         "otel_credentials": {
#             "user": "otel_admin",
#             "password": "otel_password",
#         },
#     },
# }

Sorting

sort_by_value

You can use Python's built-in sorted function to sort a dictionary by value, but that requires specifying a lambda function.

Because this task occurs frequently, UtilsX offers a shorthand to simplify it:

sort_by_value.py

from utilsx import sort_by_value

product_revenue = {
    "coffee": 2400,
    "juice": 1200,
    "cookie": 800,
    "banana": 1600,
}

# Sort products by revenue, highest first
top_selling = sort_by_value(product_revenue, reverse=True)

print(top_selling)
# {"coffee": 2400, "banana": 1600, "juice": 1200, "cookie": 800}