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What is functools.partial?

functools.partial creates a new function by partially applying arguments to an existing function. This is helpful for simplifying function calls in scenarios where certain arguments are repetitive or fixed.

The functools.partial function in Python allows you to "freeze" some portion of a function's arguments or keywords, creating a new function with fewer parameters. It's especially useful when you want to fix certain parameters of a function while keeping others flexible.

from functools import partial

Basic Syntax

partial(func, *args, **kwargs)

• func: The function to partially apply.
• *args: Positional arguments to fix.
• `kwargs`**: Keyword arguments to fix.

The returned object is a new function where the fixed arguments are "frozen," and you only need to supply the remaining ones when calling the new function.

Examples

1. Partially Fixing Arguments

def power(base, exponent):
    return base ** exponent

# Create a square function by fixing exponent = 2
square = partial(power, exponent=2)

# Now, square() only needs the base
print(square(5))  # Output: 25
print(square(10))  # Output: 100

Here, partial creates a new function square that always uses exponent=2.

2. Simplifying Function Calls

Suppose you have a function with multiple arguments, and you often call it with some fixed values.

def greet(greeting, name):
    return f"{greeting}, {name}!"

# Fix the greeting
say_hello = partial(greet, greeting="Hello")
say_goodbye = partial(greet, greeting="Goodbye")

print(say_hello("Alice"))   # Output: Hello, Alice!
print(say_goodbye("Alice")) # Output: Goodbye, Alice!

3. Partial for Use in Mapping

You can use partial to adapt a function for operations like map.

def multiply(x, y):
    return x * y

# Fix y = 10
multiply_by_10 = partial(multiply, y=10)

# Use in a map
numbers = [1, 2, 3, 4]
result = map(multiply_by_10, numbers)
print(list(result))  # Output: [10, 20, 30, 40]

4. Partial with Functions That Have Default Arguments

Partial works seamlessly with functions that already have default arguments.

def add(a, b=10):
    return a + b

# Fix b to 20
add_with_20 = partial(add, b=20)
print(add_with_20(5))  # Output: 25

5. Combining with Other Libraries (e.g., Pandas or JSON)

You can use partial with libraries like Pandas to simplify repetitive operations.

import pandas as pd

def filter_rows(df, column, value):
    return df[df[column] == value]

# Fix the column name
filter_by_age = partial(filter_rows, column="age")

# Example DataFrame
data = pd.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
result = filter_by_age(data, value=25)
print(result)

When to Use functools.partial

1. Reusable Logic:
   • When you want to create reusable versions of a function with fixed arguments.
2. Simplifying Callbacks:
   • Useful for libraries like tkinter, asyncio, or threading, where callbacks often require simpler signatures.
3. Functional Programming:
   • Works well with map, filter, or similar operations.
4. Improving Readability:
   • Makes code cleaner by reducing redundant arguments.

Notes and Best Practices

• Inspecting Partial Functions: You can inspect the frozen arguments of a partial function using partial.func, partial.args, and partial.keywords.

   print(square.func)       # Original function (power)
   print(square.keywords)   # {'exponent': 2}
   print(square.args)       # ()

• Flexibility: You can override frozen arguments when calling the partial function.

   print(square(5, exponent=3))  # Output: 125 (exponent is overridden)

Advanced Example: Using Partial for Higher-Order Functions

def apply_discount(price, discount):
    return price - (price * discount)

# Fix a 10% discount
discount_10 = partial(apply_discount, discount=0.10)

prices = [100, 200, 300]
discounted_prices = map(discount_10, prices)
print(list(discounted_prices))  # Output: [90.0, 180.0, 270.0]

Using functools.partial can simplify and clean up your code, especially when dealing with repetitive function calls or higher-order functions. Let me know if you'd like more examples or advanced use cases!