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Chain Generators with yield from in Python
Combine multiple generators into one seamless sequence using the `yield from` delegation syntax in Python.
def numbers():
yield 1
yield 2
yield 3
def letters():
yield 'a'
yield 'b'
yield 'c'
def combined():
yield from numbers()
yield from letters()
if __name__ == "__main__":
print(list(combined()))How to Compose Two Functions into a Single Callable in Python
Combine two Python functions into a single callable using a compose helper, then apply the chained call.
def add_one(x):
return x + 1
def double(x):
return x * 2
def compose(f, g):
return lambda x: f(g(x))
add_then_double = compose(double, add_one)
double_then_add = compose(add_one, double)
result1 = add_then_double(5)
result2 = double_then_add(5)
print(f"add_one then double(5) = {result1}")
print(f"doub…How to Wrap a Low Level Error in a Higher Level Exception in Python
Wrap low-level exceptions in a higher-level exception while preserving the original cause with the `from` keyword.
class LowLevelError(Exception):
pass
class HighLevelError(Exception):
pass
def low_level_operation():
raise LowLevelError("storage drive failed to respond")
def high_level_operation():
try:
low_level_operation()
except LowLevelError as e:
raise HighLevelError(f"database operation…How to Use ChainMap for Layered Config Lookup in Python
This code demonstrates using collections.ChainMap to combine multiple dictionaries into a single layered lookup, where earlier maps override later ones.
from collections import ChainMap
defaults = {"theme": "light", "lang": "en", "debug": False}
user = {"lang": "de", "auto_save": True}
runtime = {"debug": True}
config = ChainMap(runtime, user, defaults)
if __name__ == "__main__":
print("theme:", config["theme"])
print("lang:", config["lang"])
print("deb…How to Build a Fluent Interface with the Builder Pattern in Python
Learn to implement a fluent builder pattern in Python by chaining methods that return self, enabling readable object construction.
class Pizza:
def __init__(self):
self.size = None
self.toppings = []
self.crust = None
def set_size(self, size):
self.size = size
return self
def add_topping(self, topping):
self.toppings.append(topping)
return self
def set_crust(self, crust):
…How to Call a Parent Class __init__ with super() in Python
Shows how to chain __init__ calls through a class hierarchy using super(), so each class sets its own attributes while reusing the parent's initialization logic.
class Animal:
def __init__(self, name, species):
self.name = name
self.species = species
print(f"Animal init: {self.name}, {self.species}")
class Mammal(Animal):
def __init__(self, name, species, fur_color):
super().__init__(name, species)
self.fur_color = fur_color
…How to Merge Multiple Iterables with a Generator in Python
This code defines a generator function that 'chains' or merges multiple iterables into a single iterator, which is then converted to a list.
def chain(*iterables):
for iterable in iterables:
yield from iterable
def main():
list1 = [1, 2, 3]
tuple1 = (4, 5)
set1 = {6, 7}
string1 = "89"
result = list(chain(list1, tuple1, set1, string1))
print(result)
if __name__ == "__main__":
main()Chain of Thought Prompting in Python: Step-by-Step Reasoning Demo
This demo shows how to structure a function that explains its own reasoning step-by-step, mimicking chain-of-thought prompting for AI systems.
def solve_math_step_by_step(expression: str) -> str:
"""Solves a simple expression, showing each reasoning step."""
# Step 1: Parse the expression (assume "a + b" or "a - b")
parts = expression.split()
a = int(parts[0])
op = parts[1]
b = int(parts[2])
steps = []
steps.append(f"Step…How to Build a Chainable Filter Helper in Python
A beginner-friendly dataclass helper that chains filters, uniqueness, and slicing on any sequence, returning a plain list at the end.
from dataclasses import dataclass
from typing import Callable, Iterator, Sequence, TypeVar
T = TypeVar("T")
@dataclass
class FilterAssistant:
"""Beginner-friendly helper to filter any collection."""
data: Sequence[T]
def where(self, predicate: Callable[[T], bool]) -> "FilterAssistant":
return …How to Define Dagster ML Assets in Python
Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.
from dagster import asset
@asset
def raw_features():
return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}
@asset
def normalized_features(raw_features):
values = raw_features["sepal_length"]
mean = sum(values) / len(values)
std = (sum((x - mean) ** 2 for x in values) / len(values…Browse by section
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