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How to Pipe Data Through a List of Transform Functions in Python
Applies a sequence of functions to an initial value using functools.reduce, creating a reusable pipe utility.
from functools import reduce
def pipe(data, *transforms):
return reduce(lambda value, func: func(value), transforms, data)
def double(x):
return x * 2
def add_one(x):
return x + 1
def to_string(x):
return f"Result: {x}"
if __name__ == "__main__":
initial = 5
result = pipe(initial, double, …How to Use functools.reduce in Python
Apply functools.reduce with operator functions and lambda expressions to aggregate lists into sums, products, maximums, and concatenated strings.
from functools import reduce
import operator
# Sum all numbers in a list using reduce
numbers = [1, 2, 3, 4, 5]
sum_result = reduce(operator.add, numbers)
# Find the maximum value using reduce
max_result = reduce(lambda a, b: a if a > b else b, numbers)
# Multiply all numbers using reduce
product_result = reduce(la…How to Use __slots__ in Python Classes for Memory Efficiency
Defines classes with __slots__ to prevent dynamic attribute creation and reduce memory usage, including inheritance with additional slots.
```python
class Person:
__slots__ = ("name", "age")
def __init__(self, name: str, age: int):
self.name = name
self.age = age
def greet(self) -> str:
return f"Hi, I'm {self.name} and I'm {self.age} years old."
class Employee(Person):
__slots__ = ("role",)
def __init__(se…Slots Class: How to Reduce Memory Usage in Python
Use __slots__ to prevent dynamic attribute creation and reduce per-instance memory overhead, while keeping methods intact.
class SlotsDemo:
__slots__ = ("name", "age", "email")
def __init__(self, name, age, email):
self.name = name
self.age = age
self.email = email
def describe(self):
return f"{self.name}, {self.age}, {self.email}"
if __name__ == "__main__":
instance = SlotsDemo("Alice", …How to Reduce Aggregate Counts from Mapped Chunks in Python
Combine a list of mapped chunk dictionaries into a single aggregated count dictionary using functools.reduce.
from functools import reduce
from collections import defaultdict
def aggregate_chunks(mapped_chunks):
"""Combine mapped chunk counts into a single aggregate dict."""
return reduce(
lambda acc, chunk: {
**acc,
**{k: acc.get(k, 0) + v for k, v in chunk.items()}
},
…How to Reduce Instance Memory with __slots__ in Python
Demonstrates that classes with __slots__ use less memory per instance than regular classes because they skip the instance __dict__.
class SlottedPoint:
__slots__ = ('x', 'y', 'z')
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
class RegularPoint:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
if __name__ == "__main__":
regular = RegularPoint(1, 2, 3)…How to Build a Bloom Filter to Reduce Cache Misses in Python
Implement a probabilistic Bloom filter in Python that lets a cache quickly determine which keys are definitely not present, reducing expensive source lookups on cache misses.
import hashlib
import random
class BloomFilter:
def __init__(self, size=100, num_hashes=3):
self.size = size
self.num_hashes = num_hashes
self.bit_array = [0] * size
def _hashes(self, item):
result = []
for i in range(self.num_hashes):
hash_value = int(hash…How to Implement a Negative Cache with TTL in Python
This code provides a TTL mock cache that stores negative results (cache misses) for a short time to reduce repeated lookups of missing keys.
from time import time, sleep
class TTLMockCache:
def __init__(self, ttl_seconds=5):
self.ttl = ttl_seconds
self.store = {}
self.negative_cache = {}
def get(self, key):
now = time()
if key in self.store:
value, expires_at = self.store[key]
if exp…How to Implement MapReduce Word Count in Python Using a Dict
Simulate a MapReduce word count pipeline in Python with a mock dict, splitting text into words, shuffling, and reducing to frequency counts.
def map_reduce_word_count(text: str) -> dict:
"""Simulate a MapReduce pipeline to count word frequencies."""
# MAP phase: split into words and emit (word, 1) pairs
mapped = []
for word in text.lower().split():
# Clean word of punctuation
clean_word = ''.join(char for char in word if cha…How to Implement a Mock MapReduce for Word Count in Python
Simulates a MapReduce word count pipeline with mapper, shuffle, and reducer phases using Python dicts and standard library modules.
from collections import defaultdict
import re
def mapper(text):
"""Split text into words and emit (word, 1) pairs."""
words = re.findall(r'\b\w+\b', text.lower())
return [(word, 1) for word in words]
def reducer(pairs):
"""Group word-count pairs and sum counts."""
counts = defaultdict(int)
fo…How to Simulate a MapReduce Mock with Combine Phase in Python
Simulates a MapReduce pipeline with a combiner that aggregates local counts per reducer to reduce network and compute overhead.
from collections import defaultdict
def map_phase(lines):
intermediate = defaultdict(list)
for line in lines:
for word in line.strip().lower().split():
intermediate[word].append(1)
return dict(intermediate)
def combine_phase(intermediate, num_reducers=3):
combined = defaultdict(li…How to Compute CUPED Variance Reduction in Python
Implement CUPED in Python to reduce variance of A/B test treatment effect estimates using pre-experiment covariates.
import numpy as np
def compute_cuped_reduction(control, variant, covariate):
"""
Compute variance reduction using CUPED (Controlled Experiment with
Pre-Experiment Data). Uses pre-experiment covariate values to
reduce variance of the treatment effect estimate.
"""
control = np.asarray(control, …How to Eager Load with JOIN to Reduce N+1 Queries in Python
Demonstrates eager loading with a SQL JOIN to reduce N+1 query patterns down to a single database call when fetching related data.
import sqlite3
def eager_load_join_reduce(mock_db_path=":memory:"):
"""Demonstrate eager loading where joins reduce query count from N+1 to 1."""
conn = sqlite3.connect(mock_db_path)
cursor = conn.cursor()
cursor.executescript(
"""
CREATE TABLE authors (id INTEGER PRIMARY KEY, name TE…Browse by section
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