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How to Find the n Smallest Items in a Large List with heapq in Python
This code demonstrates how to efficiently extract the n smallest items from a large list using Python's heapq module and a manual max-heap approach.
import heapq
def n_smallest_iterable(data, n):
"""Return the n smallest items without loading the whole list."""
if n <= 0:
return []
return heapq.nsmallest(n, data)
def n_smallest_manual(data, n):
"""Return the n smallest using a heap, O(n log k) time."""
if n <= 0:
return []
…
How to Find Missing Values in Large Datasets in Python
Analyze missing values across multiple large pandas DataFrames with counts and percentages.
import pandas as pd
import numpy as np
def find_missing_values_summary(datasets):
"""Analyze missing values across multiple datasets (dict of name: DataFrame)."""
summary = {}
for name, df in datasets.items():
missing_count = df.isnull().sum()
total_rows = len(df)
missing_pct = (mi…
Approximate Distinct Count in Python with HyperLogLog
Mock a large data stream and estimate the number of distinct items with a HyperLogLog-style probabilistic counter to save memory.
import random
import string
from collections import Counter
import math
class ApproxCountDistinct:
def __init__(self, num_buckets=16):
self.num_buckets = num_buckets
self.max_zeros = [0] * num_buckets
def _hash(self, item):
# Simple string hash to a 32-bit integer
h = …
Bloom Filter Join Mock in Python
A mock hash join that uses a Bloom filter to pre-filter one table before performing an exact match, reducing the number of comparisons in large dataset joins.
import hashlib
import random
import string
class BloomFilter:
def __init__(self, size: int = 200, num_hashes: int = 3):
self.bits = [False] * size
self.size = size
self.num_hashes = num_hashes
def _hashes(self, item: str):
result = []
for seed in range(self.num_hashes…
Approximate Count with HyperLogLog in Python
A mock HyperLogLog implementation uses hash-based registers to estimate cardinality of large datasets with sublinear memory.
import hashlib
class HyperLogLog:
def __init__(self, precision=4):
if precision < 4 or precision > 16:
raise ValueError("precision must be between 4 and 16")
self.precision = precision
self.registers = [0] * (1 << precision)
def _hash(self, value):
return int(hashl…
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