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Find Duplicate Elements in a Python List
Identifies and returns duplicate elements from a Python list using sets for efficient membership tests.
def find_duplicates(lst):
seen = set()
duplicates = set()
for item in lst:
if item in seen:
duplicates.add(item)
else:
seen.add(item)
return list(duplicates)
if __name__ == "__main__":
sample = [1, 2, 3, 2, 4, 1, 5, 3]
print(find_duplicates(sample))How to Get the Union of Two Lists Without Duplicates in Python
Merge two lists and remove duplicate values using a set, then convert back to a list.
def union_without_duplicates(list1, list2):
return list(set(list1 + list2))
if __name__ == "__main__":
list_a = [1, 2, 3, 4]
list_b = [3, 4, 5, 6]
result = union_without_duplicates(list_a, list_b)
print(f"Union of {list_a} and {list_b}: {result}")Intersection of Two Lists Preserving Order in Python
This code returns the common elements between two lists while preserving the order they appear in the first list, filtering out duplicates.
def intersection_preserving_order(list1, list2):
"""
Return the intersection of two lists while preserving the order
of elements as they appear in list1.
"""
set2 = set(list2)
result = []
seen = set()
for item in list1:
if item in set2 and item not in seen:
resu…How to Find Duplicate Files by Size and Hash in Python
Recursively scan a directory, group files by size, then hash candidates to identify exact duplicate files.
import hashlib
from pathlib import Path
def hash_file(path, chunk_size=8192):
hasher = hashlib.md5()
with open(path, 'rb') as f:
while chunk := f.read(chunk_size):
hasher.update(chunk)
return hasher.hexdigest()
def find_duplicates(directory):
size_map = {}
for path in Path(dir…How to Count Elements and Find Duplicates in a Python List
Count occurrences of each element in a list, extract unique values, and identify duplicates using Python dictionaries and sets.
def analyze_counts(data):
"""Count elements, return unique values, and find duplicates."""
# Count occurrences using a dictionary
counts = {}
for item in data:
counts[item] = counts.get(item, 0) + 1
# Alternative compact approach with set
unique_items = set(data)
# Fi…Find Common Elements in List of Lists in Python
Return elements that appear in every sublist of a nested list, preserving duplicates with Counter intersection.
from collections import Counter
def common_elements(list_of_lists):
"""Return elements present in every sublist."""
if not list_of_lists:
return []
counts = Counter(list_of_lists[0])
for sublist in list_of_lists[1:]:
counts &= Counter(sublist)
return list(counts.elements())
if _…Find Elements in One Python List but Not Another
Return a new list containing only the elements from list A that are not present in list B, preserving duplicates and order.
def difference_elements(a, b):
"""Return elements present in list a but not in list b."""
set_b = set(b)
return [item for item in a if item not in set_b]
if __name__ == "__main__":
a = [1, 2, 3, 4, 5, 3, 2]
b = [2, 4, 6]
result = difference_elements(a, b)
print(f"A: {a}")
print(f"B: {b…Find Missing Numbers, Duplicates, and Ranges in Python
Analyze a list to identify missing numbers, duplicate values, and contiguous ranges using sets and the Counter class.
def find_missing_duplicates_ranges(numbers):
"""Find missing numbers, duplicates, and ranges in a list."""
from collections import Counter
if not numbers:
return {"missing": [], "duplicates": [], "ranges": []}
full_range = set(range(min(numbers), max(numbers) + 1))
present = set(n…How to Find Four Sum Quadruplets in Python (Sorted Demo)
Find all unique quadruplets in a sorted array that sum to a target, with duplicate skipping.
def four_sum(nums, target):
nums.sort()
result = []
n = len(nums)
for i in range(n - 3):
if i > 0 and nums[i] == nums[i - 1]:
continue
for j in range(i + 1, n - 2):
if j > i + 1 and nums[j] == nums[j - 1]:
continue
left, right = j + 1…How to Remove Duplicates in Python Preserving Order
Removes duplicate items from a list while keeping the first occurrence order intact using a set for fast membership checks.
def remove_duplicates_preserving_order(items):
seen = set()
result = []
for item in items:
if item not in seen:
seen.add(item)
result.append(item)
return result
if __name__ == "__main__":
sample = [3, 1, 2, 1, 3, 4, 2, 5]
unique_items = remove_duplicates_preserv…Python Generator to Filter Duplicates with a Seen Set
A lazily-evaluated generator function that yields only the first occurrence of each item, using a set to track seen values.
def unique_generator(items):
seen = set()
for item in items:
if item not in seen:
seen.add(item)
yield item
if __name__ == "__main__":
data = [1, 2, 2, 3, 3, 3, 4, 5, 5]
result = list(unique_generator(data))
print(result)Find and Delete Duplicate Files Using Hashing in Python
Walk a directory tree, compute SHA256 hashes for every file, and delete duplicates that share the same hash.
import hashlib
import os
from pathlib import Path
def file_hash(path, block_size=65536):
"""Return SHA256 hash of file content."""
hasher = hashlib.sha256()
with open(path, 'rb') as f:
while chunk := f.read(block_size):
hasher.update(chunk)
return hasher.hexdigest()
def find_and_d…Build a Python Utility That Detects Duplicate Records Across Multiple Excel Sheets
A Python utility that uses pandas to find overlapping records across different Excel sheets based on specified key columns.
import pandas as pd
from pathlib import Path
def find_duplicate_records_across_sheets(file_path: str, key_columns: list, sheet_names: list) -> dict:
"""
Detect duplicate records across multiple Excel sheets based on specified key columns.
Args:
file_path: Path to the Excel file
key_co…How to Deduplicate Events with At-Least-Once Delivery in Python
Implements an exactly-once processing pattern for at-least-once event delivery by tracking seen event IDs in a set, skipping duplicates.
seen_ids = set()
def process_event(event_id: str, payload: dict) -> dict:
"""Process an event exactly once, ignoring duplicates."""
if event_id in seen_ids:
return {"status": "duplicate", "event_id": event_id}
seen_ids.add(event_id)
return {"status": "processed", "event_id": event_id, **payloa…Implement Exactly-Once Transaction Log in Python
A mock transaction log that deduplicates transaction IDs so each is recorded only once, with a dataclass for records and simple in-memory storage.
from dataclasses import dataclass
from typing import Dict, Optional
@dataclass
class TxnRecord:
txn_id: str
status: str
class ExactlyOnceTxnLog:
def __init__(self) -> None:
self._log: Dict[str, TxnRecord] = {}
self._processed_ids: set = set()
def record(self, txn_id: str, status: s…Idempotent Consumer: Store Processed IDs in Python
Implement an idempotent consumer that persists processed message IDs to a JSON file, skipping duplicates on restart.
import json
from pathlib import Path
class IdempotentStore:
def __init__(self, storage_path: str = "processed_ids.json"):
self.storage_path = Path(storage_path)
self.processed_ids = self._load()
def _load(self) -> set:
if self.storage_path.exists():
with self.storage_path…Inbox pattern consumer dedupe mock in Python
Implements a mock inbox consumer that deduplicates incoming messages by ID, with automatic eviction of old seen IDs to prevent unbounded memory growth.
import json
from collections import deque
from dataclasses import dataclass, field
from hashlib import sha256
from typing import Any
@dataclass
class InboxConsumer:
max_seen: int = 1000
seen_ids: set = field(default_factory=set)
seen_history: deque = field(default_factory=deque)
def _mark_seen(self,…How to Build an Idempotency-Key POST Handler in Python
Python HTTP server mock that accepts POST requests and deduplicates them using an Idempotency-Key header, returning the same response for repeated calls.
import hashlib
import json
from http.server import BaseHTTPRequestHandler, HTTPServer
from urllib.parse import urlparse
class MockAPI(BaseHTTPRequestHandler):
responses = {}
def do_POST(self):
length = int(self.headers.get("Content-Length", 0))
body = self.rfile.read(length).decode("utf-8")
…At Least Once with Idempotent Consumer in Python
Implements a thread-safe idempotent consumer that processes each unique message exactly once, even when a producer sends duplicates under an at-least-once delivery model.
import threading
import time
import uuid
from collections import Counter
class IdempotentConsumer:
def __init__(self):
self.processed = set()
self._lock = threading.Lock()
def consume(self, message_id, payload):
with self._lock:
if message_id in self.processed:
…How to Deduplicate Messages in Python by ID
This code consumes a mock inbox of JSON messages and deduplicates them by message ID, keeping either the first or last occurrence.
import json
from collections import OrderedDict
mock_inbox = [
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 2, "message": "world", "timestamp": "2024-01-01T10:01:00Z"},
{"id": 1, "message": "hello", "timestamp": "2024-01-01T10:00:00Z"},
{"id": 3, "message": "test", "times…How to Implement an Exactly-Once Deduplication Store in Python
Implement a Python class that deduplicates keys exactly once, tracking first-seen timestamps and duplicate counts.
from datetime import datetime
from typing import Any, Hashable
class ExactlyOnceStore:
def __init__(self) -> None:
self._seen: set[Hashable] = set()
self._first_seen: dict[Hashable, datetime] = {}
self._counts: dict[Hashable, int] = {}
def add(self, key: Hashable, value: Any = None) …Idempotent Consumer Event Processing in Python
Track processed event IDs to skip duplicates and count event types for a reliable, idempotent consumer.
import json
from collections import defaultdict
class EventProcessor:
def __init__(self):
self.processed_ids = set()
self.counts = defaultdict(int)
def process_event(self, event):
event_id = event["id"]
if event_id in self.processed_ids:
return {"status": "skipped"…Browse by section
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