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How to Diff Two Dicts in Python for Config Drift
Recursively compare two dictionaries and report added, removed, and changed keys with their old and new values for debugging configuration drift.
def diff_dicts(a, b, path=""):
differences = []
for key in a.keys() | b.keys():
new_path = f"{path}.{key}" if path else key
if key not in a:
differences.append((new_path, "<missing>", b[key], "added"))
elif key not in b:
differences.append((new_path, a[key], "<…How to Log Errors with Structured Fields in Python
Logs error details as structured dictionary fields using Python's logging module with extra parameters.
import logging
import sys
def log_structured_error(operation: str, user_id: int, status_code: int, error_msg: str):
"""Log an error with structured fields using a dictionary."""
logger = logging.getLogger("structured_logger")
logger.setLevel(logging.ERROR)
# Create console handler if not already …How to Build a CSV Comparison Tool That Highlights Every Changed Cell in Python
Read two CSV files with DictReader, compare cell by cell, and return a list of dictionaries describing each changed cell using only the standard library.
import csv
from pathlib import Path
def csv_cell_diff(file_a: str, file_b: str) -> list[dict]:
rows_a = list(csv.DictReader(Path(file_a).open('r', newline='')))
rows_b = list(csv.DictReader(Path(file_b).open('r', newline='')))
if not rows_a or not rows_b:
return []
columns = list(rows_a[0].key…How to Parse Apache Log Files in Python
Parse Apache common log format lines into structured dictionaries using Python's standard library.
import re
from pathlib import Path
def parse_apache_line(line):
pattern = r'^(\S+) (\S+) (\S+) \[([^\]]+)\] "(\S+) (\S+) (\S+)" (\d{3}) (\S+)'
match = re.match(pattern, line)
if not match:
return None
ip, ident, user, timestamp, method, path, protocol, status, size = match.groups()
return …Join two CSV files on shared key column in Python
Merge rows from two CSV files by a common key column, outputting combined records to a new file.
import csv
def join_csv(file1, file2, key, output="joined.csv"):
# Read first CSV into dict keyed by the join column
with open(file1, newline="") as f1:
reader1 = csv.DictReader(f1)
data1 = {row[key]: row for row in reader1}
# Read second CSV and merge matching rows
with open(file2, n…Read Parquet-Like Columnar CSV Chunks in Python
A Python generator that reads a CSV file column-by-column, yielding dictionary chunks where each key points to a list of values—mirroring how Parquet stores data columnar.
```python
import csv
from pathlib import Path
from typing import Iterator, List
def read_parquet_like_columnar(csv_path: str, column_names: List[str], chunk_size: int = 2) -> Iterator[dict]:
"""Read CSV data in columnar chunks, similar to how parquet stores columns."""
csv_file = Path(csv_path)
with csv_f…Build a Case-Insensitive Dict with a Wrapper Class in Python
Create a custom dict subclass that treats keys as case-insensitive by normalizing them to lowercase, with a full set of common dict methods.
class CaseInsensitiveDict:
def __init__(self, data=None):
self._data = {}
if data:
self.update(data)
def __setitem__(self, key, value):
self._data[str(key).lower()] = value
def __getitem__(self, key):
return self._data[str(key).lower()]
def __delitem__(sel…Find All Leaf Paths in a Nested Dict in Python
Recursively traverse a nested dictionary and yield every leaf path as a list of keys, including paths to empty dictionaries.
def find_leaf_paths(data, path=None):
if path is None:
path = []
if not isinstance(data, dict) or not data:
yield path
return
for key, value in data.items():
yield from find_leaf_paths(value, path + [key])
if __name__ == "__main__":
nested = {
"a": 1,
…Get Nested Dict Value with Default in Python
Access values deep inside a nested dictionary using a dotted path string, returning a default when any key is missing.
def get_nested(d, path, default=None):
"""Walk a nested dict along a dotted path, returning default if missing."""
current = d
for key in path.split("."):
if isinstance(current, dict) and key in current:
current = current[key]
else:
return default
return current
…How to Build a TTL Cache Dict in Python
Create a dictionary subclass that automatically expires keys after a fixed time-to-live using timestamps.
import time
class TTLDict(dict):
def __init__(self, ttl, *args, **kwargs):
self.ttl = ttl
self._expires = {}
super().__init__(*args, **kwargs)
def __setitem__(self, key, value):
super().__setitem__(key, value)
self._expires[key] = time.time() + self.ttl
def __geti…How to Build a Two-Way Dictionary in Python
Implement a BiDict class that supports both forward key-to-value and reverse value-to-key lookups with a simple add, delete, and update API.
class BiDict:
def __init__(self, data=None):
self.forward = {}
self.backward = {}
if data:
self.update(data)
def update(self, data):
for key, value in data.items():
self[key] = value
def __setitem__(self, key, value):
self.forward[key] = val…How to Deep Merge Nested Dicts Recursively in Python
Recursively merge two Python dictionaries, with overlay values taking precedence while preserving nested structures.
def deep_merge(base, overlay):
"""
Recursively merge two dictionaries.
Values in 'overlay' take precedence over 'base'.
"""
result = base.copy()
for key, value in overlay.items():
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
result[key…How to Implement Disjoint Set Union Find in Python
Implement a Disjoint Set Union-Find data structure using a Python dictionary for parent tracking, with path compression and connectivity checks.
class DisjointSet:
def __init__(self):
self.parent = {}
def find(self, x):
# Path compression
if self.parent[x] != x:
self.parent[x] = self.find(self.parent[x])
return self.parent[x]
def union(self, x, y):
# Initialize if not present
if x not in…How to Recursively Remove None Values from Nested Dictionaries in Python
Recursively removes all None values from nested dictionaries and lists while preserving non-None data.
def prune_none(obj):
if isinstance(obj, dict):
return {
k: prune_none(v)
for k, v in obj.items()
if v is not None and prune_none(v) is not None
}
elif isinstance(obj, list):
pruned = [prune_none(item) for item in obj]
pruned = [item for item i…LRU Cache with OrderedDict in Python
Implement an LRU cache using collections.OrderedDict to track insertion order and evict the least-recently-used item when capacity is exceeded.
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity):
self.capacity = capacity
self.cache = OrderedDict()
def get(self, key):
if key not in self.cache:
return -1
self.cache.move_to_end(key)
return self.cache[key]
def put(sel…Traverse Nested Dict Paths Depth-First in Python
Recursively walk a nested dictionary depth-first and yield each full path from root to leaf as lists.
def depth_first_paths(node, path=None):
if path is None:
path = []
if not isinstance(node, dict):
yield path + [node]
return
for key, value in node.items():
new_path = path + [key]
if isinstance(value, dict):
yield from depth_first_paths(value, …Unflatten Dot Keys to Nested Dict in Python
Convert a flat dictionary with dot-separated keys into a nested dictionary structure using recursive setdefault loops.
def unflatten_dot_keys(flat_dict):
result = {}
for flat_key, value in flat_dict.items():
parts = flat_key.split(".")
current = result
for part in parts[:-1]:
current = current.setdefault(part, {})
current[parts[-1]] = value
return result
if __name__ == "__main_…Borg pattern shared state in Python
Implement the Borg pattern to share state across class instances by assigning a class-level dictionary to each instance's __dict__.
class Borg:
_shared_state = {}
def __init__(self):
self.__dict__ = Borg._shared_state
class ConfigManager(Borg):
def __init__(self):
super().__init__()
if not hasattr(self, "settings"):
self.settings = {}
def set(self, key, value):
self.settings[key] = va…How to Evaluate RPN Expressions in Python
Use a stack to evaluate Reverse Polish Notation token lists with a dictionary of operator lambdas, truncating division toward zero.
def eval_rpn(tokens):
stack = []
ops = {
'+': lambda a, b: a + b,
'-': lambda a, b: a - b,
'*': lambda a, b: a * b,
'/': lambda a, b: int(a / b) # truncate toward zero
}
for token in tokens:
if token in ops:
b = stack.pop()
a = stack.pop(…Implement Insert Delete GetRandom O(1) in Python
Build a RandomizedSet class that supports insert, delete, and get_random in average O(1) time using a list and a dictionary mapping values to indices.
import random
class RandomizedSet:
def __init__(self):
self.values = []
self.index_map = {}
def insert(self, val):
if val in self.index_map:
return False
self.index_map[val] = len(self.values)
self.values.append(val)
return True
def delete(self…How to Build a Data Helper for LLM Prompts in Python
A beginner-friendly helper class that flattens nested dictionaries, formats prompt templates, and safely parses JSON for AI/LLM pipelines.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Simple helper class for working with data in AI/LLM pipelines."""
def __init__(self, data: Optional[Dict[str, Any]] = None) -> None:
self.data = data or {}
def flatten(self, prefix: str = "") -> Dict[str, Any]…How to cache embeddings with a Python dict to avoid recomputation
Caches embeddings computed from text in a dictionary keyed by SHA-256 hash, returning cached results for repeated calls.
import hashlib
import time
class EmbeddingCache:
def __init__(self):
self.cache = {}
def _hash_text(self, text):
return hashlib.sha256(text.encode()).hexdigest()
def get_embedding(self, text, compute_func):
key = self._hash_text(text)
if key not in self.cache:
…How to parallel map embeddings with a thread pool in Python
Run embedding computations in parallel using ThreadPoolExecutor, collect results into a dict keyed by the original item.
import threading
from concurrent.futures import ThreadPoolExecutor
import time
def compute_embedding(item: int) -> tuple[int, int]:
time.sleep(0.05) # Simulate embedding work
return item, item * 10
def parallel_map_embed(items, max_workers=3):
results = {}
with ThreadPoolExecutor(max_workers=max_w…Deduplicate events by ID within a window in Python
Deduplicate event streams by ID within sliding time windows, keeping the newest occurrence per window using heaps and sets.
import heapq
from collections import defaultdict
def deduplicate_events(events, window_size):
"""Return events deduplicated by id, keeping newest within each sliding window."""
# Index events by (timestamp, id) for deterministic ordering
events_by_id = defaultdict(list)
for ts, eid, *payload in events…Browse by section
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