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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 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 …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 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 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…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_…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]…Pivot long to wide transformation dict
Transform a list of dictionaries from long format to wide format by pivoting on a key column and aggregating values, using pure Python.
def pivot_long_to_wide(rows, key_col, value_col, id_cols=None):
"""
Convert long-format data (list of dicts) to wide format.
Args:
rows: List of dicts in long format
key_col: Column name to pivot on (becomes new column headers)
value_col: Column name whose values become the cel…How to Implement an LFU Cache in Python
Implement a Least Frequently Used (LFU) cache with frequency tracking dictionaries to evict the least accessed items when capacity is reached.
class LFUCache:
def __init__(self, capacity: int):
self.capacity = capacity
self.data = {}
self.freq = {}
self.min_freq = 0
def get(self, key: int) -> int:
if key not in self.data:
return -1
self._increment_freq(key)
return self.data[key]
…How to implement the Database per service pattern in Python
Simulate separate databases per microservice in Python using dataclasses and in-memory dictionaries, showing how services own their data independently.
import json
from dataclasses import dataclass, asdict
from typing import Dict, List
@dataclass
class User:
id: int
name: str
email: str
@dataclass
class Order:
id: int
user_id: int
product: str
amount: float
class UserServiceDB:
"""Simulates a separate database for the User servic…How to Create a Mock Iceberg Snapshot Manifest in Python
Build a mock Iceberg snapshot manifest structure with metadata and data entries using Python dictionaries and JSON.
import json
from datetime import datetime, timezone
def create_mock_manifest(snapshot_id: int, file_paths: list[str]) -> dict:
"""Create a mock Iceberg snapshot manifest structure."""
manifest_file = {
"manifest_path": f"/warehouse/table/metadata/snap-{snapshot_id}-m0.avro",
"manifest_length"…Browse by section
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