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Count Data in Python with Comprehensions and Generators
Count list items with a dict comprehension and generate squares lazily with a generator expression, printing both results.
from collections import Counter
data = ["apple", "banana", "apple", "cherry", "banana", "apple"]
counts = {item: data.count(item) for item in set(data)}
square_gen = (x * x for x in range(5))
squares = list(square_gen)
if __name__ == "__main__":
print("Manual count:", counts)
print("Counter:", dict(Counter…Dict Comprehension to Map Keys to Lengths in Python
Build a dictionary that maps each word to its character count using a dictionary comprehension.
words = ["apple", "banana", "cherry", "date", "elderberry"]
word_lengths = {word: len(word) for word in words}
print(word_lengths)Generator Function to Yield an Infinite Counter in Python
This code demonstrates a generator function that yields an infinite sequence of integers starting from a given value, allowing lazy, memory-efficient iteration.
def infinite_counter(start=0):
count = start
while True:
yield count
count += 1
if __name__ == "__main__":
counter = infinite_counter(5)
for _ in range(5):
print(next(counter))How to Create a Mock LLM Judge Rubric Score in Python
Scores a response against a rubric by counting keyword matches, returning total, percentage, and per-criterion feedback.
def judge_score(response, rubric):
"""Mock LLM judge that scores a response against a rubric."""
total = 0
max_total = 0
feedback = []
for criterion, rubric_item in rubric.items():
max_points = rubric_item["max"]
description = rubric_item["description"]
# Simple mock scori…How to Estimate Token Count in Python
Estimates tokens in a text string using a whitespace and punctuation heuristic without external libraries.
def estimate_tokens(text: str) -> int:
"""Estimate token count using whitespace and punctuation heuristics."""
if not text:
return 0
words = text.split()
total_punctuation = sum(1 for char in text if char in ".,!?;:")
special_tokens = sum(1 for char in text if char in "\n\t")
# Rough …How to Filter Blocked Words in Python
Scans input text against a moderation blocklist, returning blocked terms and their counts.
MODERATION_BLOCKLIST = {"spam", "scam", "fraud", "phishing", "malware", "abuse"}
def scan_text(text: str) -> dict:
normalized = text.lower()
words = normalized.replace(".", " ").replace(",", " ").replace("!", " ").replace("?", " ").split()
found_terms = []
for word in words:
if word in MO…How to Serialize Chat Messages to a JSON File in Python
Writes a list of chat message dicts to a JSON file with metadata like export time and message count.
import json
from pathlib import Path
from datetime import datetime
def serialize_messages(messages, output_path):
data = {
"exported_at": datetime.now().isoformat(),
"count": len(messages),
"messages": messages
}
Path(output_path).write_text(
json.dumps(data, indent=2, ensu…How to compute ROUGE recall in Python
Compute ROUGE recall by counting token overlap between a reference and candidate summary with pure Python.
def rouge_recall(reference, candidate):
ref_tokens = reference.lower().split()
cand_tokens = candidate.lower().split()
ref_counts = {}
for token in ref_tokens:
ref_counts[token] = ref_counts.get(token, 0) + 1
cand_counts = {}
for token in cand_tokens:
cand_counts[token] = cand…Aggregate Log Errors Count by Hour in Python
Counts ERROR log lines per hour using regex and Counter, returning a sorted dictionary of hourly totals.
import re
from collections import Counter
from datetime import datetime
def aggregate_errors_by_hour(log_lines):
pattern = re.compile(r'^(\d{4}-\d{2}-\d{2} \d{2}):\d{2}:\d{2}.*ERROR')
hourly_counts = Counter()
for line in log_lines:
match = pattern.match(line)
if match:
ho…Batch Rename Hundreds of Files in Python
Rename all files with a given extension inside a folder using a sequential counter and a custom prefix.
import os
from pathlib import Path
def batch_rename_files(directory: str, prefix: str, extension: str = ".txt") -> None:
"""Rename all files with given extension in directory to prefix_{counter}.ext."""
path = Path(directory)
if not path.is_dir():
print(f"Directory '{directory}' does not exist.")
…Build a Live Countdown Timer for Events in Python
A Python script that displays a real-time countdown to a target date and time, updating every second in the console.
import datetime
import time
def countdown(event_name, target_datetime):
"""Displays a live countdown to a target datetime."""
while True:
now = datetime.datetime.now()
remaining = target_datetime - now
if remaining.total_seconds() <= 0:
print(f"\n🚀 {event_name} is happening…Generate a Monthly Report CSV from Log Files in Python
Reads a CSV log file, filters events by a given month, aggregates daily event counts and revenue, and writes a summarized monthly report to a new CSV.
import csv
from collections import defaultdict
from datetime import datetime
def generate_monthly_report(log_file: str, month: str, output_file: str) -> None:
events_by_date = defaultdict(int)
revenue_by_date = defaultdict(float)
with open(log_file, 'r') as f:
for line in f:
date_…How to Build an argparse Command-Line Tool in Python
Create a simple file-info CLI with argparse that counts lines and prints file size, with optional verbose and output flags.
import argparse
import os
from pathlib import Path
def process_file(filepath, verbose=False):
"""Read a file and report its size and line count."""
path = Path(filepath)
if not path.exists():
raise FileNotFoundError(f"File not found: {filepath}")
content = path.read_text()
lines = conten…How to Recover Deleted .txt Files from a Backup in Python
A Python function that searches a backup directory recursively and copies all .txt files to a destination folder, printing each recovered file name and a total count.
import os
import shutil
from pathlib import Path
def recover_deleted_txt_files(source_backup_dir: str, destination_dir: str) -> None:
"""Recover .txt files from backup directory."""
backup_path = Path(source_backup_dir)
dest_path = Path(destination_dir)
dest_path.mkdir(parents=True, exist_ok=True)
…Parse nginx access log top IPs in Python
Reads an nginx access log line by line, extracts the client IP, and returns the most frequent IPs using a regex and Counter.
import re
from collections import Counter
def top_ips(log_file, n=10):
ip_pattern = re.compile(r'^(\S+)')
ip_counts = Counter()
with open(log_file, 'r') as f:
for line in f:
match = ip_pattern.match(line)
if match:
ip_counts[match.group(1)] += 1
return…Count Records Processed per Category in Python
Use a Counter dictionary to track how many records of each type (ok, error, retry) were processed in a data pipeline.
from collections import Counter
import random
processed_counter = Counter()
def process_records(records):
for record in records:
processed_counter[record] += 1
return len(records)
if __name__ == "__main__":
sample_records = [random.choice(["ok", "error", "retry"]) for _ in range(10)]
print(f…How to Build a Simple Data Pipeline in Python
A beginner-friendly data pipeline that loads JSON, filters records by a field value, and aggregates counts per category.
import json
from pathlib import Path
def load_json(filepath: str | Path) -> list[dict]:
"""Load a JSON file containing a list of records."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def filter_records(records: list[dict], field: str, value) -> list[dict]:
"""Kee…How to Clean and Format Data in Python
This code loads JSON data, cleans records by removing empty fields and normalizing text, then summarizes the results with counts and unique keys.
import json
from pathlib import Path
def load_data(filepath: str) -> dict:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def clean_records(records: list[dict]) -> list[dict]:
"""Remove empty fields and normalize text to lowercase."""…How to Count JSON Records in Python
Read a JSON file and count the number of top-level records, handling both list and dictionary structures.
import json
from pathlib import Path
def count_records(json_file):
"""Count top-level records in a JSON file."""
with open(json_file, "r") as f:
data = json.load(f)
# Handle both list of records and dict of records
if isinstance(data, list):
return len(data)
elif isinstance(da…How to Implement Incremental Load with Watermark by updated_at in Python
Load only new or changed rows into SQLite by comparing an updated_at timestamp against a stored watermark, returning counts and the new watermark.
import sqlite3
from datetime import datetime, timedelta
def watermark_incremental_load(db_path, table_name, last_watermark, source_data):
"""Load only rows with updated_at greater than the last watermark."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Create table if it doesn't exist
…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()}
},
…Build a Simple Log Graph in Python
Create a basic one-dimensional bar chart from log lines by counting occurrences of leading numeric keys.
import heapq
def log_graph(log_lines: list[str]) -> str:
"""Build a simple per-line, one-dimensional visual graph from log entries."""
counts: dict[int, int] = {}
for line in log_lines:
tokens = line.split()
if tokens:
try:
idx = int(tokens[0])
exce…Count Unique Contributors from Git Shortlog in Python
Parses git shortlog -sn output to count the number of unique contributors, handling duplicate entries and variable whitespace.
import subprocess
from collections import Counter
# Mock shortlog output as a list of lines (simulating git shortlog -sn output)
MOCK_SHORTLOG = """ 120 Alice Johnson
88 Bob Smith
45 Alice Johnson
30 Carol Williams
25 Bob Smith
10 Dave Brown
"""
def count_contributors_from_shortlog(text):
"…How to compute diff stats (insertions, deletions) in Python
Parses a git diff text and counts the number of added and removed lines to produce insertion and deletion stats.
import re
from collections import Counter
def parse_diff(diff_text):
insertions = 0
deletions = 0
for line in diff_text.splitlines():
if line.startswith("+") and not line.startswith("+++"):
insertions += 1
elif line.startswith("-") and not line.startswith("---"):
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