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Export List of Dicts to CSV in Python
Write a list of dictionaries (dataframe-like) to a CSV file with headers using the standard library csv module and verify by reading it back.
import csv
def export_to_csv(data, filename):
"""Export a list of dicts to a CSV file."""
if not data:
print("No data to export")
return
# Get column names from the keys of the first dict
fieldnames = list(data[0].keys())
with open(filename, 'w', newline='', encoding='utf…Export SQLite Query Results to CSV in Python
Connects to a SQLite database, runs a query, and writes the result rows and column headers to a CSV file using the standard library.
import sqlite3
import csv
def export_query_to_csv(db_path, query, csv_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(query)
rows = cursor.fetchall()
column_names = [description[0] for description in cursor.description]
with open(csv_path, 'w', newline='', encodi…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 List Failed Records in a Dead Letter Queue Mock in Python
A mock Dead Letter Queue stores failed processing records with error details and timestamps, lists them, and exports to JSON.
import json
from datetime import datetime, timedelta
import random
class DeadLetterQueue:
def __init__(self):
self.failed_records = []
def add_failed_record(self, record_id, payload, error_message):
self.failed_records.append({
"record_id": record_id,
"payload": paylo…How to detect secrets in git history with Python
Scan a git history export file for common secret patterns using regex and Python.
import re
from pathlib import Path
def scan_history_for_secrets(history_file: str) -> list:
"""Scan a git history export for potential secrets using regex patterns."""
patterns = {
"AWS Access Key": r"AKIA[0-9A-Z]{16}",
"GitHub Token": r"gh[pousr]_[0-9A-Za-z]{36,255}",
"Private Key": …How to Export a Conda Environment YAML File in Python
Generate a mock conda environment YAML export with a reusable Python function and the PyYAML library.
import yaml
def conda_env_mock(name="demo_env", channels=None, packages=None):
channels = channels or ["defaults"]
packages = packages or [
"python=3.11",
"pip",
"numpy=1.24.3",
"pandas=2.0.3",
]
env_dict = {
"name": name,
"channels": channels,
…How to Mock OpenTelemetry Tracer Setup in Python
Set up a mock OpenTelemetry tracer with an in-memory span exporter to capture spans for testing and debugging.
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
def setup_tracer():
provider = TracerProvider()
exporter = InMemorySpanExpo…How to Mock an OTLP HTTP Endpoint in Python
This code implements a lightweight HTTP server that accepts OTLP/HTTP trace exports, stores spans by trace ID, and exposes them via a simple GET endpoint for debugging.
import json
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from collections import defaultdict
class TraceHandler(BaseHTTPRequestHandler):
traces = defaultdict(list)
def do_POST(self):
if self.path == "/v1/traces":
length = int(self.headers.get("Content-Length", 0))
…Python Observability Data Helper for Beginners
A beginner-friendly Python helper to log events, record metrics, summarize observability data, and export it as JSON.
import json
from datetime import datetime
from collections import defaultdict
class ObservabilityDataHelper:
"""Helper for exploring basic observability data patterns."""
def __init__(self):
self.events = []
self.metrics = defaultdict(list)
def log_event(self, service, level, message):
…Build a Data Helper Class in Python for ML Pipelines
A beginner-friendly Python class that summarizes, filters, and exports ML dataset rows as JSON.
from typing import List, Dict, Any
import json
class DataHelper:
"""Beginner-friendly helpers for ML data pipelines."""
def __init__(self, data: List[Dict[str, Any]]):
self.data = data
self.keys = list(data[0].keys()) if data else []
def summary(self) -> Dict[str, Any]:
"…Browse by section
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