Python Data Transformation Scripts

2026-03-01T08:15:00Z
2026-03-01T14:30:00Z
2026-03-01T08:45:00Z
2026-03-01T20:00:00Z

Parse ISO 8601 timestamps and count occurrences of events per UTC hour, returning a JSON object mapping hour string (e.g. '08', '14') to count.

import json
from datetime import datetime
from collections import Counter

def transform(text):
    lines = [l for l in text.strip().split('\n') if l]
    hours = [datetime.fromisoformat(l.replace('Z', '+00:00')).strftime('%H') for l in lines]
    return json.dumps(dict(Counter(hours)))

{"08": 2, "14": 1, "20": 1}

| Name | Role | Salary |
| Alice | Engineer | 90000 |
| Bob | Designer | 80000 |

Convert a Markdown table into a standard CSV string with line breaks.

import csv, io

def transform(text):
    lines = [l.strip() for l in text.strip().split('\n') if l.strip() and not set(l.strip()) <= set('|- ')]
    out = io.StringIO()
    writer = csv.writer(out)
    for line in lines:
        cols = [c.strip() for c in line.strip('|').split('|')]
        writer.writerow(cols)
    return out.getvalue().strip()

Name,Role,Salary
Alice,Engineer,90000
Bob,Designer,80000

name=Alice&age=30&city=New+York&role=Developer

Parse a URL query string into a JSON object, decoding URL-encoded characters (like '+' to space).

import json, urllib.parse

def transform(text):
    parsed = urllib.parse.parse_qs(text.strip())
    # parse_qs returns lists for values; flatten single values
    result = {k: v[0] if len(v) == 1 else v for k, v in parsed.items()}
    return json.dumps(result)

{"name": "Alice", "age": "30", "city": "New York", "role": "Developer"}