date-normalizer

dkyazzentwatwa's avatarfrom dkyazzentwatwa

Use when asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.

⭐1stars🔀0forks📁View on GitHub🕐Updated Dec 17, 2025
[Data Cleaning]

When & Why to Use This Skill

The Date Normalizer Claude skill is a robust utility designed to parse, standardize, and convert dates from over 100 different formats into consistent ISO 8601 or custom structures. It simplifies complex data preparation tasks by automatically handling international format variations, relative time expressions, and timezone conversions, making it an essential tool for maintaining data integrity in ETL pipelines and database management.

Use Cases

  • ETL Pipeline Integration: Automatically standardize inconsistent date formats from multiple legacy sources into a unified ISO 8601 format for data warehousing.
  • International Data Harmonization: Resolve ambiguity between US (MM/DD/YYYY) and EU (DD/MM/YYYY) date formats in global sales reports or user databases.
  • Log File Analysis: Convert relative timestamps like '2 hours ago' or 'last Friday' into absolute date-time objects for precise event sequencing and auditing.
  • Batch CSV Processing: Cleanse large datasets by normalizing entire columns of messy date strings, ensuring compatibility with spreadsheet software and SQL databases.
  • Data Validation and Quality Assurance: Identify and flag invalid or highly ambiguous date entries in automated workflows to prevent downstream processing errors.
namedate-normalizer
descriptionUse when asked to parse, normalize, standardize, or convert dates from various formats to consistent ISO 8601 or custom formats.

Date Normalizer

Parse and normalize dates from various formats into consistent, standardized formats for data cleaning and ETL pipelines.

Purpose

Date standardization for:

  • Data cleaning and ETL pipelines
  • Database imports with mixed date formats
  • Log file parsing and analysis
  • International data harmonization
  • Report generation with consistent dating

Features

  • Smart Parsing: Automatically detect and parse 100+ date formats
  • Format Conversion: Convert to ISO 8601, US, EU, or custom formats
  • Batch Processing: Normalize entire CSV columns
  • Ambiguity Detection: Flag dates that could be interpreted multiple ways
  • Timezone Handling: Convert and normalize timezones
  • Relative Dates: Parse "today", "yesterday", "next week"
  • Validation: Detect and report invalid dates

Quick Start

from date_normalizer import DateNormalizer

# Normalize single date
normalizer = DateNormalizer()
result = normalizer.normalize("03/14/2024")
print(result)  # {'normalized': '2024-03-14', 'format': 'iso8601'}

# Normalize to specific format
result = normalizer.normalize("March 14, 2024", output_format="us")
print(result)  # {'normalized': '03/14/2024', 'format': 'us'}

# Batch normalize CSV column
normalizer.normalize_csv(
    'data.csv',
    date_column='created_at',
    output='normalized.csv',
    output_format='iso8601'
)

CLI Usage

# Normalize single date
python date_normalizer.py --date "March 14, 2024"

# Convert to specific format
python date_normalizer.py --date "14/03/2024" --format us

# Normalize CSV column
python date_normalizer.py --csv data.csv --column date --format iso8601 --output normalized.csv

# Detect ambiguous dates
python date_normalizer.py --date "01/02/03" --detect-ambiguous

API Reference

DateNormalizer

class DateNormalizer:
    def normalize(self, date_string: str, output_format: str = 'iso8601',
                 dayfirst: bool = False, yearfirst: bool = False) -> Dict
    def normalize_batch(self, dates: List[str], **kwargs) -> List[Dict]
    def normalize_csv(self, csv_path: str, date_column: str,
                     output: str = None, **kwargs) -> str
    def detect_format(self, date_string: str) -> str
    def is_valid(self, date_string: str) -> bool
    def is_ambiguous(self, date_string: str) -> bool
    def parse_relative(self, relative_string: str) -> datetime

Output Formats

ISO 8601 (default):

'2024-03-14'  # Date only
'2024-03-14T15:30:00'  # With time
'2024-03-14T15:30:00+00:00'  # With timezone

US Format:

'03/14/2024'  # MM/DD/YYYY

EU Format:

'14/03/2024'  # DD/MM/YYYY

Long Format:

'March 14, 2024'

Custom Format:

normalizer.normalize(date, output_format='%Y%m%d')  # '20240314'

Supported Input Formats

Numeric:

  • 2024-03-14 (ISO)
  • 03/14/2024 (US)
  • 14/03/2024 (EU)
  • 14.03.2024 (German)
  • 2024/03/14 (Japanese)
  • 20240314 (Compact)

Textual:

  • March 14, 2024
  • 14 March 2024
  • Mar 14, 2024
  • 14-Mar-2024

Relative:

  • today, yesterday, tomorrow
  • next week, last month
  • 2 days ago, in 3 weeks

With Time:

  • 2024-03-14 15:30:00
  • 03/14/2024 3:30 PM
  • 2024-03-14T15:30:00Z

Ambiguity Handling

Dates like 01/02/03 are ambiguous. Specify interpretation:

# Day first (EU)
normalizer.normalize("01/02/03", dayfirst=True)
# Result: 2003-02-01

# Month first (US)
normalizer.normalize("01/02/03", dayfirst=False)
# Result: 2003-01-02

# Year first
normalizer.normalize("01/02/03", yearfirst=True)
# Result: 2001-02-03

Use Cases

Clean Messy Data:

messy_dates = [
    "March 14, 2024",
    "2024-03-15",
    "03/16/2024",
    "17-Mar-2024"
]

normalized = normalizer.normalize_batch(messy_dates)
# All converted to: ['2024-03-14', '2024-03-15', '2024-03-16', '2024-03-17']

CSV Normalization:

# Input CSV with mixed date formats
# Convert all to ISO 8601
normalizer.normalize_csv(
    'orders.csv',
    date_column='order_date',
    output='orders_normalized.csv',
    output_format='iso8601'
)

Validation:

if not normalizer.is_valid("invalid date"):
    print("Invalid date detected")

Timezone Conversion:

normalizer.normalize(
    "2024-03-14 15:30:00+00:00",
    output_timezone='America/New_York'
)

Limitations

  • Cannot parse dates from images or PDFs (use OCR first)
  • Ambiguous dates require manual specification of format
  • Very old dates (<1900) may have limited support
  • Non-Gregorian calendars not supported
  • Some regional formats may need explicit configuration