Before & After: Code Transformation with PT Dev Utility

See how PT Dev Utility simplifies your Python development workflow

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🚀 Transform Your Code from Complex to Simple

PT Dev Utility eliminates boilerplate code and provides clean, professional solutions for common development tasks. See the dramatic difference below:

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📅 Time Operations

❌ Before: Complex Date Handling

import datetime
from dateutil.relativedelta import relativedelta

# Getting current date components - messy and error-prone
def get_date_parts():
    now = datetime.datetime.now()
    date_str = now.strftime('%Y_%m_%d')
    parts = date_str.split('_')
    return {
        'year': parts[0],
        'month': parts[1], 
        'day': parts[2]
    }

# Getting timestamps - inconsistent formatting
def get_timestamp():
    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')

# Month calculations - complex and bug-prone
def get_previous_month():
    now = datetime.datetime.now()
    prev_month = now - relativedelta(months=1)
    return prev_month.strftime("%Y_%m")

# IST timezone - manual offset calculations
def get_ist_time():
    utc_now = datetime.datetime.utcnow()
    ist_offset = datetime.timedelta(hours=5, minutes=30)
    ist_time = utc_now + ist_offset
    return ist_time.replace(microsecond=0).isoformat() + '+05:30'

✅ After: Clean & Simple with PT Dev Utility

import pt_dev_utility as pdu

# Getting current date components - one line!
date_parts = pdu.get_current_date_components()

# Getting timestamps - consistent and clean
timestamp = pdu.get_current_timestamp()

# Month calculations - simple and reliable
previous_month = pdu.get_month_year_string(1)

# IST timezone - handled automatically
ist_time = pdu.get_iso_timestamp_ist()

Result: 25+ lines reduced to 4 lines with better reliability!

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📊 CSV Processing

❌ Before: Manual CSV Handling

import csv
import pandas as pd

# Reading CSV with custom headers - verbose and error-prone
def read_csv_custom(file_path, headers=None, columns=None):
    try:
        if headers:
            df = pd.read_csv(file_path, header=None, names=headers, skiprows=1)
        else:
            df = pd.read_csv(file_path)

        if columns:
            df = df[columns]

        result = []
        for _, row in df.iterrows():
            row_dict = {}
            for col in df.columns:
                row_dict[col] = row[col]
            result.append(row_dict)

        return result
    except FileNotFoundError:
        raise Exception(f"File not found: {file_path}")
    except Exception as e:
        raise Exception(f"Error reading CSV: {e}")

# Converting to SQL-like records - manual conversion
def csv_to_records(file_path):
    try:
        df = pd.read_csv(file_path)
        records = []
        for _, row in df.iterrows():
            record_tuple = tuple(row.values)
            records.append(record_tuple)
        return records
    except Exception as e:
        raise Exception(f"Failed to convert CSV: {e}")

✅ After: Elegant CSV Operations

import pt_dev_utility as pdu

# Reading CSV with custom headers - clean and flexible
data = pdu.read_csv_as_dict_list(
    'data.csv',
    custom_headers=['name', 'age', 'city'],
    selected_columns=['name', 'age']
)

# Converting to SQL-like records - one line!
records = pdu.load_csv_as_records('data.csv')

Result: 30+ lines reduced to 2 lines with better error handling!

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⏱️ Performance Monitoring

❌ Before: Manual Timing Code

import time
import functools
from datetime import datetime

# Manual timing decorator - complex and repetitive
def time_it(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()

        elapsed = end_time - start_time
        hours = int(elapsed // 3600)
        minutes = int((elapsed % 3600) // 60)
        seconds = int(elapsed % 60)

        print(f"Function {func.__name__} took {hours:02d}:{minutes:02d}:{seconds:02d}")
        return result
    return wrapper

# Usage - requires custom decorator for each project
@time_it
def slow_function():
    time.sleep(2)
    return "done"

✅ After: Professional Timing Made Easy

import pt_dev_utility as pdu

# Professional timing - ready to use!
@pdu.measure_execution_time
def slow_function():
    time.sleep(2)
    return "done"

Result: 15+ lines reduced to 1 decorator with professional formatting!

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📱 Telegram Notifications

❌ Before: Manual API Integration

import requests
import json

# Manual Telegram integration - error-prone and verbose
def send_telegram_alert(token, chat_id, message):
    url = f"https://api.telegram.org/bot{token}/sendMessage"

    payload = {
        "chat_id": chat_id,
        "text": message,
        "parse_mode": "HTML"
    }

    headers = {
        "Content-Type": "application/json"
    }

    try:
        response = requests.post(url, json=payload, headers=headers)

        if response.status_code == 200:
            return True, response.json()
        else:
            return False, response.text

    except requests.RequestException as e:
        return False, str(e)
    except Exception as e:
        return False, f"Unexpected error: {e}"

# Usage - complex error handling required
success, result = send_telegram_alert(
    "your_token", 
    "@channel", 
    "<b>Alert!</b> System is down"
)

if success:
    print("Message sent!")
else:
    print(f"Failed: {result}")

✅ After: Simple & Reliable Notifications

import pt_dev_utility as pdu

# Clean, professional notification system
success, result = pdu.send_telegram_message(
    token="your_token",
    message="<b>Alert!</b> System is down",
    chat_id="@channel"
)

if success:
    print("Message sent!")
else:
    print(f"Failed: {result}")

Result: 25+ lines reduced to 3 lines with better error handling!

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🔄 Real-World Workflow Comparison

❌ Before: A Typical Data Processing Script

import csv
import datetime
import time
import requests
import pandas as pd
from dateutil.relativedelta import relativedelta
import functools

# Timing decorator
def measure_time(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        elapsed = end - start
        hours = int(elapsed // 3600)
        minutes = int((elapsed % 3600) // 60)
        seconds = int(elapsed % 60)
        print(f"{func.__name__} took {hours:02d}:{minutes:02d}:{seconds:02d}")
        return result
    return wrapper

# CSV processing
def process_sales_data(file_path):
    try:
        df = pd.read_csv(file_path)
        records = []
        for _, row in df.iterrows():
            records.append(tuple(row.values))
        return records
    except Exception as e:
        raise Exception(f"CSV error: {e}")

# Telegram notification
def notify_completion(token, chat_id, message):
    url = f"https://api.telegram.org/bot{token}/sendMessage"
    payload = {"chat_id": chat_id, "text": message, "parse_mode": "HTML"}
    headers = {"Content-Type": "application/json"}

    try:
        response = requests.post(url, json=payload, headers=headers)
        return response.status_code == 200
    except:
        return False

# Main processing function
@measure_time
def daily_report():
    # Get current date
    now = datetime.datetime.now()
    date_str = now.strftime('%Y-%m-%d %H:%M:%S')

    # Process data
    sales_data = process_sales_data('sales.csv')

    # Send notification
    message = f"<b>Daily Report</b>\nProcessed {len(sales_data)} records at {date_str}"
    notify_completion("token", "@channel", message)

    return len(sales_data)

# Execute
result = daily_report()

✅ After: Clean & Professional with PT Dev Utility

import pt_dev_utility as pdu

@pdu.measure_execution_time
def daily_report():
    # Get current timestamp
    timestamp = pdu.get_current_timestamp()

    # Process data
    sales_data = pdu.load_csv_as_records('sales.csv')

    # Send notification
    message = f"<b>Daily Report</b>\nProcessed {len(sales_data)} records at {timestamp}"
    pdu.send_telegram_message("token", message, "@channel")

    return len(sales_data)

# Execute
result = daily_report()

Transformation Results: - Lines of Code: 50+ → 15 lines (70% reduction) - Complexity: High → Low - Maintainability: Poor → Excellent - Error Handling: Manual → Built-in - Readability: Complex → Crystal Clear

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💡 Why Choose PT Dev Utility?

🎯 Productivity Boost

• Reduce development time by 60-80%
• Focus on business logic, not boilerplate code
• Professional-grade solutions out of the box

🛡️ Reliability & Quality

• Comprehensive error handling
• Thoroughly tested functions
• Type hints for better IDE support

📚 Developer Experience

• Clean, intuitive APIs
• Extensive documentation with examples
• Consistent coding patterns

🚀 Professional Results

• Production-ready code from day one
• Standardized solutions across projects
• Reduced maintenance overhead

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🎉 Get Started Today!

pip install pt-dev-utils

Transform your Python development experience and write cleaner, more maintainable code with PT Dev Utility!