Module Integration

Package Initialization

Create a proper Python package with __init__.py that exposes the public API:

# pydatakit/__init__.py
"""PyDataKit - A comprehensive data analysis toolkit."""

__version__ = "1.0.0"
__author__ = "Your Name"

# Import main classes/functions for easy access
from .time_utils import TimeUtils, DateRange
from .math_utils import Statistics, RandomGenerator
from .data_structures import DataProcessor
from .api_client import APIClient
from .numpy_ops import ArrayOperations
from .pandas_ops import DataFrameHelper

# Define what's available with "from pydatakit import *"
__all__ = [
    'TimeUtils', 'DateRange', 'Statistics', 'RandomGenerator',
    'DataProcessor', 'APIClient', 'ArrayOperations', 'DataFrameHelper',
]

TimeUtils Class (datetime & time)

Implement comprehensive datetime utilities:

• now(tz) - Get current datetime with optional timezone
• parse_date(date_string, fmt) - Parse string to datetime
• format_date(dt, fmt) - Format datetime to string
• days_between(start, end) - Calculate days between dates
• add_business_days(start, days) - Add business days
• timestamp_to_datetime(ts) - Convert Unix timestamp
• measure_execution(func) - Decorator for timing functions

Also create a DateRange class with iteration, weekdays, weekends, and monthly split.

Statistics & RandomGenerator Classes (math & random)

Implement math and random utilities:

• Statistics.mean/median/std_dev/percentile/correlation
• RandomGenerator.random_int/random_float/random_choice
• RandomGenerator.random_sample/shuffle/generate_normal
• RandomGenerator.generate_dataset(n, columns) - Generate test data

DataProcessor Class (collections & itertools)

Implement data processing utilities:

• count_items(items) - Count occurrences using Counter
• most_common(items, n) - Get n most common items
• group_by(items, key) - Group list of dicts by key using defaultdict
• flatten(nested) - Flatten nested lists using chain
• window(items, size) - Sliding window using deque
• batch(items, size) - Split into batches
• unique_combinations(items, r) - Get combinations

APIClient Class (requests library)

Implement HTTP client using requests:

• get(endpoint, params, headers) - Make GET request
• post(endpoint, data, json_data, headers) - Make POST request
• fetch_with_retry(endpoint, max_retries, backoff) - Retry with exponential backoff
• download_file(url, filepath) - Download file
• get_request_stats() - Get request statistics

ArrayOperations Class (NumPy)

Implement NumPy array operations:

• create_array/arange/linspace/zeros/ones/random_array
• reshape(arr, new_shape) - Reshape array
• statistics(arr) - Return dict with mean, std, min, max, median, sum
• normalize/standardize - Scale data
• dot_product/matrix_multiply - Linear algebra
• filter_by_condition(arr, condition) - Boolean filtering

DataFrameHelper Class (Pandas)

Implement Pandas DataFrame operations:

• from_csv/from_json/to_csv/to_json - File I/O
• info/describe/head - Data exploration
• filter_rows(column, condition, value) - Filter by condition
• select_columns/add_column/drop_columns/rename_columns
• sort_by(columns, ascending) - Sort data
• group_aggregate(group_by, agg_dict) - Group and aggregate
• fill_missing/drop_duplicates/merge - Data cleaning

Requirements File

Create a proper requirements.txt with pinned versions:

# requirements.txt
numpy>=1.24.0
pandas>=2.0.0
requests>=2.31.0

Unit Tests

Create tests in the tests/ directory:

• test_time_utils.py - Test datetime functions
• test_math_utils.py - Test statistics and random
• test_api_client.py - Test HTTP client

Use Python's unittest module with at least 5 test cases per file.

Main Entry Point

Create main.py that demonstrates all modules:

#!/usr/bin/env python3
"""PyDataKit - Main demonstration script."""

from pydatakit import (
    TimeUtils, DateRange, Statistics, RandomGenerator,
    DataProcessor, APIClient, ArrayOperations, DataFrameHelper
)

def main():
    print("=" * 60)
    print("PyDataKit - Data Analysis Toolkit Demo")
    print("=" * 60)
    
    # Demonstrate TimeUtils
    print("\n--- DateTime Utilities ---")
    now = TimeUtils.now()
    print(f"Current time: {TimeUtils.format_date(now)}")
    
    # Demonstrate Statistics
    print("\n--- Statistics ---")
    data = [10, 20, 30, 40, 50]
    print(f"Mean: {Statistics.mean(data)}")
    print(f"Std Dev: {Statistics.std_dev(data):.2f}")
    
    # Demonstrate NumPy operations
    print("\n--- NumPy Operations ---")
    arr = ArrayOperations.create_array([1, 2, 3, 4, 5])
    stats = ArrayOperations.statistics(arr)
    print(f"Array stats: {stats}")
    
    # Demonstrate Pandas operations
    print("\n--- Pandas Operations ---")
    df = DataFrameHelper({'name': ['Alice', 'Bob'], 'age': [25, 30]})
    print(df.head())
    
    print("\n" + "=" * 60)
    print("Demo completed!")
    print("=" * 60)

if __name__ == "__main__":
    main()