semvec/pypi-packages huggingface dataset:
pylib-tzconvert 0.1.0
Time-zone conversions and aware datetimes. Timezone utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, conversion, data-processing, data-science, datetime, machine-learning, ml, nlp, timezone, utilities

# pytzconvert

Timezone conversion

## Installation

```bash
pip install pytzconvert
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_tzconvert import convert_timezone

from datetime import datetime

# Convert timezone
dt = datetime(2024, 1, 1, 12, 0)
converted = convert_timezone(dt, "UTC", "America/New_York")
# Converted datetime
```

### AI/ML Use Cases

```python
from pylib_tzconvert import convert_timezone

# Normalize timestamps for ML
normalized = convert_timezone(timestamp, source_tz, "UTC")
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-daterange 0.1.0
Generate date ranges, sequences, calendars. Date range utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, dates, datetime, machine-learning, ml, nlp, ranges, utilities

# pydaterange

Date ranges

## Installation

```bash
pip install pydaterange
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_daterange import DateRange

from datetime import datetime

# Create date range
start = datetime(2024, 1, 1)
end = datetime(2024, 1, 31)
date_range = DateRange(start, end)

# Check if date is in range
date_range.contains(datetime(2024, 1, 15))
# True
```

### AI/ML Use Cases

```python
from pylib_daterange import DateRange

# Filter training data by date range
training_range = DateRange(start_date, end_date)
filtered_data = [d for d in data if training_range.contains(d['date'])]
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-authclient 0.1.0
OAuth2 / API token helper toolkit. Authentication utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, authentication, data-processing, data-science, machine-learning, ml, nlp, oauth, security, utilities

# pyauthclient

OAuth2 helpers

## Installation

```bash
pip install pyauthclient
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_authclient import OAuthClient

# OAuth client
client = OAuthClient(client_id, client_secret)
token = client.get_access_token()
# Use token for authenticated requests
```

### AI/ML Use Cases

```python
from pylib_authclient import OAuthClient

# Authenticate AI API calls
oauth = OAuthClient(api_key, api_secret)
token = oauth.authenticate()
headers = {"Authorization": f"Bearer {token}"}
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-restmock 0.1.0
Mock external REST APIs for tests. Testing utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, api, data-processing, data-science, machine-learning, ml, mocking, nlp, testing, utilities

# pyrestmock

Mock REST APIs

## Installation

```bash
pip install pyrestmock
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_restmock import MockServer

# Create mock server
server = MockServer()
server.add_route("GET", "/api/data", {"result": "success"})
response = server.request("GET", "/api/data")
```

### AI/ML Use Cases

```python
from pylib_restmock import MockServer

# Mock AI API for testing
mock_ai_api = MockServer()
mock_ai_api.add_route("POST", "/api/predict", {"prediction": "cat"})
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-fetcher 0.1.0
Async HTTP client with caching and metrics. Network utilities for AI agents. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, async, data-processing, data-science, http, machine-learning, ml, networking, nlp, utilities

# pyfetcher

Async HTTP client

## Installation

```bash
pip install pyfetcher
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_fetcher import fetch_async

# Async fetch
import asyncio

async def get_data():
    result = await fetch_async("https://api.example.com/data")
    return result

data = asyncio.run(get_data())
```

### AI/ML Use Cases

```python
from pylib_fetcher import fetch_async

# Fetch multiple AI API responses concurrently
results = await asyncio.gather(
    fetch_async(api_url1),
    fetch_async(api_url2),
    fetch_async(api_url3)
)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-checksum 0.1.0
Compute MD5/SHA file hashes and integrity checks. Security utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, hashing, integrity, machine-learning, ml, nlp, security, utilities

# pychecksum

File hashes

## Installation

```bash
pip install pychecksum
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_checksum import md5_hash, sha256_hash

# Calculate MD5 hash
hash_value = md5_hash("data")
# MD5 hash string

# Calculate SHA256 hash
hash_value = sha256_hash("data")
# SHA256 hash string
```

### AI/ML Use Cases

```python
from pylib_checksum import md5_hash, sha256_hash

# Hash training data for caching
data_hash = sha256_hash(str(training_data))
# Use hash as cache key

# Verify model file integrity
file_hash = sha256_hash(file_content)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-compress 0.1.0
Compress/decompress files (zip, gzip, tar). File compression utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, archives, compression, data-processing, data-science, files, machine-learning, ml, nlp, utilities

# pycompress

Compress/decompress files

## Installation

```bash
pip install pycompress
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_compress import compress, decompress

# Compress data
compressed = compress("original data")
# Compressed bytes

# Decompress data
decompressed = decompress(compressed)
# 'original data'
```

### AI/ML Use Cases

```python
from pylib_compress import compress, decompress

# Compress model weights for storage
compressed_weights = compress(model_weights)

# Compress training data
compressed_data = compress(json.dumps(training_data))
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-scheduler 0.1.0
Lightweight cron/job scheduler with async support. Task scheduling utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, cron, data-processing, data-science, machine-learning, ml, nlp, scheduling, tasks, utilities

# pyscheduler

Job scheduler

## Installation

```bash
pip install pyscheduler
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_scheduler import Scheduler

# Schedule task
scheduler = Scheduler()
scheduler.schedule("daily", task_function)
scheduler.start()
```

### AI/ML Use Cases

```python
from pylib_scheduler import Scheduler

# Schedule model retraining
scheduler.schedule("weekly", retrain_model)
scheduler.schedule("daily", update_predictions)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-taskqueue 0.1.0
In-memory async queue with retry & backoff. Task processing for AI agents. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, async, data-processing, data-science, machine-learning, ml, nlp, queue, task-processing, utilities

# pytaskqueue

In-memory async queue

## Installation

```bash
pip install pytaskqueue
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_taskqueue import TaskQueue

# Create task queue
queue = TaskQueue()
queue.enqueue(task_function, args=[1, 2, 3])
queue.process()
```

### AI/ML Use Cases

```python
from pylib_taskqueue import TaskQueue

# Queue ML prediction tasks
queue.enqueue(predict_task, data=data_point)
queue.enqueue(train_task, data=training_batch)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-timer 0.1.0
Code timers, profiling decorators. Performance measurement tools.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, machine-learning, ml, nlp, performance, profiling, timing, utilities

# pytimer

Code timers

## Installation

```bash
pip install pytimer
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_timer import Timer, timeit

# Timer context manager
with Timer() as timer:
    # Your code here
    pass
print(f"Elapsed: {timer.elapsed} seconds")

# Time function execution
@timeit
def my_function():
    # Your code here
    pass
```

### AI/ML Use Cases

```python
from pylib_timer import Timer, timeit

# Time ML model training
with Timer() as timer:
    model.train(training_data)
print(f"Training took {timer.elapsed:.2f} seconds")

# Profile prediction function
@timeit
def predict_batch(data):
    return model.predict(data)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-dateutils 0.1.0
Days-between, add/subtract, format/parse helpers. Date manipulation utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, date, datetime, machine-learning, ml, nlp, time, utilities

# pydateutils

Date helpers

## Installation

```bash
pip install pydateutils
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_dateutils import days_between, add_days, format_date

from datetime import datetime

# Calculate days between dates
date1 = datetime(2024, 1, 1)
date2 = datetime(2024, 1, 15)
days = days_between(date1, date2)
# 14

# Add days to date
new_date = add_days(date1, 10)
# datetime(2024, 1, 11)

# Format date
formatted = format_date(date1, "%Y-%m-%d")
# '2024-01-01'
```

### AI/ML Use Cases

```python
from pylib_dateutils import days_between, add_days, format_date

# Calculate training duration
start_date = datetime(2024, 1, 1)
end_date = datetime(2024, 1, 20)
training_days = days_between(start_date, end_date)

# Schedule next model evaluation
last_eval = datetime.now()
next_eval = add_days(last_eval, 7)  # Weekly evaluation
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-optimize 0.1.0
Simple optimization solvers (gradient descent, LP). Machine learning utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, machine-learning, ml, nlp, optimization, utilities

# pyoptimize

Optimization solvers

## Installation

```bash
pip install pyoptimize
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_optimize import optimize

# Optimize function
def objective(x):
    return x**2 + 2*x + 1

result = optimize(objective, bounds=[-10, 10])
# Optimal value and parameters
```

### AI/ML Use Cases

```python
from pylib_optimize import optimize

# Optimize hyperparameters
def model_score(params):
    model = create_model(params)
    return model.evaluate()

optimal_params = optimize(model_score, bounds=param_bounds)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-compare 0.1.0
Deep diff for dicts/lists with patch generation. Data comparison utilities.
License: MIT
Requires Python: >=3.8
Keywords: ai, comparison, data-analysis, data-processing, data-science, diff, machine-learning, ml, nlp, utilities

# pycompare

Deep diff for dicts/lists

## Installation

```bash
pip install pycompare
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_compare import deep_diff, patch, compare

# Deep diff between dictionaries
dict1 = {"a": 1, "b": 2}
dict2 = {"a": 1, "b": 3, "c": 4}
diff = deep_diff(dict1, dict2)
# {'b': {'op': 'replace', 'old': 2, 'new': 3}, 'c': {'op': 'add', 'value': 4}}

# Apply patch
patched = patch(dict1, diff)
# {'a': 1, 'b': 3, 'c': 4}

# Compare objects
is_equal = compare(dict1, dict2)
# False
```

### AI/ML Use Cases

```python
from pylib_compare import deep_diff, patch, compare

# Track model configuration changes
old_config = {"lr": 0.01, "batch_size": 32}
new_config = {"lr": 0.001, "batch_size": 64}
changes = deep_diff(old_config, new_config)

# Compare model outputs
output1 = {"prediction": "cat", "confidence": 0.9}
output2 = {"prediction": "cat", "confidence": 0.9}
are_same = compare(output1, output2)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-serializer 0.1.0
Safe JSON/YAML serialization with circular-reference handling. Data processing utility.
License: MIT
Requires Python: >=3.8
Keywords: ai, data, data-processing, data-science, json, machine-learning, ml, nlp, serialization, utilities, yaml

# pyserializer

Safe JSON/YAML serialization

## Installation

```bash
pip install pyserializer
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_serializer import serialize, to_json, to_yaml

# Serialize to JSON
data = {"name": "John", "age": 30}
json_str = serialize(data)
# '{"name": "John", "age": 30}'

# Convert to JSON
json_output = to_json(data)

# Convert to YAML
yaml_output = to_yaml(data)
# 'name: John
age: 30'
```

### AI/ML Use Cases

```python
from pylib_serializer import serialize, to_json, to_yaml

# Serialize ML model predictions
predictions = {"class": "cat", "confidence": 0.95}
json_predictions = serialize(predictions)

# Save model config
config = {"model": "resnet50", "epochs": 100}
yaml_config = to_yaml(config)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-validator 0.1.0
Validate Python objects with schema definitions. Perfect for API validation.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, data-validation, machine-learning, ml, nlp, schema, utilities, validation

# pyvalidator

Validate Python objects

## Installation

```bash
pip install pyvalidator
```

## 💡 Usage Examples

### Basic Operations

```python
from pylib_validator import Schema, validate

# Define validation schema
schema = Schema({
    "name": lambda x: isinstance(x, str) and len(x) > 0,
    "age": lambda x: isinstance(x, int) and 0 < x < 150,
    "email": lambda x: "@" in str(x)
})

# Validate data
data = {"name": "John", "age": 30, "email": "john@example.com"}
is_valid, errors = validate(data, schema)
# (True, [])
```

### AI/ML Use Cases

```python
from pylib_validator import Schema, validate

# Validate ML model inputs
input_schema = Schema({
    "features": lambda x: isinstance(x, list) and len(x) > 0,
    "model_type": lambda x: x in ["classification", "regression"]
})
is_valid, errors = validate(model_input, input_schema)
```

## 📚 API Reference

See package documentation for complete API reference.


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT