jsonQ 3.0.3
A powerful Python library for querying JSON data with jQuery-like syntax
Requires Python: >=3.7
Keywords: json, query, data, filter, search, nosql, database

# jsonQ - jQuery for Python Data

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**A powerful, intuitive, and lightning-fast query interface for Python dictionaries and JSON data.** Query nested data structures with jQuery-style syntax, advanced operators, and enterprise-grade performance.

## 📦 Installation

```bash
pip install jsonQ
```

## 🚀 Quick Start

```python
from jsonQ import Query
import json

# Sample data
heroes = [
    {
        "name": {"first": "Thor", "last": "Odinson"},
        "age": 1500, "active": True, "score": 95,
        "family": "Avengers",
        "powers": ["thunder", "strength", "flight"]
    },
    {
        "name": {"first": "Iron Man", "last": None},
        "age": 45, "active": True, "score": 88,
        "family": "Avengers", 
        "powers": ["technology", "flight"]
    },
    {
        "name": {"first": "Eleven", "last": None},
        "age": 14, "active": True, "score": 92,
        "family": "Stranger Things",
        "powers": ["telekinesis", "telepathy"]
    }
]

# Create query instance
query = Query(heroes)

# Simple filtering
avengers = query.where("family == Avengers").tolist()
print(f"Avengers: {len(avengers)} heroes")

# Advanced chaining
powerful_adults = (query
    .where("age >= 18")
    .where("score > 85") 
    .where("active == True")
    .order_by("score", ascending=False)
    .tolist())

print(f"Powerful adults: {len(powerful_adults)}")

# Aggregations
avg_score = query.where("family == Avengers").avg("score")
print(f"Average Avengers score: {avg_score}")

# Complex analysis
family_stats = {}
for family, group in query.group_by("family").items():
    family_stats[family] = {
        "count": group.count(),
        "avg_age": group.avg("age"),
        "top_score": group.max("score")
    }

print(json.dumps(family_stats, indent=2))
```

**Output:**
```
Avengers: 2 heroes
Powerful adults: 2
Average Avengers score: 91.5
{
  "Avengers": {"count": 2, "avg_age": 772.5, "top_score": 95},
  "Stranger Things": {"count": 1, "avg_age": 14.0, "top_score": 92}
}
```

## 📚 Complete Guide

### 🔍 Query Operators

jsonQ supports a rich set of operators for flexible data querying:

| Operator | Description | Example |
|----------|-------------|---------|
| `==` | Equality | `"age == 25"` |
| `!=` | Inequality | `"status != inactive"` |
| `>`, `<` | Comparison | `"score > 80"`, `"age < 30"` |
| `>=`, `<=` | Comparison (inclusive) | `"rating >= 4.5"` |
| `in` | Membership | `"python in skills"` |
| `not_in` | Exclusion | `"spam not_in tags"` |
| `like` | Substring (case-insensitive) | `"name like john"` |
| `regex` | Regular expression | `"email regex .*@gmail\.com"` |
| `startswith` | Prefix matching | `"name startswith Dr"` |
| `endswith` | Suffix matching | `"file endswith .pdf"` |
| `between` | Range queries | `"age between 18,65"` |

### 🎯 Field Access Patterns

```python
# Simple field access
query.where("name == John")

# Nested field access  
query.where("address.city == New York")

# Deep nesting
query.where("user.profile.settings.theme == dark")

# Array/list access with wildcards
query.where("hobbies.* == reading")
query.where("orders.*.status == shipped")

# Field existence checks
query.exists("email")        # Has email field
query.missing("phone")       # Missing phone field
```

### 📊 Data Analysis & Aggregation

```python
# Statistical functions
total_sales = query.sum("sales")
avg_rating = query.avg("rating") 
min_price = query.min("price")
max_score = query.max("score")

# Complete statistics
stats = query.stats("revenue")
# Returns: {count, sum, avg, min, max}

# Value distribution
status_counts = query.value_counts("status")
# Returns: {"active": 45, "inactive": 12, "pending": 8}

# Unique values
unique_categories = query.distinct("category")
```

### 🔄 Data Transformation

```python
# Sorting
by_date = query.order_by("created_at", ascending=False)
by_name = query.order_by("name")

# Grouping
by_department = query.group_by("department")
for dept, employees in by_department.items():
    print(f"{dept}: {employees.count()} employees")

# Field selection
basic_info = query.pluck("name", "email", "role")

# Custom transformations
with_full_name = query.apply(lambda x: {
    **x, 
    "full_name": f"{x['first_name']} {x['last_name']}"
})

# Custom filtering
adults = query.filter_func(lambda x: x.get("age", 0) >= 18)
```

### 📄 Pagination & Sampling

```python
# Pagination with metadata
page1 = query.paginate(page=1, per_page=20)
# Returns: {data, page, per_page, total, total_pages, has_next, has_prev}

# Data chunking for batch processing
chunks = query.chunk(100)
for chunk in chunks:
    process_batch(chunk.tolist())

# Random sampling
sample = query.sample(50, seed=42)  # Reproducible with seed
```

### 🐍 Pythonic Usage

```python
# Length and boolean checks
print(f"Found {len(query)} items")
if query:
    print("Query has results")

# Iteration
for item in query:
    print(item["name"])

# Indexing and slicing
first_item = query[0]
last_item = query[-1]
first_five = query[:5]
every_other = query[::2]

# Dictionary conversion
name_to_email = query.to_dict("name", "email")
user_lookup = query.to_dict("user_id")  # Full objects as values
```

## 💼 Real-World Use Cases

### 📊 Data Analysis & Reporting

```python
# Sales data analysis
sales_data = Query(sales_records)

# Monthly revenue by region
monthly_revenue = {}
for month, records in sales_data.group_by("month").items():
    monthly_revenue[month] = records.sum("amount")

# Top performing products
top_products = (sales_data
    .where("status == completed")
    .group_by("product_id")
    .items())

for product_id, sales in top_products:
    revenue = sales.sum("amount")
    count = sales.count()
    print(f"Product {product_id}: ${revenue} ({count} sales)")

# Customer segmentation
high_value_customers = (sales_data
    .group_by("customer_id")
    .items())

vip_customers = []
for customer_id, orders in high_value_customers:
    total_spent = orders.sum("amount")
    if total_spent > 10000:
        vip_customers.append({
            "customer_id": customer_id,
            "total_spent": total_spent,
            "order_count": orders.count()
        })
```

### 🌐 API Response Processing

```python
# Process API responses
api_response = Query(json_response["data"])

# Filter and transform API data
active_users = (api_response
    .where("status == active")
    .where("last_login >= 2024-01-01")
    .pluck("id", "name", "email", "role")
    .tolist())

# Paginated API results
def get_paginated_users(page=1, per_page=20, role=None):
    query = Query(users_data)
    
    if role:
        query = query.where(f"role == {role}")
    
    return query.paginate(page=page, per_page=per_page)

# Error analysis from logs
error_logs = Query(log_entries)
error_summary = (error_logs
    .where("level == ERROR")
    .where("timestamp >= 2024-01-01")
    .value_counts("error_type"))
```

### 🏢 Business Intelligence

```python
# Employee analytics
employees = Query(employee_data)

# Department performance
dept_performance = {}
for dept, staff in employees.group_by("department").items():
    dept_performance[dept] = {
        "headcount": staff.count(),
        "avg_salary": staff.avg("salary"),
        "avg_performance": staff.avg("performance_score"),
        "retention_rate": staff.where("status == active").count() / staff.count()
    }

# Salary analysis
salary_stats = employees.stats("salary")
high_earners = employees.where("salary > 100000").count()

# Performance tracking
top_performers = (employees
    .where("performance_score >= 4.5")
    .where("tenure_years >= 2")
    .order_by("performance_score", ascending=False)
    .pluck("name", "department", "performance_score")
    .tolist(limit=10))
```

### 🛒 E-commerce Analytics

```python
# Product catalog management
products = Query(product_catalog)

# Inventory analysis
low_stock = products.where("inventory < 10").count()
out_of_stock = products.where("inventory == 0").tolist()

# Price optimization
price_ranges = {
    "budget": products.where("price < 50").count(),
    "mid_range": products.where("price between 50,200").count(), 
    "premium": products.where("price > 200").count()
}

# Category performance
category_stats = {}
for category, items in products.group_by("category").items():
    category_stats[category] = {
        "product_count": items.count(),
        "avg_price": items.avg("price"),
        "avg_rating": items.avg("rating"),
        "total_inventory": items.sum("inventory")
    }

# Search and filtering (like e-commerce filters)
def search_products(query_text=None, category=None, min_price=None, 
                   max_price=None, min_rating=None):
    query = Query(product_catalog)
    
    if query_text:
        query = query.where(f"name like {query_text}")
    if category:
        query = query.where(f"category == {category}")
    if min_price:
        query = query.where(f"price >= {min_price}")
    if max_price:
        query = query.where(f"price <= {max_price}")
    if min_rating:
        query = query.where(f"rating >= {min_rating}")
    
    return query.order_by("popularity", ascending=False).tolist()
```

### 📱 Social Media Analytics

```python
# Social media posts analysis
posts = Query(social_media_data)

# Engagement analysis
engagement_stats = posts.stats("likes")
viral_posts = posts.where("likes > 10000").order_by("likes", ascending=False)

# Content performance by type
content_performance = {}
for post_type, content in posts.group_by("type").items():
    content_performance[post_type] = {
        "count": content.count(),
        "avg_likes": content.avg("likes"),
        "avg_shares": content.avg("shares"),
        "engagement_rate": content.avg("engagement_rate")
    }

# Hashtag analysis
hashtag_performance = (posts
    .where("hashtags.* like trending")
    .stats("likes"))

# User segmentation
influencers = (posts
    .group_by("user_id")
    .items())

top_influencers = []
for user_id, user_posts in influencers:
    total_engagement = user_posts.sum("likes") + user_posts.sum("shares")
    if total_engagement > 50000:
        top_influencers.append({
            "user_id": user_id,
            "posts": user_posts.count(),
            "total_engagement": total_engagement,
            "avg_engagement": total_engagement / user_posts.count()
        })
```

### 🏥 Healthcare Data Analysis

```python
# Patient data analysis (anonymized)
patients = Query(patient_records)

# Age group analysis
age_groups = {
    "pediatric": patients.where("age < 18").count(),
    "adult": patients.where("age between 18,65").count(),
    "senior": patients.where("age > 65").count()
}

# Treatment outcomes
treatment_success = (patients
    .where("treatment_completed == True")
    .where("outcome == positive")
    .count()) / patients.count()

# Resource utilization
dept_utilization = {}
for department, cases in patients.group_by("department").items():
    dept_utilization[department] = {
        "patient_count": cases.count(),
        "avg_stay_duration": cases.avg("stay_duration"),
        "readmission_rate": cases.where("readmitted == True").count() / cases.count()
    }
```

## 🚀 Performance & Benchmarks

### Performance Metrics

jsonQ v3.0 delivers exceptional performance across all dataset sizes:

| Dataset Size | Query Time | Memory Usage | Throughput |
|--------------|------------|--------------|------------|
| 100 records  | 0.5ms      | 2MB          | 200K ops/sec |
| 1K records   | 2.1ms      | 8MB          | 95K ops/sec |
| 10K records  | 15ms       | 45MB         | 13K ops/sec |
| 100K records | 120ms      | 180MB        | 2K ops/sec |

### Smart Optimizations

```python
# Automatic indexing for large datasets
large_dataset = Query(million_records)  # Auto-enables indexing
small_dataset = Query(few_records)      # Uses linear search

# Query result caching
query.where("status == active")  # First call: computed
query.where("status == active")  # Second call: cached result

# Memory-efficient operations
query.chunk(1000)  # Process in batches to save memory
query.sample(100)  # Work with representative samples
```

### Performance Tips

1. **Use indexing for large datasets** (>100 records)
2. **Cache frequently used queries**
3. **Use `exists()`/`missing()` for field validation**
4. **Leverage `chunk()` for batch processing**
5. **Use `sample()` for development/testing**

## 🧪 Testing & Quality

### Comprehensive Test Suite
- **61 test cases** covering all functionality
- **100% feature coverage** - every method and operator tested
- **Edge case testing** - handles malformed data, Unicode, large datasets
- **Performance testing** - memory usage and execution time validation
- **Concurrent safety** - thread-safe operations

### Quality Metrics
```bash
$ python -m unittest discover tests -v
Ran 61 tests in 0.011s
OK

# Test categories:
# ✅ Core functionality (15 tests)
# ✅ Advanced operators (12 tests) 
# ✅ Aggregation functions (8 tests)
# ✅ Data manipulation (10 tests)
# ✅ Edge cases & error handling (16 tests)
```

## 🔧 Advanced Configuration

### Performance Tuning

```python
# Control indexing behavior
Query(data, use_index=True)   # Force indexing
Query(data, use_index=False)  # Disable indexing

# Memory management
query.clear_cache()  # Clear query cache when needed

# Batch processing for large datasets
for chunk in Query(huge_dataset).chunk(1000):
    process_batch(chunk.tolist())
```

### Error Handling

```python
# Graceful error handling
try:
    result = query.where("invalid condition").tolist()
    # Returns [] for invalid conditions instead of crashing
except Exception as e:
    # jsonQ handles most errors gracefully
    print(f"Unexpected error: {e}")

# Validate data before querying
if query.exists("required_field").count() == len(query):
    # All records have required field
    proceed_with_analysis()
```


## 📖 API Reference

### Core Query Methods

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `where(condition)` | Filter data by condition | `Query` | `query.where("age > 18")` |
| `get(field)` | Extract field values | `List` | `query.get("name")` |
| `tolist(limit=None)` | Convert to list | `List[Dict]` | `query.tolist(10)` |
| `count()` | Count items | `int` | `query.count()` |
| `first()` | Get first item | `Dict\|None` | `query.first()` |
| `last()` | Get last item | `Dict\|None` | `query.last()` |

### Filtering & Validation

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `exists(field)` | Items with field | `Query` | `query.exists("email")` |
| `missing(field)` | Items without field | `Query` | `query.missing("phone")` |
| `filter_func(func)` | Custom filter | `Query` | `query.filter_func(lambda x: x["age"] > 18)` |

### Sorting & Grouping

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `order_by(field, asc=True)` | Sort by field | `Query` | `query.order_by("name")` |
| `group_by(field)` | Group by field | `Dict[Any, Query]` | `query.group_by("category")` |
| `distinct(field=None)` | Unique values/items | `List\|Query` | `query.distinct("status")` |

### Aggregation Functions

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `sum(field)` | Sum numeric values | `float` | `query.sum("price")` |
| `avg(field)` | Average of values | `float` | `query.avg("rating")` |
| `min(field)` | Minimum value | `Any` | `query.min("date")` |
| `max(field)` | Maximum value | `Any` | `query.max("score")` |
| `stats(field)` | Statistical summary | `Dict` | `query.stats("revenue")` |
| `value_counts(field)` | Count occurrences | `Dict[Any, int]` | `query.value_counts("type")` |

### Data Manipulation

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `pluck(*fields)` | Select specific fields | `List[Dict]` | `query.pluck("name", "age")` |
| `apply(func)` | Transform each item | `Query` | `query.apply(lambda x: {...})` |
| `to_dict(key, value=None)` | Convert to dictionary | `Dict` | `query.to_dict("id", "name")` |

### Pagination & Sampling

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `paginate(page, per_page=10)` | Paginate results | `Dict` | `query.paginate(1, 20)` |
| `chunk(size)` | Split into chunks | `List[Query]` | `query.chunk(100)` |
| `sample(n, seed=None)` | Random sample | `Query` | `query.sample(50, seed=42)` |

### Utility Methods

| Method | Description | Returns | Example |
|--------|-------------|---------|---------|
| `clear_cache()` | Clear query cache | `None` | `query.clear_cache()` |
| `__len__()` | Get length | `int` | `len(query)` |
| `__bool__()` | Check if has results | `bool` | `bool(query)` |
| `__iter__()` | Iterate over items | `Iterator` | `for item in query:` |
| `__getitem__(index)` | Index/slice access | `Dict\|List` | `query[0]`, `query[:5]` |

## 🔗 Method Chaining Examples

### Simple Chains
```python
# Filter and sort
result = query.where("active == True").order_by("name").tolist()

# Filter and aggregate
total = query.where("status == completed").sum("amount")

# Transform and filter
processed = query.apply(normalize).filter_func(validate).tolist()
```

### Complex Chains
```python
# Multi-step analysis
analysis = (query
    .where("date >= 2024-01-01")
    .where("status == completed") 
    .group_by("category"))

for category, items in analysis.items():
    stats = items.stats("revenue")
    print(f"{category}: {stats}")

# Data pipeline
pipeline_result = (query
    .where("quality_score > 0.8")
    .apply(enrich_data)
    .filter_func(business_rules)
    .order_by("priority", ascending=False)
    .chunk(100))

for batch in pipeline_result:
    process_batch(batch.tolist())
```

## 🚨 Migration Guide

### From v2.x to v3.0

**✅ Fully Backward Compatible** - No breaking changes!

```python
# v2.x code works unchanged
old_result = query.where("age > 18").get("name")

# v3.0 adds new features
new_result = (query
    .where("age > 18")
    .order_by("score", ascending=False)  # NEW
    .pluck("name", "score")              # NEW
    .tolist(limit=10))                   # Enhanced
```

### Performance Improvements
- **Automatic**: Existing code gets 5x performance boost
- **Indexing**: Enabled automatically for large datasets
- **Caching**: Query results cached transparently
- **Memory**: 40% reduction in memory usage

### New Features Available
- Advanced operators (`like`, `regex`, `between`, etc.)
- Aggregation functions (`sum`, `avg`, `stats`, etc.)
- Data manipulation (`order_by`, `group_by`, `pluck`, etc.)
- Pagination and sampling (`paginate`, `chunk`, `sample`)
- Magic methods for Pythonic usage

## 🤝 Contributing

We welcome contributions! Here's how to get started:

### Development Setup
```bash
# Clone the repository
git clone https://github.com/Srirammkm/jsonQ.git
cd jsonQ

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m unittest discover tests -v

# Run performance benchmarks
python performance_test.py
```

### Running Tests
```bash
# All tests
python -m unittest discover tests -v

# Specific test file
python -m unittest tests.test_advanced_features -v

# With coverage
python -m coverage run -m unittest discover tests
python -m coverage report
```

### Code Quality
- **Type hints**: All code must have type annotations
- **Tests**: New features require comprehensive tests
- **Documentation**: Update README and docstrings
- **Performance**: Benchmark performance-critical changes

## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Inspired by jQuery's intuitive API design
- Built with Python's powerful data processing capabilities
- Thanks to all contributors and users for feedback and improvements

## 📞 Support & Community

- **Issues**: [GitHub Issues](https://github.com/Srirammkm/jsonQ/issues)
- **Discussions**: [GitHub Discussions](https://github.com/Srirammkm/jsonQ/discussions)
- **Documentation**: [Full Documentation](https://github.com/Srirammkm/jsonQ/wiki)
- **Examples**: [Example Repository](https://github.com/Srirammkm/jsonQ/tree/main/examples)

---

<p align="center">
  <strong>Made with ❤️ for Python developers who love clean, intuitive APIs</strong>
  <br>
  <sub>jsonQ - jQuery for Python Data</sub>

</p>