# Data Analysis Guide
Comprehensive guide to data analysis workflows with hawk.
## 📖 Table of Contents
- [Data Analysis Fundamentals](#data-analysis-fundamentals)
- [Exploratory Data Analysis](#exploratory-data-analysis)
- [Statistical Operations](#statistical-operations)
- [Data Filtering and Selection](#data-filtering-and-selection)
- [Grouping and Aggregation](#grouping-and-aggregation)
- [Data Transformation](#data-transformation)
- [Time Series Analysis](#time-series-analysis)
- [Performance Analytics](#performance-analytics)
- [Business Intelligence](#business-intelligence)
- [Advanced Analytics Patterns](#advanced-analytics-patterns)
## Data Analysis Fundamentals
### The hawk Analytics Workflow
```bash
3. Data Filtering → hawk '.[] | select(.condition)'
4. Data Aggregation → hawk '.[] | group_by(.field) | agg_function'
5. Results Export → hawk '.results[]' --format csv > output.csv
```
### Understanding Your Data Structure
Before analysis, always understand your data:
```bash
# Get basic information
# Count total records
# Sample the first few records
hawk '.[0:5]' data.json
# Check for missing values
## Exploratory Data Analysis
### Data Overview and Profiling
```bash
# Dataset summary
# Record count by category
# Unique values in a field
# Data quality check
### Sample Data Analysis Workflow
Let's work with a sample sales dataset:
```json
{
"sales": [
{
"date": "2024-01-15",
"product": "Laptop",
"category": "Electronics",
"amount": 1200,
"quantity": 1,
"region": "North",
"salesperson": "Alice"
},
{
"date": "2024-01-15",
"product": "Mouse",
"category": "Electronics",
"amount": 25,
"quantity": 3,
"region": "South",
"salesperson": "Bob"
},
{
"date": "2024-01-16",
"product": "Desk",
"category": "Furniture",
"amount": 300,
"quantity": 2,
"region": "North",
"salesperson": "Alice"
},
{
"date": "2024-01-16",
"product": "Chair",
"category": "Furniture",
"amount": 150,
"quantity": 4,
"region": "South",
"salesperson": "Carol"
}
]
}
```
**Basic Analysis:**
```bash
# Total sales count
# Total revenue
# Average sale amount
# Sales by category
# Top performing regions
## Statistical Operations
### Descriptive Statistics
```bash
# Central tendency
# Variability
hawk '.[] | stddev(.field)' data.json # Standard deviation
# Distribution
```
### Advanced Statistical Analysis
```bash
# Quartile analysis (using slicing)
# Range analysis
# Frequency analysis
### Statistical Comparisons
```bash
# Compare groups
# Performance metrics
# Correlation analysis (manual)
## Data Filtering and Selection
### Conditional Filtering
```bash
# Numeric conditions
# String conditions
# Date filtering (string-based)
```
### Complex Multi-condition Filtering
```bash
# Multiple AND conditions
# Range filtering
# Category filtering
# Data quality filtering
### Sampling and Data Selection
```bash
# Random sampling (using slicing)
# Stratified sampling
# Top/Bottom N
```
## Grouping and Aggregation
### Basic Grouping Operations
```bash
# Group by single field
hawk '.[] | group_by(.category) | avg(.price)' products.json
# Group by multiple criteria (sequential)
### Advanced Aggregation Patterns
```bash
# Multiple aggregations per group
hawk '.[] | group_by(.department) | avg(.salary)' employees.json # Average per group
hawk '.[] | group_by(.department) | sum(.salary)' employees.json # Total per group
# Performance analytics
hawk '.[] | group_by(.server) | min(.cpu_usage)' performance.json
```
### Business Intelligence Aggregations
```bash
# Sales analysis
hawk '.[] | group_by(.region) | count' customers.json
# Financial analysis
# User behavior analysis
```
## Data Transformation
### Data Cleaning and Normalization
```bash
# Clean text data
# Normalize numeric data
# Handle missing data
```
### Feature Engineering
```bash
# Extract date components
# Categorize numeric data
### Data Reshaping
```bash
# Extract specific fields
## Time Series Analysis
### Date-based Analysis
```bash
# Group by time periods
hawk '.[] | group_by(.date | substring(0, 7))' time_series.json # By year-month
# Trend analysis
```
### Sales Trend Analysis
```bash
# Monthly sales trends
# Daily transaction counts
# Seasonal analysis
# Growth analysis
```
### Performance Over Time
```bash
# System performance trends
# User engagement trends
# Error rate analysis
## Performance Analytics
### Application Performance Analysis
```bash
# Response time analysis
hawk '.[] | group_by(.endpoint) | min(.response_time)' api_logs.json
# Error rate calculation
# Throughput analysis
### System Resource Analysis
```bash
# Memory usage analysis
# CPU utilization
# Disk usage trends
### User Performance Analysis
```bash
# Page load times
# User session analysis
# Conversion rate analysis
## Business Intelligence
### Sales Analytics
```bash
# Revenue analysis
hawk '.[] | group_by(.region) | sum(.revenue)' regional_sales.json
# Profitability analysis
# Sales performance
```
### Customer Analytics
```bash
# Customer segmentation
# Customer behavior
# Retention analysis
### Marketing Analytics
```bash
# Campaign performance
# Channel effectiveness
# ROI analysis
## Advanced Analytics Patterns
### Cohort Analysis
```bash
# User cohorts by signup month
# Retention by cohort
### Funnel Analysis
```bash
# Conversion funnel
hawk '.[] | select(.stage == "purchase") | count' funnel.json
# Drop-off analysis
### A/B Testing Analysis
```bash
# Test group comparison
# Statistical significance (basic)
### Anomaly Detection (Basic)
```bash
# Outlier detection using statistical methods
# Threshold-based anomalies
```
## Export and Reporting
### Data Export Formats
```bash
# Export to JSON
hawk '.summary' --format json > summary_report.json
# Export specific fields
### Report Generation
```bash
# Summary statistics report
echo "=== Sales Summary ===" > report.txt
hawk '.[] | count' sales.json >> report.txt
```
## Best Practices
### Data Analysis Workflow
1. **Start with exploration**: Always use `hawk '. | info'` first
2. **Sample your data**: Use slicing `.[0:100]` for large datasets
3. **Check data quality**: Filter out invalid records early
4. **Build incrementally**: Add complexity step by step
5. **Validate results**: Cross-check with known values
### Performance Optimization
```bash
# ✅ Filter early in pipeline
# ❌ Filter late in pipeline
# ✅ Use appropriate data types
# ✅ Sample large datasets
### Common Pitfalls
```bash
# ❌ Ignoring missing data
# ✅ Handle missing data
# ❌ Not validating data types
# ✅ Validate data types
---
**Related Documentation:**
- [Getting Started](getting-started.md) - Basic introduction
- [Query Language Reference](query-language.md) - Complete syntax
- [String Operations](string-operations.md) - Text processing
- [Examples](examples/) - Real-world use cases