Camelot: PDF Table Extraction for Humans
Python library for extracting tables from PDF files with high accuracy:
Key Features:
- Multiple Parsing Methods: Stream and Lattice parsers for different table
types
- High Accuracy: Better results than traditional PDF parsing tools
- Pandas Integration: Direct conversion to pandas DataFrames
- Visual Debugging: Plot detected table areas for validation
- Customizable: Fine-tune detection parameters
Usage Examples:
1
2
3
4
5
6
7
8
9
10
11
12
| import camelot
# Extract tables from PDF
tables = camelot.read_pdf('document.pdf')
print(f"Found {tables.n} tables")
# Convert to pandas DataFrame
df = tables[0].df
# Export to various formats
tables.export('output.csv', f='csv')
tables.export('output.xlsx', f='excel')
|
When to Use:
- Financial reports with tabular data
- Research papers with data tables
- Government documents with statistics
- Any structured data trapped in PDFs
PostgreSQL Row Level Security for Multi-Tenancy
Multi-tenant data isolation with PostgreSQL Row Level Security
Advanced PostgreSQL feature for implementing secure multi-tenant applications:
What is Row Level Security (RLS):
- Fine-Grained Access: Control access at the row level within tables
- Policy-Based: Define security policies for different user types
- Transparent: Application code doesn’t need complex filtering logic
- Performance: Database-level enforcement with optimized execution
Implementation Pattern:
1
2
3
4
5
6
7
8
9
10
| -- Enable RLS on table
ALTER TABLE customer_data ENABLE ROW LEVEL SECURITY;
-- Create policy for tenant isolation
CREATE POLICY tenant_isolation ON customer_data
FOR ALL TO tenant_user
USING (tenant_id = current_setting('app.current_tenant')::integer);
-- Set tenant context in application
SET app.current_tenant = '123';
|
Benefits:
- Data Isolation: Strong tenant boundaries
- Security: Prevents data leaks between tenants
- Simplicity: Reduces application complexity
- Compliance: Meets regulatory requirements
Use Cases:
- SaaS applications with multiple customers
- Healthcare systems with patient data isolation
- Financial applications with account separation
- Any system requiring strict data boundaries
Zerodha Varsity - Free Trading Education
Varsity by Zerodha – Markets, Trading, and Investing Simplified
Comprehensive free educational platform for learning about financial markets:
What It Covers:
Trading Fundamentals:
- Technical Analysis: Chart patterns, indicators, candlesticks
- Fundamental Analysis: Financial statement analysis, valuation
- Options Trading: Strategies, Greeks, risk management
- Futures Trading: Contract specifications, margin requirements
Investment Concepts:
- Portfolio Management: Asset allocation, diversification
- Risk Management: Position sizing, stop losses
- Market Psychology: Behavioral finance concepts
- Regulatory Framework: SEBI guidelines and compliance
Learning Approach:
- Module-Based: Structured progression from basics to advanced
- Practical Examples: Real market scenarios and case studies
- Interactive Content: Charts, calculators, and tools
- Free Access: No cost for high-quality financial education
Value Proposition:
- Democratizes financial education
- Reduces dependence on expensive courses
- Provides practical, actionable knowledge
- Builds informed investor community
Write yourself a Git!
Educational project to understand Git internals by implementing it from scratch:
Learning Objectives:
- Version Control Concepts: How distributed version control works
- Git Internals: Objects, references, and storage mechanisms
- Data Structures: Trees, graphs, and hash-based storage
- File System Operations: Blob storage and retrieval
Key Components to Implement:
Core Git Objects:
- Blobs: File content storage
- Trees: Directory structure representation
- Commits: Snapshots with metadata
- Tags: Named references to commits
Repository Operations:
- Initialize: Create new repository structure
- Add: Stage files for commit
- Commit: Create snapshot with message
- Checkout: Switch between branches/commits
Implementation Benefits:
- Deep Understanding: Learn Git’s internal architecture
- Debugging Skills: Better troubleshoot Git issues
- System Design: Understand distributed system principles
- Programming Practice: File I/O, data structures, algorithms
Technologies Used:
- Python for implementation simplicity
- File system operations for storage
- Hashing algorithms for content addressing
- Command-line interface design
Each resource provides deep technical knowledge in its domain - from practical
data extraction to advanced database security, financial literacy, and version
control system design.
