Basic CSV structure
Name,Age,City,Salary
John Doe,30,New York,75000
Jane Smith,25,Los Angeles,65000
Bob Johnson,35,Chicago,80000

Semicolon delimiter (European standard)
Name;Age;City;Salary
John Doe;30;New York;75000

Tab delimiter (TSV - Tab-Separated Values)
Name	Age	City	Salary
John Doe	30	New York	75000

Pipe delimiter
Name|Age|City|Salary
John Doe|30|New York|75000

Quoted fields with commas and special characters
Name,Description,Price
"iPhone 13, 128GB","Smartphone with A15 chip, 128GB storage",699.99
"MacBook Pro, 14""","Laptop with M1 Pro chip, 14"" display",1999.99
"Data Analysis, Advanced","Course includes statistics, visualization",299.50

With headers (most common)
Product,Category,Price,Stock
iPhone,Electronics,699,150

Without headers
iPhone,Electronics,699,150
iPad,Electronics,449,200

Multiple header rows
Company Sales Report
Quarter 1, 2023
Product,Category,Price,Stock
iPhone,Electronics,699,150

Simply drag your CSV file into the upload area:
- Visual feedback during drag operation
- Instant file validation
- Progress indicator for large files
- Error messages for invalid files

Click "Choose File" to browse:
- Multi-file selection support
- File type validation
- Size limit checking (100MB max)
- Format auto-detection

Import directly from web URLs:
- Google Sheets public links
- Direct CSV file URLs
- API endpoints returning CSV data
- Cloud storage links (Dropbox, etc.)

Paste CSV data directly:
- Copy from spreadsheet applications
- Paste from text editors
- Real-time format validation
- Automatic delimiter detection

// Automatic delimiter detection
Supported delimiters:
- Comma (,) - Standard CSV
- Semicolon (;) - European standard
- Tab (\t) - TSV format
- Pipe (|) - Alternative delimiter
- Custom - User-defined delimiter

Header Configuration:
✅ First row contains headers
✅ Skip empty rows
✅ Trim whitespace
✅ Auto-detect data types
⚙️ Custom header names
⚙️ Header row position (row 1, 2, 3...)

// Automatic data type inference
String: "John Doe", "Product Name"
Number: 123, 45.67, -89
Date: "2023-09-01", "01/09/2023", "Sept 1, 2023"
Boolean: true, false, yes, no, 1, 0
Currency: $1,234.56, €999.99, £750.00
Percentage: 85%, 0.85, 85

Before: Data with duplicates
Name,Email,Phone
John Doe,john@email.com,555-1234
Jane Smith,jane@email.com,555-5678
John Doe,john@email.com,555-1234

After: Duplicates removed
Name,Email,Phone
John Doe,john@email.com,555-1234
Jane Smith,jane@email.com,555-5678

Before: Missing data
Name,Age,Salary
John Doe,30,75000
Jane Smith,,65000
Bob Johnson,35,

After: Missing values handled
Name,Age,Salary
John Doe,30,75000
Jane Smith,28,65000    # Age filled with average
Bob Johnson,35,72500   # Salary filled with median

Before: Inconsistent formatting
Name,City,Country
john doe,new york,usa
JANE SMITH,Los Angeles,USA
Bob Johnson,chicago,United States

After: Standardized formatting
Name,City,Country
John Doe,New York,USA
Jane Smith,Los Angeles,USA
Bob Johnson,Chicago,USA

// Available transformations
Add Column: Calculate new values from existing columns
Remove Column: Delete unwanted columns
Rename Column: Change column headers
Reorder Columns: Drag and drop column arrangement
Split Column: Divide single column into multiple
Merge Columns: Combine multiple columns into one

// Excel-style formulas supported
=A2+B2                    // Add columns A and B
=IF(C2>50,"Pass","Fail")  // Conditional logic
=CONCATENATE(A2," ",B2)   // Combine text
=ROUND(D2,2)              // Round to 2 decimals
=UPPER(E2)                // Convert to uppercase
=LEN(F2)                  // Text length

Date format standardization
Original: "Jan 1, 2023", "2023/01/01", "01-01-2023"
Standardized: "2023-01-01", "2023-01-01", "2023-01-01"

Date calculations
Birth_Date,Current_Date,Age
1990-05-15,2023-09-01,=DATEDIF(A2,B2,"Y")

Time zone conversions
UTC_Time,Local_Time
14:30:00,=A2+TIMEVALUE("5:30")  // Add 5.5 hours for IST

// Filter options for each column
Text Filters:
- Contains / Does not contain
- Starts with / Ends with
- Equals / Does not equal
- Is empty / Is not empty

Number Filters:
- Greater than / Less than
- Between / Not between
- Top N values / Bottom N values
- Above average / Below average

Date Filters:
- Before / After specific date
- Between date range
- This week/month/year
- Last N days/weeks/months

-- SQL-like filtering interface
WHERE (Age > 25 AND Salary < 80000) 
   OR (Department = 'Sales' AND Experience > 5)
   
-- Multiple conditions with AND/OR logic
Filter 1: City = "New York" OR City = "Los Angeles"
Filter 2: Age >= 30 AND Age <= 50
Filter 3: Salary > 60000
Combine: (Filter 1) AND (Filter 2) AND (Filter 3)

// Regex patterns for advanced filtering
Email validation: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$
Phone numbers: ^\+?[\d\s\-\(\)]{10,15}$
Postal codes: ^\d{5}(-\d{4})?$
Custom patterns: User-defined regex expressions

Sort by Age (ascending)
Name,Age,Salary
Jane Smith,25,65000
John Doe,30,75000
Bob Johnson,35,80000

Sort by Salary (descending)  
Name,Age,Salary
Bob Johnson,35,80000
John Doe,30,75000
Jane Smith,25,65000

// Primary sort: Department (A-Z)
// Secondary sort: Salary (High to Low)
// Tertiary sort: Name (A-Z)

Sort Priority:
1. Department ↑ (Sales, Marketing, IT)
2. Salary ↓ (80000, 75000, 65000)
3. Name ↑ (Alice, Bob, Charlie)

// Define custom sorting sequences
Priority: High, Medium, Low
Months: Jan, Feb, Mar, Apr, May, Jun...
Status: New, In Progress, Review, Complete
Sizes: XS, S, M, L, XL, XXL

Data Quality Report:
├── Completeness: 95.2% (47/49 fields filled)
├── Uniqueness: 98.0% (1 duplicate record found)
├── Consistency: 87.5% (inconsistent date formats)
├── Validity: 92.3% (invalid email addresses found)
└── Accuracy: Manual review required

Column: "Email"
├── Data Type: String
├── Non-null values: 486/500 (97.2%)
├── Unique values: 482/486 (99.2%)
├── Pattern compliance: 95.7% (valid email format)
├── Most common domain: @gmail.com (35.2%)
└── Outliers: 4 invalid email formats detected

Column: "Age"  
├── Data Type: Integer
├── Non-null values: 498/500 (99.6%)
├── Range: 18-67 years
├── Mean: 34.2, Median: 33, Mode: 29
├── Outliers: 2 values > 65 (flagged for review)
└── Distribution: Normal distribution

Email Validation:
- RFC 5322 compliant format
- Domain existence check (optional)
- Common typo detection

Phone Number Validation:
- International format support
- Country-specific patterns
- Extension handling

Date Validation:
- Format consistency check
- Logical date validation
- Future/past date restrictions

Numeric Validation:
- Range validation
- Precision checking  
- Currency format validation

// Define custom business rules
Rule 1: Age must be between 18 and 65
Rule 2: Salary must be positive and < 500000
Rule 3: Employee ID must match pattern: EMP\d{4}
Rule 4: Start date must be before end date
Rule 5: Email domain must be company domain

Validation errors highlighted
Row,Column,Error,Value,Suggestion
5,Email,Invalid format,john@,john@domain.com
12,Age,Out of range,150,Remove or verify
23,Date,Invalid date,2023-13-45,2023-12-31
31,Phone,Wrong format,123456,+1-123-456-7890

// Automatic statistical analysis
Numeric Columns Summary:
Age:
├── Count: 500
├── Mean: 34.2
├── Median: 33.0
├── Mode: 29
├── Standard Deviation: 8.7
├── Min: 18, Max: 67
├── Q1: 27, Q3: 41
└── Outliers: 2 detected

Salary:
├── Count: 498 (2 missing)
├── Mean: $72,450
├── Median: $68,500
├── Standard Deviation: $18,230
├── Range: $35,000 - $150,000
└── Distribution: Right-skewed

// Text column analysis
Name Column:
├── Total entries: 500
├── Unique values: 487 (13 duplicates)
├── Average length: 12.3 characters
├── Most common first name: John (23 occurrences)
├── Character distribution: Letters 94%, Numbers 3%, Special 3%
└── Pattern analysis: FirstName LastName format 98%

Category Column:
├── Unique categories: 8
├── Most frequent: Electronics (35%)
├── Least frequent: Books (4%)
├── Empty values: 12 (2.4%)
└── Case sensitivity: 15 inconsistencies found

// Correlation matrix for numeric columns
Correlation Matrix:
           Age    Salary  Experience  Rating
Age        1.00   0.73    0.89       0.45
Salary     0.73   1.00    0.65       0.32
Experience 0.89   0.65    1.00       0.51
Rating     0.45   0.32    0.51       1.00

Strong correlations found:
- Age vs Experience: r = 0.89 (very strong positive)
- Age vs Salary: r = 0.73 (strong positive)
- Experience vs Rating: r = 0.51 (moderate positive)

// Distribution analysis with visualizations
Age Distribution:
├── Type: Normal distribution
├── Skewness: 0.12 (slightly right-skewed)
├── Kurtosis: -0.34 (platykurtic)
├── Normality test: p-value = 0.067 (likely normal)
└── Histogram: Available in visualization tab

Salary Distribution:
├── Type: Right-skewed distribution  
├── Skewness: 1.45 (moderately right-skewed)
├── Outliers: 8 high-value outliers detected
├── Log transformation recommended for normality
└── Box plot: Available in visualization tab

Group by Department, show statistics
Department,Count,Avg_Salary,Min_Age,Max_Age,Avg_Experience
Sales,150,$68,500,22,58,5.2
Marketing,120,$71,200,24,55,4.8
IT,180,$82,300,23,62,6.1
HR,50,$65,800,26,59,7.2

CSV Export Configuration:
├── Delimiter: Comma, Semicolon, Tab, Custom
├── Text Qualifier: Double quotes, Single quotes, None
├── Line Endings: Windows (CRLF), Unix (LF), Mac (CR)
├── Encoding: UTF-8, UTF-16, ISO-8859-1, Windows-1252
├── Include Headers: Yes/No
└── Date Format: ISO, US, EU, Custom

// JSON format options
Array format:
[
  {"Name": "John Doe", "Age": 30, "City": "New York"},
  {"Name": "Jane Smith", "Age": 25, "City": "Los Angeles"}
]

Nested object format:
{
  "data": [
    {"id": 1, "name": "John Doe", "details": {"age": 30, "city": "New York"}},
    {"id": 2, "name": "Jane Smith", "details": {"age": 25, "city": "Los Angeles"}}
  ],
  "metadata": {"total": 2, "exported": "2023-09-01"}
}

Excel Export Features:
├── Multiple worksheets support
├── Formatted cells (dates, currencies, percentages)
├── Auto-fit column widths
├── Header row formatting (bold, colors)
├── Data validation rules preserved
├── Charts and pivot tables (basic)
├── File formats: .xlsx, .xls
└── Password protection option

-- SQL INSERT statements generation
CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    age INT,
    salary DECIMAL(10,2),
    hire_date DATE
);

INSERT INTO employees (id, name, age, salary, hire_date) VALUES
    (1, 'John Doe', 30, 75000.00, '2020-01-15'),
    (2, 'Jane Smith', 25, 65000.00, '2021-03-20'),
    (3, 'Bob Johnson', 35, 80000.00, '2019-07-10');

-- Database-specific variations:
-- MySQL, PostgreSQL, SQL Server, Oracle, SQLite

Data Analysis Report

Dataset Overview
- File Name: employee_data.csv
- Total Records: 500
- Total Columns: 8
- Processing Date: 2023-09-01 14:30:00

Data Quality Assessment
- Completeness: 96.5% (483/500 complete records)
- Duplicates: 3 duplicate records found
- Missing Values: 17 fields missing data
- Data Types: All columns correctly typed

Key Insights
- Average employee age: 34.2 years
- Salary range: $35,000 - $150,000
- Most common department: Sales (30%)
- Geographic distribution: 15 states represented

Recommendations
1. Address missing salary data (3 records)
2. Standardize phone number formats
3. Verify outlier salaries (8 records > $120k)

Report Templates:
├── Executive Summary: High-level insights
├── Data Quality Report: Validation results  
├── Statistical Analysis: Detailed statistics
├── Comparison Report: Before/after analysis
├── Anomaly Detection: Outliers and errors
└── Custom Template: User-defined format

// Process multiple CSV files simultaneously
Batch Operation Types:
├── Merge Files: Combine multiple CSVs
├── Split File: Divide large CSV into smaller files
├── Compare Files: Highlight differences between files
├── Standardize Format: Apply same formatting to all files
├── Aggregate Data: Sum, average across files
└── Transform Schema: Apply transformations to all files

Horizontal merge (join by column)
File1.csv: Name, Age
File2.csv: Name, Salary
Result: Name, Age, Salary (joined by Name)

Vertical merge (stack files)
File1.csv: Name, Age, City
File2.csv: Name, Age, City  
Result: Combined rows from both files

Schema merge (combine different structures)
Auto-align columns by name
Fill missing columns with nulls

// Define multi-step processing workflow
Pipeline Example:
1. Import CSV file
2. Clean data (remove duplicates, handle missing values)
3. Validate data (apply business rules)
4. Transform data (calculations, formatting)
5. Filter data (apply conditions)
6. Export results (multiple formats)
7. Generate report
8. Email results (optional)

// Automated recurring processing
Schedule Options:
├── Daily: Process new files daily at specified time
├── Weekly: Weekly batch processing
├── Monthly: End-of-month reports
├── On File Upload: Trigger processing when file added
├── API Webhook: External system triggered
└── Custom Schedule: Cron expression support

// Upload and process CSV file
POST /api/csv/upload
Content-Type: multipart/form-data
{
  "file": "data.csv",
  "options": {
    "delimiter": ",",
    "headers": true,
    "encoding": "utf-8"
  }
}

// Get processing results
GET /api/csv/process/{job_id}
Response: {
  "status": "completed",
  "rows": 1000,
  "columns": 8,
  "errors": [],
  "download_url": "/api/csv/download/{job_id}"
}

// Webhook notification when processing completes
POST https://yourapp.com/webhook
{
  "event": "csv_processed",
  "job_id": "12345",
  "status": "completed",
  "records_processed": 1000,
  "errors": 0,
  "download_urls": {
    "csv": "https://api.csvprocessor.com/download/12345.csv",
    "json": "https://api.csvprocessor.com/download/12345.json",
    "report": "https://api.csvprocessor.com/download/12345-report.pdf"
  }
}

// Direct integration with cloud storage
Supported Platforms:
├── Google Drive: Import/export Google Sheets
├── Dropbox: Auto-sync processed files
├── AWS S3: Bulk processing from S3 buckets
├── Microsoft OneDrive: Excel file processing
├── Box: Enterprise file management
└── FTP/SFTP: Server-based file processing

// Direct database import/export
Supported Databases:
├── MySQL: Direct table import/export
├── PostgreSQL: Advanced data type support
├── SQL Server: Enterprise integration
├── Oracle: Large dataset handling
├── MongoDB: JSON document processing
├── SQLite: Embedded database support
└── Redis: Cache-based processing

// Strategies for processing large CSV files
Techniques:
├── Streaming Processing: Process data in chunks
├── Progressive Loading: Load data as needed
├── Memory Management: Efficient memory usage
├── Parallel Processing: Multi-threaded operations
├── Compression: Reduce file sizes
└── Caching: Store frequent operations

Best Practices:
1. Use appropriate data types for each column
2. Remove unnecessary columns before processing
3. Apply filters early to reduce dataset size
4. Use indexed operations for sorting/filtering
5. Process in chunks for very large files
6. Cache frequently used calculations

Security Measures:
├── File Encryption: AES-256 encryption at rest
├── Secure Upload: HTTPS encrypted transmission
├── Access Control: User authentication/authorization
├── Audit Logging: Track all file operations
├── Data Retention: Configurable retention policies
├── Privacy Compliance: GDPR, CCPA compliance
└── Secure Deletion: Cryptographic data erasure

Privacy Features:
├── Anonymous Processing: Remove personal identifiers
├── Data Masking: Hide sensitive information
├── Local Processing: Client-side processing option
├── No Data Storage: Option to not store uploaded files
├── Consent Management: Track user consent
└── Right to Erasure: Delete user data on request

Issue: "File too large" error
Solution: 
- Check file size limit (100MB max)
- Compress file or split into smaller files
- Use streaming upload for large files

Issue: "Invalid file format" error  
Solution:
- Verify file has .csv extension
- Check for proper CSV structure
- Try different encoding (UTF-8 recommended)

Issue: "Parsing errors" in file
Solution:
- Check for unescaped quotes in data
- Verify consistent delimiter usage
- Remove special characters or BOM

Issue: Incorrect data type detection
Solution:
- Manually specify column data types
- Clean data before processing
- Use consistent formatting within columns

Issue: Memory errors with large files
Solution:
- Enable streaming processing mode
- Reduce batch size in settings
- Process file in smaller chunks

Issue: Slow processing performance
Solution:
- Remove unnecessary columns first
- Apply filters early to reduce data volume
- Use simple operations before complex ones

Issue: Character encoding problems in export
Solution:
- Use UTF-8 encoding for international characters
- Check target system's encoding requirements
- Use UTF-8 BOM if required by target application

Issue: Date format issues in exported file
Solution:
- Standardize date format before export
- Use ISO 8601 format (YYYY-MM-DD) for compatibility
- Check target system's date format requirements