### Installation

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Install the bun-csv package using Bun.

```bash
bun add @nds-stack/bun-csv
```

--------------------------------

### Real-World Example

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

A comprehensive example demonstrating parsing a CSV file, filtering and transforming data, and writing it back as a new CSV file, including streaming from a URL.

```typescript
import { parse, stringify, parseStream } from "@nds-stack/bun-csv";
import { write } from "bun";

// Parse downloaded CSV
const file = Bun.file("./users.csv");
const text = await file.text();
const users = parse(text, { cast: true });

// Filter + transform
const activeUsers = users.data.filter(u => u.active === true);
const transformed = activeUsers.map(u => ({
  name: u.name,
  email: u.email,
  age: u.age,
}));

// Write back as CSV
const output = stringify(transformed);
await write("./active-users.csv", output);

// Or stream directly from URL
const response = await fetch("https://example.com/report.csv");
const text = await response.text();
const data = parse(text);
console.log(`Parsed ${data.rows} rows`);
```

--------------------------------

### Multi-Instance / Cross-Boundary Usage

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Example demonstrating stateless parser usage across multiple instances or workers.

```typescript
// Worker 1
const result1 = parse(csv1);

// Worker 2 (same import)
const result2 = parse(csv2);

// Streaming in separate processes
for await (const chunk of parseStream(file)) { ... }
```

--------------------------------

### Streaming Large File

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Demonstrates parsing a large file efficiently using streams, processing data in chunks.

```typescript
import { parseStream } from "@nds-stack/bun-csv";

const file = Bun.file("./massive.csv");
for await (const chunk of parseStream(file)) {
  await processBatch(chunk.data);
}
```

--------------------------------

### Extending Parser

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Shows how to extend the parser functionality by creating a custom parsing function with added validation.

```typescript
import { parse } from "@nds-stack/bun-csv";

function parseWithValidation(csv: string) {
  const result = parse(csv);
  if (result.rows === 0) throw new Error("Empty CSV");
  return result;
}
```

--------------------------------

### Headerless Mode

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Parses CSV data when there is no header row, assigning default column names.

```typescript
const result = parse(csv, { hasHeader: false });
// headers: ["column0", "column1", ...]
```

--------------------------------

### Custom Delimiter (TSV)

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Parses and stringifies data using a custom delimiter, specifically tab for TSV.

```typescript
import { parse, stringify } from "@nds-stack/bun-csv";

const tsv = parse(data, { delimiter: "\t" });
const output = stringify(tsv.data as Record<string, unknown>[], { delimiter: "\t" });
```

--------------------------------

### ParseResult Interface

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Interface for the result of the parse function.

```typescript
interface ParseResult<T = Record<string, string>> {
  data: T[]
  headers: string[]
  rows: number
}
```

--------------------------------

### Auto-Type Casting

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Enables automatic type casting for values during parsing, converting strings to their appropriate types.

```typescript
const result = parse(csv, { cast: true });
// "30" → 30, "true" → true, "false" → false
```

--------------------------------

### StreamChunk Interface

Source: https://github.com/nds-stack/bun-csv/blob/main/README.md

Interface for chunks yielded by parseStream.

```typescript
interface StreamChunk<T = Record<string, string>> {
  data: T[]
  chunkIndex: number
  byteOffset: number
}
```

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