Python to Go Converter
Transform Python scripts into high-performance Go microservices with AI-powered conversion achieving 10-100x speedups. Converts Python classes to Go structs with methods, asyncio to goroutines with channels, and dynamic types to statically-checked interfaces. Eliminates GIL concurrency bottlenecks, compiles to single binaries for containerized deployment, and replaces runtime interpretation with native machine code execution.
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Issue Description
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How It Works
- Step 1: Paste your Python source code including class definitions, async/await functions, type hints (if available), and list/dict comprehensions.
- Step 2: The AI analyzes Python's dynamic patterns inferring static Go types from usage, identifying async operations for goroutine conversion, and detecting mutable structures.
- Step 3: Advanced transformation generates Go code with explicit error handling (replacing exceptions), struct-based data with receivers, and goroutines with channel communication.
- Step 4: Download production-ready Go code with static typing, memory-safe concurrency, and native compilation—ready for microservice deployment with 10-100x performance gains.
Python vs Go: Language Comparison
| Feature | Python | Go |
|---|---|---|
| Type System | Dynamic, duck typing | Static, compile-time checks |
| Performance | Interpreted, GIL limitations | Compiled, native execution |
| Concurrency | Threading (GIL), asyncio | Goroutines, channels (CSP) |
| Memory Management | Reference counting + GC | Efficient garbage collection |
| Deployment | Requires runtime | Single static binary |
Code Conversion Examples
Example 1: Class to Struct with Methods
class DataProcessor:
def __init__(self, batch_size=100):
self.batch_size = batch_size
self.processed = 0
def process_items(self, items):
results = []
for item in items:
if item > 0:
results.append(item * 2)
self.processed += 1
return results
def get_stats(self):
return {
'batch_size': self.batch_size,
'processed': self.processed
}
# Usage
processor = DataProcessor(batch_size=50)
data = [1, -2, 3, 4, -5]
output = processor.process_items(data)
stats = processor.get_stats()package main
type DataProcessor struct {
batchSize int
processed int
}
func NewDataProcessor(batchSize int) *DataProcessor {
return &DataProcessor{
batchSize: batchSize,
processed: 0,
}
}
func (dp *DataProcessor) ProcessItems(items []int) []int {
results := make([]int, 0, len(items))
for _, item := range items {
if item > 0 {
results = append(results, item*2)
dp.processed++
}
}
return results
}
type Stats struct {
BatchSize int
Processed int
}
func (dp *DataProcessor) GetStats() Stats {
return Stats{
BatchSize: dp.batchSize,
Processed: dp.processed,
}
}
// Usage
processor := NewDataProcessor(50)
data := []int{1, -2, 3, 4, -5}
output := processor.ProcessItems(data)
stats := processor.GetStats()Key Changes:
Python classes convert to Go structs with receiver methods. The __init__ constructor becomes a factory function (NewDataProcessor) returning a pointer. Python's self becomes Go's receiver (dp *DataProcessor). Dynamic Python dictionaries transform to typed structs for return values. List comprehensions convert to explicit for-range loops with conditional appends. Field names change from snake_case to camelCase following Go conventions. The method signatures gain explicit parameter and return types.
Example 2: Async/Await to Goroutines
import asyncio
import aiohttp
async def fetch_url(session, url):
async with session.get(url) as response:
return await response.text()
async def fetch_multiple(urls):
async with aiohttp.ClientSession() as session:
tasks = [fetch_url(session, url) for url in urls]
results = await asyncio.gather(*tasks)
return results
# Usage
urls = ['http://api.example.com/1', 'http://api.example.com/2']
results = asyncio.run(fetch_multiple(urls))package main
import (
"io/ioutil"
"net/http"
"sync"
)
func fetchURL(url string, results chan<- string, wg *sync.WaitGroup) {
defer wg.Done()
resp, err := http.Get(url)
if err != nil {
results <- ""
return
}
defer resp.Body.Close()
body, err := ioutil.ReadAll(resp.Body)
if err != nil {
results <- ""
return
}
results <- string(body)
}
func fetchMultiple(urls []string) []string {
results := make(chan string, len(urls))
var wg sync.WaitGroup
for _, url := range urls {
wg.Add(1)
go fetchURL(url, results, &wg)
}
wg.Wait()
close(results)
output := make([]string, 0, len(urls))
for result := range results {
output = append(output, result)
}
return output
}
// Usage
urls := []string{"http://api.example.com/1", "http://api.example.com/2"}
results := fetchMultiple(urls)Key Changes:
Python's async/await converts to goroutines with channels for communication. AsyncIO's gather pattern becomes WaitGroup for synchronization. Async context managers (async with) convert to defer statements for cleanup. Python's aiohttp becomes Go's native http package. Error handling changes from exceptions to explicit error returns checked at each step. The channel pattern (results chan<- string) replaces Promise-based async coordination. List comprehensions creating tasks convert to for-range loops spawning goroutines.
Frequently Asked Questions
Can I customize how Python types map to Go interfaces?
The converter infers static Go types from Python usage patterns by default. Duck typing converts to interface{} or specific interfaces when patterns are clear. Advanced customization (forcing specific struct types, using generics for type parameters) requires manual post-processing. The tool prioritizes compile-safe Go over literal Python translation.
Is my Python code sent to external servers?
Yes. AI conversion requires server-side LLM processing. Your Python source is transmitted over HTTPS to our API, converted, then discarded. We do not store code or train models on submissions. Review data policies before converting proprietary algorithms.
What Python features have no Go equivalent?
Python's dynamic attribute access (__getattr__), multiple inheritance, decorators with complex closures, and list comprehensions with side effects lack direct Go mappings. The GIL-dependent threading model differs fundamentally from Go's goroutines. The tool excels at algorithmic Python but struggles with metaprogramming (metaclasses, exec()).