Contrarian View: Why You Should Stop Using VS Code 2026 for Python Development—Use PyCharm 2026

import logging\nfrom fastapi import FastAPI, HTTPException, Depends\nfrom pydantic import BaseModel, Field\nfrom typing import List, Optional\nimport uvicorn\n\n# Configure logging for the application\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n)\nlogger = logging.getLogger(__name__)\n\nclass BookBase(BaseModel):\n    \"\"\"Base Pydantic model for book data validation\"\"\"\n    title: str = Field(..., min_length=1, max_length=200)\n    author: str = Field(..., min_length=1, max_length=100)\n    published_year: int = Field(..., ge=1900, le=2026)\n    isbn: str = Field(..., pattern=r\"^(?=(?:\D*\d){10}(?:(?:\D*\d){3})?$)[\d-]+$\")\n\nclass BookCreate(BookBase):\n    \"\"\"Model for creating a new book, inherits from BookBase\"\"\"\n    pass\n\nclass Book(BookBase):\n    \"\"\"Model for returning book data, includes generated ID\"\"\"\n    id: int\n\napp = FastAPI(title=\"Library API\", version=\"1.0.0\")\n\n# In-memory storage for books (replace with DB in production)\nbooks_db: List[Book] = []\nbook_id_counter: int = 1\n\ndef get_book_by_id(book_id: int) -> Optional[Book]:\n    \"\"\"Helper function to retrieve a book by its ID, returns None if not found\"\"\"\n    for book in books_db:\n        if book.id == book_id:\n            return book\n    return None\n\n@app.post(\"/books/\", response_model=Book, status_code=201)\nasync def create_book(book: BookCreate) -> Book:\n    \"\"\"Create a new book entry in the library\"\"\"\n    global book_id_counter\n    try:\n        new_book = Book(id=book_id_counter, **book.model_dump())\n        books_db.append(new_book)\n        book_id_counter += 1\n        logger.info(f\"Created new book with ID {new_book.id}: {new_book.title}\")\n        return new_book\n    except Exception as e:\n        logger.error(f\"Failed to create book: {str(e)}\")\n        raise HTTPException(status_code=500, detail=\"Internal server error creating book\")\n\n@app.get(\"/books/\", response_model=List[Book])\nasync def list_books(skip: int = 0, limit: int = 10) -> List[Book]:\n    \"\"\"List all books with pagination\"\"\"\n    return books_db[skip : skip + limit]\n\nif __name__ == \"__main__\":\n    # Run the application with hot reload disabled for benchmark consistency\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000, reload=False)\n

import logging\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db import transaction\nfrom myapp.models import LegacyUser, NewUser\nfrom typing import List, Dict\nimport csv\nfrom pathlib import Path\n\nlogger = logging.getLogger(__name__)\n\nclass Command(BaseCommand):\n    \"\"\"Django management command to migrate legacy user data to new user model\"\"\"\n    help = \"Migrates legacy user records from CSV to new NewUser model with validation\"\n\n    def add_arguments(self, parser):\n        \"\"\"Add command line arguments for the migration command\"\"\"\n        parser.add_argument(\n            \"--csv-path\",\n            type=str,\n            required=True,\n            help=\"Path to the legacy user CSV file (must have columns: id, username, email, join_date)\"\n        )\n        parser.add_argument(\n            \"--batch-size\",\n            type=int,\n            default=1000,\n            help=\"Number of records to process in a single transaction batch (default: 1000)\"\n        )\n        parser.add_argument(\n            \"--dry-run\",\n            action=\"store_true\",\n            help=\"Run validation without writing to the database\"\n        )\n\n    def handle(self, *args, **options):\n        \"\"\"Main entry point for the command\"\"\"\n        csv_path = Path(options[\"csv-path\"])\n        batch_size = options[\"batch-size\"]\n        dry_run = options[\"dry-run\"]\n\n        if not csv_path.exists():\n            raise CommandError(f\"CSV file not found at {csv_path}\")\n\n        self.stdout.write(self.style.SUCCESS(f\"Starting legacy user migration from {csv_path}\"))\n        if dry_run:\n            self.stdout.write(self.style.WARNING(\"Running in dry-run mode: no data will be written\"))\n\n        migrated_count = 0\n        error_count = 0\n        batch: List[NewUser] = []\n\n        try:\n            with open(csv_path, \"r\", encoding=\"utf-8\") as f:\n                reader = csv.DictReader(f)\n                for row_num, row in enumerate(reader, start=1):\n                    try:\n                        # Validate row data\n                        legacy_id = int(row[\"id\"])\n                        username = row[\"username\"].strip()\n                        email = row[\"email\"].strip()\n                        if not username or not email:\n                            raise ValueError(f\"Empty username or email in row {row_num}\")\n\n                        # Check if user already exists\n                        if NewUser.objects.filter(legacy_id=legacy_id).exists():\n                            logger.warning(f\"User with legacy ID {legacy_id} already exists, skipping\")\n                            continue\n\n                        # Create new user instance\n                        new_user = NewUser(\n                            legacy_id=legacy_id,\n                            username=username,\n                            email=email,\n                            join_date=row[\"join_date\"]\n                        )\n                        batch.append(new_user)\n\n                        # Process batch if size is reached\n                        if len(batch) >= batch_size:\n                            if not dry_run:\n                                self._process_batch(batch)\n                            migrated_count += len(batch)\n                            self.stdout.write(f\"Processed batch of {len(batch)} records (total: {migrated_count})\")\n                            batch = []\n\n                    except Exception as e:\n                        error_count += 1\n                        logger.error(f\"Failed to process row {row_num}: {str(e)}\")\n                        if error_count > 100:\n                            raise CommandError(\"Too many errors, aborting migration\")\n\n                # Process remaining batch\n                if batch:\n                    if not dry_run:\n                        self._process_batch(batch)\n                    migrated_count += len(batch)\n\n            self.stdout.write(self.style.SUCCESS(\n                f\"Migration complete: {migrated_count} users migrated, {error_count} errors\"\n            ))\n\n        except Exception as e:\n            raise CommandError(f\"Migration failed: {str(e)}\")\n\n    def _process_batch(self, batch: List[NewUser]) -> None:\n        \"\"\"Process a batch of users in a single transaction\"\"\"\n        with transaction.atomic():\n            NewUser.objects.bulk_create(batch)\n        logger.info(f\"Committed batch of {len(batch)} users to database\")\n

import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import datasets, transforms\nimport logging\nfrom typing import Tuple\nimport os\nfrom pathlib import Path\n\n# Configure logging\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\nlogger = logging.getLogger(__name__)\n\nclass CustomMNISTDataset(Dataset):\n    \"\"\"Custom dataset wrapper for MNIST with additional preprocessing\"\"\"\n    def __init__(self, train: bool = True):\n        self.transform = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize((0.1307,), (0.3081,))\n        ])\n        self.mnist = datasets.MNIST(\n            root=\"./data\",\n            train=train,\n            download=True,\n            transform=self.transform\n        )\n\n    def __len__(self) -> int:\n        return len(self.mnist)\n\n    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:\n        return self.mnist[idx]\n\nclass SimpleCNN(nn.Module):\n    \"\"\"Simple CNN model for MNIST classification\"\"\"\n    def __init__(self):\n        super(SimpleCNN, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.fc1 = nn.Linear(64 * 7 * 7, 128)\n        self.fc2 = nn.Linear(128, 10)\n        self.dropout = nn.Dropout(0.5)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.pool(nn.functional.relu(self.conv1(x)))\n        x = self.pool(nn.functional.relu(self.conv2(x)))\n        x = x.view(-1, 64 * 7 * 7)\n        x = self.dropout(nn.functional.relu(self.fc1(x)))\n        x = self.fc2(x)\n        return x\n\ndef train_model(\n    model: nn.Module,\n    train_loader: DataLoader,\n    val_loader: DataLoader,\n    epochs: int = 10,\n    learning_rate: float = 0.001,\n    device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n) -> None:\n    \"\"\"Train the CNN model with validation\"\"\"\n    model.to(device)\n    criterion = nn.CrossEntropyLoss()\n    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n    for epoch in range(epochs):\n        model.train()\n        train_loss = 0.0\n        correct = 0\n        total = 0\n\n        for batch_idx, (data, target) in enumerate(train_loader):\n            data, target = data.to(device), target.to(device)\n            optimizer.zero_grad()\n            output = model(data)\n            loss = criterion(output, target)\n            loss.backward()\n            optimizer.step()\n\n            train_loss += loss.item()\n            _, predicted = output.max(1)\n            total += target.size(0)\n            correct += predicted.eq(target).sum().item()\n\n            if batch_idx % 100 == 0:\n                logger.info(f\"Epoch {epoch+1}, Batch {batch_idx}, Loss: {loss.item():.4f}\")\n\n        # Validation phase\n        model.eval()\n        val_loss = 0.0\n        val_correct = 0\n        val_total = 0\n\n        with torch.no_grad():\n            for data, target in val_loader:\n                data, target = data.to(device), target.to(device)\n                output = model(data)\n                val_loss += criterion(output, target).item()\n                _, predicted = output.max(1)\n                val_total += target.size(0)\n                val_correct += predicted.eq(target).sum().item()\n\n        logger.info(\n            f\"Epoch {epoch+1} complete: \"\n            f\"Train Acc: {100. * correct / total:.2f}%, \"\n            f\"Val Acc: {100. * val_correct / val_total:.2f}%, \"\n            f\"Val Loss: {val_loss / len(val_loader):.4f}\"\n        )\n\nif __name__ == \"__main__\":\n    try:\n        # Load datasets\n        train_dataset = CustomMNISTDataset(train=True)\n        val_dataset = CustomMNISTDataset(train=False)\n\n        train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n        val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n\n        # Initialize model\n        model = SimpleCNN()\n        logger.info(f\"Model initialized: {model.__class__.__name__}\")\n\n        # Train model\n        train_model(model, train_loader, val_loader, epochs=5)\n\n        # Save model\n        save_path = Path(\"./models/mnist_cnn.pth\")\n        save_path.parent.mkdir(exist_ok=True)\n        torch.save(model.state_dict(), save_path)\n        logger.info(f\"Model saved to {save_path}\")\n\n    except Exception as e:\n        logger.error(f\"Training failed: {str(e)}\")\n        raise\n

def calculate_discount(price: float, is_premium: bool) -> float:\n    # PyCharm will catch this return type error immediately, Pylance may miss it in large projects\n    return \"10%\" if is_premium else price * 0.9

from django.db import models\n\nclass Product(models.Model):\n    name = models.CharField(max_length=200)\n    # PyCharm will warn if this field isn't in the database schema after migration\n    sale_price = models.DecimalField(max_digits=10, decimal_places=2)

# Run this on your remote instance, PyCharm will automatically forward port 8000 to localhost\nif __name__ == \"__main__\":\n    import uvicorn\n    from fastapi import FastAPI\n    app = FastAPI()\n    uvicorn.run(app, host=\"0.0.0.0\", port=8000)