python

import django_filters
from .models import Product


class ProductFilter(django_filters.FilterSet):
    name = django_filters.CharFilter(lookup_expr='icontains')

import re


class APIVersionMiddleware:
    """Extract API version from request and add to request object."""

from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(X)

from django.db import models
from django.core.exceptions import ValidationError
from django.utils import timezone


class Event(models.Model):

# Find products with specific spec value
products = Product.objects.filter(specs__weight__gte=100)

# Check if JSON key exists
products = Product.objects.filter(specs__has_key='color')

from products.models import Product


def import_products_bulk(product_data):
    """Import thousands of products efficiently."""
    products = [

import pandas as pd
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader

class ProductImageDataset(Dataset):

from rest_framework import serializers
from .models import Author, Book


class BookSerializer(serializers.ModelSerializer):
    class Meta:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report

pipeline = Pipeline([

from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier

import asyncio
import time
from dataclasses import dataclass, field


@dataclass

import pandas as pd

df = pd.read_csv(
    'orders.csv',
    parse_dates=['created_at'],
    dtype={