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Biblioteca de Prompts
Consultor de código
Sugiere mejoras para optimizar el rendimiento del código Python.
¡Copia este prompt en nuestra Consola para desarrolladores para probarlo tú mismo!
Contenido | |
---|---|
System | Tu tarea es analizar el fragmento de código Python proporcionado y sugerir mejoras para optimizar su rendimiento. Identifica áreas donde el código puede hacerse más eficiente, más rápido o menos intensivo en recursos. Proporciona sugerencias específicas para la optimización, junto con explicaciones de cómo estos cambios pueden mejorar el rendimiento del código. El código optimizado debe mantener la misma funcionalidad que el código original mientras demuestra una eficiencia mejorada. |
User | def fibonacci(n): if n <= 0: return [] elif n == 1: return [0] elif n == 2: return [0, 1] else: fib = [0, 1] for i in range(2, n): fib.append(fib[i-1] + fib[i-2]) return fib |
Ejemplo de Salida
Python
def fibonacci(n):
a, b = 0, 1
for _ in range(n):
yield a
a, b = b, a + b
Explicación: Usar una función generadora con yield es más eficiente en memoria para generar la secuencia de Fibonacci. Evita crear y almacenar toda la secuencia en una lista. En su lugar, genera cada número sobre la marcha según sea necesario.
Solicitud API
import anthropic
client = anthropic.Anthropic( # por defecto usa os.environ.get("ANTHROPIC_API_KEY")
api_key="my_api_key",
)
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1000,
temperature=0,
system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib",
}
],
}
],
)
print(message.content)
import anthropic
client = anthropic.Anthropic( # por defecto usa os.environ.get("ANTHROPIC_API_KEY")
api_key="my_api_key",
)
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1000,
temperature=0,
system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib",
}
],
}
],
)
print(message.content)
import Anthropic from "@anthropic-ai/sdk";
const anthropic = new Anthropic({
apiKey: "my_api_key", // por defecto usa process.env["ANTHROPIC_API_KEY"]
});
const msg = await anthropic.messages.create({
model: "claude-opus-4-20250514",
max_tokens: 1000,
temperature: 0,
system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages: [
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
});
console.log(msg);
from anthropic import AnthropicBedrock
# Ver https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
# para opciones de autenticación
client = AnthropicBedrock()
message = client.messages.create(
model="anthropic.claude-opus-4-20250514-v1:0",
max_tokens=1000,
temperature=0,
system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
)
print(message.content)
import AnthropicBedrock from "@anthropic-ai/bedrock-sdk";
// Ver https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
// para opciones de autenticación
const client = new AnthropicBedrock();
const msg = await client.messages.create({
model: "anthropic.claude-opus-4-20250514-v1:0",
max_tokens: 1000,
temperature: 0,
system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages: [
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
});
console.log(msg);
from anthropic import AnthropicVertex
client = AnthropicVertex()
message = client.messages.create(
model="claude-3-7-sonnet-v1@20250219",
max_tokens=1000,
temperature=0,
system="Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
)
print(message.content)
import { AnthropicVertex } from '@anthropic-ai/vertex-sdk';
// Lee de las variables de entorno `CLOUD_ML_REGION` y `ANTHROPIC_VERTEX_PROJECT_ID`.
// Además, sigue el flujo estándar de `google-auth-library`.
const client = new AnthropicVertex();
const msg = await client.messages.create({
model: "claude-3-7-sonnet-v1@20250219",
max_tokens: 1000,
temperature: 0,
system: "Your task is to analyze the provided Python code snippet and suggest improvements to optimize its performance. Identify areas where the code can be made more efficient, faster, or less resource-intensive. Provide specific suggestions for optimization, along with explanations of how these changes can enhance the code's performance. The optimized code should maintain the same functionality as the original code while demonstrating improved efficiency.",
messages: [
{
"role": "user",
"content": [
{
"type": "text",
"text": "def fibonacci(n):\n if n <= 0:\n return []\n elif n == 1:\n return [0]\n elif n == 2:\n return [0, 1]\n else:\n fib = [0, 1]\n for i in range(2, n):\n fib.append(fib[i-1] + fib[i-2])\n return fib"
}
]
}
]
});
console.log(msg);
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