10 Killer AI Programming Assistants That Make Python Development Easier

In today’s world, AI programming assistants have become indispensable allies for Python developers. These AI tools leverage intelligent features to enhance coding efficiency, allowing developers to produce high-quality code with less effort.

This article shares 10 highly effective AI tools for Python code generation.

1. GitHub Copilot

GitHub Copilot is an AI coding partner created by GitHub. It provides real-time suggestions for complete lines of code and entire functions as you type. Powered by OpenAI Codex, it has been trained on billions of lines of public code.

Key Features:

• Supports various languages, including Python, providing precise contextual code completions.
• Seamlessly integrates with popular IDEs like Visual Studio Code.
• Incorporates user feedback to continually optimize suggestions.

For instance, when you need to read a file and print its contents, Copilot can generate the following efficient Python code:

import fileinput

for line in fileinput.input():
    print(line)

2. TabNine

TabNine is an AI assistant designed by Codota to enhance developer productivity. It specializes in code completion and supports over 24 programming languages.

Key Features:

• Quickly provides code completions based on context.
• Embeds entire libraries for accurate suggestions.
• Continuously updates via a cloud-connected system.

For example, when sorting a list in Python, TabNine can generate the following code:

my_list = [5, 3, 8, 2]
my_list.sort()
print(my_list)

3. AWS Copilot

AWS Copilot is an intelligent tool designed for deploying containerized applications on Amazon Web Services. It automatically generates Dockerfiles, CI/CD pipelines, and deployment configurations based on simple commands.

Key Features:

• One-click command to generate a complete application framework.
• Supports popular development frameworks like React and PyTorch.
• Deep integration with AWS services to simplify deployment processes.

For instance, to create a simple “Hello World” web application, AWS Copilot generates the following files:

Copilot init → Do you want to initialize a new application? Yes

Copilot web init → Set up a Hello World web application? Yes

✅ Created Dockerfile

✅ Created buildspec.yml with test commands

✅ Created a temporary directory for demonstration code

4. AI Assistant

AI Assistant is an IDE plugin for IntelliJ and WebStorm that suggests code based on comments and tests. It uses GPT-3 in the background to generate contextually relevant code.

Key Features:

• Write code by describing it in simple English.
• Automatically generated unit tests to validate code quality.
• Supports many JVM languages, such as Java and Kotlin.

For example, when tasked with writing a program to read a text file and count words, AI Assistant can generate the following Java code:

import java.io.File;  
import java.util.Scanner;

public class Main {
  public static void main(String[] args) throws Exception {
    File textFile = new File("data.txt");
    Scanner sc = new Scanner(textFile);
    int numWords = 0;
    while (sc.hasNext()) {
      sc.next();
      numWords++;
    }
    System.out.println("Number of words: " + numWords);  
  }
}

5. Pythia

Pythia is a Python AI assistant developed by Anthropic that aims to provide helpful, harmless, and honest experiences. It generates entire functions or classes based on natural language descriptions and input/output examples, complete with detailed type annotations.

Key Features:

• High accuracy and reliable code completions.
• Quantifies uncertainty to avoid overstepping.
• Provides backtracking and error corrections for improvement.

For example, when asked to “convert a list of integers to floats,” Pythia responds:

def convert_to_float(int_list: List[int]) -> List[float]:
    """Convert a list of integers to floats."""
    return [float(x) for x in int_list]

6. Codex

OpenAI Codex is the AI system that powers tools like Copilot and AI Assistant. Developers can access it directly via the OpenAI API to generate code in over a dozen languages.

Key Features:

• Latest capabilities in code generation.
• Natural language processing to interpret prompts.
• Control parameters such as temperature and frequency penalties.

Here’s an example of Python code generated by Codex to print the squares of numbers from 1 to 10:

for i in range(1, 11):
    print(f"Square of {i} is {i**2}")

7. Tabnine Professionally Supported

Tabnine Professionally Supported offers customized support and advanced features for organizations looking to maximize Tabnine’s code completion capabilities.

Key Features:

• Code completions fine-tuned for proprietary systems.
• Priority support with responses within 4 hours.
• Management dashboard for user administration.

Here’s an example of Python code that Tabnine might generate when trained on internal libraries:

import custom_utils

def process_data(input_df):
    cleaned_df = custom_utils.clean(input_df)
    analyzed_df = custom_utils.analyze(cleaned_df)
    return analyzed_df

8. Amazon Lex

Amazon Lex enables developers to build conversational interfaces for applications using the same AI that powers Alexa. With Lex Code Hook, developers can automatically generate backend logic based on interaction models, simplifying the development process.

Key Features:

• Managed service that handles speech recognition and language understanding.
• Generates logic in multiple languages, including Node.js, Java, and Python through Code Hook.
• Tight integration with serverless functions like AWS Lambda for easy deployment and scaling.

For example, here’s a Python code snippet generated using Lex that demonstrates how to handle a pizza order intent:

# Get slot values
pizza_type = intent_request['currentIntent']['slots']['pizzaType']
pizza_size = intent_request['currentIntent']['slots']['pizzaSize']

# Call pizza order function
order = order_pizza(pizza_type, pizza_size)

# Build response
response = {
    "sessionAttributes": {},
    "dialogAction": {
      "type": "Close",
      "fulfillmentState": "Fulfilled",
      "message": {
        "contentType": "PlainText",
        "content": f"Thanks, your {order['pizza']} has been ordered!"
      }
    }
}
return response

9. PyCap

Anthropic’s PyCap generates complete Python functions from natural language descriptions and input/output examples. It aims to provide a helpful, harmless, and honest experience.

Key Features:

• Quantifies uncertainty in generated outputs.
• Provides backtracking and error corrections for improvement.
• Clarifies questions in English to refine prompts.

For example, given the description “split a string by spaces,” PyCap responds:

def split_on_spaces(text: str) -> List[str]:
    """Split a string by spaces."""
    return text.split()

10. Runpod AutoCompute

Runpod AutoCompute is an intelligent tool that customizes optimized TensorFlow, PyTorch, and SQL code for your data. It uses AutoML technology to recommend the most suitable neural network architectures and data preprocessing steps for developers.

Key Features:

• Instantly generates optimized deep learning and SQL code.
• Supports one-click deployment to Google Cloud or your own servers.
• Developers retain ownership and control over all generated code assets.

For example, AutoCompute can generate the following Python code for preprocessing image data:

import tensorflow as tf

def preprocess(image):
    image = tf.image.resize(image, [224, 224])
    image = tf.keras.applications.inception_v3.preprocess_input(image)
    return image

As we look ahead to the future of AI programming tools in 2024, one thing is clear: the integration of AI into the software development workflow is no longer a novelty, but a necessity. These cutting-edge AI assistants are revolutionizing the way Python developers work, boosting productivity, enhancing code quality, and enabling the creation of more sophisticated and impactful applications. By embracing these AI tools and leveraging their capabilities, Python developers can stay ahead of the curve, tackle complex challenges with greater efficiency, and push the boundaries of what’s possible in the world of software development.