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Python for AI

Since its initial release in 1991, Python has become one of the most popular and used programming languages by developers. In actuality, it is one of the essential practices to use Python for Artificial Intelligence. There are other programming languages as well which support working with Artificial Intelligence. Some of these languages are C++, Lisp, Prolog, Java, and R programming languages. These are the best languages when it comes to developing software using AI. The developers use Python widely for complicated tasks due to its ability to be a high-level programming language. The usability of code increases exponentially after using Python.

Using Python for AI

Despite other languages supporting AI platforms, developers worldwide prefer Python for artificial intelligence due to its remarkable features. Following are some of the most popular features due to which Python is the most feasible language for its usage in artificial intelligence projects:

Pre-Built Libraries

There are pre-built libraries in Python. For example, Numpy helps in complicated scientific computations. Moreover, there is the library Scipy which helps with advanced computations in python. Finally, there is a library, Pybrain, which is crucial for performing machine learning in Python. Due to the Pybrain library being available in Python, computer scientists consider python one of the best languages for AI.

Available Tutorials

There are countless helping materials and tutorials available for Python and AI. Experts worldwide have shared their comprehensive guides and assist the developers by addressing their queries via various platforms.

Platform Independency

Python provides platform independence by showing flexibility towards numerous platforms and technologies. It supports the flexible transition to cross-platform projects. It provides the most negligible modification to the code while working with cross platforms and technologies.

Flexible Approach

Python is a flexible choice for working with complicated tasks in machine learning. The code needed to get the work done reduces in number from 100 to 20 percent while using Python as a platform for AI. It is the best choice among other object-oriented programming languages due to its flexibility. Moreover, there is no need in Python to utilizing different algorithms to get the required output. Additionally, the libraries and IDE provide much more usability than others.

Python Environment for AI

Python provides packages like NumPy, Scikit-learn, iPython Notebook, and matplotlib for the primary user to start a new Python project for AI. Moreover, pandas is a sound open-source library that provides different data structures and analytical tools for storage and calculations. The most commonly used libraries for AI include AIMA, pyDatalog, SimpleAI, EasyAi, PyBrain, Scikit, MDP, and PyML.


Following is a simple example of using Python for AI. The users need to install the libraries using the following command as a sample piece of code.

pip install numpy scipy scikit-learn

Following is an example code for implementing the KNN-Classification algorithm by using Python on the IRIS dataset.

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np
X_train, X_test, y_train, y_test = train_test_split(iris_dataset["data"], iris_dataset["target"], random_state=0)
kn = KNeighborsClassifier(n_neighbors=1), y_train)
x_new = np.array([[5, 2.9, 1, 0.2]])
prediction = kn.predict(x_new)
print("Predicted target value: {}\n".format(prediction))
print("Predicted feature name: {}\n".format
print("Test score: {:.2f}".format(kn.score(X_test, y_test)))


Firstly, the user subjects the machine to the iris flower data set for training. Then it is provided with a test value to predict according to the training dataset. The machine looks for the most suitable choice out of three available: Iris setosa, Iris virgincia, and Iris versicolor). Following is the output of the program mentioned above.

Predicted target name: [0]
Predicted feature name: [‘setosa’]
Test score: 0.97


Python Libraries Used for AI

Numerous Python libraries are pretty helpful in working with projects for Artificial Intelligence. Following are some of the Python libraries which are beneficial for general AI implementations.

  • AIMA library is derived from a book named Artificial Intelligence-A Modern Approach, commonly known as AIMA. The code is free to use and works under the MIT License. It works best for the Python version from 2.7 to 2.9. This library provides Python implementation of the algorithms by Stuart Russell and Peter Norvig.
  • pyDatalog helps in adding the logic programming paradigm into the Python toolbox. The primary purpose of using pyDatalog is to introduce the Datalog in a Domain Specific Language (DSL) within Python syntax and programs. This library is quite helpful in running logic queries on databases or Python objects, using logic clauses to define Python classes. In short, the library works as a query language for the developers.
  • SimpleAI library works on implementing various artificial intelligence algorithms available in the book “Artificial Intelligence-A Modern Approach” by Russell and Norvig. The primary purpose of using this library is to provide the developers with an easy-to-use, well-documented, and tested library. The implementation consists of searching algorithms like traditional search algorithms, local search algorithms, and interactive execution viewers for search algorithms. Moreover, it provides constraint satisfaction problems algorithms, machine learning, and statistical classification.
  • EasyAI is a pure Python artificial intelligence framework that is best known for two-player games. These games include Tic Tac Toe, Connect 4, Reversi. This Python engine uses concepts such as Negamax, alpha-beta pruning, transposition tables, and game solving. Zulko developed EasyAI originally, and it works under the MIT License. This open-source engine works best for the clarity and simplicity of code, but it works slower than similar engines.

Moreover, various machine learning libraries are available for Python, including PyBrain, PyML, Scikit-learn, and MDP-Toolkit. There are also Python libraries to work alongside Natural Language and Text Processing which include NLTK. Therefore, Python is Preferable for working with AI instead of other languages because it provides simplicity, better performance, easy-to-use syntax, compatibility, and more straightforward implementation. Moreover, Python is a popular language for AI over C++, with 57% of votes by the developing community.

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