What are Entities in Sympy

This recipe explains what are Entities in Sympy

Recipe Objective - What are Entities in Sympy?

The geometry module in SymPy is the base class for all geometrical entities and allows the creation of two-dimensional objects such as lines, circles, etc. Then we can get information about it, such as checking for collinearity or finding intersections.

Build a Chatbot in Python from Scratch! 

For more related projects -

https://www.projectpro.io/projects/data-science-projects/deep-learning-projects
https://www./projectpro.io/data-science-projects/keras-deep-learning-projects

Point:
The class of points represents a point in Euclidean space. The following example checks the collinearity of the points:

# Example 1:

# Importing libraries
from sympy.geometry import Point
from sympy.abc import p,q,r

# Defining some points
p = Point(2,4)
q = Point(4,6)
r = Point(6,7)

print("Are points P and Q collinear ? => ",Point.is_collinear(p,q))
print("Are points P, Q and R collinear ? => ",Point.is_collinear(p,q,r))

Output - 
Are points P and Q collinear ? =>  True
Are points P, Q and R collinear ? =>  False

The distance() method of the Point class calculates the distance between two points.

# Example 2:

# Importing libraries
from sympy.geometry import Point
from sympy.abc import p,q
from sympy import pprint

# Defining some points
p = Point(2,4)
q = Point(12,6)

print("Distance between P and Q => ")
pprint(p.distance(q))

Output - 
Distance between P and Q => 
2.√26

Line:
The line entity is obtained from two-point objects. The intersection() method returns an intersection point when two lines intersect.

# Example 1:

# Importing libraries
from sympy.geometry import Point, Line

# Defining some lines
line1 = Line(Point(0,0), Point(10,10))
line2 = Line(Point(10,0), Point(0,10))

# intersection point of 2 lines
line1.intersection(line2)

Output - 
[Point2D(5, 5)]

In this way here, we learned about entities in sympy.

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

Recommender System Machine Learning Project for Beginners-4
Collaborative Filtering Recommender System Project - Comparison of different model based and memory based methods to build recommendation system using collaborative filtering.

ML Model Deployment on AWS for Customer Churn Prediction
MLOps Project-Deploy Machine Learning Model to Production Python on AWS for Customer Churn Prediction

Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP

Image Classification Model using Transfer Learning in PyTorch
In this PyTorch Project, you will build an image classification model in PyTorch using the ResNet pre-trained model.

Build a Credit Default Risk Prediction Model with LightGBM
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.

Deploy Transformer BART Model for Text summarization on GCP
Learn to Deploy a Machine Learning Model for the Abstractive Text Summarization on Google Cloud Platform (GCP)

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

Deploying Machine Learning Models with Flask for Beginners
In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.