applied machine learning in python assignment 2

Applied Machine Learning - Welcome

Applied machine learning - welcome #, introduction #.

This course provides an overview of key algorithms and concepts in machine learning, with a focus on applications. Introduces supervised and unsupervised learning, including logistic regression, support vector machines, neural networks, Gaussian mixture models, as well as other methods for classification, regression, clustering, and dimensionality reduction. Covers foundational concepts such as overfitting, regularization, maximum likelihood estimation, generative models, latent variables, and non-parametric methods. Applications include data analysis on images, text, time series, and other types of data using modern software tools such as numpy, scikit-learn, and pytorch.

What’s Inside #

Prerequisites #.

This masters-level course requires a background in mathematics and programming at the level of introductory college courses. Experience in Python is recommended, but not required. A certain degree of ease with mathematics will be helpful.

Programming experience (ideally Python; Cornell CS 1110 or equivalent)

Linear algebra. (Cornell MATH 2210, MATH 4310 or equivalent)

Statistics and probability. (Cornell STSCI 2100 or equivalent)

Instructors #

These lecture notes accompany CS5785 Applied Machine Learning at Cornell University and Cornell Tech, as well as the open online version of that course. They are based on materials developed at Cornell by:

• Volodymyr Kuleshov , Assistant Professor, Computer Science, Cornell Tech
• Nathan Kallus , Associate Professor, Operations Research, Cornell Tech
• Serge Belongie , Professor, Computer Science, University of Copenhagen

The open version of CS5785 and the accompanying online lectures have been produced by Hongjun Wu . We are also grateful to over a dozen teaching assistants that have helped with drafts of these lecture notes.

Table of Contents #

• Lecture 1: Introduction to Machine Learning
• 1.1. What is Machine Learning?
• 1.2. Three Approaches to Machine Learning
• 1.3. Logistics and Course Information.
• Lecture 2: Supervised Machine Learning
• 2.1. Elements of A Supervised Machine Learning Problem
• 2.2. Anatomy of a Supervised Learning Problem: The Dataset
• 2.3. Anatomy of a Supervised Learning Problem: The Learning Algorithm
• Lecture 3: Linear Regression
• 3.1. Calculus Review
• 3.2. Gradient Descent in Linear Models
• 3.3. Ordinary Least Squares
• 3.4. Non-Linear Least Squares
• Lecture 4: Classification and Logistic Regression
• 4.1. Classification
• 4.2. Logistic Regression
• 4.3. Maximum Likelihood
• 4.4. Learning a Logistic Regression Model
• 4.5. Softmax Regression for Multi-Class Classification
• 4.6. Maximum Likelihood: Advanced Topics
• Lecture 5: Regularization
• 5.1. Two Failure Cases of Supervised Learning
• 5.2. Evaluating Supervised Learning Models
• 5.3. A Framework for Applying Supervised Learning
• 5.4. L2 Regularization
• 5.5. L1 Regularization and Sparsity
• 5.6. Why Does Supervised Learning Work?
• Lecture 6: Generative Models and Naive Bayes
• 6.1. Text Classification
• 6.2. Generative Models
• 6.3. Naive Bayes
• 6.4. Learning a Naive Bayes Model
• Lecture 7: Gaussian Discriminant Analysis
• 7.1. Revisiting Generative Models
• 7.2. Gaussian Mixture Models
• 7.3. Gaussian Discriminant Analysis
• 7.4. Discriminative vs. Generative Algorithms
• Lecture 8: Unsupervised Learning
• 8.1. Introduction to Unsupervised Learning
• 8.2. The Language of Unsupervised Learning
• 8.3. Unsupervised Learning in Practice
• Lecture 9: Density Estimation
• 9.1. Outlier Detection Using Probabilistic Models
• 9.2. Kernel Density Estimation
• 9.3. Nearest Neighbors
• Lecture 10: Clustering
• 10.1. Gaussian Mixture Models for Clustering
• 10.2. Expectation Maximization
• 10.3. Expectation Maximization in Gaussian Mixture Models
• 10.4. Generalization in Probabilistic Models
• Lecture 12: Support Vector Machines
• 12.1. Classification Margins
• 12.2. The Max-Margin Classifier
• 12.2.2. Algorithm: Linear Support Vector Machine Classification
• 12.3. Soft Margins and the Hinge Loss
• 12.4. Optimization for SVMs
• Lecture 13: Dual Formulation of Support Vector Machines
• 13.1. Lagrange Duality
• 13.2. Dual Formulation of SVMs
• 13.3. Practical Considerations for SVM Duals
• Lecture 14: Kernels
• 14.1. The Kernel Trick in SVMs
• 14.2. Kernelized Ridge Regression
• 14.3. More on Kernels
• Lecture 15: Tree-Based Algorithms
• 15.1. Decision Trees
• 15.2. Learning Decision Trees
• 15.3. Bagging
• 15.4. Random Forests
• Lecture 16: Boosting
• 16.1. Defining Boosting
• 16.2. Structure of a Boosting Algorithm
• 16.3. Adaboost
• 16.4. Ensembling
• 16.5. Additive Models
• 16.6. Gradient Boosting

• Description
• Announcements
• Class Logistics

Live Session Plan

• Assignments and Final Project Submission Guidelines

DataSci W207: Applied Machine Learning

Lecture: mo, wed, th, office hours: tu, 8-9 am pt.

This course provides a practical introduction to the rapidly growing field of machine learning— training predictive models to generalize to new data. We start with linear and logistic regression and implement gradient descent for these algorithms, the core engine for training. With these key building blocks, we work our way to understanding widely used neural network architectures, focusing on intuition and implementation with TensorFlow/Keras. While the course centers on neural networks, we will make sure to cover key ideas in unsupervised learning and nonparametric modeling.

Along the way, weekly short coding assignments will connect lectures with concrete data and real applications. A more open-ended final project will tie together crucial concepts in experimental design and analysis with models and training.

This class meets for one 90 min class periods each week.

All materials for this course are posted on GitHub in the form of Jupyter notebooks.

• Please fill out this PRE-COURSE survey so I can get to know a bit more about you and your programming background.
• Due to a large number of private Slack inquiries, I encourage you to first read this website for commonly asked questions.
• Any questions regarding course content and organization (including assignments and final project) should be posted on my Slack channel. You are strongly encouraged to answer other students' questions when you know the answer.
• If there are private matters specific to you (e.g., special accommodations), please contact me directly.
• If you miss a class, watch the recording and inform me here .
• If you want to stay up to date with recent work in AI/ML, start by looking at the conferences NeurIPS and ICML .
• ML study guidelines: Stanford's super cheatsheet .

Core data science courses: research design, storing and retrieving data, exploring and analyzing data.

Undergraduate-level probability and statistics. Linear algebra is recommended.

Python (v3).

Jupiter and JupiterLab notebooks. You can install them in your computer using pip or Anaconda . More information here .

Git(Hub), including clone/commmit/push from the command line. You can sign up for an account here.

If you have a MacOS M1, this .sh script will install everything for you (credit goes to one of my former students, Michael Tay)

Mac/Windows/Linux are all acceptable to use.

• Raschka & Mirjalili (RM) , Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2.
• Weekly coding assignments, submitted via GitHub and Digital Campus (see notes below).
• You will present your final project in class during the final session. You are allowed to work in teams.
• You will submmit your code and presentation slides via GitHub and Digital Campus (see notes below).

Communication channel

For the final project you will form a group (3-4 people are ideal; 2-5 people are allowed; no 1 person group allowed). Grades will be calibrated by group size. Your group can only include members from the section in which you are enrolled.

Do not just re-run an existing code repository; at the minimum, you must demonstrate the ability to perform thoughtful data preprocessing and analysis (e.g., data cleaning, model training, hyperparameter selection, model evaluation).

The topic of your project is totally flexible (see also below some project ideas).

• week 04: inform me here about your group, question and dataset you plan to use.
• week 08: prepare the baseline presentation of your project. You will present in class (no more than 10 min).
• week 16: prepare the final presentation of your project. You will present in class (no more than 20 min).
• Can we predict solar panel electricity production using equipment and weather data?
• Predict Stock Portfolio Returns using News Headlines
• Pneumonia Detection from Chest Xrays
• Predicting Energy Usage from Publically Available Building Performance Data
• Can we Predict What Movies will be Well Received?
• ML for Music Genre Classification
• Predicting Metagenome Sample Source Environment from Protein Annotations
• California Wildfire Prediction
• Title, Authors
• What is the question you will be working on? Why is it interesting?
• What is the data you will be using? Include data source, size of dataset, main features to be used. Please also include summary statistics of your data.
• What prediction algorithms do you plan to use? Please describe them in detail.
• How will you evaluate your results? Please describe your chosen performance metrices and/or statistical tests in detail.
• (15%) Motivation: Introduce your question and why the question is interesting. Explain what has been done before in this space. Describe your overall plan to approach your question. Provide a summary of your results.
• (15%) Data: Describe in detail the data that you are using, including the source(s) of the data and relevant statistics.
• (15%) Approach: Describe in detail the models (baseline + improvement over baseline) that you use in your approach.
• (30%) Experiments: Provide insight into the effect of different hyperperameter choices. Please include tables, figures, graphs to illustrate your experiments.
• (10%) Conclusions: Summarize the key results, what has been learned, and avenues for future work.
• (15%) Code submission: Provide link to your GitHub repo. The code should be well commented and organized.
• Contributions: Specify the contributions of each author (e.g., data processing, algorithm implementation, slides etc).
• Step 1: Create GitHub repos for Assignments 1-10 and Final Project
• Step 2: If weekly assignments, upload .ipynb file in Gradescope. If final project, upload an .ipynb file that contains the link to your group GitHub repo (add your presentation slides to the repo; each team member submits in Gradescope)

Integrating a diverse set of experiences is important for a more comprehensive understanding of machine learning. I will make an effort to read papers and hear from a diverse group of practitioners, still, limits exist on this diversity in the field of machine learning. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was created. I would like to nurture a learning environment that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, veteran status, etc.) in the spirit of the UC Berkeley Principles of Community.

To help accomplish this, please contact me or submit anonymous feedback through I School channels if you have any suggestions to improve the quality of the course. If you have a name and/or set of pronouns that you prefer I use, please let me know. If something was said in class (by anyone) or you experience anything that makes you feel uncomfortable, please talk to me about it. If you feel like your performance in the class is being impacted by experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you. Also, anonymous feedback is always an option, and may lead to me to make a general announcement to the class, if necessary, to address your concerns.

As a participant in teamwork and course discussions, you should also strive to honor the diversity of your classmates.

If you prefer to speak with someone outside of the course, MICS Academic Director Lisa Ho, I School Assistant Dean of Academic Programs Catherine Cronquist Browning, and the UC Berkeley Office for Graduate Diversity are excellent resources. Also see the following link.

CodeFatherTech

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An Introduction to Applied Machine Learning in Python

When was the last time your computer or an app offered suggestions for you based on something you might like or enjoy? That is one of the benefits of Applied Machine Learning. 

The world is advancing fast, Machine learning and Artificial Intelligence based projects are what the future holds. This article provides insight into the exciting course of Applied Machine Learning with the Python programming language. 

What is Applied Machine Learning (AML)?

The term “Applied Machine Learning” refers to the use of Machine Learning (ML) to solve various data-related problems. 

Applied Machine Learning (AML) differs from theoretical machine learning – which is about understanding underlying algorithms, and statistics at the most fundamental level. It is not just about understanding these concepts, but also about solving real-world problems with them.

It is a subset of Artificial Intelligence that enables computer systems to learn and predict based on input data. The aim is for systems to learn automatically without human intervention and act accordingly.

Brief History of Applied Machine Learning

Machine learning is based on a model of brain-cell interaction. Donald Hebb developed this model in 1949 in his book titled The Organization of Behavior . The book discusses Hebb’s theories on neuron excitement and communication.

The first AML program was written by Arthur Samuel in 1952. The program’s goal was to play a game of checkers and the program was taught to correct its mistakes and improve its checker skills.

Applied Machine Learning Examples 

AML has enabled our apps to see, hear, and respond, therefore improving user experience and adding value across many industries. It has also enabled more personalized recommendations and improved search functionality.

Here are some examples of the unique power of Applied Machine Learning:

Speech Recognition

When you ask Siri to perform an action with your iPhone (like opening an app, calling a friend, or searching the Internet) you are witnessing AML in action.

Siri is programmed to convert spoken words into actions. It constantly learns new words through user interaction to provide users with a better experience. 

Another speech recognition assistant that you may be familiar with is Alexa .

Related Items

What do you notice when you look at a product on Amazon?

At the bottom of the product description, you will most likely find a section labeled “Customers also bought these items”. For example, when viewing a Laptop listing, Amazon will suggest a laptop bag, micro-USB cable, and mouse, as well as different variations of the laptop.

This is another example of AML in action known as “learning association.” Machines are taught to link one object to another.

Pattern Recognition

Pattern recognition in AML refers to the process of labeling specific data based on patterns. For example, if you keep watching Sci-Fi movies on Netflix, Netflix will recognize that pattern and recommend movies in the same genre.

The Basic Process of Applied Machine Learning

Here’s a breakdown of the basic process of Applied Machine Learning.  

Step 1: Understand Machine Learning Models and Algorithms

You need to have a good understanding of machine learning concepts, models, algorithms, and statistics so you can learn how to apply them in practice.

Step 2: Identify the Problem You Want to Solve

The second step in the process is to define the problem you want to solve. The problem usually centers primarily on improving user experience and increasing value across a specific industry.

Step 3: Gather Necessary Data

AML models are created by training computer systems on large samples of data. The data is usually available in your specific business domain (e.g. historical user behavior data for an eCommerce website) and it needs to be relayed to developers for data analysis.

Step 4: Choose Your Programming Language

You will need a programming background to create and use machine learning models to solve real-world business problems.

Programming languages which are often used for machine learning include Python, R programming language, Java, Javascript, and Julia.

Step 5: Build Your Experience with Practice

Writing code is the best way to build your experience as a developer. You can start with one of the machine learning courses available to make sure you create a strong foundation for yourself in this area.

What Makes Python the Best Programming Language for AML?

Choosing a programming language for your machine learning project is an important step to get started.

Numerous programming languages are available and in the next sections, you will see why Python is a great programming language for machine learning.

Why is Python Recommended for Machine Learning?

Python ranks first in IEEE Spectrum’s annual ranking of popular programming languages, with over 8.2 million developers worldwide using it for coding.

It is the preferred programming language for some IT industry titans, including Google, Instagram, Facebook, and Netflix.

Python has quickly become the preferred language for Data Analytics, Data Science, and Applied Machine Learning. Here is why:

Extensive Collection of Libraries and Frameworks

Programmers use Python frameworks and libraries to reduce development time. Python’s built-in libraries and frameworks provide pre-written code, allowing machine learning engineers to avoid starting from scratch. 

Data processing is required for machine learning, and Python has built-in libraries and packages for every programming task, including data processing. When working with complex machine learning applications, this helps machine learning engineers reduce development time and increase productivity. 

The best thing about these libraries and packages is that there is a small learning curve. Once you understand Python programming fundamentals, you can begin using these libraries.

• Working with textual data? Use NLTK , scikit-learn, Pandas , and NumPy .
• Working with images? Use scikit-image and OpenCV .
• Are you working with audio? Use Librosa.
• Implementing Deep Learning? Use TensorFlow, Keras, and PyTorch.
• Implementing basic Machine Learning algorithms? Use Sci-Kit- learn .
• Want to do scientific computing? Use Sci-Py.
• Want to visualize a dataset? Use Matplotlib , Sci-Kit, and Seaborn. 

Use these Python libraries to make sure your team does not reinvent the wheel!

Below you can see an example of Python code to plot random data points with two distinct clusters. This could represent a very simple clustering task in machine learning:

When you try this on your computer remember to install the matplotlib module.

In this code you do the following:

• Generate random data points centered around two different means (1 and 4 for both x and y coordinates), simulating two clusters.
• Use the scatter function of the Matplotlib module to plot these points.
• Add a title and axis labels to make the plot informative.
• Finally, call the show() function to display the plot.

This type of data distribution is typical in clustering problems in machine learning, where the goal is to identify distinct groups in the data.

Here is the graph you plot with the code:

Code Readability

Python is widely regarded as a highly effective coding language due to its simple syntax and readability .

The math behind machine learning is typically complex. As a result, code readability is critical for successfully implementing complex machine-learning algorithms. This allows machine learning engineers to focus on the logic to write rather than on the syntax of the programming language.

Powerful and Fast

Python saves time and effort by presenting ready-made solutions when performing tasks like scientific calculations, and processing images. This is an advantage because it means a faster return on investment (ROI) due to the ability to quickly tailor software based on feedback from real users.

Flexibility

Python is capable of interacting with code written in other programming languages. This means you can create projects that combine Python with other programming languages (such as C++) to get the best of both worlds.

Additionally, Python is available for almost every operating system, including UNIX, Windows, macOS, iOS, and Android. It is also available on various platforms, including IBM, AIX, Solaris, and VMS.

Great Community and Popularity

Python was among the top five most popular programming languages in the Developer Survey 2020 by Stack Overflow . As a result, you can easily find and hire developers with the necessary skill set to build your AML-based project.

Python AI community has expanded globally in the last few years and it represents a great support for you as a developer.

Platform Independence

Python’s popularity stems from the fact that it is platform-independent. As a result, you can use Python code to create Machine Learning programs for most common operating systems.

Developers typically use computing services such as Google or Amazon. Companies and data scientists, on the other hand, frequently use their machines with powerful Graphics Processing Units (GPUs) to train their Machine Learning models. And the fact that Python is platform-independent makes this training much more affordable and simple.

Final Thoughts

Applied machine learning has become essential to modern business and research in many organizations. It has evolved into a critical tool for cloud computing and eCommerce, and it is now part of a wide range of cutting-edge technologies. 

Python remains one of the best programming languages to help you explore Applied Machine Learning techniques. We have seen that it offers plenty of machine-learning libraries.

Claudio Sabato is an IT expert with over 15 years of professional experience in Python programming, Linux Systems Administration, Bash programming, and IT Systems Design. He is a professional certified by the Linux Professional Institute .

With a Master’s degree in Computer Science, he has a strong foundation in Software Engineering and a passion for robotics with Raspberry Pi.

Related posts:

• A Beginner’s Guide to Practical Machine Learning
• How To Read CSV Files Using Pandas: Step-By-Step
• How To Install Pandas And Use It In Your Python Programs
• Deploy a Machine Learning Model using Flask: Step-By-Step

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Applied Machine Learning in Python

Welcome to Applied Machine Learning with Python. This is a draft of an in-depth guide to machine learning in Python with scikit-learn.

It’s based on my course on Applied Machine Learning that I held at Columbia.

The book is available online at https://amueller.github.io/aml . and all notebooks are available on github .

Please feel free to open issues and pull requests there to improve the book.

This book is aimed at practitioners that have some experience with Python, but not necessarily a strong mathematical background. Therefore, I tried to avoid going into too much detail on the mathematical details of particular methods. I highly recommend the book Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman and Machine Learning: a Probabilistic Perspective Kevin Murphy for those that are interested.

Mathematical Background

In some places, there are side-notes with mathematical details where those can be helpful for understanding the materials. However, these parts are optional and not required to follow the main text.

This is an early draft, and feedback is very welcome! It’s based on scikit-learn 0.23, though might require some features of scikit-learn 0.24 when finished.

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Applied Machine Learning in Python

Description.

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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Applied Data Science with Python

U-M Credit Eligible

Kevyn Collins-Thompson

Associate Professor

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Python Operators

Precedence and associativity of operators in python.

• Python Arithmetic Operators
• Difference between / vs. // operator in Python
• Python - Star or Asterisk operator ( * )
• What does the Double Star operator mean in Python?
• Division Operators in Python
• Modulo operator (%) in Python
• Python Logical Operators
• Python OR Operator
• Difference between 'and' and '&' in Python
• not Operator in Python | Boolean Logic

Ternary Operator in Python

• Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

• Walrus Operator in Python 3.8
• Increment += and Decrement -= Assignment Operators in Python
• Merging and Updating Dictionary Operators in Python 3.9
• New '=' Operator in Python3.8 f-string

Python Relational Operators

• Comparison Operators in Python
• Python NOT EQUAL operator
• Difference between == and is operator in Python
• Chaining comparison operators in Python
• Python Membership and Identity Operators
• Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

• OPERATORS: These are the special symbols. Eg- + , * , /, etc.
• OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

• Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

• P – Parentheses
• E – Exponentiation
• M – Multiplication (Multiplication and division have the same precedence)
• D – Division
• A – Addition (Addition and subtraction have the same precedence)
• S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

• x % 10 -> yields the last digit
• x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

• Logical not
• logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

• Bitwise NOT
• Bitwise Shift
• Bitwise AND
• Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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