35 Python Programming Exercises and Solutions
To understand a programming language deeply, you need to practice what you’ve learned. If you’ve completed learning the syntax of Python programming language, it is the right time to do some practice programs.
In this article, I’ll list down some problems that I’ve done and the answer code for each exercise. Analyze each problem and try to solve it by yourself. If you have any doubts, you can check the code that I’ve provided below. I’ve also attached the corresponding outputs.
1. Python program to check whether the given number is even or not.
2. python program to convert the temperature in degree centigrade to fahrenheit, 3. python program to find the area of a triangle whose sides are given, 4. python program to find out the average of a set of integers, 5. python program to find the product of a set of real numbers, 6. python program to find the circumference and area of a circle with a given radius, 7. python program to check whether the given integer is a multiple of 5, 8. python program to check whether the given integer is a multiple of both 5 and 7, 9. python program to find the average of 10 numbers using while loop, 10. python program to display the given integer in a reverse manner, 11. python program to find the geometric mean of n numbers, 12. python program to find the sum of the digits of an integer using a while loop, 13. python program to display all the multiples of 3 within the range 10 to 50, 14. python program to display all integers within the range 100-200 whose sum of digits is an even number, 15. python program to check whether the given integer is a prime number or not, 16. python program to generate the prime numbers from 1 to n, 17. python program to find the roots of a quadratic equation, 18. python program to print the numbers from a given number n till 0 using recursion, 19. python program to find the factorial of a number using recursion, 20. python program to display the sum of n numbers using a list, 21. python program to implement linear search, 22. python program to implement binary search, 23. python program to find the odd numbers in an array, 24. python program to find the largest number in a list without using built-in functions, 25. python program to insert a number to any position in a list, 26. python program to delete an element from a list by index, 27. python program to check whether a string is palindrome or not, 28. python program to implement matrix addition, 29. python program to implement matrix multiplication, 30. python program to check leap year, 31. python program to find the nth term in a fibonacci series using recursion, 32. python program to print fibonacci series using iteration, 33. python program to print all the items in a dictionary, 34. python program to implement a calculator to do basic operations, 35. python program to draw a circle of squares using turtle.
For practicing more such exercises, I suggest you go to hackerrank.com and sign up. You’ll be able to practice Python there very effectively.
Once you become comfortable solving coding challenges, it’s time to move on and build something cool with your skills. If you know Python but haven’t built an app before, I suggest you check out my Create Desktop Apps Using Python & Tkinter course. This interactive course will walk you through from scratch to building clickable apps and games using Python.
I hope these exercises were helpful to you. If you have any doubts, feel free to let me know in the comments.
I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.
11 thoughts on “ 35 Python Programming Exercises and Solutions ”
I don’t mean to nitpick and I don’t want this published but you might want to check code for #16. 4 is not a prime number.
Thanks man for pointing out the mistake. I’ve updated the code.
# 8. Python program to check whether the given integer is a multiple of both 5 and 7:
You can only check if integer is a multiple of 35. It always works the same – just multiply all the numbers you need to check for multiplicity.
For reverse the given integer n=int(input(“enter the no:”)) n=str(n) n=int(n[::-1]) print(n)
very good, tnks
Please who can help me with this question asap
A particular cell phone plan includes 50 minutes of air time and 50 text messages for $15.00 a month. Each additional minute of air time costs $0.25, while additional text messages cost $0.15 each. All cell phone bills include an additional charge of $0.44 to support 911 call centers, and the entire bill (including the 911 charge) is subject to 5 percent sales tax.
We are so to run the code in phyton
this is best app
Hello Ashwin, Thanks for sharing a Python practice
May be in a better way for reverse.
#”’ Reverse of a string
v_str = str ( input(‘ Enter a valid string or number :- ‘) ) v_rev_str=” for v_d in v_str: v_rev_str = v_d + v_rev_str
print( ‘reverse of th input string / number :- ‘, v_str ,’is :- ‘, v_rev_str.capitalize() )
#Reverse of a string ”’
Problem 15. When searching for prime numbers, the maximum search range only needs to be sqrt(n). You needlessly continue the search up to //n. Additionally, you check all even numbers. As long as you declare 2 to be prime, the rest of the search can start at 3 and check every other number. Another big efficiency improvement.
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Top 12 Fascinating Python Applications in Real-World 
It is a well-established fact that Python is one of the most popular programming languages in both the coding and Data Science communities. But have you ever wondered why Python is so popular? What is the secret behind Python’s worldwide success and fame?
We’ll give you the answer in one line – Python is one of the top programming languages of all with a slew of applications of Python.
Whatever be your development and Data Science need, you name it – Python can take care of it as well as other related Python applications. Python is an open-source, high-level, general-purpose programming language that incorporates the features of object-oriented, structural, and functional programming.
Some believe that Java is a better language. However, it goes with saying that the former is much faster, but Python is easier to handle/read, versatile, and comes with a simple syntax. As per Stack Overflow, Python—general use and interpreted language rank fourth on the list of most popular languages for coding. It also finds immense use for Python applications.
Created way back in 1989 by Guido Van Rossum, Python stresses on the DRY (Don’t Repeat Yourself) principle, which enhances the readability of Python code. Python’s robust string manipulation, a massive collection of user-friendly libraries, and easy shell access make it a useful tool for quickly automating repetitive tasks.
While Python’s simple syntax allows for writing readable code, which can be further applied to complex software development processes to facilitate test-driven software application development, machine learning, and data analytics. Python can run on all the major operating systems, including Windows, Linux, and iOS.
Since it functions on cross-platform operating systems, Python can be used to develop a host of applications, including web apps, gaming apps, enterprise-level applications, ML apps, image processing, text processing, and so much more.
But beyond its innate simplicity and versatility, what makes Python stand out are its vast assortments of libraries and packages that can cater to a wide range of development as well as Data Science requirements.
Understanding Python applications
Given that Python is now used in data science and related applications, its growing popularity among developers is natural. The common applications of Python are so relevant that it has now become a significant resource for those looking to begin a career in data science. Having a good grasp of Python allows you to strengthen your analytical skills. Today, a data scientist or even someone in the IT sector is expected to come with relevant and new-age skills.
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A common query few might have in mind is, whether Python-run blockchain is tough to learn. It is a technology that is complex yet groundbreaking after all! However, this must never deter you from trying to get a grasp on the same. The core Blockchain concepts are mining, decentralization, and consensus mechanism. This is an immutable blockchain that is also secured cryptographically.
If you want to kick-start a career in this domain, begin by understanding the fundamentals of the same and upskill your Python programming skills to develop blockchain applications. Start your journey by studying these top four concepts of blockchain first-
- Smart Contracts
- Shared Ledger
- Consensus/Trust Mechanism
What makes Python the “Best of the Best?”
The top application of Python ensures that the language remains popular at a professional level. Here are some of the most noteworthy features of Python that make it an excellent tool for Python application professionals of all skill levels:
Python = Simplicity
We cannot stress this point enough, but Python is not only easy to learn but also easy to use and implement across any application of python. With a syntax similar to English, you can master the nitty-gritty of python application and coding in a few days. Moreover, Python is dynamically-typed, which makes indentation mandatory, thereby enhancing its readability factor.
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It is an open-source language
You don’t need to pay charges to install and use Python – it is open-source. What this means is that the source code of Python is freely available to the public. You can download it from Python’s official website . Not only that, Python supports the FLOSS (Free/Libre and Open Source Software) model, which means you can also change it and distribute it. This allows the Python community to tweak it and improve its features continuously.
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It is a high-level language
Since Python is a high-level language, you need not remember its system architecture, not do you need to perform memory management. This feature contributes to Python’s user-friendliness.
It is interpreted
Unlike compiled languages like C++ and Jave wherein you must compile the code and then run it, Python is an interpreted language. What this means is that instead of executing the source code all at once, Python executes it line by line. This makes it easier to debug a Python code because you can do it while writing the code.
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It is both object-oriented and functional
An object-oriented programming language is one that can model real-world data, while a functional language focuses on functions (code that can be reused). Python supports both object-oriented and functional programming features. Also, unlike Java, Python supports multiple inheritances. Naturally, this opens up a lot of scope around the topic- what are the applications of python?
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It is portable.
Python is portable and highly flexible, meaning, a Python code written for a Windows machine or a Linux machine can also run on iOS, and vice versa – you don’t need to make any alterations in the code. So, with Python eliminates the need to write different code for different machines (just make sure there’s no system-dependent feature in your Python code).
It is extensible and embeddable
Python is an extensible language, as it allows you to write specific parts of your Python code in other programming languages such as C++. Similarly, you can also embed your Python code in the source code of other languages. This allows you to integrate Python’s scripting functionalities into a code written in another language. This also leads to a number of applications of Python programming being developed as per need. When you look up on what are the applications of Python programming , you can get an idea of myriad Python uses .
It comes with a vast collection of libraries
When you download Python, you will automatically download the extensive collection of Python libraries with it. These libraries are built-in, so you don’t have to write individual code for every single thing. Python has libraries and packages for web browsers, threading, databases, regular expressions, image manipulation, documentation-generation, unit-testing, CGI, email, and much more.
Now that we’ve talked at length about how great a tool Python is let’s check out twelve real-world applications of Python or popular python uses.
One of the great things about Python is it owns a huge collection of data science libraries like SciPy, NumPy, and Matplotlib. All these libraries help you to conduct visualization and data analysis easily.
Python is famous in the scientific community because of its large collection of libraries for physics, math, machine learning, and engineering. For instance, TensorFlow is a Google Brain library used in ML projects. So, there are plenty of application areas of Python .
It supports different paradigms
Python is a versatile programming language and supports multiple paradigms like functional, object-oriented, and procedural programming. It suggests that you can choose the paradigm that best outfits your requirements and preferences.
Python features a simple syntax for ease of learning and use. This characteristic helps you to explore various application areas of Python, including automation.
It should be your foremost choice to learn various programming paradigms.
Python owns a huge standard library that contains modules for string handling, data manipulation, Internet protocols, file I/O, and operating system interfaces. Python’s syntax makes it easy to learn and understand python programs that are typically used for automation tasks.
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It owns a strong community of developers
Python comes with a powerful community of developers who are always dedicated to assisting you. Many online resources like chat rooms and forums are available. It aids you in solving different Python programming problems.
Python provides support for debugging tools and unit testing. It is, therefore, an ideal language for even advanced software development. You get ample resources to get started with the application of Python due to its vast and friendly community.
It is popular in data science and machine learning
Python has become quite popular in machine learning and data science applications. Its flexibility and readability make it a wonderful choice for prototyping new ideas and algorithms. Many libraries are available to help you easily execute complex data analysis tasks.
For instance, the SciPy library contains scientific and mathematical computation tools. The Pandas library is widely used for manipulation and data analysis. These are only a few of the numerous libraries that Python offers.
One of the key reasons why Python is also a popular programming language in machine learning is that it offers various powerful tools for data handling. These tools help you to conduct tasks like data modelling, transformation, and cleaning. Moreover, various machine learning libraries like Theano and TensorFlow help you to build an advanced application of Python . So, Python is worth considering if you are fascinated by machine learning and/or data science.
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12 Real-world Applications of Python
Python is a very stable programming language choice that is in use at the developers’ end as well as thought of as an apt choice for automation of deployment automation. Of course, it has a lot of uses in web-related development. Even the non-developer groups believe that once they have a hang of the framework in Python, it is a preferred language for conducting data-related work.
1. Web Development
When it comes to web development, Python should be your go-to tool. Why?
That’s because Python offers numerous options for web development. For instance, you have Django, Pyramid, Flask, and Bottle for developing web frameworks and even advanced content management systems like Plone and Django CMS. These web frameworks are packed with standard libraries and modules which simplify tasks like content management, database interaction, and interfacing with internet protocols like HTTP, SMTP, XML, JSON, FTP, IMAP, and POP.
Python web frameworks are known for their security, scalability, and flexibility. To add to that, Python’s Package Index comes with useful libraries like Requests, BeautifulSoup, Paramiko, Feedparser, and Twisted Python.
Web development is an amazing application of Python programming . The reason is it offers a broad range of frameworks like Flask, Django, Bottle, and more that streamline developers’ tasks. Python also contains inbuilt libraries and tools that make the web development process quite effortless. You can effectively build the best Python app using its characteristics like wonderful visualization, convenience in development, enhanced security, and quick development process.
2. Game Development
As we mentioned earlier, Python comes loaded with many useful extensions (libraries) that come in handy for the development of interactive games. For instance, libraries like PySoy (a 3D game engine that supports Python 3) and PyGame are two Python-based libraries used widely for game development . Python is the foundation for popular games like Battlefield 2, Frets on Fire, World of Tanks, Disney’s Toontown Online, Vega Strike, and Civilization-IV.
Apart from game development, game designers can also use Python for developing tools to simplify specific actions such as level design or dialog tree creation, and even use those tools to export those tasks in formats that can be used by the primary game engine. Also, Python is used as a scripting language by many game engines.
Python is also used to develop many contemporary popular game titles like World of Tanks, Sims 4, Eve Online, and Civilization IV. A few other titles that use Python are Doki Doki Literature Club, Mount & Blade, Disney’s Toontown Online, and Frets on Fire. So, the application of Python programming is not only limited to the professional world but game development too.
3. Scientific and Numeric Applications
Thanks to its massive library base, Python has become a crucial tool in scientific and numeric computing. In fact, Python provides the skeleton for applications that deal with computation and scientific data processing. Apps like FreeCAD (3D modeling software) and Abaqus (finite element method software) are coded in Python.
Some of the most useful Python packages for scientific and numeric computation include:
- SciPy (scientific numeric library)
- Pandas (data analytics library)
- IPython (command shell)
- Numeric Python (fundamental numeric package)
- Natural Language Toolkit (Mathematical And text analysis)
4. Artificial Intelligence and Machine Learning
AI and ML models and projects are inherently different from traditional software models. When we talk about AI/ML projects, the tools and technologies used and the skillset required is totally different from those used in the development of conventional software projects. AI/ML applications require a language that is stable, secure, flexible, and is equipped with tools that can handle the various unique requirements of such projects. Python has all these qualities, and hence, it has become one of the most favored languages of Data Science professionals and Python is a must tool in data science courses.
Python’s simplicity, consistency, platform independence, great collection of resourceful libraries, and an active community make it the perfect tool for developing AI and ML applications. Some of the best Python packages for AI and ML are:
- SciPy for advanced computing
- Pandas for general-purpose data analysis
- Seaborn for data visualization
- Keras, TensorFlow, and Scikit-learn for ML
- NumPy for high-performance scientific computing and data analysis
Apart from these libraries, there are also other Python-based libraries like NLTK, Caffee, PyTorch, and Accord.NET, that are useful for AI and ML projects.
Two of the trendiest subjects right now are Artificial Intelligence and Machine Learning. With the inbuilt tools and libraries, it facilitates the development of ML and AI algorithms. Moreover, it provides easy, concise, and readable code that makes it simpler for developers to write complicated algorithms.
Few of the built-in tools and libraries that enhance ML and AI processes through the application of Python programming are:
- Keras for Machine learning
- Numpy for complex data analysis
- SciPy for technical computing
Python not only boasts of an English-like syntax, but it also features a modular architecture and the ability to work on multiple operating systems. These aspects, combined with its rich text processing tools, make Python an excellent choice for developing desktop-based GUI applications.
Python offers many GUI toolkits and frameworks that make desktop application development a breeze. PyQt, PyGtk, Kivy, Tkinter, WxPython, PyGUI, and PySide are some of the best Python-based GUI frameworks that allow developers to create highly functional Graphical User Interfaces (GUIs).
Python is a dynamic programming language that assists developers in easily and efficiently creating GUIs. It features a long list of inbuilt tools like kivy, PyQT,wxWidgets, and several other libraries. These libraries help you to efficiently and securely build a functional GUI.
Python’s modular programming approach and easy-to-understand syntax are the basis for responsive and super-fast GUI. So, they streamline the whole development process. A few of the prominent tools available for GUI development using applications of Python are Tkinter, PyQt, wxWidgets, Python GTK+, and Kivy.
6. Software Development
Python packages and applications aim to simplify the process of software development. From developing complex applications that involve scientific and numeric computing to developing desktop and web applications, Python can do it all. This is the reason why Software Developers use Python as a support language for build control, testing, and management.
For instance, SCons is designed explicitly for build control, Buildbot and Apache Gump allow for automated continuous compilation and testing, and Roundup and Trac are great for bug tracking and project management.
Python also supports data analyzation and visualization, thereby further simplifying the process of creating custom solutions minus the extra effort and time investment.
Python is ideal for software development. Famous applications like Google, Reddit, and Netflix use Python. It offers the following great features for software development:
- Platform independence
- High compatibility
- Inbuilt frameworks and libraries to streamline development
- Enhanced code reusability and readability
Python also provides enhanced features to work with swiftly growing technologies like AI and ML. These features make applications of Python the famous choice for software development.
7. Enterprise-level/Business Applications
Enterprise-level software or business applications are strikingly different from standard applications, as in the former demands features like readability, extensibility, and scalability. Essentially, business applications are designed to fit the requirements of an organization rather than the needs of individual customers.
Thus, these applications must be capable of integrating with legacy systems like existing databases and non-web apps. Since business applications are developed, keeping in mind the custom requirements to cater to the specific needs of an organization’s operating model, the entire development process becomes very complicated.
This is where Python can make a significant difference. Python high performance, scalability, flexibility, and readability are just the features required for developing fully-functional and efficient business applications. Furthermore, Python has other tools for business application development, like:
- Odoo, an all-in-one management software that forms a complete suite of enterprise management applications.
- Tryton, a three-tier, high-level, general-purpose application platform, is another fantastic tool for building business applications.
Learn more about: Top Python tools
Business applications vastly vary from average consumer software. Firstly, provide a set of explicit features instead of plenty of features. Secondly, they target a small user group, commonly an organization. One of the best things about Python is that it perfectly delivers performance-efficient custom solutions. So, it can work on both business applications and consumer applications.
One of the most crucial facets of any application is security. Python’s security features are standout when it comes to business applications. This is because it is built considering information security. One of the great applications of Python programming is scalability through which a business can expand its horizon.
8. Education programs and training courses
If there’s any beginner-friendly programming language, it is Python. We’ve said it many times before, and we’re repeating it – Python has an extremely straightforward syntax that’s similar to the English language. It has a short learning curve and hence, is an excellent choice for beginners. Python’s easy learning curve and simplicity are the two main reasons why it is one of the most used programming languages in educational programs, both at beginner and advanced levels.
However, Python is not just great as an introductory language – even professional developers and coders all around the world rely heavily on Python.
Python features a shorter learning curve compared to other programming languages. So, you can quickly learn the development of applications of Python programming. This facet makes it one of the best options for educational programs. Platforms like Coursera, Udemy, edX, Harvard, and Python Institute are among the leading online providers of Python educational courses.
9. Language Development
Over the years, Python’s design and module architecture has been the inspiration behind the development of many new programming languages such as Boo, Swift, CoffeeScript, Cobra, and OCaml. All of these languages share numerous similarities with Python on grounds like object model, syntax, and indentation.
10. Operating Systems
Yes, Python is the secret ingredient behind many operating systems as well, most popularly of Linux distributions. Linux-based Ubuntu’s Ubiquity Installer and Fedora and Red Hat Enterprise’s Anaconda Installer are coded in Python. Even Gentoo Linux leverages Python Portage (package management system). Usually, Python is combined with the C programming language to design and develop operating systems.
11. Web Scraping Applications
Python is a nifty tool for extracting voluminous amounts of data from websites and web pages. The pulled data is generally used in different real-world processes, including job listings, price comparison, R&D, etc.
BeautifulSoup, MechanicalSoup, Scrapy, LXML , Python Requests, Selenium, and Urllib are some of the best Python-based web scraping tools.
In other words, web scraping is an automated process for easily and quickly extracting information from websites. Python presents various features that make it appropriate for web scraping and justify the applications of Python programming . Some of the features are:
- Easy to understand and use
- A concise syntax that improves the readability and saves your time
- The web scraping process is made easy and efficient with various tools and libraries like matplotlib, Pandas, and Selenium
12. Image Processing and Graphic Design Applications:
Alongside all the uses mentioned above, Python also finds a unique use case in image processing and graphic design applications. The programming language is used globally to design and build 2D imaging software like Inkscape, GIMP, Paint Shop Pro, and Scribus. Also, Python is used in several 3D animation packages such as Blender, Houdini, 3ds Max, Maya, Cinema 4D, and Lightwave, to name a few.
With so many uses up its sleeve, Python ranks as a highly loved language for programming. It is a top pick of software engineers and hackers, too, since it is laced with flexibility, versatility, and object-oriented specifications.
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Other real time applications of python.
- Python in IoT (Internet of Things)
Python has emerged as a prominent language in the realm of Internet of Things (IoT) development. Its versatility, simplicity, and a rich ecosystem of libraries make it an ideal choice for building robust IoT solutions. Python facilitates the development of IoT applications, from sensor data processing to communication with cloud platforms, ensuring seamless connectivity in the IoT ecosystem.
Python seamlessly integrates with a wide array of IoT devices and platforms. Its adaptability allows developers to connect and communicate with various sensors, actuators, and IoT hardware. Python’s compatibility with popular IoT platforms, such as Raspberry Pi and Arduino, empowers developers to create innovative IoT projects with ease.
Python serves as the backbone for numerous IoT projects, ranging from smart home automation to industrial IoT applications. Frameworks like MicroPython and Zerynth provide Python support for microcontrollers, enabling developers to deploy Python code directly on resource-constrained IoT devices. Additionally, popular IoT frameworks like Home Assistant leverage Python for building comprehensive home automation solutions.
- Advancements in Python Web Frameworks
Modern Python web frameworks play a pivotal role in web development, offering efficient tools for building scalable and feature-rich applications. Frameworks such as Django, Flask, and FastAPI provide developers with the scaffolding needed to streamline development workflows. Django, known for its batteries-included approach, offers a comprehensive set of features, while Flask embraces simplicity and flexibility. FastAPI, a newcomer, stands out for its speed and automatic OpenAPI documentation.
Each Python web framework has its unique strengths and use cases. Django, a high-level framework, is renowned for rapid development and a built-in admin interface. Flask, a microframework, grants developers more flexibility by allowing them to choose components as needed. FastAPI, designed for building APIs quickly, stands out for its automatic validation and support for asynchronous programming. Comparing these frameworks helps developers choose the one that aligns with their project requirements.
Python web development continues to evolve with emerging trends. Serverless architecture, powered by platforms like AWS Lambda and Azure Functions, is gaining traction, allowing developers to build scalable applications without managing server infrastructure. Microservices architecture, supported by Python frameworks, is fostering the development of modular and maintainable web applications. Moreover, the rise of frontend technologies like Vue.js and React, coupled with Python backend services, contributes to a more dynamic and interactive user experience.
- Python forNatural Language Processing (NLP)
Python has become a leading language for Natural Language Processing (NLP), enabling developers to work with and analyze human language data. Its simplicity and a wealth of NLP libraries make it accessible for tasks such as text parsing, sentiment analysis, and language translation. Python provides a conducive environment for handling the intricacies of natural language, making it a preferred choice for NLP practitioners.
Python boasts powerful libraries dedicated to NLP tasks. The Natural Language Toolkit (NLTK) offers a comprehensive set of tools for tasks like tokenization, stemming, and part-of-speech tagging. SpaCy, another popular library, stands out for its speed and efficiency in processing large amounts of text. These libraries empower developers to implement sophisticated NLP algorithms with ease, making Python a cornerstone in the field.
Python’s influence extends to the creation of advanced language models and chatbots. With frameworks like TensorFlow and PyTorch, developers can build and train complex language models for tasks like language generation and understanding. Python’s simplicity and extensive community support also contribute to the development of conversational agents and chatbots, enhancing user interactions across various platforms.
Some other real-world applications of Python:
- Automation and robotics through inbuilt tools and libraries like Dart, PyDy, pyro, and PyRobot
- Image processing through tools and libraries like OpenCV, Blender, PIL, and Houdini
- Scientific applications and the best Python app are developed through libraries like Pandas, SciPy, Matplotlib
Latest Trends and Updates in Python
To stay ahead in the tech world, it’s crucial for developers and enthusiasts alike to be aware of the latest trends, updates, and developments in the Python ecosystem. Let’s delve into the exciting advancements that are shaping the future of Python with the help of python course for beginners.
- Adoption of Python in Machine Learning and AI:
Python’s dominance in the realm of machine learning and artificial intelligence continues to grow. Libraries such as TensorFlow and PyTorch are witnessing widespread adoption, empowering developers to create sophisticated models and applications. The seamless integration of Python with machine learning frameworks reinforces its position as the go-to language for AI enthusiasts.
- Web Development with FastAPI:
FastAPI, a modern, fast, and highly performant web framework for building APIs with Python 3.7 and above, has gained significant traction. Its simplicity, automatic validation, and support for asynchronous programming make it an attractive choice for developers working on web applications and APIs. FastAPI’s rising popularity showcases Python’s adaptability to evolving web development needs.
- Serverless Computing and Python:
Serverless architecture has become a popular paradigm in cloud computing, and Python is playing a pivotal role in this space. Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions seamlessly support Python, enabling developers to build scalable and cost-effective serverless applications of python.
- Increased Focus on Data Science and Visualization:
With the rise of data-driven decision-making, Python’s role in data science and visualization is more prominent than ever. Libraries like Pandas, NumPy, and Plotly continue to be instrumental in handling, analyzing, and visualizing data. Python’s simplicity and extensive ecosystem contribute to its widespread adoption in data-centric applications.
- Microservices Architecture
Python’s versatility extends to microservices architecture, with frameworks like Flask and Django providing robust support. Microservices enable developers to build scalable and maintainable applications by breaking them down into smaller, independent services. Python’s ease of use and diverse ecosystem contribute to the efficiency of microservices development.
- Enhanced Type Hinting and Static Analysis
Type hinting, introduced in Python 3.5, has gained momentum, leading to improved static analysis tools and support in popular IDEs. The push towards more statically-typed Python code enhances code quality, readability, and developer collaboration. Tools like MyPy and Pyright contribute to the growing emphasis on static analysis in the Python community.
- Python in DevOps and Automation
Python’s simplicity and readability make it a preferred choice for DevOps tasks and automation scripts. Infrastructure as Code (IaC) tools like Ansible leverage Python for defining and managing infrastructure. The language’s extensive standard library and third-party modules facilitate seamless automation across various domains.
- Quantum Computing with Python
As quantum computing gains momentum, Python is becoming a language of choice for quantum programming. Libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python’s accessibility is lowering the barrier for entry into the fascinating field of quantum computing.
After reading about all these versatile and diverse real-world applications of Python , it is safe to conclude that Python is capable of handling almost any development requirement. In the last few years, Python applications have gained newfound traction in the field of Data Science as well, particularly in Machine Learning.
Python has brought in a lot of changes to the industry given it is easy to use as well as comes packed with powerful libraries. Additionally, it offers a wide range of applications that boost productivity. The jobs in Python pay hefty packages over time and Python developers are in high demand. The ease of learning Python makes it an option to bag a steady and well-paying job too.
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Frequently Asked Questions (FAQs)
Python has a solution for every field. It is the most versatile language till now and has a bright future ahead. There is a long list of fields where Python is considered to be the most suitable programming language. Developers in this language are sought after because the language is gradually becoming the go to solution in a diverse set of different areas. The major fields include Machine Learning and AI, Web Development, Data Analytics, Game Development, IoT, Application Development, and Game Development. Many sectors including the healthcare sector, finance sector, aerospace sector, and banking sector rely heavily on Python. There are many big names that have either built their applications on Python or have completely shifted their tech stack to Python. Some of these include YouTube, Google, Instagram, and Pinterest.
Python is a very versatile language and if you have a good knowledge of Python, there can be various career opportunities in your hand. Some of these opportunities are as follows: You can be a Python developer right after acquiring the Python knowledge. Python developers are responsible for building websites, optimize data algorithms, or write clean and efficient Python codes. A data analyst has to deal with large sets of data, analyze them and create visualizations out of them. If you are a Python geek and love to play with data then this job is for you. Project management is in high demand as a project manager is highly responsible for the business and marketing of the companies. A machine learning engineer trains the machines or models for making predictions on the basis of the data provided to them.
Python has a wide range of rich libraries and modules but being a Python geek, you must be handy with the top and most used Python libraries. The following are some of the most popular Python libraries: TensorFlow is a boon to Machine Learning engineers. This library is developed by Google and can be considered a computational library. Numpy is again a machine learning library used by other Python libraries like TensorFlow to perform internal operations. Keras is another popular Python library that provides a convenient mechanism for neural networks.
Python has become a dominant language in Machine Learning and AI due to its simplicity, extensive libraries (such as TensorFlow and PyTorch), and readability. Real time applications of python provides a conducive environment for developing complex algorithms, making it a preferred choice for data scientists and machine learning engineers.
Python's popularity in software development can be attributed to its platform independence, high compatibility, extensive built-in frameworks and libraries, enhanced code reusability, and readability. These features make it an excellent tool for developing applications ranging from web development to enterprise-level solutions.
Python plays a crucial role in quantum computing, and libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python's accessibility and support for quantum programming are contributing to its adoption in this cutting-edge field.
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Say "Hello, World!" With Python Easy Max Score: 5 Success Rate: 96.29%
Python if-else easy python (basic) max score: 10 success rate: 89.76%, arithmetic operators easy python (basic) max score: 10 success rate: 97.44%, python: division easy python (basic) max score: 10 success rate: 98.68%, loops easy python (basic) max score: 10 success rate: 98.13%, write a function medium python (basic) max score: 10 success rate: 90.31%, print function easy python (basic) max score: 20 success rate: 97.25%, list comprehensions easy python (basic) max score: 10 success rate: 97.71%, find the runner-up score easy python (basic) max score: 10 success rate: 94.14%, nested lists easy python (basic) max score: 10 success rate: 91.63%.
What can you do with Python? 5 real-world Python applications
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The word is out on Python. The simplicity, limitless range of external libraries, and committed community of Pythonistas are fundamentals of Python that even a beginner developer has probably heard before. These features have also distinguished Python as the most used programming language in the world, currently  .
You’re not here to learn all the reasons why you should learn Python as either your first introduction to coding or your next learning endeavor in a long line of languages. You’re here to be shown why learning to code in Python is worth your time based on the career and project you see in your future. For our purposes, It helps to view Python as a tool . We’ll explore not necessarily the tool itself, but rather all that can be built by that tool . It’s pointless to learn how to grip and strike with a hammer if you don’t have an idea of what to do with it once you achieve proficiency. If you don’t know what your future holds just yet, that’s more than OK. Hopefully, the following sections can provide some inspiration through examples. Many major industries and companies are already using Python’s boundless applications to turn their visions into reality.
Python is a versatile and powerful tool that stretches to every corner of our world. The use cases of Python are in everything from Luke Skywalker’s lightsaber to your tedious health care plan. You don’t know Python until you know what it can do.
3. education, 5. health care, what python’s presence means for you, wrapping up and next steps, 5 real-world applications of python.
Python code is in everything. The Python-based libraries and modules that can be freely and easily used in any project make certain that the language can be everywhere. Some examples of these libraries are NumPy for machine learning and Pandas for data analysis . Python and its endless list of libraries are things that even the most different industries and companies have in common. As a tool, Python can lead you down any career path that you could dream of. Let’s take a look at just a few examples of where Python thrives.
The Python programming language brings you personalized playlists to brighten up your day. Spotify uses Python to support its back-end web development and data science . Have you ever wondered how Spotify knows exactly what to put in your personalized playlists? You can thank the data analysis capabilities of Python . Over 80% of Spotify’s back-end web development and data analysis processes are written in Python.
Spotify is also a vocal and proud member of the Python community , sponsoring large conferences such as PyCon and local groups such as NYC PyLadies. A thriving company like Spotify doesn’t connect its name to a language so enthusiastically unless it performs sensationally. Spotify is always hiring Python developers. If you’ve always pictured yourself working with music while using your favorite programming language, then these are opportunities you should keep an eye out for in the future.
You don’t have to write or act to get into the entertainment business. You can code too ! Python appears frequently in the entertainment media industry. Industrial Light and Magic, the visual effects company behind films such as Star Wars and Jurassic Park, has been using Python to run its CGI operating systems and lighting automation for decades. And Netflix has been becoming more and more Python-oriented every year. The company depends on Python to run its Cassandra database. Cassandra clusters and modules are used for automation (including the recommendations page that everybody loves), data analytics, and error monitoring.
Metaflow, a Python web framework , is responsible for machine learning projects at Netflix from the prototype to the production stage. The framework handles millions of data points and organizes them among thousands of CPUs. YouTube was also initially built using mostly Python and still heavily uses it today among other languages. Not just exclusive to Netflix, the machine learning abilities of Python are extensively used in our modern entertainment landscape.
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Applying Functional Programming in Python to Solve Real-World Problems
Functional programming is a programming paradigm that models computations as the evaluation of mathematical functions. It emphasizes pure functions, immutable data, and avoidance of side effects. While Python is not a fully functional language, it does provide useful tools to apply functional programming concepts and techniques to solve real-world problems in fields like data science, machine learning, and web development. This guide will examine key functional programming principles and demonstrate how to leverage them in Python to write cleaner, more maintainable and testable code.
Table of Contents
Overview of functional programming, 1. first-class functions, 2. pure functions, 3. higher-order functions, 4. list comprehensions, 5. immutability, 6. recursion, benefits of functional programming in python, 1. use pure functions wherever possible, 2. make use of higher-order functions, 3. embrace immutability, 4. utilize list comprehensions, 5. write recursive algorithms, 6. pass functions as arguments, 7. return functions from other functions, data processing pipeline, parsing complex json, managing application state, parallel processing, when to avoid functional programming in python.
Functional programming languages like Haskell, Erlang, and Scala are built around mathematical functions and shun side effects. In contrast, Python is a multi-paradigm language that supports imperative, object-oriented, and functional styles. While not inherently functional, Python offers features like first-class functions, list comprehensions, and built-in functions like map() , filter() , and reduce() that facilitate a functional approach.
The key principles of functional programming include:
- Pure Functions - Functions that always return the same output for the same input and avoids manipulating external state.
- Immutable Data - Data that cannot be modified after creation. In Python, tuples are immutable while lists are mutable.
- First-class Functions - Treating functions as values that can be passed as arguments to other functions, returned from functions, or assigned to variables.
Higher-order Functions - Functions that take other functions as arguments or return functions as output. Examples are map() , filter() , reduce() .
Recursion - Solving problems by having functions call themselves until reaching a base case. Useful for tasks like tree traversal.
Avoid Side Effects - Not mutating state that exists outside a function’s scope. Enforces isolation and testing.
Following these principles leads to code that is more modular, testable, and reasoned about.
Key Functional Concepts in Python
While Python is not a functional language, it has adopted several functional concepts into its design:
Treating functions as first-class objects provides the basis for implementing core functional techniques in Python:
Pure functions always return the same output for the same input and avoid side effects:
Pure functions are easier to reason about and test.
Higher-order functions take other functions as input or return functions as output:
map() , filter() , reduce() facilitate functional programming.
List comprehensions provide a concise syntax for mapping and filtering without mutating existing data:
Using immutable objects prevents unintended side effects:
Python tuples , strings , numbers are immutable while lists and dictionaries are mutable.
Recursion allows elegant solutions by breaking problems down into simpler subproblems:
Recursive algorithms shine for tasks like tree traversal.
By leveraging these concepts, we can write more functional Python code.
Applying functional programming principles in Python code offers several advantages:
Modularity - Pure functions with no side effects are easier to combine into pipelines.
Testability - Immutable data and lack of side effects make functional code much easier to test.
Readability - Concise declarative syntax like list comprehensions are easier to read.
Parallelization - No shared state enables trivial parallelization of functional code.
Mathematical - Mapping functions over data structures has an elegant mathematical appeal.
While functional code can sometimes be less performant due to immutability, the benefits of modularity and testability often outweigh any minor performance costs.
Functional Programming Techniques in Python
Let’s explore some of the main techniques for applying functional programming in Python:
Pure functions that avoid side effects facilitate code isolation and testing:
Use built-in higher order functions like map() , filter() , reduce() for data processing pipelines:
Use immutable objects like tuples and strings instead of lists and dictionaries:
List comprehensions provide a concise way to transform lists by mapping and filtering:
Use recursion techniques to traverse trees, break down problems, and enable elegant solutions:
Leverage first-class functions to pass behavior into other functions:
This allows abstraction and reusability.
Use closures to factory functions that can configure and return behavior:
Encapsulates logic elegantly.
By applying these techniques, we can reap the benefits of functional programming in Python.
Real World Examples
Let’s examine some real-world examples that demonstrate where a functional approach in Python shines:
Data pipelines often involve chaining together mapping, filtering, and reducing operations:
Functional programming allows clean data pipelines.
Parsing nested JSON can be elegantly handled via recursion:
Recursive algorithms shine for tree-like structures.
Representing state as immutable data helps manage complex applications:
Immutable state and pure functions provide stability.
Functional programming enables trivial parallelization:
No side effects means safely parallelizable.
As we’ve seen, taking a functional approach in Python can simplify everything from data processing to concurrency and state management.
While functional programming excels in many areas, there are a few cases where it may not be the best approach in Python:
Performance Critical Situations - Operations on immutable data and function calls can sometimes impose performance costs. Needs that demand optimized speed may justify an imperative approach.
Stateful Algorithms - Heavily stateful algorithms like simulations, neural networks, and physics engines can be awkward to express in a functional style. Mutable state offers a more natural fit.
Convenient APIs - Many Python libraries for tasks like machine learning and web development use stateful APIs for convenience. Interfacing with these may require non-functional design.
Legacy Systems - Integrating with older imperative systems can be difficult using pure functional code. Pragmatic compromises are sometimes necessary.
Functional programming emphasizes elegance, testability, and modularity, but integration with external systems and stateful algorithms may necessitate a more imperative style. The ideal approach depends heavily on the specific use case and problem domain.
Functional programming techniques enable writing Python code that is concise, readable, testable and modular. Key concepts like immutability, pure functions and avoidance of side effects can facilitate elegantly solving problems involving data processing, concurrency, and complex state management. While Python is not a purely functional language, its support of features like list comprehensions, higher order functions, and recursion allow idiomatic functional patterns that can simplify coding and testing. Learning to apply functional programming principles can make you a better and more versatile Python developer.
Solving Real World Problems with Regular Expressions in Python
Regular Expressions are a tool for searching and manipulating text. Most popular programming languages feature support for them and they are used widely across different disciplines. Regardless of whether you are a developer or engineer, being comfortable reading and writing regular expressions will benefit you.
They are a declarative way of specifying the desired structure of a piece of text. A regular expression can be used to extract information from a larger text, and to validate smaller pieces of text.
In this set of labs, you will use the Python programming language to learn the basics of how to use a regular expression and you'll learn about the different character classes available for matching different types of characters.
Upon completion of these beginner-level labs, you will be able to:
- Implement a Regular Expression using Python
- Use different features of Regular Expressions to match subsets of a piece of text
- Recognize when Regular Expressions are a good solution and when something else should be preferred
Familiarity with the Python programming language will be beneficial but is not required.
Andrew is a Labs Developer with previous experience in the Internet Service Provider, Audio Streaming, and CryptoCurrency industries. He has also been a DevOps Engineer and enjoys working with CI/CD and Kubernetes.
He holds multiple AWS certifications including Solutions Architect Associate and Professional.
The Python Workshop: Write Python code to solve challenging real-world problems
Gain proficiency, productivity, and power by working on projects and kick-starting your career in Python with this comprehensive, hands-on guide.
- Understand and utilize Python syntax, objects, methods, and best practices
- Explore Python’s many features and libraries through real-world problems and big data
- Use your newly acquired Python skills in machine learning as well as web and software development
What you will learn.
- Write efficient and concise functions using core Python methods and libraries
- Build classes to address different business needs
- Create visual graphs to communicate key data insights
- Organize big data and use machine learning to make regression and classification predictions
- Develop web pages and programs with Python tools and packages
- Automate essential tasks using Python scripts in real-time execution
Who this book is for
This book is for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner’s course, you’ll learn more easily if you already have an understanding of standard programming topics like variables, if-else statements, and functions. Experience with another object-oriented program, though not essential, will also be beneficial. If Python is your first attempt at computer programming, this book will help you understand the basics with adequate detail for a motivated student.
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Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio
February 15, 2024
Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription . The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio .
Today, we’re also excited to introduce our next-generation Gemini 1.5 model , which uses a new Mixture-of-Experts (MoE) approach to improve efficiency. It routes your request to a group of smaller "expert” neural networks so responses are faster and higher quality.
Developers can sign up for our Private Preview of Gemini 1.5 Pro , our mid-sized multimodal model optimized for scaling across a wide-range of tasks. The model features a new, experimental 1 million token context window, and will be available to try out in Google AI Studio . Google AI Studio is the fastest way to build with Gemini models and enables developers to easily integrate the Gemini API in their applications. It’s available in 38 languages across 180+ countries and territories .
1,000,000 tokens: Unlocking new use cases for developers
Before today, the largest context window in the world for a publicly available large language model was 200,000 tokens. We’ve been able to significantly increase this — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model. Gemini 1.5 Pro will come with a 128,000 token context window by default, but today’s Private Preview will have access to the experimental 1 million token context window.
We’re excited about the new possibilities that larger context windows enable. You can directly upload large PDFs, code repositories, or even lengthy videos as prompts in Google AI Studio. Gemini 1.5 Pro will then reason across modalities and output text.
Upload multiple files and ask questions We’ve added the ability for developers to upload multiple files, like PDFs, and ask questions in Google AI Studio. The larger context window allows the model to take in more information — making the output more consistent, relevant and useful. With this 1 million token context window, we’ve been able to load in over 700,000 words of text in one go. Gemini 1.5 Pro can find and reason from particular quotes across the Apollo 11 PDF transcript.
[Video sped up for demo purposes]
Query an entire code repository The large context window also enables a deep analysis of an entire codebase, helping Gemini models grasp complex relationships, patterns, and understanding of code. A developer could upload a new codebase directly from their computer or via Google Drive, and use the model to onboard quickly and gain an understanding of the code. Gemini 1.5 Pro can help developers boost productivity when learning a new codebase.
Add a full length video Gemini 1.5 Pro can also reason across up to 1 hour of video. When you attach a video, Google AI Studio breaks it down into thousands of frames (without audio), and then you can perform highly sophisticated reasoning and problem-solving tasks since the Gemini models are multimodal. Gemini 1.5 Pro can perform reasoning and problem-solving tasks across video and other visual inputs.
More ways for developers to build with Gemini models
In addition to bringing you the latest model innovations, we’re also making it easier for you to build with Gemini:
Easy tuning. Provide a set of examples, and you can customize Gemini for your specific needs in minutes from inside Google AI Studio. This feature rolls out in the next few days.
New developer surfaces . Integrate the Gemini API to build new AI-powered features today with new Firebase Extensions , across your development workspace in Project IDX , or with our newly released Google AI Dart SDK .
Lower pricing for Gemini 1.0 Pro . We’re also updating the 1.0 Pro model, which offers a good balance of cost and performance for many AI tasks. Today’s stable version is priced 50% less for text inputs and 25% less for outputs than previously announced. The upcoming pay-as-you-go plans for AI Studio are coming soon.
Since December, developers of all sizes have been building with Gemini models, and we’re excited to turn cutting edge research into early developer products in Google AI Studio . Expect some latency in this preview version due to the experimental nature of the large context window feature, but we’re excited to start a phased rollout as we continue to fine-tune the model and get your feedback. We hope you enjoy experimenting with it early on, like we have.
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Don’t Let Gen AI Limit Your Team’s Creativity
Treat it as a partner in a structured conversation.
No one doubts ChatGPT’s ability to generate lots of ideas. But are those ideas any good? A recent real-world experiment showed that teams engaged in a creative problem-solving task saw only modest gains from AI assistance for the most part—and some underperformed. Surveys conducted before and after the exercise showed that the teams using AI gained far more confidence in their problem-solving abilities than the others did, but that much of their confidence was misplaced.
But don’t blame the technology, says Kian Gohar, CEO of the leadership-development firm GeoLab and one of the study’s authors. “Brainstorming with generative AI requires rethinking your ideation workflow and learning new skills,” Gohar says. This article offers guidance for approaching the exercise as a structured, ongoing conversation, opening up a staggering capacity to develop better and more-creative ideas faster.
No one doubts ChatGPT’s ability to generate lots of ideas. But are those ideas any good? In a recent real-world experiment, teams engaged in a creative problem-solving task saw modest gains from AI assistance for the most part—and some underperformed. Don’t blame the technology, says Kian Gohar, CEO of the leadership-development firm GeoLab and one of the study’s authors. Common misconceptions about generative AI, problem-solving, and the creative process are causing workers and their managers to use the tools improperly, sometimes leaving them worse off than if they’d proceeded without AI input.
Read our research on: Immigration & Migration | Podcasts | Election 2024
Regions & Countries
How americans view the situation at the u.s.-mexico border, its causes and consequences, 80% say the u.s. government is doing a bad job handling the migrant influx.
Pew Research Center conducted this study to understand the public’s views about the large number of migrants seeking to enter the U.S. at the border with Mexico. For this analysis, we surveyed 5,140 adults from Jan. 16-21, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .
Here are the questions used for the report and its methodology .
The growing number of migrants seeking entry into the United States at its border with Mexico has strained government resources, divided Congress and emerged as a contentious issue in the 2024 presidential campaign .
Americans overwhelmingly fault the government for how it has handled the migrant situation. Beyond that, however, there are deep differences – over why the migrants are coming to the U.S., proposals for addressing the situation, and even whether it should be described as a “crisis.”
Factors behind the migrant influx
Economic factors – either poor conditions in migrants’ home countries or better economic opportunities in the United States – are widely viewed as major reasons for the migrant influx.
About seven-in-ten Americans (71%), including majorities in both parties, cite better economic opportunities in the U.S. as a major reason.
There are wider partisan differences over other factors.
About two-thirds of Americans (65%) say violence in migrants’ home countries is a major reason for why a large number of immigrants have come to the border.
Democrats and Democratic-leaning independents are 30 percentage points more likely than Republicans and Republican leaners to cite this as a major reason (79% vs. 49%).
By contrast, 76% of Republicans say the belief that U.S. immigration policies will make it easy to stay in the country once they arrive is a major factor. About half as many Democrats (39%) say the same.
For more on Americans’ views of these and other reasons, visit Chapter 2.
How serious is the situation at the border?
A sizable majority of Americans (78%) say the large number of migrants seeking to enter this country at the U.S.-Mexico border is eithera crisis (45%) or a major problem (32%), according to the Pew Research Center survey, conducted Jan. 16-21, 2024, among 5,140 adults.
Related: Migrant encounters at the U.S.-Mexico border hit a record high at the end of 2023 .
- Republicans are much more likely than Democrats to describe the situation as a “crisis”: 70% of Republicans say this, compared with just 22% of Democrats.
- Democrats mostly view the situation as a major problem (44%) or minor problem (26%) for the U.S. Very few Democrats (7%) say it is not a problem.
In an open-ended question , respondents voice their concerns about the migrant influx. They point to numerous issues, including worries about how the migrants are cared for and general problems with the immigration system.
Yet two concerns come up most frequently:
- 22% point to the economic burdens associated with the migrant influx, including the strains migrants place on social services and other government resources.
- 22% also cite security concerns. Many of these responses focus on crime (10%), terrorism (10%) and drugs (3%).
When asked specifically about the impact of the migrant influx on crime in the United States, a majority of Americans (57%) say the large number of migrants seeking to enter the country leads to more crime. Fewer (39%) say this does not have much of an impact on crime in this country.
Republicans (85%) overwhelmingly say the migrant surge leads to increased crime in the U.S. A far smaller share of Democrats (31%) say the same; 63% of Democrats instead say it does not have much of an impact.
Government widely criticized for its handling of migrant influx
For the past several years, the federal government has gotten low ratings for its handling of the situation at the U.S.-Mexico border. (Note: The wording of this question has been modified modestly to reflect circumstances at the time).
However, the current ratings are extraordinarily low.
Just 18% say the U.S. government is doing a good job dealing with the large number of migrants at the border, while 80% say it is doing a bad job, including 45% who say it is doing a very bad job.
- Republicans’ views are overwhelmingly negative (89% say it’s doing a bad job), as they have been since Joe Biden became president.
- 73% of Democrats also give the government negative ratings, the highest share recorded during Biden’s presidency.
For more on Americans’ evaluations of the situation, visit Chapter 1 .
Which policies could improve the border situation?
There is no single policy proposal, among the nine included on the survey, that majorities of both Republicans and Democrats say would improve the situation at the U.S.-Mexico border. There are areas of relative agreement, however.
A 60% majority of Americans say that increasing the number of immigration judges and staff in order to make decisions on asylum more quickly would make the situation better. Only 11% say it would make things worse, while 14% think it would not make much difference.
Nearly as many (56%) say creating more opportunities for people to legally immigrate to the U.S. would make the situation better.
Majorities of Democrats say each of these proposals would make the border situation better.
Republicans are less positive than are Democrats; still, about 40% or more of Republicans say each would improve the situation, while far fewer say they would make things worse.
Opinions on other proposals are more polarized. For example, a 56% majority of Democrats say that adding resources to provide safe and sanitary conditions for migrants arriving in the U.S. would be a positive step forward.
Republicans not only are far less likely than Democrats to view this proposal positively, but far more say it would make the situation worse (43%) than better (17%).
Building or expanding a wall along the U.S.-Mexico border was among the most divisive policies of Donald Trump’s presidency. In 2019, 82% of Republicans favored expanding the border wall , compared with just 6% of Democrats.
Today, 72% of Republicans say substantially expanding the wall along the U.S. border with Mexico would make the situation better. Just 15% of Democrats concur, with most saying either it would not make much of a difference (47%) or it would make things worse (24%).
For more on Americans’ reactions to policy proposals, visit Chapter 3 .
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Table of contents, fast facts on how greeks see migrants as greece-turkey border crisis deepens, americans’ immigration policy priorities: divisions between – and within – the two parties, from the archives: in ’60s, americans gave thumbs-up to immigration law that changed the nation, around the world, more say immigrants are a strength than a burden, latinos have become less likely to say there are too many immigrants in u.s., most popular.
About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .