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Artificial intelligence is revolutionizing industries across the globe, and Python has become the go-to language for AI development. As you dive into the world of AI, mastering Python skills is crucial to unlock advanced possibilities and create cutting-edge solutions. With its versatility and extensive libraries, Python empowers you to tackle complex AI challenges and bring innovative ideas to life.

In this article, you’ll discover the essential Python skills needed to excel in AI development. We’ll explore how Python serves as the foundation for machine learning frameworks, enabling you to build powerful AI applications. You’ll learn advanced Python techniques specifically tailored for AI projects, and gain insights into responsible AI practices. Whether you’re a beginner or an experienced developer, this guide will equip you with the knowledge to harness the full potential of AI using Python.

Python: The Foundation of AI Development

Why Python is Essential for AI

Python has become the go-to language for AI development, and for good reason. Its simplicity and readability make it accessible to newcomers, while its powerful features enable rapid development and testing of complex models. You’ll find that Python’s versatility allows you to choose between object-oriented programming or scripting, giving you the flexibility to solve problems efficiently.

One of Python’s greatest strengths is its extensive ecosystem of libraries and tools specifically designed for AI and machine learning. These libraries provide pre-written code that allows you to perform complex tasks without starting from scratch. This wealth of resources significantly reduces development time and enables you to focus on creating innovative AI solutions.

Key Python Libraries for AI

To harness the full potential of AI with Python, you’ll want to familiarize yourself with these essential libraries:

1. TensorFlow: This open-source library, developed by Google, enables you to construct deep learning and machine learning models, regardless of your skill level.
2. NumPy: As a numerical library, NumPy excels in handling large, multi-dimensional arrays and matrices, making it ideal for scientific computations in AI.
3. Keras: This high-level API is perfect for experimenting with neural networks and can be integrated with other popular libraries like TensorFlow and PyTorch.
4. Scikit-learn: Supporting classical machine learning algorithms, Scikit-learn is a favorite among beginners and professionals for prediction modeling.
5. Pandas: This library serves as a data analysis hub, helping you work with time series and structured multidimensional data.

Python’s Data Handling Capabilities

In AI development, effective data management is crucial. Python’s robust data structures make it an excellent tool for AI engineers. Here’s how Python enhances your data handling capabilities:

1. Data Preprocessing: Python’s functional programming features, including lambda functions and map, improve code efficiency for tasks like data filtering, sorting, and transformation.
2. Data Organization: Python’s data classes serve as structured toolboxes, helping you organize datasets, encapsulate model evaluation metrics, and handle predictive analytics data.
3. Type Validation: Python’s type hinting feature ensures code clarity and reduces errors by validating data types fed into models.
4. File Handling: The pathlib library simplifies reading and writing files, which is essential for dataset management, model configuration, and documenting AI experiments.

By mastering these Python skills and libraries, you’ll be well-equipped to tackle complex AI challenges and create cutting-edge solutions. Python’s simplicity, coupled with its powerful capabilities, makes it the ideal foundation for your AI development journey.

Machine Learning Frameworks in Python

TensorFlow: Google’s Powerhouse

TensorFlow, developed by Google, has become a cornerstone in AI and machine learning projects. This open-source framework offers an extensive and adaptable ecosystem of tools, libraries, and community resources. You’ll find TensorFlow’s versatility allows for both high-level APIs like Keras and more intricate low-level APIs, giving you the flexibility to create and train models efficiently.

One of TensorFlow’s standout features is its graph-based computation model. This approach represents operations as nodes in a data flow graph, enabling efficient utilization of CPU and GPU resources. You’ll appreciate TensorFlow’s scalability, as it’s capable of running on both desktops and large-scale distributed systems.

For visualization and debugging, TensorFlow provides TensorBoard, a unique tool that helps you understand and refine your models. With its broad adoption in industry and academia, you’ll benefit from a large community of developers and researchers supporting the framework.

Scikit-learn: The Swiss Army Knife of ML

Scikit-learn stands out as one of the most useful libraries for machine learning in Python. It offers a wide array of efficient tools for machine learning and statistical modeling, including classification, regression, clustering, and dimensionality reduction.

With scikit-learn, you’ll have access to various algorithms such as gradient boosting, nearest neighbors, random forest, and logistic regression. The library excels in tasks like spam detection, image recognition, drug response prediction, and customer segmentation.

One of scikit-learn’s strengths is its simplicity and efficiency for predictive data analysis. It’s designed to be accessible to everybody and reusable in various contexts. Built on NumPy, SciPy, and matplotlib, scikit-learn integrates seamlessly with other scientific Python libraries.

By mastering these frameworks, you’ll be well-equipped to tackle a wide range of AI and machine learning challenges, from research and experimentation to large-scale production deployments.

Advanced Python Techniques for AI

Working with Neural Networks

To unlock the full potential of AI with Python, you need to master neural networks. These powerful tools enable you to build systems that learn and make predictions. You can create a neural network from scratch or use libraries like Keras and TensorFlow. When building from scratch, you’ll need to understand the basics of vectors, layers, and linear regression.

To get started, import necessary libraries and define your input data using NumPy. Next, add weights and bias to your input features. These are learnable parameters that adjust during training. The training process involves making predictions, comparing them to desired outputs, and adjusting the network’s internal state.

Natural Language Processing in Python

Natural Language Processing (NLP) is a crucial field in AI that focuses on making human language usable by computers. Python’s NLTK package is an excellent tool for NLP tasks. To begin, you’ll need to preprocess your text data, which involves cleaning and standardizing it.

Key NLP techniques include:

1. Syntactical parsing: Analyzing words for grammar and relationships
2. Named Entity Recognition (NER): Detecting names of people, places, and organizations
3. Topic modeling: Identifying hidden patterns in text corpora
4. Word embedding: Representing words as vectors to preserve contextual similarity

Computer Vision Applications

Computer vision enables machines to interpret and analyze visual data, opening up a wide range of applications. Python libraries like OpenCV, SimpleCV, and scikit-image provide powerful tools for image processing and analysis.

Some key applications of computer vision include:

1. Facial recognition for security systems
2. Self-driving cars for autonomous navigation
3. Medical imaging for disease detection
4. Agricultural monitoring for crop health assessment
5. Quality control in manufacturing

To implement computer vision projects, you can use frameworks like TensorFlow or PyTorch, which offer pre-trained models and flexible tools for creating custom solutions.

Conclusion

Python’s role in AI development is undeniable, offering a robust foundation for creating cutting-edge solutions. Its versatility, coupled with powerful libraries and frameworks, enables developers to tackle complex challenges in machine learning, natural language processing, and computer vision. The language’s simplicity and extensive ecosystem make it an ideal choice to build and deploy AI applications across various industries.

To wrap up, mastering Python skills is crucial to unlock advanced AI potential. By leveraging Python’s data handling capabilities and machine learning frameworks, developers can create innovative solutions that have an impact on real-world problems. As AI continues to evolve, Python remains at the forefront, providing the tools and flexibility needed to push the boundaries of what’s possible in artificial intelligence.

Tags: Python, python development

Python has become a powerhouse in the world of programming, and mastering the art of writing Python code is crucial for modern developers. Whether you’re crafting a simple script or building complex software written in Python, understanding the latest techniques and best practices can significantly boost your productivity and code quality. You’ll find that staying up-to-date with modern Python features is not just beneficial—it’s essential to stay competitive in today’s fast-paced tech landscape.

In this article, you’ll discover key strategies to elevate your Python programming skills. We’ll explore how type hinting can improve your code’s reliability, delve into effective project management and dependency handling, and uncover the power of new Python syntax. By the end, you’ll have a solid grasp on writing modern Python programs that are not only efficient but also maintainable and scalable. Get ready to transform your approach to writing Python and take your development skills to the next level.

Elevating Code Quality with Type Hinting

Type hinting, introduced in Python 3.5, has become a powerful tool to enhance code quality and reliability. By providing a way to specify expected types for variables, function parameters, and return values, type hints offer a map for developers to understand data flow and prevent potential errors .

Introduction to Type Hints

Type hints allow you to annotate your code with expected types, making it easier to catch bugs before runtime. For instance, you can specify function parameters and return types like this:

def greet(name: str) -> str:
   return f"Hello, {name}"

This simple annotation indicates that the name parameter should be a string, and the function will return a string . While Python remains dynamically typed, these hints serve as a guide for developers and tools alike.

Advanced Type Hinting Techniques

As you delve deeper into type hinting, you’ll encounter more complex scenarios. The typing module provides several advanced features to handle these cases:

1. Generic Types: Use TypeVar to create generic functions or classes that work with different types .
2. Union Types: Employ Union to specify that a variable can be one of several types .
3. Optional Types: Utilize Optional for values that might be None .
4. Callable Types: Use Callable to hint at function types with specific input and output types .

Here’s an example of a more complex type hint:

from typing import List, Union

def process_data(items: List[Union[int, str]]) -> None:
   for item in items:
       print(item)

This function accepts a list containing either integers or strings .

Integrating Type Hints with IDEs and Linters

One of the most significant benefits of type hinting is its integration with development tools. IDEs and linters can leverage these hints to provide better code completion, error checking, and refactoring suggestions .

Tools like mypy and pyright perform static type checking based on your type hints, alerting you to potential errors before runtime . To get the most out of type hinting, consider these steps:

1. Use a type-aware IDE like PyCharm or Visual Studio Code.
2. Integrate a static type checker into your development workflow.
3. Set up pre-commit hooks to enforce type checking in your projects .

By incorporating these practices, you’ll find that type hints not only improve code quality but also enhance the overall development experience. They make your code more self-documenting, easier to understand, and less prone to errors .

Optimizing Project Management and Dependency Handling

Virtual Environments: Beyond venv

Virtual environments are crucial for Python development, providing lightweight and isolated spaces for your projects. They help you avoid system pollution, sidestep dependency conflicts, and minimize reproducibility issues . While the built-in venv module is commonly used, there are alternatives worth exploring.

One such alternative is microvenv, which offers a simpler approach by omitting activation scripts. This can be beneficial if you’re working with tools like VS Code that are moving towards implicit activation . Remember, you don’t always need to activate a virtual environment to use it – you can simply use the absolute path to the Python interpreter in your virtual environment when running your script.

Modern Dependency Management Tools

The landscape of Python packaging has evolved significantly in recent years. While pip and setup.py were once the standard, newer tools offer more comprehensive solutions for managing dependencies and project configurations.

1. Poetry: This tool provides a single pyproject.toml file for configuration, developer-friendly tooling for adding dependencies, and a lock file for consistent deployments .
2. Pipenv: Similar to npm for Node.js or bundler for Ruby, Pipenv manages dependencies on a per-project basis. It creates a [Pipfile](https://packaging.python.org/en/latest/tutorials/managing-dependencies/) to track project dependencies .
3. Hatch: This tool adheres strictly to Python PEP standards and offers innovative features like grouping dependencies and scripts into custom environments .
4. PDM: Following current PEP standards, PDM provides good support for cross-platform lock files and includes a useful pdm export command to output a standard requirements.txt file .
5. Rye: Developed by the creator of Flask, Rye has quickly gained popularity due to its opinionated approach to building Python packages .

Adopting pyproject.toml for Project Configuration

The pyproject.toml file has become a standard for configuring Python projects. It’s used not only by packaging tools but also by linters, type checkers, and other development tools .

Key sections in pyproject.toml include:

1. [build-system]: Specifies the build backend and dependencies needed to build your project.
2. [project]: Contains basic metadata like dependencies, name, and version.
3. [tool]: Includes tool-specific configurations.

By adopting pyproject.toml, you’re standardizing your project configuration, making it easier to switch between different package managers if needed. This flexibility allows you to choose the tool that best suits your workflow, improving your overall development experience.

Harnessing the Power of New Python Syntax

Exploring Pattern Matching

Python 3.10 introduced structural pattern matching, a powerful feature that goes beyond a simple switch statement. This new syntax allows you to match and destructure complex data structures efficiently. With pattern matching, you can elegantly handle different cases in your code, making it more readable and maintainable .

To use pattern matching, you employ the match and case keywords. For example:

match command:
   case "push":
       print("pushing")
   case "pull":
       print("pulling")
   case _:
       print("Command not implemented")

This syntax enables you to match against various patterns, including sequences, mappings, and objects. You can even use wildcards and capture variables within patterns, making it a versatile tool for handling complex data structures .

Efficient Coding with the Walrus Operator

The walrus operator (:=), introduced in Python 3.8, allows you to assign values to variables within expressions. This operator can help you write more concise and efficient code. For instance, you can use it to simplify loop conditions or avoid redundant computations .

Here’s an example of how the walrus operator can streamline your code:

while (user_input := input("Enter command: ")) != "quit":
   process_command(user_input)

This syntax combines the assignment and condition evaluation in one step, making your code more compact and potentially improving performance .

Enhancing Function Definitions with New Parameter Types

Python’s type hinting system has evolved to provide more precise ways to specify function parameters and return types. You can now use advanced type hints to make your code more self-documenting and easier to understand .

For example, you can use Callable to annotate functions:

from collections.abc import Callable

def process(func: Callable[[int], str]) -> None:
   result = func(42)
   print(result)

This annotation indicates that func should be a function that takes an integer and returns a string .

By leveraging these new Python syntax features, you can write more expressive, efficient, and maintainable code. Embrace these modern Python capabilities to enhance your development experience and produce higher-quality software.

Conclusion

To wrap up, mastering modern Python development has a significant impact on a programmer’s efficiency and code quality. The integration of type hinting, advanced project management tools, and new syntax features equips developers to create more reliable and maintainable software. These advancements not only streamline the coding process but also enhance collaboration among team members, leading to more robust and scalable applications.

As the Python ecosystem continues to evolve, staying up-to-date with these practices is crucial to remain competitive in the fast-paced tech world. By embracing these modern techniques, developers can unlock new possibilities in their projects and push the boundaries of what’s achievable with Python. The journey to become a skilled Python developer is ongoing, but these essential keys provide a strong foundation to build upon and excel in the ever-changing landscape of software development.

Tags: Development, Python, python development

Python is an interpreted, high-level object-oriented programming language. It comes with built-in data structures, dynamic typing(a process wherein type checks are done during the runtime), and binding(mapping of different objects with one another), which makes it a top language used for the development of applications. Python syntaxes are simple, easy to read, and easy to learn.

R is a programming language for statistical analysis or computing and graphics. R comes with a wide range of statistical techniques such as linear modeling, non-linear modeling, statistical tests, clustering, etc. One of R’s strengths is the ease at which a plot can be produced, including the mathematical notations and formulas.

R and Python are both excellent choices for data science, but each has advantages and disadvantages. Accordingly, if you’re new to data science, one option may be more appropriate than the other, and if you already know one, learning the other may still be worthwhile.

There is no question that Python and R can handle the majority of data science tasks; however, there are other considerations that may influence your decision. One tool might be more useful for a particular task, might be simpler to learn for some users than for others, might lead to more opportunities in the workforce, and the list goes on.

Making the right decision is important because learning something new is challenging. Before you start learning Python and/or R for data science, you should be aware of the following.

Which background you’re from?

Consider your background when selecting between Python and R if you’re new to data science. Learning a new programming language like Python or R wouldn’t be challenging if you have years of coding experience, but things are different if you have only recently used programs like Excel or SPSS. Let’s examine who makes use of Python and R, as well as their purposes.

The programming language R, which was developed by statisticians, is primarily employed for statistical computing. Nevertheless, R is used by more than just statisticians; it is also employed by data miners, bioinformaticians, and other experts who perform data analysis and create statistical software.

On the other hand, Python is a general-purpose language that is used for creating GUIs, games, websites, and other things in addition to data science. Python is used for a wide range of tasks by experts like software engineers, web developers, data analysts, and business analysts.

In conclusion, R would probably be simpler to learn if you’re coming from Excel, SAS, or SPSS, but Python would be simpler to use and get used to if you’ve been coding in other programming languages for a while and have a programming mindset.

Which one is more popular for data science?

Before learning a tool, it’s important to keep its popularity in mind. You don’t want to learn anything that has no practical application, I assure you.

On Google Trends, a quick comparison of the keywords “python data science” (blue) and “r data science” (red) reveals the growth in popularity of both programming languages over the previous five years.

Without a doubt, Python is more widely used for data science than R.

Employers, however, look for different things in Python and R experts when it comes to data science. The most prevalent data science tools and techniques that appear in each set of job postings were identified through a comparison of job postings that contain the terms data science and R (but not python) and data science and Python (but not R).

The wordcloud reveals that while job postings with the terms data science and Python include “machine learning,” “SQL,” “research,” and tools like AWS and Spark, those with the terms data science and R frequently include terms like “research,” “SQL,” and “statistics.”

Which one offers the best tools for data science?

The workflow for data science includes activities like data collection, exploration, and visualization. Despite the fact that both Python and R will do the job, each language’s tools and package offerings have advantages and disadvantages.

Data Collection: R and Python both support a wide range of file formats, including CSV and JSON, and R also enables you to convert files created in Minitab or SPSS into datasets. Both platforms also let you use website data extraction to create your own datasets, but Python has more sophisticated tools like Selenium and full frameworks like Scrapy.

Data Exploration: Take a look at the packages used in both R and Python because this is the step where data scientists spend the majority of their time. While R has a variety of packages designed for data exploration, we typically use Pandas and Numpy to explore datasets in Python. Since a picture speaks a thousand words, check out these straightforward exploratory data analyses carried out in R and Python to learn more about the tools employed.

Data Visualization: Basic graphs can be created in Python using the Pandas library, but for customizable and sophisticated visualizations, you must learn libraries like Matplotlib and Seaborn. The issue is that Python visualizations aren’t the most aesthetically pleasing and can be challenging to learn (and remember their syntax for). R excels at data visualization, in contrast. Many common graphs are already supported by R by default, and it also offers sophisticated tools like ggplot2 to enhance the look and feel of your graphs.

Wrapping Up

You already likely know which tool is best for you at this point, but allow me to share what some of the people I know do.

Some people favor Python over R because of its versatility and flexibility, which enable them to perform powerful data science tasks as well as go beyond them, while others prefer Python over R because of its statistics-oriented strength and excellent visualization capabilities.

For the various job opportunities and tools they offer, learning the other would be worthwhile even if you already know one.

Tags: Python, Python vs r, R

Data scientists, data engineers, and application developers now have better programmability thanks to new updates from Snowflake, a provider of data clouds.

This week at its yearly user conference, Snowflake Summit 2022, in Las Vegas, the company made the update public.

With the release of Snowpark for Python, which is currently in public preview, and a native integration with Streamlit for quick application development and iteration, both of which are currently under development, Snowflake’s most recent innovations put Python in the spotlight. Along with making data stored in open formats and on-premises accessible in the Data Cloud, Snowflake is also streamlining access to more data with new improvements for working with streaming data.

These improvements make it simpler for data professionals and developers to build and collaborate with data quickly while utilizing Snowflake’s platform’s speed, simplicity, consistent governance, and security.

Increasing Python’s Use in Machine Learning and Application Development

These improvements make it simpler for data professionals and developers to build and collaborate with data quickly while utilizing Snowflake’s platform’s speed, simplicity, consistent governance, and security.

Data scientists, data engineers, and application developers now have access to a rich programming environment with Snowpark, the developer framework for Snowflake, allowing them to create scalable pipelines, applications, and machine learning (ML) workflows directly within Snowflake using their preferred languages and libraries. By facilitating seamless access to Python’s rich ecosystem of open-source packages and libraries in the Data Cloud, Snowflake is expanding what users can create with Snowpark for Python.

Snowpark for Python runs on the same Snowflake compute infrastructure as Snowflake pipelines and applications written in other languages thanks to a highly secure Python sandbox. As a result, developers can expect the same scalability, elasticity, security, and compliance benefits from Snowpark for Python as they have come to expect from Snowflake. Developers now have the exceptional chance to consolidate their Python-based data processing in Snowflake using Snowpark, streamlining and modernizing their data processing architecture.

Along with Snowpark for Python, other updates include:

• With the help of Python and Snowpark’s DataFrame APIs for Python, users can create pipelines, machine learning models, and applications directly in Snowsight, the Snowflake user interface. Development is sped up by code auto-complete and the quick productization of custom logic.
• With the help of Snowflake’s Streamlit Integration, which is still under development, users will be able to build interactive applications, securely share them with business teams to iterate, and work together to increase the impact of development.
• The currently under development Large Memory Warehouses gives users the ability to safely carry out memory-intensive operations, like feature engineering and model training on sizable datasets, using well-liked Python open-source libraries accessible through the Anaconda integration.
• SQL Machine Learning gives SQL users the ability to incorporate ML-powered predictions into their routine business intelligence and analytics to increase decision quality and speed, starting with time-series forecasting, which is currently in private preview.

Python is a well-liked choice among developers due to its robust syntax and extensive ecosystem of open-source packages. Thanks to Snowflake’s ongoing partnership with Anaconda, more Python packages are now seamlessly accessible in Snowflake, and all code is run in a highly secure sandboxed environment. As a result of Snowflake’s Python developments, the Snowpark Accelerated program has also continued to expand, with more partners using Python to increase the Data Cloud’s functionality in their preferred language.

In order to support machine learning (ML) and artificial intelligence (AI) solutions that make use of data in the Allegis Enterprise Data Platform on Snowflake, Allegis Group, a global talent solutions company, depends on Snowpark.

Joe Nolte, AI & MDM Architect, Allegis Group, said: “At its core, Snowpark is all about extensibility, and Snowpark for Python provides us with the tools we need to work with data effectively in our programming language of choice.”

“Snowpark is becoming our preferred framework for data science and application development, providing our teams with a seamless experience to easily collaborate with data and bring everyone onto the same platform for accelerated time-to-value.”

For developers to work more productively, to create more accurate ML models, and to offer more potent applications, they need quick and easy access to the right data. The upgrades to Snowflake let teams to experiment more quickly and with access to more data, resulting in improved programming capabilities and more insightful user experiences.

New innovations include:

• With Snowpipe Streaming, which is currently in private preview and allows for the serverless ingestion of streaming data, and Materialized Tables, which are currently under development and make declarative transformation of streaming data simple, Streaming Data Support aims to do away with the distinctions between streaming and batch pipelines.
• The currently under development Iceberg Tables in Snowflake, which will allow users to work with Apache Iceberg, a well-liked open table format, in external storage while utilizing the platform’s simplicity, performance, and consistent governance. This will streamline overall data management and increase architectural flexibility.
• With Snowflake’s External Tables for On-Premises Storage, customers can access their data in on-premises storage systems like Dell Technologies, Pure Storage, and others to take use of the Data Cloud’s elasticity without relocating that data. This feature is currently in private preview.

Christian Kleinerman, senior VP of product, Snowflake, said: “We are heavily investing in Python to make it easier for data scientists, data engineers, and application developers to build even more in the Data Cloud, without governance trade-offs.

“Our latest innovations extend the value of our customers’ data-driven ecosystems, enabling them with more access to data and new ways to develop with it directly in Snowflake. These capabilities, paired with Snowflake’s best of class data security and privacy, are changing the way teams experiment, iterate, and collaborate with data to drive value.”

Tags: Python, snowflake, snowpark

Python is a very popular programming language today and often does not need an introduction. It is widely used in various business sectors, such as programming, web development, machine learning, and data science. Given its widespread use, it’s not surprising that Python has surpassed Java as the top programming language. In this article, you will discover the top ten reasons why you should learn Python.

What is the Python

Python is a high-level, object-oriented programming language with built-in data structures and dynamic semantics. It supports multiple programming paradigms, such as structures, object-oriented, and functional programming, which was created by Guido van Rossum. It is an interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically typed and garbage-collected. It supports different modules and packages, which allows program modularity and code reuse.

Python was initially started as a successor for the ABC programming language. According to the LaTeX-based early Python documentation (1991), the goal of Python was to offer a better programming language for scripting by filling the gap between C and traditional Shell scripting languages. The issue is that you can’t access C-based operating system APIs natively in Bash. On the other hand, writing Shell scripts in C is indeed more time-consuming than Bash. Python became one of the most popular languages because of the simple syntax, full-featured standard library, rich open-source library ecosystem, and advanced frameworks. New features like type hints and impressive open-source libraries/frameworks make Python suitable for enterprise apps.

Better Practical Alternative

A lot of tech companies do a series of interviews to find top engineering candidates. These interviews usually include technical, HR, and management, etc. In technical interviews, interviewers often ask candidates to write pseudocodes for various algorithmic challenges. Pseudocodes are good, but they come with a small problem. Pseudocodes typically don’t have a standard syntax, so candidates often tend to borrow some syntax from their favorite languages. As a result, candidates write various pseudocodes for one technical problem.

What if we have a standard pseudocode syntax? How about pseudocode syntax, which actually works as a programming language? Writing the Python code is undoubtedly more productive than writing traditional pseudocodes. Almost all on-site development interviews typically test candidates’ analytical skills — not how many fancy syntaxes they know in a specific programming language, so using Python in technical interviews saves everyone’s time.

Usability & Flexibility

Programmers initially used Python on personal computers for various general-purpose scripting requirements like automation. Later, programmers started writing GUI apps and web apps with Python. Now, Python programmers can use the Kivy. Again, not only is Python easy to learn but also, it’s flexible. Over 125,000 third-party Python libraries exist that enable you to use Python for machine learning, web processing, and even biology. Also, its data-focused libraries like pandas, NumPy, and matplotlib make it very capable of processing, manipulating, and visualizing data — which is why it’s favored in data analysis. It’s so accommodating, it’s often called the “Swiss Army Knife” of computer languages.

Career & Earning Potential

Going hand-in-hand with lightning speed growth, Python programming is in high demand for jobs. Based on the number of job postings on one of the largest job search platforms, LinkedIn.com, Python ranks #2 in the most in-demand programming languages of 2020.

As Python is the second-highest paid computer language, you can expect an average salary of USD 110,026 per year. Nothing to cry about! If you can land a job with Selby Jennings, you’ll earn the most. The average salary there is USD 245,862. Amazing!

Python Security

The Python Software Foundation and the Python developer community take security vulnerabilities ‌seriously. A Python Security Response Team has been formed that does triage on all reported vulnerabilities and recommends appropriate countermeasures. To reach the response team, send an email to security at python dot org. Only the response team members will see your email, and it will be treated confidentially.

The PSRT mailing list is tightly controlled, so you can have confidence that your security issue will only be read by a highly trusted cabal of Python developers. If for some reason you wish to further encrypt your message to this mailing list (for example, if your mail system does not use TLS), you can use our shared OpenPGP key, which is also available on the public key servers.

Incredibly supportive community

While programming is often misinterpreted as a solo-sport, one of the greatest tools a programmer will ever have is the support of their community. Thanks to online forums, local meet-ups, and the open source community, programmers continue to learn from and build on the success of their predecessors. GitHub is where developers store project code and collaborate with other developers. With over 1.5M repositories on GitHub and over 90,000 users committing or creating issues in these repositories, Python has the second largest GitHub community.

In addition to online communities, Python User Groups are places where developers can meet others working with Python to share resources and solutions and cheesy Python jokes.

Conclusion

Now that you know the reasons to learn Python Programming, and how it can give you a career boost, the next step is simple. You just have to learn the code and start utilizing it. Python has become the language of choice for AI researchers, who have produced numerous packages for it. Reusing, recycling and improving other programmers’ code is fundamental to being a successful programmer, which is why Python’s robust programming communities help make it a solid programming language to learn.

Tags: Development, programming language, Python

Debate over the most popular programming language can become an emotional, almost religious battle.  And sometimes there’s no debate at all, such as when a developer is assigned to repair legacy software.  “It was written in COBOL?” is a popular refrain.

A programming language is just one tool in a developer’s expansive collection of specialty software and hardware.  So does it really matter which programming language a developer uses, as long as he or she is meeting customer requirements on time and within budget?

Yes, yes it does.  Ford or Chevy.  Stihl or Husky.  Coke or Pepsi.  Let’s face it, we all get passionate about our tools.

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Tags: C#, Delphi, Development, Java, JavaScript, Languages, Perl, PHP, Programming-Languages, Python, Ruby, VB, Visual Basic, Visual-Basic-.NET