Reliable Python Development Company

Python overtook other backend programming languages, according to the Stack Overflow Developer Survey Healthtech startups that use Python Aledade Aledade is not a traditional startup, but it still feels like one when it comes to innovation and its heavy use of technology. Python plays a big role in what they do. So, they fit right in on a list of companies using Python in healthcare. Aledade is a healthcare company that partners with independent primary care physicians to create and manage Accountable Care Organizations (ACOs). Their platform offers data analytics, guided workflows, healthcare policy expertise, and payer relationships to support practices through value-based care models. Aledade has raised a total funding of $780M over 11 rounds from 17 investors. Senior software engineers at Aledade use Python to maintain, improve, and expand their web applications and data pipelines. They are responsible for API design and development, building backend services, developing serverless functions, automating cloud workflows, and managing infrastructure. They also work with server-side web technologies, including Python (along with Java, Scala, C#, C++, and Go), and web frameworks like Django, FastAPI, and Flask, in combination with SQL and NoSQL databases (e.g., Postgres, Databricks, Snowflake). Their work ensures scalable systems, optimized query performance, and cleanly abstracted interfaces to support web applications, business analytics, and artificial intelligence data consumers. They integrate and process cloud-based data pipelines, design, build, and optimize ETL processes, work with data storage technologies, manage data ingestion systems, and optimize large-scale data processing performance. Python is also used to build and improve the AI/ML infrastructure for developing, training, and deploying models like LLMs and other generative AI. After analyzing their software product usage, they make decisions on where generative AI can be better implemented and create APIs to integrate these models into healthcare systems. Their staff AI researchers rely on Python to work with large healthcare datasets, including Electronic Health Records (EHRs), clinical data, and other medical data collected from millions of patients. They address data quality issues like incomplete or mislabeled data and use Python's statistical packages alongside R and SAS for data analysis. They also design feature engineering pipelines (data processing, feature extraction, and transformation to optimize model performance), fine-tune generative models to specific healthcare domain tasks, build custom machine learning systems, and design and implement deep learning architectures with major frameworks (e.g., PyTorch, TensorFlow, Keras). All these efforts lead to the development of working prototypes and the delivery of Proof of Concept (POC) solutions for healthcare. Their engineering teams that maintain innovative patient outreach platforms use Python for developing server-side components and building APIs for SMS and calling communication channels. They rely on Python to integrate with SMS gateways and calling APIs, handle high-volume communication traffic, develop caching mechanisms, optimize database queries, and manage asynchronous communication and load balancing for SMS and calling systems. Their staff security engineers (application security architects) leverage Python to design and deploy security controls that protect networks, systems, and applications. Python is often used alongside Terraform and CloudFormation for automated security testing and validation, as well as for identifying, evaluating, and triaging vulnerabilities through Static and Dynamic Application Security Testing (SAST/DAST) tools. In cloud-native environments (AWS, Azure, GCP), Python supports secure application deployment, and workflow automation. They also use Python—alongside other languages—to secure AI/LLM and machine learning architectures. Python helps address OWASP Top 10 vulnerabilities and implement security measures in web-based SaaS applications, such as API security and WAF configurations. Their senior security engineers develop and deploy Python-based automation scripts to perform advanced log analysis, detect threats, security incidents across cloud environments, and anomalous behaviors, and support incident response workflows. They also use Python to automate vulnerability scanning processes, perform data security checks, and facilitate proactive threat hunting by correlating data from various sources (network, endpoint, application) to identify potential security threats that may bypass traditional detection systems. Their network security engineers may use Python with SIEM systems like Sumo Logic to automate log parsing, alert generation, and network incident handling, as well as to automate packet analysis workflows and tasks for forensic investigations. Python contributes to securing PHI/PII by automating encryption key management, configuring access control policies, and generating compliance reports. It's also commonly used to create interactive, real-time dashboards for visualizing security metrics. Health Catalyst Health Catalyst is a healthcare performance improvement company focused on driving clinical, operational, and financial improvements for healthcare organizations in the United States. Health Catalyst has raised a total funding of $392M over 10 rounds. Their Migration (Data) Engineers in the Migration Team (Analytics Services) use Python to modernize legacy healthcare data warehousing platforms. These platforms are designed to collect and manage raw data from Electronic Medical Records (e.g., Epic, Cerner, NextGen), Financial Systems (e.g., EPSi, Peoplesoft, Lawson), Patient Satisfaction Systems (e.g., Press Ganey, NRC Picker), HR Systems (e.g., Lawson, Peoplesoft, Ultipro), Administrative Systems (e.g., API Time Tracking), and Claims Systems (e.g., Medicare, Private Payers). The data is standardized using common identifiers (e.g., patients, labs, encounters, diagnoses, medications, and more) and fed into Subject Area Data Marts, which are designed for specific analytical purposes. Their Data Engineers use Python for migrating client data marts and reports, legacy data models, identifying and resolving issues during migration, enhancing automated tooling, reducing migration timelines, leading data validation tests to ensure consistent and accurate results, and writing and optimizing Databricks queries. Their Software Engineers on the Technology (Platform) team use Python, along with C#, JavaScript, and SQL to develop cloud-based RESTful API analytics and ML platform solutions on Azure services. They are responsible for data governance of cloud storage and compute, auditing data access (including tenant and identity management), and event-based downstream process executions. Innovaccer Innovaccer is a healthcare technology company that uses advanced analytics, artificial intelligence (AI), and machine learning (ML) to help payers, providers, and life sciences companies make better decisions. Innovaccer's healthcare data cloud platform integrates data from multiple sources, enabling solutions for population health management, value-based care, and clinical analytics. Innovaccer has raised a total funding of $379M over 7 rounds from 31 investors. Python is used by backend software development engineers at Innovaccer to develop various AI products. It's widely used with Django and FastAPI for backend development, and MongoDB, PostgreSQL, Redis, Snowflake, AWS, and Azure are commonly integrated into their Python projects for data storage and cloud computing. Python also allows for developing efficient and optimized APIs and microservices to support various frontend applications and external integrations. It is also used to implement data models and database schemas. Python is great for things like prototyping quickly, integrating backends, and automating testing and QA. It’s also used for working with BigData, distributed systems, and async programming, which makes it perfect for handling real-time data pipelines with tools like Kafka or RabbitMQ. Innovaccer uses Python to build analytical models, including descriptive, predictive, and prescriptive models, and to develop automation scripts and tools for data preprocessing, model training, and evaluation workflows. Python works seamlessly with SQL/NoSQL databases and ETL processes. Python is a preferred language for designing ML pipelines, managing model deployments, and developing monitoring solutions. Innovaccer uses Hugging Face for integrating model hubs and deploying or fine-tuning ML models, and AWS Bedrock to facilitate access to foundation models through its no-code platform and Python SDKs. Python is used by Innovaccer's database reliability engineers for the end-to-end automation of database management of large-scale distributed systems in a multi-cloud environment (cloud managed databases and self hosted databases), including administration, observability, troubleshooting, configuration changes, upgrades, and migrations to ensuring the reliability, scalability, and performance of their database systems. It is also used for automating backup, replication, and failover tasks, scripting access controls, enforcing policies, and automating HIPAA data compliance checks and audits. Additionally, Python is employed for analyzing usage patterns to assist with capacity planning for large-scale database clusters. It is compatible with monitoring tools (New Relic, DataDog, PagerDuty) for custom monitoring and alerts. Python may enhance SQL operations and serve as a connector for syncing data between systems such as PostgreSQL and ElasticSearch or Snowflake and ElasticSearch. Their software development engineers in test use Python to ensure the quality and performance of their software products by designing, developing, and maintaining automated test frameworks for UI, API, and database applications. They also develop and execute performance and load-testing scripts using tools and frameworks with strong Python support (Selenium, and Playwright). However, Python is not the only programming language Innovaccer uses. Their software development engineers develop and deliver some backend solutions (backend microservices, data, cloud, observability, etc.) using C# and .NET Core, where Python serves as a secondary language of choice. For the architecture, design, and development of the frontend stack (including micro-frontends), they use TypeScript, modern frontend frameworks like React (with Redux), and the server-side JavaScript framework Node.js. For their AI-powered medical scribe, software development engineers use Flutter and Dart to build the next generation of mobile applications for Android and iOS. Qventus Qventus is a healthcare operations platform designed to help hospitals and healthcare organizations automate and streamline their processes (patient flow, resource management, and clinical decision-making) by integrating data from electronic health records (EHR), staffing systems, and patient monitoring systems and using AI and machine learning. Qventus has raised a total of $98.3 million over eight funding rounds. Qventus engineers, including Senior Data Scientists, LLM Engineers, Data Engineers, and QA Automation Engineers, rely on Python as a foundational tool to build scalable innovative solutions in healthcare: AI-driven and data-intensive applications. Engineers use Python for cleaning and preprocessing datasets, uncovering patterns, identifying anomalies, and extracting insights from complex healthcare data. Python’s libraries (e.g., Pandas, PySpark) enable analysis and preparation of data for machine learning and analytics workflows. Python is used to design, build, and optimize scalable, reliable data pipelines that support AI and ML solutions. Engineers use Python to write scripts for data transformation pipelines. Python supports the discovery, evaluation, and integration of new datasets while enabling data modeling. Python is a key tool for developing and fine-tuning machine learning models, including Generative AI (GANs, VAEs, Transformers) and LLM-based solutions such as Retrieval-Augmented Generation (RAG). Python frameworks like TensorFlow and PyTorch are employed to create intelligent agent-based systems capable of processing multimodal inputs (text, image, audio) and outputs. Engineers use Python for rapid AI product development, supporting workflows from concept to deployment. Python’s ecosystem supports integration with cloud platforms such as AWS (Glue, Lambda, S3, RDS), Databricks, and DBT to deliver modular, secure, and scalable data systems. Python supports workflows in high-regulation ecosystems like HIPAA, requiring maintaining data privacy and security standards. Python integrates with tools like Looker and Tableau to enable actionable insights and facilitate cross-functional collaboration with clinical and product teams. QA engineers use Python to build and maintain automated testing frameworks (e.g., Playwright, Cypress, Selenium), script test cases, and automate regression and API testing, ensuring product reliability. AiCure AiCure is a New York-based AI and advanced data analytics company that uses AI to understand how patients respond to treatment and provides real-time monitoring of patient dosing and behavior. AiCure has raised a total funding of $51.8M over 5 rounds from 15 investors. Python is central to many of AiCure’s operations. The team uses Python for exploratory data analysis, statistical testing, and creating predictive models of patient behavior with machine learning techniques. Libraries such as Scikit-learn are commonly used, while frameworks like PyTorch or TensorFlow are applied for deep learning tasks when necessary. Python is used for data quality assessment and managing ETL processes with SQL for data extraction and transformation. Python also supports data visualization, helping the team analyze complex biomarker data and assess model performance. Additionally, Python is critical for deploying machine learning models in cloud environments like AWS or GCP. Python is alo used to develop APIs. Fathom Health Fathom Health is a medical coding automation AI platform based on deep learning and Natural Language Processing (NLP), trained on over 400 million coded encounters (terabytes of clinician notes) contained within the EHRs of the world’s largest health systems. Its deep learning engine automates the translation of patient records into billing codes used for healthcare provider reimbursement, helping to prevent errors and denied claims. Fathom has raised a total funding of $21.8M over 3 rounds from 19 investors. Their senior software engineers (infrastructure and backend/data), distributed across Toronto, San Francisco, and New York, ensure the stability, security, and performance of Fathom’s platform by developing internal tools using Python to make machine learning and software engineers more efficient. These tools support tasks such as infrastructure deployment, continuous integration, and testing. They are also responsible for building, controlling, and monitoring secure cloud-based platforms, leveraging Python’s compatibility with Google Cloud services. They focus on developing data infrastructure using Python to ingest, sanitize, and normalize a wide range of medical data, including electronic health records, journals, established medical ontologies, crowd-sourced labeling, and other human inputs. They build performant and expressive interfaces to the data and create infrastructure to support scaling data ingestion and large-scale cloud-based machine learning workflows. Their responsibilities include developing backend systems, data pipelines, and integrations, where Python plays a central role, particularly in production settings. Python Safety: is Python safe? Data security (patient privacy) has become especially critical to the healthcare industry with the adoption of electronic health records (EHR). Is Python a safe language for building healthcare apps? Or maybe there are more secure programming languages? This is the responsibility of the coder to know what can be done and what to avoid. An application becomes secure when the developer adopts the best practice and best security policies and techniques. The more a programming language is popular, the more it is safe because the more security vulnerabilities are widely known and the more of them could be fixed by professional Python developers. There were a lot of talks that Python 2 was not so secure, so even OWASP created a project pythonsecurity.org to highlight this issue. However, with the arrival of version 3 of Python, there is no need for this project anymore, and the website no longer gets updated or opens. Now, the Python team (Python Software Foundation) itself checks Python's security and lists potential vulnerabilities. Anyone who works with the Python code can apply appropriate solutions in advance. They can also report issues to the Python Software Foundation. According to Reddit users, some corporate IT departments ban older Python versions. However, blocking older versions alone is not a complete solution. Not all 3rd-party modules support the latest Python version, but they may be critical for other systems to function correctly. Python is a free programming language, and some modules are developed by enthusiastic contributors in their spare time. So they simply do not have enough resources to update them. Therefore, custom modifications of outdated Python modules may be required sometimes. Django, a Python-based framework, was released in July 2005 for the purpose of creating web applications, including medical apps. A good Django dev is a good Python dev. Django simplifies web application creation by reducing the amount of code that your developer needs to write. Instead of creating modules from scratch, Django offers a solid set of built-in blocks (such as packages for the admin interface, user authentication, chat functionality, etc.). Django also provides the protection against the three main types of web app attacks (SQL injection, XSS, and CSRF). Using Django security best practices, you can be sure of the safety of your healthcare app. Flask is a Python-based microframework primarily used for building API. Though it can be extended to a full-stack framework with the help of existing extensions. While Flask has fewer users than Django and takes more time for the configuration, it is often preferred for building prototypes because you can get going much more quickly with it. Which framework is easier to secure? Django.  Python / Django and HIPAA As a rule, development of a medical software application is associated with handling medical data that needs to be protected under the HIPAA compliance requirements. Are Python and Django or other Python-based frameworks secure enough to be HIPAA compliant? In fact, the HIPAA is a checklist that does not depend on a programming language or a framework. "You want to provide your clients the assurance that the information being presented is meeting the HIPAA requirements. This is not entirely a Django/Python implementation, but falls in line with the Database back-end support". "HIPAA factors like "how you store your data" and "how often sysadmins review logs" and "what the access control policies at the data center are" will probably play a bigger role than which programming framework you use, so you need to make sure to have good answers to those questions". "There are about 100 individual checkboxes that you'll need to hit to be HIPAA compliant. Approximately 90% of the requirements can be satisfied by having good engineering/risk management practices and documenting them. For example, one requirement is that you need to have a formal policy on use of patient information" (Patrick McKenzie).

Python Source: https://pixabay.com/ ‘A programming language changes the world.’ Python.org According to Wikipedia, Python was released in 1991 as a high-level language for general-purpose programming. But it actually dates back to 1989. The interest around Python began to rise only around 2000, and today Python is loved for its highly readable code. That is why many programmers believe it's the easiest language to get started with. Why is that? Well, the story starts decades ago. In the 90s, if you wanted a managed, Object Oriented language, there was surprisingly little choice. C# and Java didn't exist, nor did Ruby. We had a sloppy Perl and TCL that had OOP only as an extra feature. Above all, lots of languages were Windows-only like Delphi or Visual Basic. In the meantime, C++ was frightening to beginners, and Smalltalk always felt like a nice idea, but not something you actually wrote software with. And here we go, Python seemed better than everything else what was out there. Java was around since 1995, but it seemed too 'corporate'. In 2000 C# was coming out, but it was, again, Windows only. Source: https://www.reddit.com Today Python is one of the most important and popular languages in the world and is still gaining our attention. Java Source: https://bgr.com/ ‘Java is arguably the most popular programming language as 90% of the Fortune 500 companies heavily use it. Its famous slogan “write once, run anywhere” captures one of the keys that makes Java so valuable — its powerful Java Virtual Machine (JVM), which makes it cross-platform compatible.’ Alexander Petkov, Medium.freecodecamp.org It was 1995 and C++ was the language of choice for building large-scale software systems. It was a powerful object-oriented language, the successor of widely used C. But not only was C++ powerful, it was also quite complicated. Here’s the point where Java was released. Java is a high-level object-oriented language. Initially conceived and developed in 1991 under the name OAK, it was designed for handheld devices and set-top boxes, but due to some circumstances the name was changed to Java and modified the target to the then evolving World Wide Web. So, at the moment, Java is one of the most widely-used languages for developing and delivering content on the web plus developing enterprises applications and Android apps. An estimated 9 million developers use it and more than 3 billion mobile phones run it. #1: History: long ride vs short bite Both languages were developed relatively at the same time, in 1991. But started to draw attention in quite different periods: Java wasn’t really itself back then, and Python just started to open his little pixel-eyes. Nevertheless, they both are actively engaged in making improvements and perfecting the current results: Python. Since the popularity reached a peak, it had given rise to long discussions. Source: https://octoverse.github.com/ 15 most popular languages according to GitHub in 2017 So the answer to “How did Python transform itself into one of the most popular languages?” is still in demand. A few things  had happened that shifted the attitude towards Python and made it more viable as a web development language: FastCGI came, and then WSGI (before that, you had to run Python scripts as ordinary Common Gateway Interface, which wasn’t fast enough). So instead of creating a new process for each request, FastCGI uses permanent processes to handle a series of requests. It increases the speed of implementation and reduces time on development. Prominent universities began using Python in their curriculum, teaching algorithm and other classes (e.g. University of Oxford, Harvard, Binghamton University, Carnegie Mellon University, New York University Polytechnic School of Engineering etc.). Visible developers and standards developers, such as Joe Gregorio and Mark Pilgrim were both using Python to implement the prototypes of the Atom protocol. During that time, Python was stable and has implemented features such as Unicode support, a new Garbage Collector, generators and functional methods, etc. A huge step was applying the Django framework - it’s fast, secure and makes the code clean and easy to write. And Google, of course. It was the main reason why Python had risen so tremendously in literally several years. Google adopted Python back in 2006. They’ve used it as a top-tier implementation language for many platforms and applications since. By reinvesting in development of the language they perfected it and built up the trust. Source: https://stackoverflow.blog/ Java. ‘Java code may never dominate all computers or all platforms, but it is as close to a lingua franca as there is.’ Peter Wayner, InfoWorld. Java is the one that always has been there. It was developed at Sun Microsystems in 1991, and released in 1995. The first ten years were packed with intensive adoption and explosive growth. The developers managed to create something simpler than C++ which equals its performance capabilities. Over time, Sun has released three major versions of Java: 1.0, 1.1 and 1.2. Version 1.2 seems to finally bring Java into the prime time, in particular where user interface tools are concerned. A couple of years later J2SE 1.3 added something new, including the Hotspot JVM, a new version of the Java Virtual Machine (JVM), the sound API and improved debugging. Java core was simple and powerful compared to what they had back there. The role of JVM was crucial. It broke the limitations of that time and significantly drew attention to the Sun’s flagship. Then there were libraries. A huge impact was made, just because from now on developers shifted their focus from struggles with the infrastructure to the application itself. This aspect made Java even more popular than it used to be. Source: StackOverflow Survey 2018 Source: StackOverflow Survey 2018 In 2002, version 1.4 added new tools in the language. And then in 2004, J2SE 5.0 was released, known internally as 1.5. From this point forward Java would be known as Java 5, Java 6, Java 7, and Java 8 and so on. Unfortunately, several years ago Sun Microsystems had ceased to exist being swallowed by the Oracle on January 27, 2010. In the end, Java’s still expected to be everywhere, whether it is a web, desktop or mobile devices. It’s gradually expanding the influence on the whole of modern programming industry. TIOBE index for August 2018 #2:  Functionality Static & Compiled vs Dynamic & Interpreted Static and Dynamic typing: The main idea here is when the types of variables are checked. Statically typed languages define the type of a variable at compile time. In short, types are checked before run-time. For some languages, this means that the programmer must specify what type each variable is (Java, C, C++). The main advantage here is that all kinds of checking can be done by the compiler, and therefore a lot of trivial bugs are caught at a very early stage. Dynamically typed languages are checking types on the fly, during execution. This means that a programmer writes a little quicker because he does not have to specify types every time. Languages that support this type are Perl, Ruby, and Python. Most scripting languages have this feature as there is no compiler to do static type-checking anyway, but you may find yourself searching for a bug that is due to the interpreter misinterpreting the type of a variable. Dynamic typing Static typing Comparison of Dynamic vs. Static Typing in Programming: Dynamic typing associates a type with the value of a variable at runtime, while static typing enforces type checks at compile-time Supporting dynamic typing is an important reason why programmers can be more productive with Python. They simply don’t have to deal with the overhead of Java’s static typing. In the meantime, BECAUSE it is dynamic, there are many design restrictions that are reported by some Python developers and the need to search for bugs every time the code has an error (in static typing the compiler makes it for you). It also requires more testing time, and the errors show up when the applications are finally run. The visible difference looks like that: Java Python Source: https://www.quora.com Interpreted and compiled languages: Python is an interpreted language. In an interpreted language most of its implementations execute the code directly and freely, without previously compiling a program into machine-language instructions. So, while a compiled language after the implementation has ready-to-go instructions for the device you use, in the interpreted language an interpreter interprets the original code every time the program runs. Javacan be either interpreted or compiled. As a compiled language, it has its implementations executed by compilers (translators that generate machine code from source code). Java, C/C++, Assembler, COBOL and many others are translated by running the source code through a compiler. This results in very efficient code that can be executed any number of times. The overhead for the translation is incurred just once when the source is compiled; thereafter, it need only be loaded and executed. Interpreted languages, in contrast, must be parsed, interpreted, and executed each time the program runs, thereby greatly adding to the cost of running the program. Advantages and disadvantages: Interpreted languages: Pros: Good when it comes to the projects with time restrictions on development Ease of future changes to the program Are generally more suited to ad hoc requests, rather than predefined (fixing, editing happens on-the-go) Easier to use (errors are immediately displayed and corrected by a user until the program is able to be executed) Smaller executable package size comparing with a compiled source code Cons: Higher execution costs - more time required for the implementation The interpreted program must be translated every time it is executed Less efficient than compiled programs Interpreters can be 2 to 10 times slower than compilers (they have to translate the same statements over and over again) Compiled languages: Pros: Optimized for the target hardware Fast execution Optimizing compilers may increase speed and decrease the size of a file. Compilers ensure that static variables can be created so that providing less probability of runtime errors Security (the compiled version of a code is not clear and cannot be read this makes application hard for reverse engineering). Cons: Require a compiler (developers need to spend the time to install additional software before they start working on a project) Editing + deploying the code is sometimes much slower than interpreters Performance Python executes with the help of an interpreter instead of the compiler, which causes it to slow down because compilation and execution help it to work normally. On the other hand, it can be seen that it is fast for many web applications too. Python is one of the slowest popular languages around. If you do the same amount of work as a JavaScript program, python takes upwards of 10x the processing, e.g. upwards of 1000x the processing to do the same amount of work as Java or C#): Source: https://benchmarksgame-team.pages.debian.net/benchmarksgame/ The full benchmark of Python vs Java fastest programs (and some more languages) is available here. The speed isn’t Python strong suit. In contrast, Java is faster - high-level languages are expected to act on a high velocity and be genuinely effective, as represented in the graph below: Source: https://medium.freecodecamp.org/ As you can see, the program written in different languages runs consuming a different amount of time. E.g. Python handled this test for 694 secs, comparing to Java that required only 6 seconds. #3: Application Python. ‘Python was so friendly and versatile to begin with that it became viral.’ By Toni Alatalo on Quora.com The most basic use case for Python is as a scripting and automation language. Python isn’t just a replacement for shell scripts or batch files; it is also used to automate interactions with web browsers or application GUIs (Graphical User Interface). Python is also used for system operations, web development, machine learning, server and administrative tools, deployment, scientific modeling and much more. Source: https://stackoverflow.blog/ But, surprisingly, many developers don’t pick up Python as their primary language. Because it’s so easy to use and learn, they choose it as a second or third language. Java is a cross-platform language that is applied in many fields including: Healthcare Finances Government Science Education Transportation, etc. Moreover, Java is highly portable as it must be executed through a cross-platform compatible Java Virtual Machine (JVM). That is why it’ is easily applied on a wide range of hardware devices like television sets, VCRs, toasters and many other devices. Java was, is and will be successfully applied in many domains including, first of all, enterprise and Android applications, experimental stuff, web development and desktop. It won’t be first forever, but shining in its element. Source: http://codingnomads.co/ #4: Ease of use There are two schools of thought about language complexity. One school believes that in order for a programming language to be fully “expressive,” you need an abundance of sophisticated features. Even if these features make the language much bigger, more complex, and much more difficult to learn. Examples include C++, D, Scala, Vala, Rust, and JavaScript. The other school believes in minimalism, in keeping the language small, simple, easy to learn, and easy to use. Examples include Smalltalk, Scheme, Forth, Go, Pascal, and Oberon. Python. Python allows programmers to code faster with less effort. Above all, due to its simplicity and usability, Python is a great language to learn first. But, here comes a disadvantage: while Python lovers become so accustomed to its features and its extensive libraries, they face problem in learning or working on other programming languages. Python experts may see even the requirements of adding curly braces or semicolons as an onerous task. However, because ease of use and fast learning made their contribution, Python drew attention in the late 90s and up to present days. And the output you get is that Python requires less effort compared to other giants, such as C++, Java or even Javascript. Collections described in both languages: Source: https://python-scripts.com/ Java. Java’s simplicity, comparing to its predecessors, also explains the position of Java as one of the most popular programming languages in the world. Add a huge ecosystem packed with libraries and frameworks, a super-optimized JVM runtime, and you have the formula for a killer language. Anyways, nobody refers to Java as the most simple language that is readable for kids. See the example below: “Hello world” written in Java and Python: Source: https://python-scripts.com/ Null in both languages: Source: https://python-scripts.com/ Working with files in Java and Python: Source: https://python-scripts.com/ Java can’t be classified as one of the complex and difficult ones. However, this language is not a piece of cake so you’ve got to make an effort to become totally comfortable with it.   Conclusion The famous guy who created C++ said once: ‘There are only two kinds of programming languages: those people always b*tch about and those nobody uses.’ Bjarne Stroustrup Today, Python is popular because it's easy to get started with, has a lot of libraries and frameworks available, and if you're OK with dynamic types, it's a decent enough language. On the other hand, we have Java. A top-notch language that is applied literally everywhere. A unique set of standard libraries doesn’t concede in any place. The virtual machine that performs greatly on any platform, sturdy garbage collection and the overall ecosystem itself define Java as a beautiful creation that will be in the forefront for a long time. Make your choice that is based on your specific experience and remember, that both Python and Java are worthy to be on top. So, do these programming languages really need to fight? Well, the thing is, they were created for particular purposes and can be successfully implemented in different domains showing unique performance.

The dedicated Python developer model allows for payment only for the work completed, eliminating the need for in-house hires and minimizing administrative expenses. Why Hire Dedicated Python Developers From Belitsoft?We help you design, develop and launch secure, robust, and performant web applications.   Your Intellectual Property Protection  Belitsoft takes confidentiality seriously. We ensure that client intellectual property is protected at all times, from signing an NDA, to keeping code secure in private Git, to delivering the project with all formalities, including code ownership, copyrights, etc. Our policy is to not reuse license keys, patented processes, trademarks, proprietary algorithms, or any other specific elements used only for your project. You will have full ownership of your project. Expert Talent Save on significant recruitment costs by hiring pre-screened Python experts who have passed our multi-stage vetting process evaluating their technical abilities, English proficiency, and soft skills. All of our Python developers have years of experience in the industry and require a short ramp-up time. We value our employees and have established performance management programs to ensure that they are up-to-date with industry and technology best practices. Our Python developers are highly motivated to bring your project to success and we foster long-term relationships with our employees. We value both our employees and clients and have established effective staff retention policies and project health management programs to ensure a successful journey. Scalable Development With Belitsoft's dedicated Python developers, you can easily adjust the size of your team based on your project's needs without compromising on quality or time. Our services focus on handling any change in the number of dedicated Python programmers you require. We can quickly add or remove Python developers as needed, along with administrative support. If you're not satisfied with your assigned team, we'll provide a quick replacement with no questions asked. We also offer end-to-end development expertise, including a range of tools, technologies, and frameworks such as Python + React/Angular/Vue+MongoDB/AWS, to build scalable and flexible web architectures. Our development team is well-versed.  Cost-effective Solutions Save time and money by hiring pre-vetted remote Python developers on a part-time or monthly basis with Belitsoft. Our combination of cost-effective rates and quality gives you control over the budget for your Python development. We accurately estimate project timelines to avoid budget overruns. Flexible Engagement Models Belitsoft offers flexibility in selecting from different engagement and hiring models. Our agile Python developers come with onboarding, infrastructure, administrative, payroll, compliance, and project support, removing the hassle of managing administrative aspects such as paperwork, team setup, and payroll operations. Seamless Communication At Belitsoft, we prioritize the convenience of both our clients and developers by offering flexible time zones for regular meetings and calls. Our remote Python developers are available up to 4 hours from regular office hours for US companies. You'll have direct communication with your developers and full control over every step of the process with daily code reviews and retrospectives. Maintenance and Support Belitsoft adept Python developers take care of all your app support and maintenance needs, ensuring a bug-free and high-performing app that is up-to-date with industry trends and technology updates. Get reliable maintenance and support from our teams throughout the development and delivery process. We offer post-app development support through a flexible model, allowing you to hire our web developers for dedicated app support and maintenance services as needed. Our Python development services range from full-cycle custom development from scratch to developing existing products. Finish your incomplete projects with us! If you have a mission-critical app development project that was left unfinished, Belitsoft can help. Our experts would love to discuss your ideas, find the right solution, and bring your app to life as soon as possible. Schedule a call with us today. Types of Python Developers We Offer Python Consultants Get expert Python consulting services to develop revenue-generating solutions for your business-critical app ideas. Python Web and Back-end Developers Create powerful and scalable custom web and back-end applications. Belitsoft develops robust mission-critical applications like eCommerce, ERP, and SaaS using the best Python frameworks at an affordable price.   We provide dedicated offshore developers for building high-performance B2B enterprise apps for large-scale industries.  Python AI/ML Developers Belitsoft's development team creates AI/ML solutions using the full potential of Python to power up your business processes with proactive learning intelligence.  Python simplifies content management, system administration, and database interaction. Python supports Big Data and AI, making it ideal for forecasting and data analysis. Hire a Python expert to extract insights and build predictive models to make data-driven decisions for your business. Python API Developers Belitsoft's dedicated Python developers design and develop APIs using Python, allowing seamless communication between applications and external services. Python App Migration Experts Our skilled Python developers smoothly move your legacy solutions to the Python ecosystem without any data loss, using proper migration plans and cutting-edge tools. How Much Does It Cost to Hire Python Developers The cost of hiring a Python developer is directly impacted by the amount of work you require for your project. More complex features require more time to implement. Outline the features and functionalities you want to integrate into the application to get the estimation.  The timeline for your project is another cost-influencing factor. If you need certain tasks to be completed within a short timeframe, more developers may be needed. The cost of hiring a Python developer also depends on their experience and expertise level. Hiring a developer with 1 year of experience will cost less compared to a senior-level Python developer.   Further application maintenance to ensure a seamless user experience may also influence project cost.   Get a personalized quote for your project Skills to Look for in an Expert Python Developer The following tools and technologies are what Python developers are proficient in using for various Python app development needs: Frameworks: FastAPI, Flask, Django, Pyramid, Web2py, Tornado Libraries: TensorFlow, PyTorch, SciKit-Learn, Pandas, NumPy, SciPy, Matplotlib, Graphene, Selenium Tools & Utilities: Anaconda, TensorBoard, Jupyter, Tableau, PowerBI, MATLAB, Apache Hadoop Databases: PostgreSQL, MySQL, MongoDB, DynamoDB, Oracle, Firebase, Redis, SQLite For a Python developer to be effective, they must have a solid understanding of the core concepts of Python, including data structure, data types and variables, file handling, exception handling, and object-oriented programming. This is crucial for adapting to different project requirements, both big and small. A skilled Python developer should be able to utilize the vast Python ecosystem of over 250,000 projects to their advantage. They should be able to find, study, and implement packages with ease. To be a successful Python developer, one must have a strong understanding of Python language concepts including data structures, data types, variables, file handling, generators, iterators, and object-oriented programming.  A skilled Python developer should have hands-on experience with various Python implementations, including web development, game development, AI/ML development, and data visualization. They should also be familiar with popular full-stack and asynchronous Python frameworks and libraries. Knowledge of multi-process architecture and code behavior during deployment is also important. The ideal Python developer should be well-versed in agile project management tools, such as Azure Boards, Notion, and Jira, and be able to follow agile best practices to meet deadlines. They should also be able to adapt to new technology-specific methodologies as they emerge, and have a diverse portfolio of app development projects across industries and technologies. Effective communication skills are also important for success in this role.