Python Reviews & Product Details

Just starting out, I found Python approachable and surprisingly powerful. I was able to automate tasks, process data, and solve real problems with very little coding experience. The language feels clean and logical, and there’s a huge community sharing helpful resources and examples. Tools and libraries make everything easier, and running scripts to save time and handle batch processes has opened up a ton of new possibilities for me. Review collected by and hosted on G2.com.

Occasionally, figuring out the right packages or dependencies takes a bit of research, especially for more complex tasks. But honestly, Python’s support and documentation are so extensive that I always find answers when I need them. Review collected by and hosted on G2.com.

What I like best about Python is its ease of use and simplicity compared to other applications—the syntax feels very natural and readable, which makes coding smooth even for beginners. I have started my journey with Python until I have now earned the title of expert. Implementation is straightforward, and projects can be set up quickly without unnecessary complications.

Python also shines in its vast number of features and libraries, covering everything from web development to data science, machine learning, and automation. This makes it incredibly versatile.

In terms of integration, Python works well with other languages, tools, and platforms, making it easy to fit into different environments. The community support is another strong point—there’s always documentation, tutorials, or forums available to find help. this was very helpful throughout my career in this field.

This is why Python is so special to me and a language I use frequently and confidently.. Review collected by and hosted on G2.com.

Python is one of the most powerful languages I have ever used in my life. However, sometimes I find it slower compared to other languages, and I feel it carries some limitations when used in mobile and low-level devices. Sometimes customer service are stuck and needed to find out alternative ways for good responses. Review collected by and hosted on G2.com.

Python is very easy to learn compared to other programming languages. We just needed to import one package and it will be having all the functions in it to use. No complex programming and way simpler. Vey easy to create application like django and easy to integrate the same. Review collected by and hosted on G2.com.

As far as there is no dislike as such. But I heard it wont be able to with stand complex programming, but I have not experienced anything as such because I'm not a developer but into data role. Review collected by and hosted on G2.com.

Simple and readable syntax, python can be used in many fields like web development, data science, machine learning, fintech etc. it has huge eco systems and cross platform compatibility if it is windows or mac or any other platforms it could be used.ideal for beginners and pros. Strong support in education and research Review collected by and hosted on G2.com.

For me it is week in mobile development, also it need high memory capacity it is more difficult in practical.limited browser based application, that’s it Review collected by and hosted on G2.com.

What i like best about python is how easy it is to learn and use, even for someone without a strong programming background. The syntax feels natural and doesn't get in the way of solving problems. it has a huge collection of libraries for almost anything whether its data analysis, machine learning, web development or automation. Plus the community is super helpful so whenever i get stuck there is always a solution online. it just makes building things faster and more enjoyable. Review collected by and hosted on G2.com.

One thing i dislike about Python is its slower speed compared to some other languages like C++ or Java. It is not always the best choice for performance heavy tasks. Also, sometimes debugging issues related to indentation or dynamic typing can be bit tricky, especially in large project. And when working with different Python version or package dependencies , managing environment can get frustrating without tools like venv or conda. Review collected by and hosted on G2.com.

Python is easy to learn and easy to read. I use it daily for data work, automation, and scripting. The best part is its huge library support, you can find a package for almost anything, from data analysis to machine learning.

It also works well across different areas: data engineering, web apps, AI, and even small automation scripts. That flexibility is what makes it my go-to language. Review collected by and hosted on G2.com.

Python can be slow compared to other languages, especially for heavy computations. For big data jobs, you need frameworks like PySpark or extra tuning.

Sometimes managing environments and dependencies can get messy too, especially when working on multiple projects. Review collected by and hosted on G2.com.

In our office case, we have linked Python and Smartsheet to perform various tasks. Our team has written the codes in Python to interact with Smartsheet and G-Drive. There are multiple use cases where we use Python in background as well in the form of an user interface for ease of use. It is easy to follow and understand the Python codes that I find it easy and comfortable to make minor changes within the codes to adapt to the new process changes or additions. We use this integrated approach of Python and Smartsheet quite extensively and frequently. Multiple features of Python are being used to perform various tasks like:

1) Clicking photographs - In an UI based python code we enter a date of receipt, upon which the Python code refers a particular Smartsheet, pull the data and number of rows, creates respective folders and starts saving the images in the respective folders while renaming them as per our requirement as we keep clicking the photos is the assets through a camera. This is an integration of Python, Smartsheet and a physical external Camera

2) Batching - In this UI based Python code, we enter a batch number, upon which it reads the data from the respective Smartsheet, creates multiple folders and downloads assets from G-drive and save it in the respective newly made folders. This is an integration of Python, Smartsheet and G-drive

3) Asset update - In this UI based Python script, the assets are compared with Smartsheet data and the processed assets saved in batches in Git folder, upon which new .xml and .json files are written and Smartsheets are updated accordingly. This is an integration of Python, Smartsheet and Git

4) Validation - In this Python script, the processed batches are validated for existence and accuracy of the processed assets within the batch folders. This is an integration of Python, Smartsheet, .xml and .json files

5) Uploading - In this script based Python (no UI), upon completion of the process and validations, the assets are uploaded on a cloud storage. This is an integration of Python, Smartsheet and a cloud storage

Considering the widely available feature and ease of implementation, we were able to automate most of our activities using the integration of Python and Smartsheet. Review collected by and hosted on G2.com.

While, our activities are not time bound or use up extensive processing, the widely know issues like slowness, memory allocation or design restriction, they do not bother us as such. Our work gets done, so for now we don't see any issue to be disliked. Review collected by and hosted on G2.com.

Python is very easy to learn. There are so many library available so we can use for Test automation. Review collected by and hosted on G2.com.

There is no issue with python until now. Visual Studio Code is great IDE and i can get easily debug error. Review collected by and hosted on G2.com.

• Python stands out as a high-level, general-purpose programming language with a design philosophy focused on code readability and simplicity. The syntax is clean and uncluttered, relying on indentation rather than braces or semicolons to define code blocks, which not only makes the code visually appealing but also reduces the likelihood of syntactic errors. This approach aligns with the Zen of Python principles, emphasizing that “readability counts” and “simple is better than complex,” which are deeply ingrained in the language’s evolution.

• Python supports multiple programming paradigms, including object-oriented, procedural, and functional programming. This multi-paradigm nature allows developers to choose the most suitable approach for the problem at hand, whether it involves building modular, reusable components using classes, or leveraging functional constructs like map, filter, and reduce for data processing. The language also features advanced constructs such as list comprehensions, generators, and decorators, enabling concise expression of complex logic.

• A major strength of Python is its dynamic typing system, where variable types are determined at runtime. This feature enables rapid prototyping and flexibility in code, as developers are not required to declare variable types explicitly. Python’s interpreted nature means code is executed line by line, facilitating interactive development, immediate feedback, and easier debugging. The interactive shell and bundled development environments like IDLE further enhance this experience, making it straightforward to test ideas and troubleshoot issues.

• The standard library is extensive, covering everything from file I/O and regular expressions to networking and web services. This rich ecosystem reduces the need for third-party dependencies for common tasks and accelerates development by providing well-tested, ready-to-use modules. In addition, Python’s integration capabilities are robust: it can interface with C, C++, Java, and other languages, and is frequently used as a “glue language” to connect disparate systems.

• Python’s open-source status and cross-platform compatibility are significant advantages. The language runs natively on Windows, macOS, Linux, and Unix, and unofficial builds exist for mobile platforms. The vibrant, global community contributes to a wealth of documentation, tutorials, and third-party libraries, ensuring that support is readily available for both beginners and experienced developers. The ecosystem includes popular frameworks for web development (Django, Flask), data analysis (pandas, NumPy), machine learning (TensorFlow, scikit-learn), and more, making Python a versatile choice across domains.

• Python’s suitability for rapid development and prototyping is well recognized. Its concise syntax and high-level abstractions allow for quick translation of ideas into working code, which is particularly valuable in fast-paced environments or when developing minimum viable products. The language’s automatic memory management and built-in exception handling further streamline the development process, reducing boilerplate and minimizing the risk of memory leaks or unhandled errors.

• Python’s role in modern technology stacks is prominent. It is widely adopted in fields such as data science, artificial intelligence, web development, automation, and Internet of Things (IoT). The language’s flexibility allows it to be used for scripting, building complex applications, or as an embedded scripting language within larger systems. Its popularity is reflected in the job market and community activity, ensuring continued evolution and relevance. Review collected by and hosted on G2.com.

• Performance limitations: As an interpreted language, Python generally executes code slower than compiled languages like C++ or Java. This can be a bottleneck for compute-intensive or real-time applications.

• Memory consumption: Python’s dynamic typing and high-level abstractions can result in higher memory usage, especially when handling large datasets or complex data structures.

• Global Interpreter Lock (GIL): The GIL restricts true multi-threaded execution in CPython, limiting concurrency for CPU-bound tasks and affecting scalability in certain scenarios.

• Dependency management: The vast ecosystem of third-party packages can lead to version conflicts and intricate dependency trees, complicating project maintenance.

• Mobile development: Native support and tooling for mobile platforms are less mature compared to other languages, making Python less suitable for mobile-first projects.

• Dynamic typing pitfalls: While dynamic typing increases flexibility, it can also lead to runtime errors that are harder to detect during development, particularly in large codebases. Review collected by and hosted on G2.com.

Python shines with its clean and readable syntax, which allows developers to write less code and accomplish more. It's extremely powerful enough for large scale applications. The rich ecosystem of libraries and frameworks, especially for web development, data science and automation, makes it a go to language across many domains. The active community and extensive documentation also make problem solving and learning much smoother. Review collected by and hosted on G2.com.

While Python's dynamic typing is convenient, its type system still feels limited compared to more better solutions like TypeScript. Even with the addition of type hints and tools like mypy, the enforcement is optional and lacks the strictness and tooling support that make TypeScript's typing so effective. This can lead to runtime issues that would be caught during compile time in statically typed languages. Review collected by and hosted on G2.com.