The immense number of libraries available for data analysis and Machine Learning, such as Pandas, NumPy, and Scikit-learn. The syntax is very clean and readable, almost like reading English, which allows for prototyping complex solutions in a very short time compared to C++ or Java. Review collected by and hosted on G2.com.

The execution speed. Being an interpreted language, it is notably slower than compiled languages like C or Rust in pure calculation tasks if not optimized. Also, managing virtual environments and dependencies (pip vs conda) can sometimes become a headache. Review collected by and hosted on G2.com.

Python has an easy syntax, and it’s very easy to learn compared to other programming languages. It also has vast libraries, providing libraries for almost every task. Since it’s widely used by the AI community, it comes with lots of tools and features that support out-of-the-box integration with many AI tools. Review collected by and hosted on G2.com.

-Slower compared to GO & Rust

-Run time evaluation can cause error that are not detected initially. Review collected by and hosted on G2.com.

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.

I like Python because it's user-friendly and its libraries like NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, PyTorch, and Keras really enhance its capabilities. It helps me with multiple access and cleaning of data, performing mathematical analysis, drawing diagrams, conducting research, and running regressions. The initial setup of Python was easy too. Review collected by and hosted on G2.com.

I think Python could be better connected with deep learning and backpropagation along with machine learning. Also, I would like to see improvements in human involvement interaction and how data is fed into the system, so models can be generated to enhance the feasibility of the data. 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.

The best thing Python offers me is the ability to develop specific routines to implement them in process automation (RPA). Review collected by and hosted on G2.com.

Until now, I have only worked with traditional frameworks for web development, which makes it difficult for me to use Python for this purpose. 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.