In the ever-evolving landscape of modern software development, Python has managed to solidify its position as one of the most popular coding languages. Its simplicity, versatility, and the vibrancy of its user community has drawn an ever-increasing number of developers to its fold. However, as is the case with any domain that experiences a rapid surge in popularity, there are a multitude of misconceptions that surround Python and its developers. The intent of this discourse is to dismantle these myths, providing a clearer, more accurate perspective of Python developers and the landscape within which they operate.

Myth 1: Python is Slow

One of the most prevalent misconceptions is that Python is slow. While it can be argued that Python's execution speed may not match that of its statically-typed counterparts such as C++ or Java, this does not equate to an inherent inefficiency. Python's dynamism and readability can often lead to more efficient problem-solving, which may more than compensate for any differences in execution speed. Moreover, when tasks are I/O bound or network-bound, the language efficiency becomes substantively irrelevant.

Myth 2: Python Developers are Beginners

Another misleading notion is that Python is only for beginners, and as such, Python developers are often dismissed as newbies. While Python is indeed an excellent language for learning the basics of coding, it's also employed for advanced tasks by seasoned developers in fields such as data science and machine learning.

Myth 3: Python is Only Good for Web Development

While Python does indeed excel in web development, thanks to frameworks such as Django and Flask, it is not limited to this domain. Python has found an important place in scripting, automation, data analysis, machine learning, and Artificial Intelligence, to name just a few areas.

Myth 4: Python is not Suitable for Mobile Applications

This myth stems from the fact that traditional mobile development technologies like Swift for iOS and Java/Kotlin for Android are more popular. However, Python can be used for mobile app development, with frameworks like Kivy and BeeWare, though it may be less common.

Myth 5: Python is not for Large Scale Applications

This misconception is based on Python’s simplicity. But in the arsenal of Python, there are tools like Django and Pyramid which are great for developing large scale applications. Also, let's not forget Python is used by tech giants like Google and Facebook.

Myth 6: Python Developers are not Well-Paid

Contrary to this myth, Python developers are among the highest earners in the field. According to Indeed, the average Python developer salary in the U.S in 2019 was $119,082.

Myth 7: Python is not Secure

Python is as secure or insecure as any other language—it largely depends on the skills of the developer and the best practices they follow. In fact, Python provides several libraries for secure coding, including PyCrypto and hashlib.

Myth 8: Python's GIL is a Serious Handicap

GIL or Global Interpreter Lock is seen as Python's Achilles heel, limiting it to single-threaded execution. However, this limitation is frequently overstated, especially considering the rise of multi-process architectures and Python's robust support for asynchronous programming paradigms.

Myth 9: Python is Dying

Python’s increasing popularity in emerging fields like machine learning, AI, and data science contradicts this claim. According to RedMonk's rankings, Python is the second most popular language after JavaScript.

Myth 10: Python Developers are Jacks of all Trades, Masters of None

Python’s versatility prompts this myth. However, mastery depends more on an individual developer's depth of knowledge, experience, and expertise rather than the language itself.

In conclusion, it's essential to approach Python and its developers without preconceptions. As is evident, Python's ease of use and versatility make it a formidable language for a variety of applications, and far from being a 'beginners only' language, it's a tool of choice for many expert developers in various sophisticated domains. The myths surrounding Python and its developers, as we've seen, do little justice to the language's potential and the robust community that upholds it.