Python is one of the most popular programming languages. As we wrote, at the beginning of 2026 it became the leader of the monthly Tiobe programming language ranking for the second time in its history. The growth in Python’s popularity over the year amounted to 1.86%.
The popularity of the language is due to its relative simplicity — even a beginner can start working with it quickly. Of course, no one says that such a beginner will immediately be able to write high-load projects. But solving basic-level tasks is вполне possible. However, there are still problems even here, and we will talk about them in this article. We analyze the pitfalls of Python together with Alexey Nekrasov, the head of the Python direction at MTS, program director of the Python direction and a speaker of the “Python Developer” profession at Skillbox.
---
A little about the advantages of the language
There are many articles about the advantages of Python, so we will not repeat them. We will only say that the language is truly famous for its simplicity. Over time, a lot of “syntactic sugar” has appeared in it, which allows it to be concise and understandable, similar to pseudocode. Everything complex is hidden “under the hood” so as not to distract the developer.
Simplicity makes it possible to quickly create prototypes and test hypotheses. Because of this, it is easiest for beginners to start learning programming with Python.
What is syntactic sugar? It is a set of syntactic constructions whose use does not affect the behavior of the program, but makes the language more convenient for humans to use.
For example, the “decorator” pattern:
def decorator_log(func):
def wrapper(*args, **kwargs):
print("function call with parameters", args, kwargs)
res = func(*args, **kwargs)
return res
return wrapper
def func_1():
print('Test function')
func_1 = decorator_log(func_1)
# Instead of func_1 = decorator_log(func_1) you can now write:
@decorator_log
def func_1():
print('Test function')
There are many such constructions in the language; they simplify reading and working with code.
The Python interpreter takes over all the boring and complex work of managing memory, threads, etc. But there is also a problem. Because of all these pleasant advantages, Python loses in speed compared to other programming languages.
---
Details about the problems
In addition to performance loss, there are other pitfalls that beginner developers do not always know about. Among them, two major problems stand out: dynamic typing and the so-called “new sugar”.
---
Dynamic typing
Python is a dynamically typed programming language. This allows developers to quickly create prototypes and write code. Dynamic typing, simply put, means that the same variable at different times can refer to data of different types.
For example:
data = input() or '123456' # now the variable data contains a string
data = int(data) # now the variable data contains an integer
And this will work.
Statically typed programming languages, for example C, do not allow this. But dynamic typing has a catch.
If everything is fine in small projects, the larger a Python project becomes, the more errors related to variable types appear.
For example, somewhere in the code there is a function that receives the variable data entered by the user. The function checks that the entered number is two-digit:
if len(data) != 2:
print("You must enter a two-digit number")
When executing this code, we suddenly get an error:
TypeError: object of type 'int' has no len()
This means that somewhere in the code the string has already been converted into a number and placed into the variable data.
Such errors are not rare. Most often they occur in projects where two or more developers work together. To avoid errors, tests must be written to check whether variables contain the correct data format. Statically typed programming languages do not have such problems.
Another solution is the use of pseudo-static typing and checking it with the static analyzer mypy.
If problems with data types are undesirable, the code must be written as follows:
data: str = input() or '123456'
number: int = int(data)
if len(number) != 2:
print("You must enter a two-digit number")
After writing the code, mypy must be run to check it. The system verifies that an integer is not used as a string anywhere. When running mypy, an error will appear about passing the wrong variable type to the len function:
error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
Why is the term “pseudo-static typing” used here? Because when running a Python script, the check will not be performed automatically. Therefore, the developer will not immediately know whether the value is passed correctly to the len function. The error will only appear when the program is executed with input data.
---
New syntactic sugar and increasing complexity
Python is constantly evolving. Each new version introduces new features that allow developers to solve tasks with fewer lines of code than before.
But this also creates a problem — programmers may become too enthusiastic about writing constructions that occupy fewer lines but significantly reduce code readability.
For example, consider the task of cleaning input data and converting it to integers.
Previously, the program code would look like this:
from typing import Optional
def to_int(string: str) -> Optional[int]:
try:
return int(string)
except ValueError:
return None
data = ['sadf', '12', '1', 'a1']
filter_data = []
for i_str in data:
i_number = to_int(i_str)
if i_number is not None:
filter_data.append(i_number)
print(filter_data)
But starting from version 3.8 the for block can be written as follows:
filter_data = [y for x in data if (y := to_int(x)) is not None]
An experienced developer will understand this without problems, but for a beginner reading such code can be difficult.
---
What other difficulties exist?
In general, there are quite a few. Learning Python can be divided into several stages, and each stage has its own difficulties.
Stage 1 — Learning basic syntax
Data types, functions, classes, etc.
At this stage it is necessary to understand basic data structures and practice solving many simple tasks.
Stage 2 — Learning language patterns
Decorators, iterators, generators, context managers, etc.
At this stage developers begin to understand design patterns and how they are implemented directly in the language.
Stage 3 — Learning applied frameworks
Web frameworks: Flask, FastAPI, Django
Working with databases: SQLAlchemy, sqlite3, Tortoise ORM
Working with data: NumPy, pandas, marshmallow, pydantic
Here developers face the difficulty of choosing a development direction and building a learning path for additional tools. Beginners are advised to consult experienced developers or mentors.
Stage 4 — Advanced language study
Metaclasses, descriptors, etc.
In everyday work developers rarely encounter these topics because about 90% of tasks can be solved without them. However, if you want to become a first-class developer, you cannot avoid these topics.
---
What about the future of the language?
In short — the prospects definitely exist, and learning Python is worth it.
If we describe it in more detail, the requirements for beginners from employers are increasing every year.
Five years ago, to get a job as a junior Python web developer it was enough to:
solve algorithmic problems
know the basics of the programming language
But by the beginning of 2026 the following skills have been added:
knowledge of one framework Flask or Django (preferably also an asynchronous framework such as FastAPI)
knowledge of SQL and working with PostgreSQL
knowledge of testing frameworks pytest and unittest
knowledge of containerization and Docker
knowledge of Linux basics
In the next three to five years the demand will grow for middle and senior developers because they perform most of the tasks in IT companies.
If we look at the demand for interns and junior developers, the situation is more complicated for several reasons.
A junior developer initially brings losses to the company because they require a mentor (middle or senior level). As a result, solving tasks with a trainee takes more time than if the mentor completed the task alone.
Often after six months or a year, an intern or junior leaves the company where they were trained and moves to another company with an average salary increase of about two times.
There are also inflated expectations among interns and juniors. Many people come from other fields where they were already used to higher incomes and are not ready to drop from, for example, 100,000 rubles per month to 50,000 rubles.
Within three to four years, interns and junior developers may also face competition from artificial intelligence. Recently, the company DeepMind (a subsidiary of Alphabet) completed ten tests on the Codeforces platform and ranked among the top 54% of participants. It is quite possible that in the near future AI will be able to solve simple, template tasks that are usually given to interns and junior developers.
---
Conclusion
Summarizing everything above, if you want to move into IT as a Python developer you need to:
focus as much as possible on learning — there will be a lot to study and it will take time
be prepared that during the first six months to a year your salary may not exceed 100,000 rubles per month
try to get your first job and learn from more experienced colleagues
However, there is no need to be afraid of this. Learning Python often allows you to introduce automation into the field where you already work. This increases your value as a specialist and helps automate routine tasks. In addition, you gain commercial development experience.
The popularity of the language is due to its relative simplicity — even a beginner can start working with it quickly. Of course, no one says that such a beginner will immediately be able to write high-load projects. But solving basic-level tasks is вполне possible. However, there are still problems even here, and we will talk about them in this article. We analyze the pitfalls of Python together with Alexey Nekrasov, the head of the Python direction at MTS, program director of the Python direction and a speaker of the “Python Developer” profession at Skillbox.
---
A little about the advantages of the language
There are many articles about the advantages of Python, so we will not repeat them. We will only say that the language is truly famous for its simplicity. Over time, a lot of “syntactic sugar” has appeared in it, which allows it to be concise and understandable, similar to pseudocode. Everything complex is hidden “under the hood” so as not to distract the developer.
Simplicity makes it possible to quickly create prototypes and test hypotheses. Because of this, it is easiest for beginners to start learning programming with Python.
What is syntactic sugar? It is a set of syntactic constructions whose use does not affect the behavior of the program, but makes the language more convenient for humans to use.
For example, the “decorator” pattern:
def decorator_log(func):
def wrapper(*args, **kwargs):
print("function call with parameters", args, kwargs)
res = func(*args, **kwargs)
return res
return wrapper
def func_1():
print('Test function')
func_1 = decorator_log(func_1)
# Instead of func_1 = decorator_log(func_1) you can now write:
@decorator_log
def func_1():
print('Test function')
There are many such constructions in the language; they simplify reading and working with code.
The Python interpreter takes over all the boring and complex work of managing memory, threads, etc. But there is also a problem. Because of all these pleasant advantages, Python loses in speed compared to other programming languages.
---
Details about the problems
In addition to performance loss, there are other pitfalls that beginner developers do not always know about. Among them, two major problems stand out: dynamic typing and the so-called “new sugar”.
---
Dynamic typing
Python is a dynamically typed programming language. This allows developers to quickly create prototypes and write code. Dynamic typing, simply put, means that the same variable at different times can refer to data of different types.
For example:
data = input() or '123456' # now the variable data contains a string
data = int(data) # now the variable data contains an integer
And this will work.
Statically typed programming languages, for example C, do not allow this. But dynamic typing has a catch.
If everything is fine in small projects, the larger a Python project becomes, the more errors related to variable types appear.
For example, somewhere in the code there is a function that receives the variable data entered by the user. The function checks that the entered number is two-digit:
if len(data) != 2:
print("You must enter a two-digit number")
When executing this code, we suddenly get an error:
TypeError: object of type 'int' has no len()
This means that somewhere in the code the string has already been converted into a number and placed into the variable data.
Such errors are not rare. Most often they occur in projects where two or more developers work together. To avoid errors, tests must be written to check whether variables contain the correct data format. Statically typed programming languages do not have such problems.
Another solution is the use of pseudo-static typing and checking it with the static analyzer mypy.
If problems with data types are undesirable, the code must be written as follows:
data: str = input() or '123456'
number: int = int(data)
if len(number) != 2:
print("You must enter a two-digit number")
After writing the code, mypy must be run to check it. The system verifies that an integer is not used as a string anywhere. When running mypy, an error will appear about passing the wrong variable type to the len function:
error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
Why is the term “pseudo-static typing” used here? Because when running a Python script, the check will not be performed automatically. Therefore, the developer will not immediately know whether the value is passed correctly to the len function. The error will only appear when the program is executed with input data.
---
New syntactic sugar and increasing complexity
Python is constantly evolving. Each new version introduces new features that allow developers to solve tasks with fewer lines of code than before.
But this also creates a problem — programmers may become too enthusiastic about writing constructions that occupy fewer lines but significantly reduce code readability.
For example, consider the task of cleaning input data and converting it to integers.
Previously, the program code would look like this:
from typing import Optional
def to_int(string: str) -> Optional[int]:
try:
return int(string)
except ValueError:
return None
data = ['sadf', '12', '1', 'a1']
filter_data = []
for i_str in data:
i_number = to_int(i_str)
if i_number is not None:
filter_data.append(i_number)
print(filter_data)
But starting from version 3.8 the for block can be written as follows:
filter_data = [y for x in data if (y := to_int(x)) is not None]
An experienced developer will understand this without problems, but for a beginner reading such code can be difficult.
---
What other difficulties exist?
In general, there are quite a few. Learning Python can be divided into several stages, and each stage has its own difficulties.
Stage 1 — Learning basic syntax
Data types, functions, classes, etc.
At this stage it is necessary to understand basic data structures and practice solving many simple tasks.
Stage 2 — Learning language patterns
Decorators, iterators, generators, context managers, etc.
At this stage developers begin to understand design patterns and how they are implemented directly in the language.
Stage 3 — Learning applied frameworks
Web frameworks: Flask, FastAPI, Django
Working with databases: SQLAlchemy, sqlite3, Tortoise ORM
Working with data: NumPy, pandas, marshmallow, pydantic
Here developers face the difficulty of choosing a development direction and building a learning path for additional tools. Beginners are advised to consult experienced developers or mentors.
Stage 4 — Advanced language study
Metaclasses, descriptors, etc.
In everyday work developers rarely encounter these topics because about 90% of tasks can be solved without them. However, if you want to become a first-class developer, you cannot avoid these topics.
---
What about the future of the language?
In short — the prospects definitely exist, and learning Python is worth it.
If we describe it in more detail, the requirements for beginners from employers are increasing every year.
Five years ago, to get a job as a junior Python web developer it was enough to:
solve algorithmic problems
know the basics of the programming language
But by the beginning of 2026 the following skills have been added:
knowledge of one framework Flask or Django (preferably also an asynchronous framework such as FastAPI)
knowledge of SQL and working with PostgreSQL
knowledge of testing frameworks pytest and unittest
knowledge of containerization and Docker
knowledge of Linux basics
In the next three to five years the demand will grow for middle and senior developers because they perform most of the tasks in IT companies.
If we look at the demand for interns and junior developers, the situation is more complicated for several reasons.
A junior developer initially brings losses to the company because they require a mentor (middle or senior level). As a result, solving tasks with a trainee takes more time than if the mentor completed the task alone.
Often after six months or a year, an intern or junior leaves the company where they were trained and moves to another company with an average salary increase of about two times.
There are also inflated expectations among interns and juniors. Many people come from other fields where they were already used to higher incomes and are not ready to drop from, for example, 100,000 rubles per month to 50,000 rubles.
Within three to four years, interns and junior developers may also face competition from artificial intelligence. Recently, the company DeepMind (a subsidiary of Alphabet) completed ten tests on the Codeforces platform and ranked among the top 54% of participants. It is quite possible that in the near future AI will be able to solve simple, template tasks that are usually given to interns and junior developers.
---
Conclusion
Summarizing everything above, if you want to move into IT as a Python developer you need to:
focus as much as possible on learning — there will be a lot to study and it will take time
be prepared that during the first six months to a year your salary may not exceed 100,000 rubles per month
try to get your first job and learn from more experienced colleagues
However, there is no need to be afraid of this. Learning Python often allows you to introduce automation into the field where you already work. This increases your value as a specialist and helps automate routine tasks. In addition, you gain commercial development experience.
