DEV Community: Vadim Kolobanov

Would you mentor me?

Of course I am ready to participate in the training. The big difficulty is that English is not my native language and it will be possible to communicate through a translator

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Poetry vs pip: Or How to Forget Forever "requirements.txt" Cheat Sheet for Beginners

Vadim Kolobanov — Mon, 06 Dec 2021 13:43:45 +0000

You can find many interesting articles on my website, please visit it Happy Python Website

Automatic translation in the browser will help you to study everything conveniently.

If the material is useful, I will try to translate all my articles for you

Poetry is a dependency management tool in Python projects (analogous to the built-in pip).

It will be vital for beginners in Python to get acquainted with this tool, as it is a very simple and easy-to-use tool, the use of which can simplify the management and development of the project.

Installation

You can install poetry on windows either using pip:

pip install poetry

or with PowerShell (Windows)

(Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py -UseBasicParsing).Content | python -

pip stores dependency data in a file requirements.txt (or something else, but more often it is), poetry stores information in the pyproject.toml file, however, in the case of pip, only a list of dependencies and versions is stored in its file, and all basic information about the project is stored in .toml. It is very convenient, you can always find all the information in one place

To install dependencies in pip, you need to run:

pip install -r requirements.txt

poetry makes it easier and more beautiful

installation

poetry install

update

poetry update

Viewing dependencies in pip is done like this

pip freeze

You will only be able to see the current versions of the libraries and will not get the structure of all packages with their dependencies. In poetry, in poetry.the lock file, you can view information about all installed packages, the command:

poetry show --tree

It will show the tree structure of packages with their personal dependencies.

Also, launching a project in pip (in the case of a virtual environment) creates inconveniences, since the first thing you need to do is go into this very environment.

There is no need to activate the virtual environment in poetry, just go to the project folder and start using the commands. Poetry will find the right environment by itself.
You can also change the python version in poetry without having to change the old virtual environment.

This is only a small part of the benefits.
Now a little bit about the .toml file

[tool.poetry]
name = "new_proj"
version = "0.1.0"
description = "DEVoneLove"
authors = ["Vadim Kolobanov <titanyforgame@gmail.com>"]

[tool.poetry.dependencies]
python = "^3.10"
pygame = "^2.1.0"
icecream = "^2.1.1"
requests = "^2.26.0"
psycopg2 = { version = "^2.7", optional = true }
pymysql = { version = "1.0.2", optional = true }

[tool.poetry.dev-dependencies]
Pympler = "^0.9"

[tool.poetry.urls]
"My GitHub" = "https://github.com/vadimkolobanov"

[tool.poetry.scripts]
run-main = "new_proj.main:main_def"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

[tool.poetry] - contains basic information about the project
[tool.poetry.dependencies] - contains a description of all project dependencies. A link to Github is specified.
[tool.poetry.scripts] - contains scripts that need to be run when installing dependencies
[tool.poetry.extras] - dependency groups for a separate installation
[tool.poetry.urls] - Along with the main URLs, you can specify your own links

Conclusion

The study and effective use of new programming language features distinguishes a real programmer from a populist who talks about his skills more than he knows how.

(c) Vadim Kolobanov 2021 AD

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You can read the original article in Russian here

To beat depression, try to just quit #programming 🤪
— Vadim Kolobanov (@decodesperato) <a

Detailed Python: or How to Cross the Border of Knowledge

Vadim Kolobanov — Mon, 29 Nov 2021 09:57:56 +0000

Photo by Emile Perron on Unsplash

Do you want your code to look high-quality and concise and speak about you as an experienced developer? Then let's dive a little deeper into the basic knowledge and see how much you can simplify your code, how to make it readable and not lose the desire to return to your work again. Welcome to detailed Python. It's time to get better!

If you started learning Python, watched a dozen training videos, took several free courses, intensive courses and meetups, and are trying to write your first project, then this article will certainly help you. A superficial analysis of Russian-language Python training materials on the Internet suggested that a novice Python developer is rarely shown the beauty and effectiveness of this language. Basic (often non-professional) training involves familiarity with the simplest mechanics, which are often found in other languages. The road will be mastered by the walking one, which means let's strive for more.

In this article you will see:

List comprehensions (List Generator)
Unpacking items from a list
Slicing (Slices or Slices)
Some nice Python language tricks
Conclusion

List comprehensions (List generator)

In most cases, list generators are used to create a list from a set of values - so as not to resort to more complex constructions via for and append.

Speaking in detail, the list generator can create a collection of values in just one line. Let's see an example:

lst = []
for x in range(10):
    lst.append(x**2)
print (lst)

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

In the example, we took a sequence of numbers from 0 to 9 (our range) and squared each iteration of the loop, after which we wrote the result to the end of the empty list declared above.

So, the result:

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Great. And it is difficult to criticize this method because it works, is readable, not cumbersome, right?

Example using a list generator:

print([x**2 for x in range(10)])

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

From four lines to one. Is it beautiful, readable, concise?

Now learn more about the syntax of the generator. In general, it looks like this:


[ expression for iterator in iterable object if condition ]

Yes, the generator can also contain a condition under which the iterated elements will fall into the list. I'll show you an example:

print([x for x in range(20) if x%2==0])

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Only even numbers were included in the list (if read by condition, these are those that are divisible by 2 without remainder)

Here the efficiency of the generator is even more pronounced. You combine both a loop and a conditional operator in one expression and get an ordered, mutable list at the output.

Now, raise the level:

def some_function(y):
    return (y + 5) / 2

print([some_function(x) for x in range(10)])

[2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0]

You can use an expression as a function argument. This is convenient, and your code will leave a pleasant impression about you.

It is worth mentioning that the generator exists not only for lists, but also for dict and set and is called Dict comprehensions and Set comprehensions, respectively. Their basic syntax is similar. I will show the differences with an example:

dict = {num: num**2 for num in range(5)}
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

set = {x for x in range(10)}
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}

Pay attention to the type of brackets in the list and in these two examples.

Unpacking items from a list

If you need to get a certain element from the collection, then the first and obvious method is to get it by index.

lst = [1,2,3,4] when lst[1] = 2

This method can be used effectively if your collection is immutable, and lst[1] will always contain the value you need. The disadvantage of the approach is the so-called "magic numbers". Imagine that you are reading someone else's code, in which the developer gets the value, as in the example. You won't have any questions: "What is lst[1]? Why 1 and not 2 and not 20?" That's why they call such numbers in parentheses "magic numbers". Appeared out of nowhere, denote something inside. You should learn how to use unpacking.

Python allows you to assign collection values to individual variables. This is effective if the collection is small. Read more in the example:

lst = [1, 2, 3 ]
x, y, z = lst
print(x, y, z)

1 2 3

At the same time, it's worth looking at how to assign multiple values to multiple variables at once, and how to replace variables beautifully.

Multiple assignment

x, y, z = 1, 2, 3

This expression is equivalent to :


x = 1
y = 2
z = 3

But you took 3 lines instead of one. Is it normal if your project will be 50 lines, and if 300 or 800?

Replacing variables.

In order to swap variables, you can do this:

a=10
b=15
_tmp=a 
a=b 
b=_tmp
print(a, b)
15 10

But there is a way to make this record shorter:


a, b = 10, 15
a, b = b, a
print(a, b)

15 10

One of the principles of the Python language states:

Readability counts

So let's be real "pythonists" in our works.

Slicing

Speaking of large collections. What if we need multiple values from a collection? And what if they are needed from the middle or through one? Python provides such a mechanism, which is called Slicing or Slices. The syntax is quite simple:

sequence[start:stop:step]

start = index of the element from which the slice starts;
stop = index of the element that ends the slice;
step = slice step.

Example:

x = [10, 5, 13, 4, 12, 43, 7, 8]
print( x[1:6:2])

[5, 4, 43]

We took every second item in the list from between 1 and 6 indexes. In your work, you will often resort to the help of slices, do not underestimate them.

Slices can be written easier. If the beginning or end of the slice is equivalent to the beginning and end of the list, they can be omitted. It looks like this:

x = [10, 5, 13, 4, 12, 43, 7, 8]
print( x[:3])

[10, 5, 13]

Here we take a slice from 0 to 2 of the index of the element with a step of 1. We didn't specify the beginning or the step, but only the end of the slice. Note that 3 is the index of the final element, but it does not fall into the final list, so the slice will be from 0 to 2 of the index.

Now an example indicating the start:

x = [10, 5, 13, 4, 12, 43, 7, 8]
print( x[3:])

[4, 12, 43, 7, 8]

The slice started with index 3, but in this situation the element with index 3 got into the slice. A similar principle works in range. Just remember that. This way you will eliminate a number of errors in your code.

Some nice Python language tricks

Many people believe that it was fashionable to monitor memory in the era of assembly language programming. Many, but not all. I'll give you a clear example of why sometimes it's worth thinking about which function to use:

import sys

range_list = range(0, 10000)
print(sys.getsizeof(range_list))

48 # bytes

Imagine that a sequence of numbers from 0 to 9999 takes up only 48 bytes.

And here is an example with the same sequence:

import sys

real_list = [x for x in range(0, 10000)]
print(sys.getsizeof(real_list))

87616 # bytes

Two identical sequences from 0 to 9999. They occupy memory with a difference of almost 2000 times. And if the program contains 100 such lists?

The fact is that range only pretends to be a list, behaves like a list, but in fact, the range function returns a class and, of course, loads less memory.

And finally, a small focus on the number of repetitions of the value in the collection (a favorite task in various tutorials and courses):

from collections import Counter
print(Counter('abracadabra').most_common(1))

[('a', 5)]

You can use a list instead of a string:

from collections import Counter
lst = [2, 2, 2, 5, 5, 6, 7, 6, 9, 2, 4, 8, 5]
print(Counter(lst).most_common(1))

[(2, 4)]

The argument in most_common specifies the number of repeatable elements that need to be counted:

from collections import Counter
print(Counter('abracadabra').most_common(3))

[('a', 5), ('b', 2), ('r', 2)]

It is worth noting that for the mechanisms described in the article to work correctly, it is necessary to have a full understanding of lists, dictionaries, sets, and tuples. These types of data are very insidious and have important differences that are worth paying attention to.

Conclusion

Deeper immersion in the technology being studied always brings its own result. After reading this material, you will get the opportunity to distinguish the code of a beginner (or lazy) from the code of a developer who loves his work, immerses himself in it, tries to do not only for himself but also for others. You will want to establish contact with such people, make a joint project or invite them to work. Writing code is like drawing: all paints are the same color, but some get a house with a triangular roof, while others have magnificent landscapes. The question is effort.

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FAQ

I am a beginner, how should I learn Python?

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To beat depression, try to just quit #programming 🤪
— Vadim Kolobanov (@decodesperato) <a

What's New at Python 3.10 And Why You Should Know That ?

Vadim Kolobanov — Mon, 22 Nov 2021 10:38:59 +0000

Hi, have you already learned Python 3.10? And what if you meet a project written on this version? How fast can you figure out this code? Most people know that Python 3.10 has been released, but some beginner developers do not even pay attention to the features of the new version, because they are now working on 3.8, for example, and 3.10 is unnecessary for them. Soon most of the code will be written on version 3.10 and without knowing the specifics, you simply will not be able to read someone else's code correctly. Let's get acquainted with some innovations.

Improvements in type checking

If you use type checking, you will be happy to hear that Python 3.10 includes many improvements in type checking, among them the type union operator, whose syntax is now cleaner.


# Function that accepts either `int` or `float`
# Old:
def func(value: Union[int, float]) -> Union[int, float]:
    return value

# New:
def func(value: int | float) -> int | float:
    return value

Population Count (popcount)

Starting with Python 3.10, to count the number of bits in the binary representation of an integer, you can call int.bit_count().


value = 42
print(bin(value))
# '0b101010'
print(value.bit_count())
# 3

Performance improvements

As with all recent Python releases, performance improvements will come with Python 3.10. The first is an optimization of the constructors str(), bytes(), and bytearray(), which should become about 30% faster (fragment adapted from the example in the Python bug tracker):


~ $ ./python3.10 -m pyperf timeit -q --compare-to=python "str()"
Mean +- std dev: [python] 81.9 ns +- 4.5 ns -> [python3.10] 60.0 ns +- 1.9 ns: 1.36x faster (-27%)
~ $ ./python3.10 -m pyperf timeit -q --compare-to=python "bytes()"
Mean +- std dev: [python] 85.1 ns +- 2.2 ns -> [python3.10] 60.2 ns +- 2.3 ns: 1.41x faster (-29%)
~ $ ./python3.10 -m pyperf timeit -q --compare-to=python "bytearray()"
Mean +- std dev: [python] 93.5 ns +- 2.1 ns -> [python3.10] 73.1 ns +- 1.8 ns: 1.28x faster (-22%)

Better error messages

SyntaxErrors

Yes, they added more accurate information in the syntax error message

SyntaxError: '{' was never closed
SyntaxError: invalid syntax. Perhaps you forgot a comma?
SyntaxError: did you forget parentheses around the comprehension target?
SyntaxError: ':' expected after dictionary key
SyntaxError: expected 'except' or 'finally' block
SyntaxError: cannot assign to attribute here. Maybe you meant '==' instead of '='?
SyntaxError: f-string: cannot use starred expression here

IndentationError

Now this exception has more context as to which block was expecting an indent, and where the operator is located.

def foo():
if some:
x = 2

IndentationError: expected an indented block after 'if' statement in line 2

AttributeError

Now it will prompt suggestions of similar attribute names in the object


collections.namedtoplo

AttributeError: module 'collections' has no attribute 'namedtoplo'. Did you mean: namedtuple?

NameError

it will also work by offering you a similar name from your code as AttributeError

Structural Pattern Matching(Match-Case)

Before version 3.10, we had to use the if ... elif operator. Python version 3.10 introduced the match-case operator, which has more features than a simple selection operator. Its syntax is as follows:


def foo(key):
   match key:
       case 'python':
          # do something with py
       case 'c++':
          # do something with cpp
       case _:
          # default

In fact, this is not all his opportunity. Let's look at some simple examples with the new operator

Structural patterns

For example, you need to check whether the analyzed object is a sequence (list or set):

case ['a', 'b']:
   pass

What to do if we don't know the length of the incoming list, but only the first two elements are known. The match operator allows you to select the remaining elements of the sequence in a variable.


match some_seq:
   case [2, 4, *others]: # The first two elements are known
       print("Yes", *others)
   case _:
       print("No")

If, however, the length of the sequence is known, and some elements may change, then they can also be set as variable:

match some_seq:
   case (1, two, three, 4):
       print("Yes", two, three)

Another unexpected feature of structural templates is that you can check their values. For example, so:

case (y, x) if x == y:
   print("number")
case (y, *other) if len(other) == 3:
   print(other)

Dictionaries as structural patterns

Dictionaries can also serve as structural patterns. Now you can compare dictionaries by keys:

match some_dict:
   case {"move": direction, "distance": km}:
       move(direction, km)
   case {"stop": duration}:
       sleep(duration)

Using OR in a match-case

If some cases are handled in a similar way, then we can combine them as a logical expression using the OR (|) operation. Moreover, the OR operation can be used with literals, sequences, and dictionaries. Below is the Python code where the template with OR is used.

match k:
   case 1 | 2:
       print("number")
   case (1, _) | (_, 2):
       print("sequence")

Conclusion

A complete list of all changes is described in the Python documentation on the website Docs Python
We looked at the most interesting and understandable changes for beginners. It should be understood that the Python 3.10 version is not stable yet and I would not recommend using it as the main interpreter for projects temporarily. You can install it for tests or training.

Why is it useful for all Python developers to know these innovations? Soon we will all be faced with a new style of writing code, new operators, features, and syntactic changes from Python 3.10. The best will be the one who will be able to understand the new code before everyone else, having a lot of practice on previous versions.

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To beat depression, try to just quit #programming 🤪
— Vadim Kolobanov (@decodesperato) November 22, 2021

6 Elegant Tricks in Python for Beginners

Vadim Kolobanov — Sun, 21 Nov 2021 05:33:37 +0000

Hello everyone! Let's see together what elegant mechanics professional Python developers use in their code

I very often see such examples in some public Git repositories of experienced programmers.
Let's get started

Sorting data by multiple keys


peoples = [
{ 'name': 'John', "age": 64 },
{ 'name': 'Janet', "age": 34 },
{ 'name': 'Ed', "age": 24 },
{ 'name': 'Sara', "age": 64 },
{ 'name': 'John', "age": 32 },
{ 'name': 'Jane', "age": 34 },
{ 'name': 'John', "age": 52 },
]

peoples.sort(key=lambda item: (item['name'], item['age']))
print(people)

#Output
[
 {'name': 'Ed',   'age': 24},
 {'name': 'Jane', 'age': 34},
 {'name': 'Janet','age': 34},
 {'name': 'John', 'age': 32},
 {'name': 'John', 'age': 52},
 {'name': 'John', 'age': 64},
 {'name': 'Sara', 'age': 64}
]

at the output, we get alphabetical sorting, and if the values match, the sorting takes place by the second key. This will not have an effect if there are no duplicate values in your data.

List comprehensions (List Generator)

I have already shown an understanding of the list in my previous articles, here I will just show you a few possible uses

Basic syntax

[ expression for item in list if conditions ]

A very simple example to fill in a list is a sequence of numbers:

mylist = [i for i in range(10)]
print(mylist)
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

And since you can use an expression, you can also do some math.:


squares = [x**2 for x in range(10)]
print(squares)

#output
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

You can call a function as an expression

def some_function(a):
    return (a + 5) / 2

result = [some_function(i) for i in range(10)]
print(result)

#output

[2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0]

And finally, you can use "if" to filter the list. In this case, we only keep the values that are divisible by 2:

filtered = [i for i in range(20) if i%2==0]
print(filtered)

#output
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Control of memory in use by objects

I have also already mentioned sys.getsizeof() in the articles, but here I want to show you an interesting example that will show all the usefulness of memory control

import sys

mylist = range(0, 10000)
print(sys.getsizeof(mylist))

#output
48

Just 48 bytes? Seriously? The list is from 0 to 9999?
Yes, guys and why is this happening, I'll tell you now

The fact is that range only pretends to be a list, behaves like a list, but in fact, the range function returns a class and it certainly loads less memory.

For comparison, let's create a real list with the same range

import sys

myreallist = [x for x in range(0, 10000)]
print(sys.getsizeof(myreallist))

#output
87616

Agree, the numbers vary greatly, so it's not so useless to monitor memory.

Dataclasses

Since version 3.7, Python offers data classes. There are several advantages over regular classes or other alternatives, such as returning multiple values or dictionaries:

a data class requires a minimal amount of code
that you can compare data classes because there is "__ eq __"
you can easily output a data class for debugging because you can use "__ repr __"
data classes that require type hints, this reduces the likelihood of errors

Here is an example of a data class in action:

from dataclasses import dataclass


@dataclass
class Card:
    rank: str
    suit: str


card = Card("Q", "hearts")

print(card == card)
# True

print(card.rank)
# 'Q'

print (card.suit)
# 'hearts'

print(card)
#Card(rank='Q', suit='hearts')

Finding the most common value

A fairly simple and elegant example of searching for repetitions in a sequence

from collections import Counter
print(Counter('abracadabra').most_common(1))

#output
[('a', 5)]

print(Counter('abracadabra').most_common(2))

#output
[('a', 5), ('b', 2)]

print(Counter('abracadabra').most_common(3))

#output
[('a', 5), ('b', 2), ('r', 2)]

As an argument for Counter, you can pass, for example, list. This will also work.

Merging Dictionaries (Python 3.5+)

Starting with Python 3.5, it's easier to combine dictionaries:

dict1 = { 'a': 1, 'b': 2 }
dict2 = { 'b': 3, 'c': 4 }
merged = { **dict1, **dict2 }
print (merged)

#output
{'a': 1, 'b': 3, 'c': 4}

If there are overlapping keys, the keys from the first dictionary will be overwritten.

In Python 3.9, combining dictionaries becomes even cleaner. The above merge in Python 3.9 can be rewritten as:

dict1 = { 'a': 1, 'b': 2 }
dict2 = { 'b': 3, 'c': 4 }
merged = dict1 | dict2
print (merged)

#output
{'a': 1, 'b': 3, 'c': 4}

Conclusion

The use of some complex (to learn) mechanics in a programming language seems unnecessary to many novice programmers.
But have you ever wondered why more experienced developers use such mechanisms more often?. Simplicity for a beginner and simplicity for a professional are two completely different things. What seems complicated and cumbersome for a beginner is actually the simplest and lightest solution for a more experienced programmer. Each of you will come to these methods and solutions by yourself. Sooner or later. It's just that in our world today, you can learn everything. Let's learn properly!

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FAQ

I am a beginner, how should I learn Python?

Look into the following series:

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Python features you'll want to repeat

Vadim Kolobanov — Tue, 16 Nov 2021 15:07:37 +0000

File server on a computer with mobile access

You can access your file system by running a file server on your computer. Navigate to the desired directory that you want to access and run the following command:

# python version >=  3.X
python -m http.server
# python version >=  3.X
python3 -m http.server #for Linux

# If Python version >= 2.X and < 3.X
python -m SimpleHTTPServer
#default port: 8000

This command starts a file server that can be accessed on the same network. To access your files on a mobile device, simply connect to the same network (Wi-Fi or use the phone's access point on a laptop). Now open the browser in your phone.

<your-computer-ip>:port

Check your IP by running - ifconfig. Check your local IP

Example:

192.168.43.155:8000
And on the mobile phone, you will see the current directory

Convert CSV to JSON 1 line

If you're a programmer, you'll probably be lazy and want to automate pretty much everything you can, right?

Don't worry, the python will cover you. There are a lot of things that you can automate in just 4-5 lines of code. From installing cron jobs and reminders to uploading your favorite YouTube videos, you can do it all with a few lines in python.

Example - convert CSV to JSON
You can convert a CSV file to JSON with just one command in Python!

python -c "import csv,json;print json.dumps(list(csv.reader(open('your_csv_file.csv'))))"

Replace it with anyfile.csv and you will get JSON output!

Easy, right?

Comics

Yes, these are comics in python!


 import antigravity

Will they add brackets in python?


from __future__ import braces
  File "<stdin>", line 1
SyntaxError: not a chance

A simple life lesson


>>> import this
>>> love = this
>>> this is love
True
>>> love is True
False
>>> love is False
False
>>> love is not True or False
True
>>> love is not True or False; love is love  
True

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I am a beginner, how should I learn Python?

Look into the following series:

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Soft skills that will improve your work and life. With self-check

Vadim Kolobanov — Mon, 15 Nov 2021 20:38:08 +0000

Being a programmer in 2021 is not the same as ten years ago. The world is constantly changing, and many of the skills that were important back then have faded into the background over time. And what skills should a developer have in order to remain a sought-after specialist, for example, in five years? If you don't have them, maybe you should think about their development now?

In this post, I want to talk about skills that will help you stay in demand as a developer even in conditions of constant change. Usually, IT specialists rely on the scientific and technical base, which is important for understanding the functioning of the system as a whole. This includes knowledge of the operating principles of the OS, the network stack, databases, and the mathematical apparatus that underlies any algorithm or protocol, etc. However, hard skills alone are not enough — communication skills, the ability to build a workflow with a team and/or strangers, flexibility in decision-making, and other soft skills are also needed. Moreover, it is important not just to have the — soft skills that should be integrated into the overall development process and go in close conjunction with professional skills. Seven of them will be discussed today.

1. Comprehensive problem solving

The demand for specialists who are able to approach the task from different sides is steadily growing. Most people cannot solve complex problems because they often use technologies aimed at the process, not the result. A developer using an integrated approach can not only identify the problem, but also determine its causes, and most importantly, "calculate" further steps.

Self-test:

Colleagues complain about a recurring problem. If it arose in your team, where would you start solving it?
Do you calculate the possible risks when choosing a certain solution?
How to understand that you are really solving the problem, and not eliminating its "symptoms"?

2. The ability to convince

The ability to convince — that is, to present your idea, make arguments and tell a story so that listeners are passionate - is necessary not only for those who constantly communicate with clients. This skill will help to defend your point of view, inspire like-minded people. And in order for colleagues to listen to you not out of politeness, but attentively and intently, it is highly desirable to make your story not only convincing but also interesting.

For example: when preparing a presentation about a release, you try not to load your colleagues with a dry list of facts with technical details, but build a story: add explanations, vivid examples, think about how you will manage the attention of listeners, and prepare arguments for answers to possible questions.

Self-test:

Have you noticed that you are interrupted at meetings? Was there a feeling that no one was listening to you?
Do you try to give illustrative cases when defending your point of view?
How will you start the story about yourself? Rehearse a small speech.
Remember the case when, after a conversation with you, a colleague changed his mind. What exactly helped you convince him?
How do you respond to objections?

3. Cooperation and teamwork skills

I think many will agree that the community plays a significant role in the IT environment: experienced developers help colleagues and willingly share their experiences. Nevertheless, there is a stereotype that it is normal for a programmer to be closed, but it is difficult to interact with a closed person since he almost never offers ideas and is not inclined to discuss current processes. Therefore, more and more companies are paying attention to whether a specialist is able to work in a team, whether he understands colleagues as well as the project itself, whether he is able to give and receive feedback.

Self-test:

You need to talk to a colleague you didn't know before. Where will you start?
One of the team members repeatedly passes the work with errors. What will you do?
What words will you choose to make the criticism sound friendly?
Do you celebrate the successes of your colleagues and talk about them publicly?
What helps you feel the mood of the interlocutor and tune in to his wave?

4. Ability to plan your work

Employers value specialists who are able to plan, give accurate estimates of the complexity of tasks and adhere to deadlines regardless of the language, technologies, and methodologies used.

Example: to organize the work of a group (if you are a leader) or your own, you plan sprints, make sure that there is a time interval between the task and the solution, which will allow you to organize things and prioritize. During the day, you stick to what you have planned and do not get distracted by other requests so that the work becomes more calm and predictable.

Self-test:

You are asked to name the deadlines for the project's readiness. What will guide you in the assessment?
It's the middle of the working day, and suddenly a colleague sends you a link. You know that there is a useful and important article there. What will you do?
Do you make work and personal plans for the day/week/month? How many of them can be completed?
Are you familiar with the Eisenhower matrix time planning method?
Do you know how much time it takes you on average for each type of task?
How many minutes/hours a day do you spend on social media? Do you track these statistics in any way? Do you use time trackers?
Do you practice taking a break from social networks and the Internet, digital detox?

5. Critical thinking

The ability to think critically is one of the key skills in our time. In a world where knowledge is power and the flow of information increases exponentially, you need to be able to ask questions, compare data and be critical of what others write. If we talk about professional development, critical thinking allows you to determine which set of skills you need. Someone needs clean code, knowledge of the version control system, and system thinking to work, and someone who is engaged in high-tech projects needs deep immersion in special sections of mathematics or physics.

Self-test:

Do you think when reading materials on the Internet about who publishes them and for what purpose?
How do you determine which information can be trusted? What criteria do you rely on when choosing information sources?
Do you check website addresses and connection security when clicking on unfamiliar links?
Do you have a list of trusted sources of information?

6. Continuous learning — long life learning

Many leading companies seek to hire full-stack developers who work freely with different technologies and platforms. Such specialists understand the principles of any development, starting from the network core level and ending with the interface level. This is impossible to achieve if you do not make it a rule to constantly learn new things. A sought-after developer is determined not only by the possession of a rare stack but also by the ability to easily "switch" between different languages and the desire to learn new tools every day.For example: although Java and C# have occupied a significant part of the market for several years, IT companies are increasingly using newer technologies (Kotlin, Swift, etc.). And, logically, developers who are proficient in several languages will give odds to those who are familiar with one or two technologies. In five years, new stacks will surely appear, so to remain in demand, it is important to constantly learn and be able to choose the right tool for solving a specific task, based on a comprehensive analysis of factors.

Self-test:

Do you participate in the development of open-source projects? How many commits have you made over the past year? How many bugs and suggestions have you created in the tracker? How many questions were answered?
How long have you been studying an unfamiliar technology for yourself?
What would you do if you found out about the emergence of a new language that is gaining popularity?
How do you react if you are offered to go to a training event?
What professional conferences do you attend?
What have you learned in your professional field over the past six months?

7. Compliance with work-life balance

Compliance with work-life balance and your well-being directly affect productivity: those who take care of themselves usually live longer, work more efficiently and have time to pay due attention to other important matters. The topic of health is closely connected with all spheres of life, including work. I won't say anything about biohacking and implanting chips under the skin (this is the topic of a separate post — or even more than one), this item is more about attention to the body, to the psycho-emotional state, and also that the separation of personal and professional life helps them mutually support each other. Try to be more tolerant of others, especially if their point of view contradicts yours, express feelings without harming others, and be aware of what you can influence and what you can't. Find a hobby to your liking and remember that your career success will partly depend on how your life is organized after work and study.

For example: in order to feel cheerful and have more time, you go to bed on time, do not use the phone two hours before bedtime and do not grab it first thing in the morning, do not overeat. Thanks to this, you stay attentive and energetic longer.

Self-test:

When was the last time you underwent a comprehensive examination?
What do you do to ensure that your diet is balanced?
How many hours a day do you sleep? Do you get enough sleep? If not, what can you do now to improve the quality of your sleep? In the morning you feel cold, but you need to go to work. What will you do?
Do you work on vacation? Do you finish projects on weekends? Do you write to work chats in the evenings?
What are your hobbies? Have you been interested in something new for yourself for a long time?

This skill set does not appear with birth and is not strictly mandatory. But the development of these skills will not only give you success in work or in a team but will also make your life much better.

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Instructive Python code examples for Beginners

Vadim Kolobanov — Wed, 10 Nov 2021 20:09:30 +0000

Photo by Pakata Goh on Unsplash

Hi guys! I want to show you beautiful and instructive python code examples. Using them in training, you will discover new language features and your code will look more professional.
Let's get started!

1. We get vowels

This example returns the found vowels "a e i o u" in the string. This can be useful when searching for or detecting vowels.

def get_vowels(String):
    return [each for each in String if each in "aeiou"]
get_vowels("animal") # [a, i, a]
get_vowels("sky") # []
get_vowels("football") # [o, o, a]

2. The first letter in uppercase

This works with a string of one or more characters and will be useful when analyzing text or writing data to a file. You can refine this code and make all letters uppercase to use case-independent search.

def capitalize(String):
    return String.title()
capitalize("shop") # [Shop]
capitalize("python programming") # [Python Programming]
capitalize("how are you!") # [How Are You!]

3. Print the line N times

Loops are a hard part of the language. You don't have to use them to output strings.

Look how it's easy

n=5
string="Hello World "
print(string * n)  #Hello World Hello World Hello World Hello World Hello World

4. Combine two dictionaries

Many of you guys, in order to combine 2 sequences, start overwriting one into the other. And again cycles, conditions, append and other horror... Catch a life hack, and please learn correctly!

def merge(dic1,dic2):
    return {**dic1,**dic2}
dic1={1:"Hello", 2:"Dev"}
dic2={2:"Python", 4:"Programming"}
# In these examples, Python merges dictionary keys
# in the order listed in the expression, overwriting 
# duplicates from left to right.
merge(dic1,dic2) # {1: 'Hello', 2: 'Dev', 3: 'Python', 4: 'Programming'}

5. Calculate the execution time

This example is useful when you need to know how long it takes to execute a program or function.

import time
start_time= time.time()
def fun():
    a=2
    b=3
    c=a+b
fun()
end_time= time.time()
timetaken = end_time - start_time
print("Your program takes: ", timetaken) # 0.0345
# I love you Dev =)

6. Exchange of values between variables

Learn to use fewer variables guys. This option will look more elegant

a=3
b=4
a, b = b, a
print(a, b) # a= 4, b =3

7. Checking duplicates

Here I draw attention once again to the fact that set contains immutable data. Just remember this

def check_duplicate(lst):
    return len(lst) != len(set(lst))
check_duplicate([1,2,3,4,5,4,6]) # True
check_duplicate([1,2,3]) # False
check_duplicate([1,2,3,4,9]) # False

8. Filtering False values (or other)

An easy way to remove values from the list. Beautiful, elegant, and will show your code as the work of a professional.


def Filtering(lst):
    return list(filter(None,lst))
lst=[None,1,3,0,"",5,7]
Filtering(lst) #[1, 3, 5, 7]

9. Size in bytes

This example returns the length of a string in bytes, which is convenient when you need to know the size of a string variable.


def ByteSize(string):
    return len(string.encode("utf8"))
ByteSize("Python") #6
ByteSize("Data") #4

10. Load memory

It will be useful if you want to keep track of the memory that your variables occupy. The main thing is to remember:
Simple is better than complex.
Complex is better than complicated.


import sys
var1="Python"
var2=100
var3=True
print(sys.getsizeof(var1)) #55
print(sys.getsizeof(var2)) #28
print(sys.getsizeof(var3)) #28

does not work with a recursive function

11. Anagrams

This code is useful for finding Anagrams. An anagram is a word obtained by rearranging the letters of another word.


from collections import Counter
def anagrams(str1, str2):
    return Counter(str1) == Counter(str2)
anagrams("abc1", "1bac") # True

12. Sorting the list

This example sorts the list. Sorting is a frequently used task that can be implemented with many lines of code with a loop, but you can speed up your work using the built-in sorting method. Study Python deeper! And you will be happy.


my_list = ["leaf", "cherry", "fish"]
my_list1 = ["D","C","B","A"]
my_list2 = [1,2,3,4,5]

my_list.sort() # ['cherry', 'fish', 'leaf']
my_list1.sort() # ['A', 'B', 'C', 'D']
print(sorted(my_list2, reverse=True)) # [5, 4, 3, 2, 1]

13. Converting a comma-separated list to a string

This code converts a comma-separated list into a single string. It is very convenient to list all the values of the list in one line. And besides, JOIN is a useful method in python

my_list1=["Python","JavaScript","C++"]
my_list2=["Java", "Flutter", "Swift"]
#example 1
"My favourite Programming Languages are" , ", ".join(my_list1))
#My favourite Programming Languages are Python, JavaScript, C++
print(", ".join(my_list2))  # Java, Flutter, Swift

14. Shuffling the list

Everything is clear from the name here. But you didn't know that, did you? Really?

from random import shuffle
my_list1=[1,2,3,4,5,6]
my_list2=["A","B","C","D"]
shuffle(my_list1) # [4, 6, 1, 3, 2, 5]
shuffle(my_list2) # ['A', 'D', 'B', 'C']

15 Splitting into fragments

This example will show how to split the list into fragments and divide it into smaller parts. Not so useful, but interesting


def chunk(my_list, size):
    return [my_list[i:i+size] for i in range(0,len(my_list), size)]
my_list = [1, 2, 3, 4, 5, 6]
chunk(my_list, 2) # [[1, 2], [3, 4], [5, 6]]

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