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Python is a no-BS programming language. Readability and simplicity of design are two of the biggest reasons for its immense popularity.
Part of the reason for this popularity is its simplicity and easiness to learn it.
If you are reading this, then it is highly likely that you already use Python or at least have an interest in it.
In this article, we will briefly see 30 short code snippets that you can understand and learn in 30 seconds or less.
The following tricks will prove handy in your day-to-day coding exercises.
The following method checks whether the given list has duplicate elements. It uses the property of set() which removes duplicate elements from the list.
def all_unique(lst):
return len(lst) == len(set(lst))
x = [1,1,2,2,3,2,3,4,5,6]
y = [1,2,3,4,5]
all_unique(x) # False
all_unique(y) # True
This method can be used to check if two strings are anagrams. An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.
from collections import Counter
def anagram(first, second):
return Counter(first) == Counter(second)
anagram("abcd3", "3acdb") # True
This snippet can be used to check the memory usage of an object.
import sys
variable = 30
print(sys.getsizeof(variable)) # 24
This method returns the length of a string in bytes.
def byte_size(string):
return(len(string.encode('utf-8')))
byte_size('😀') # 4
byte_size('Hello World') # 11
This snippet can be used to print a string n times without having to use loops to do it.
n = 2;
s ="Programming";
print(s * n); # ProgrammingProgramming
This snippet simply uses the method title() to capitalize first letters of every word in a string.
s = "programming is awesome"
print(s.title()) # Programming Is Awesome
This method chunks a list into smaller lists of a specified size.
def chunk(list, size):
return [list[i:i+size] for i in range(0,len(list), size)]
This method removes falsy values (False, None, 0 and “”) from a list by using filter().
def compact(lst):
return list(filter(None, lst))
compact([0, 1, False, 2, '', 3, 'a', 's', 34]) # [ 1, 2, 3, 'a', 's', 34 ]
This snippet can be used to transpose a 2D array.
array = [['a', 'b'], ['c', 'd'], ['e', 'f']]
transposed = zip(*array)
print(transposed) # [('a', 'c', 'e'), ('b', 'd', 'f')]
You can do multiple comparisons with all kinds of operators in a single line.
a = 3
print( 2 < a < 8) # True
print(1 == a < 2) # False
This snippet can be used to turn a list of strings into a single string with each element from the list separated by commas.
hobbies = ["basketball", "football", "swimming"]
print("My hobbies are:") # My hobbies are:
print(", ".join(hobbies)) # basketball, football, swimming
This method gets vowels (‘a’, ‘e’, ‘i’, ‘o’, ‘u’) found in a string.
def get_vowels(string):
return [each for each in string if each in 'aeiou']
get_vowels('foobar') # ['o', 'o', 'a']
get_vowels('gym') # []
This method can be used to turn the first letter of the given string into lowercase.
def decapitalize(str):
return str[:1].lower() + str[1:]
decapitalize('FooBar') # 'fooBar'
decapitalize('FooBar') # 'fooBar'
The following methods flatten a potentially deep list using recursion.
def spread(arg):
ret = []
for i in arg:
if isinstance(i, list):
ret.extend(i)
else:
ret.append(i)
return ret
def deep_flatten(xs):
flat_list = []
[flat_list.extend(deep_flatten(x)) for x in xs] if isinstance(xs, list) else flat_list.append(xs)
return flat_list
deep_flatten([1, [2], [[3], 4], 5]) # [1,2,3,4,5]
This method finds the difference between two iterables by keeping only the values that are in the first one.
def difference(a, b):
set_a = set(a)
set_b = set(b)
comparison = set_a.difference(set_b)
return list(comparison)
difference([1,2,3], [1,2,4]) # [3]
The following method returns the difference between two lists after applying a given function to each element of both lists.
def difference_by(a, b, fn):
b = set(map(fn, b))
return [item for item in a if fn(item) not in b]
from math import floor
difference_by([2.1, 1.2], [2.3, 3.4], floor) # [1.2]
difference_by([{ 'x': 2 }, { 'x': 1 }], [{ 'x': 1 }], lambda v : v['x']) # [ { x: 2 } ]
You can call multiple functions inside a single line.
def add(a, b):
return a + b
def subtract(a, b):
return a - b
a, b = 4, 5
print((subtract if a > b else add)(a, b)) # 9
The following method checks whether a list has duplicate values by using the fact that set() contains only unique elements.
def has_duplicates(lst):
return len(lst) != len(set(lst))
x = [1,2,3,4,5,5]
y = [1,2,3,4,5]
has_duplicates(x) # True
has_duplicates(y) # False
The following method can be used to merge two dictionaries.
def merge_two_dicts(a, b):
c = a.copy() # make a copy of a
c.update(b) # modify keys and values of a with the ones from b
return c
a = { 'x': 1, 'y': 2}
b = { 'y': 3, 'z': 4}
print(merge_two_dicts(a, b)) # {'y': 3, 'x': 1, 'z': 4}
In Python 3.5 and above, you can also do it like the following:
def merge_dictionaries(a, b)
return {**a, **b}
a = { 'x': 1, 'y': 2}
b = { 'y': 3, 'z': 4}
print(merge_dictionaries(a, b)) # {'y': 3, 'x': 1, 'z': 4}
The following method can be used to convert two lists into a dictionary.
def to_dictionary(keys, values):
return dict(zip(keys, values))
keys = ["a", "b", "c"]
values = [2, 3, 4]
print(to_dictionary(keys, values)) # {'a': 2, 'c': 4, 'b': 3}
This snippet shows that you can use enumerate to get both the values and the indexes of lists.
list = ["a", "b", "c", "d"]
for index, element in enumerate(list):
print("Value", element, "Index ", index, )
# ('Value', 'a', 'Index ', 0)
# ('Value', 'b', 'Index ', 1)
#('Value', 'c', 'Index ', 2)
# ('Value', 'd', 'Index ', 3)
This snippet can be used to calculate the time it takes to execute a particular code.
import time
start_time = time.time()
a = 1
b = 2
c = a + b
print(c) #3
end_time = time.time()
total_time = end_time - start_time
print("Time: ", total_time)
# ('Time: ', 1.1205673217773438e-05)
You can have an else clause as part of a try/except block, which is executed if no exception is thrown.
try:
2*3
except TypeError:
print("An exception was raised")
else:
print("Thank God, no exceptions were raised.")
#Thank God, no exceptions were raised.
This method returns the most frequent element that appears in a list.
def most_frequent(list):
return max(set(list), key = list.count)
numbers = [1,2,1,2,3,2,1,4,2]
most_frequent(numbers)
This method checks whether a given string is a palindrome.
def palindrome(a):
return a == a[::-1]
palindrome('mom') # True
The following snippet shows how you can write a simple calculator without the need to use if-else conditions.
import operator
action = {
"+": operator.add,
"-": operator.sub,
"/": operator.truediv,
"*": operator.mul,
"**": pow
}
print(action['-'](50, 25)) # 25
This snippet can be used to randomize the order of the elements in a list. Note that shuffle works in place, and returns None.
from random import shuffle
foo = [1, 2, 3, 4]
shuffle(foo)
print(foo) # [1, 4, 3, 2] , foo = [1, 2, 3, 4]
This method flattens a list similarly like [].concat(…arr) in JavaScript.
def spread(arg):
ret = []
for i in arg:
if isinstance(i, list):
ret.extend(i)
else:
ret.append(i)
return ret
spread([1,2,3,[4,5,6],[7],8,9]) # [1,2,3,4,5,6,7,8,9]
A really quick way for swapping two variables without having to use an additional one.
a, b = -1, 14
a, b = b, a
print(a) # 14
print(b) # -1
This snippet shows how you can get a default value in case a key you are looking for is not included in the dictionary.
d = {'a': 1, 'b': 2}
print(d.get('c', 3)) # 3
#python
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Thanks for sharing :)
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i’m impressed, Keep it up
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My favorite way to "Merge two dictionaries" in python 3:
```
(c := a.clone()).update(b)
```
And it will be available at c
!
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
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This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!
#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development