Python is an interpretive programming language that can be used on various platforms with a design philosophy that focuses on code readability and is one of the popular languages related to Data Science, Machine Learning, and the Internet of Things (IoT). The advantages of Python which are interpretive are also widely used for prototyping, scripting in infrastructure management, to the creation of large-scale websites.
2. Conditional Statements
3. Looping / Iterator
4. List: Collection | Array | Data Structure
5. Dictionary: Key-Value Data Structure
6. Object & Class Python
We learned a lot of things about Python basics:
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.
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.
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.
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you’re embarking on your journey into data science and everyone recommends that you start with learning how to code. You decided on Python and are now paralyzed by the large piles of learning resources that are at your disposal. Perhaps you are overwhelmed and owing to analysis paralysis, you are procrastinating your first steps in learning how to code in Python.
In this article, I’ll be your guide and take you on a journey of exploring the essential bare minimal knowledge that you need in order to master Python for getting started in data science. I will assume that you have no prior coding experience or that you may come from a non-technical background. However, if you are coming from a technical or computer science background and have knowledge of a prior programming language and would like to transition to Python, you can use this article as a high-level overview to get acquainted with the gist of the Python language. Either way, it is the aim of this article to navigate you through the landscape of the Python language at their intersection with data science, which will help you get started in no time.
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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')
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Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips
Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.
The artcile covers arrays that you create by importing the
array module. We won't cover NumPy arrays here.
Let's get started!
Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.
Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.
Lists are one of the most common data structures in Python, and a core part of the language.
Lists and arrays behave similarly.
Just like arrays, lists are an ordered sequence of elements.
They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.
However, lists and arrays are not the same thing.
Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.
As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.
Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the
array module in order to be used.
Arrays of the
array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.
They are also more compact and take up less memory and space which makes them more size efficient compared to lists.
If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.
In order to create Python arrays, you'll first have to import the
array module which contains all the necassary functions.
There are three ways you can import the
import arrayat the top of the file. This includes the module
array. You would then go on to create an array using
import array #how you would create an array array.array()
array.array()all the time, you could use
import array as arrat the top of the file, instead of
import arrayalone. You would then create an array by typing
arracts as an alias name, with the array constructor then immediately following it.
import array as arr #how you would create an array arr.array()
from array import *, with
*importing all the functionalities available. You would then create an array by writing the
from array import * #how you would create an array array()
Once you've imported the
array module, you can then go on to define a Python array.
The general syntax for creating an array looks like this:
variable_name = array(typecode,[elements])
Let's break it down:
variable_namewould be the name of the array.
typecodespecifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
elementsthat would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing
variable_name = array(typecode)alone, without any elements.
Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:
|TYPECODE||C TYPE||PYTHON TYPE||SIZE|
|'q'||signed long long||int||8|
|'Q'||unsigned long long||int||8|
Tying everything together, here is an example of how you would define an array in Python:
import array as arr numbers = arr.array('i',[10,20,30]) print(numbers) #output #array('i', [10, 20, 30])
Let's break it down:
import array as arr.
import array as arr.
array()constructor, we first included
i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with
Hfor example, would mean that no negative values are allowed.
Keep in mind that if you tried to include values that were not of
i typecode, meaning they were not integer values, you would get an error:
import array as arr numbers = arr.array('i',[10.0,20,30]) print(numbers) #output #Traceback (most recent call last): # File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module> # numbers = arr.array('i',[10.0,20,30]) #TypeError: 'float' object cannot be interpreted as an integer
In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.
Another way to create an array is the following:
from array import * #an array of floating point values numbers = array('d',[10.0,20.0,30.0]) print(numbers) #output #array('d', [10.0, 20.0, 30.0])
The example above imported the
array module via
from array import * and created an array
numbers of float data type. This means that it holds only floating point numbers, which is specified with the
To find out the exact number of elements contained in an array, use the built-in
It will return the integer number that is equal to the total number of elements in the array you specify.
import array as arr numbers = arr.array('i',[10,20,30]) print(len(numbers)) #output # 3
In the example above, the array contained three elements –
10, 20, 30 – so the length of
Each item in an array has a specific address. Individual items are accessed by referencing their index number.
Indexing in Python, and in all programming languages and computing in general, starts at
0. It is important to remember that counting starts at
0 and not at
To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.
The general syntax would look something like this:
Here is how you would access each individual element in an array:
import array as arr numbers = arr.array('i',[10,20,30]) print(numbers) # gets the 1st element print(numbers) # gets the 2nd element print(numbers) # gets the 3rd element #output #10 #20 #30
Remember that the index value of the last element of an array is always one less than the length of the array. Where
n is the length of the array,
n - 1 will be the index value of the last item.
Note that you can also access each individual element using negative indexing.
With negative indexing, the last element would have an index of
-1, the second to last element would have an index of
-2, and so on.
Here is how you would get each item in an array using that method:
import array as arr numbers = arr.array('i',[10,20,30]) print(numbers[-1]) #gets last item print(numbers[-2]) #gets second to last item print(numbers[-3]) #gets first item #output #30 #20 #10
You can find out an element's index number by using the
You pass the value of the element being searched as the argument to the method, and the element's index number is returned.
import array as arr numbers = arr.array('i',[10,20,30]) #search for the index of the value 10 print(numbers.index(10)) #output #0
If there is more than one element with the same value, the index of the first instance of the value will be returned:
import array as arr numbers = arr.array('i',[10,20,30,10,20,30]) #search for the index of the value 10 #will return the index number of the first instance of the value 10 print(numbers.index(10)) #output #0
You've seen how to access each individual element in an array and print it out on its own.
You've also seen how to print the array, using the
print() method. That method gives the following result:
import array as arr numbers = arr.array('i',[10,20,30]) print(numbers) #output #array('i', [10, 20, 30])
What if you want to print each value one by one?
This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.
For this you can use a simple
import array as arr numbers = arr.array('i',[10,20,30]) for number in numbers: print(number) #output #10 #20 #30
You could also use the
range() function, and pass the
len() method as its parameter. This would give the same result as above:
import array as arr values = arr.array('i',[10,20,30]) #prints each individual value in the array for value in range(len(values)): print(values[value]) #output #10 #20 #30
To access a specific range of values inside the array, use the slicing operator, which is a colon
When using the slicing operator and you only include one value, the counting starts from
0 by default. It gets the first item, and goes up to but not including the index number you specify.
import array as arr #original array numbers = arr.array('i',[10,20,30]) #get the values 10 and 20 only print(numbers[:2]) #first to second position #output #array('i', [10, 20])
When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:
import array as arr #original array numbers = arr.array('i',[10,20,30]) #get the values 20 and 30 only print(numbers[1:3]) #second to third position #output #rray('i', [20, 30])
Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.
Let's see some of the most commonly used methods which are used for performing operations on arrays.
You can change the value of a specific element by speficying its position and assigning it a new value:
import array as arr #original array numbers = arr.array('i',[10,20,30]) #change the first element #change it from having a value of 10 to having a value of 40 numbers = 40 print(numbers) #output #array('i', [40, 20, 30])
To add one single value at the end of an array, use the
import array as arr #original array numbers = arr.array('i',[10,20,30]) #add the integer 40 to the end of numbers numbers.append(40) print(numbers) #output #array('i', [10, 20, 30, 40])
Be aware that the new item you add needs to be the same data type as the rest of the items in the array.
Look what happens when I try to add a float to an array of integers:
import array as arr #original array numbers = arr.array('i',[10,20,30]) #add the integer 40 to the end of numbers numbers.append(40.0) print(numbers) #output #Traceback (most recent call last): # File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module> # numbers.append(40.0) #TypeError: 'float' object cannot be interpreted as an integer
But what if you want to add more than one value to the end an array?
extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.
import array as arr #original array numbers = arr.array('i',[10,20,30]) #add the integers 40,50,60 to the end of numbers #The numbers need to be enclosed in square brackets numbers.extend([40,50,60]) print(numbers) #output #array('i', [10, 20, 30, 40, 50, 60])
And what if you don't want to add an item to the end of an array? Use the
insert() method, to add an item at a specific position.
insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.
import array as arr #original array numbers = arr.array('i',[10,20,30]) #add the integer 40 in the first position #remember indexing starts at 0 numbers.insert(0,40) print(numbers) #output #array('i', [40, 10, 20, 30])
To remove an element from an array, use the
remove() method and include the value as an argument to the method.
import array as arr #original array numbers = arr.array('i',[10,20,30]) numbers.remove(10) print(numbers) #output #array('i', [20, 30])
remove(), only the first instance of the value you pass as an argument will be removed.
See what happens when there are more than one identical values:
import array as arr #original array numbers = arr.array('i',[10,20,30,10,20]) numbers.remove(10) print(numbers) #output #array('i', [20, 30, 10, 20])
Only the first occurence of
10 is removed.
You can also use the
pop() method, and specify the position of the element to be removed:
import array as arr #original array numbers = arr.array('i',[10,20,30,10,20]) #remove the first instance of 10 numbers.pop(0) print(numbers) #output #array('i', [20, 30, 10, 20])
And there you have it - you now know the basics of how to create arrays in Python using the
array module. Hopefully you found this guide helpful.
You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.
Thanks for reading and happy coding!
Original article source at https://www.freecodecamp.org