Modular programming refers to the process of breaking a large, unwieldy programming task into separate, smaller, more manageable subtasks or modules. Individual modules can then be cobbled together like building blocks to create a larger application.
In this article I will show you the rationale behind a Python module, the differences with a package and how the two things interact together.
A module is a Python file which contains code, saved with the .py extension. Every time you write a function, a class or a statement and save it to a .py file you are actually creating a new module. A module can be executed by the Python interpreter directly, e.g. python script.py and thus called main module, or imported by other modules. Modules are a way to lay out your program in different files for easier maintenance.
When you have many modules in your project it’s good practice to organize them into folders. For example, say I’m working on a very primitive game in Python called Fancy Game: I would like to structure the directory as follows:
fancy_game/ models/ player.py monster.py audio/ mixer.py effects.py player.py graphics/ renderer.py screen.py common/ constants.py main.py
A package is simply a collection of Python modules organized into folders. In my Fancy Game the packages could be: models, audio, graphics and common. The fancy_game folder is not a package itself, because it is intended to be run directly by Python (i.e. python main.py). Sometimes you want to create a library instead to be imported in other Python programs, so the entire root folder would become a package too (made of many sub-packages).
Having a project or a library organized into packages is a good thing: a) your source code is even more modularized and b) packages provide protection against name clashes with other modules. We’ll see why in a minute.
You may also like: How to create the Structure of a Python Project
Python has to be instructed about which directory should become a package. To do this, simply add an empty file called
__init__.py inside each desired folder. This is a special file used to mark directories on disk as Python package directories. So, my Fancy Game folder structure would be:
fancy_game/ models/ __init__.py <--- new __init__.py file added player.py monster.py audio/ __init__.py <--- new __init__.py file added mixer.py effects.py player.py graphics/ __init__.py <--- new __init__.py file added renderer.py screen.py common/ __init__.py <--- new __init__.py file added constants.py main.py
Notice how there is no init.py in the root folder: this is because my game (i.e. main.py) is intended to be run directly from the Python interpreter. In case of a library, simply add the special file into the root directory as well.
Now that the whole structure has been set up, the code inside main.py needs to import some modules from the various packages in order to make the game work. To import a module from a package you have to follow the dotted module name syntax. For example, in the main module I want to import the player module from the audio package:
# main.py import audio.player
More generally, the rule is
import [package].[module]. This also works in case you have nested packages:
import [package1].[package2].[module] and so on.
Once imported, the module must be referenced with its full name. So if I want to use the function
play_sound() from within the
audio.player module I have to call it as
audio.player.play_sound(). As mentioned above, this is a good way to avoid name clashes across different modules: I can easily import the module
model.player without messing up with its homonym
# main.py import audio.player import model.player # Two modules with the same name: no problem audio.player.play_sound() model.player.run()
Sometimes a deep-buried module needs to import stuff from the upper level. For example, the
audio.player module might need something inside
common.constants. There are two ways of doing it:
absolute import — import the module as if the importer is located in the root directory. Python is able to figure out the right path. For example, in
audio.player just do import
common.constants. This is my favorite option;
relative import — use the formula
from [module] import [name] with dots to indicate the current and parent packages involved in the import. For example, in
audio.player you can call
from .. import common.constants. One dot means the current package, two dots is up one level, three dots is up two levels and so on. I’m not a huge fan of this one, as relative imports break easily when you move modules around.
Using long names such as
models.monster.Skeleton is quite inconvenient. You can shorten a module name while importing it with an alias, for example:
# main.py import models.monster as monster
models.monster is available as
monster. Just keep in mind that this way might lead to name clashes across modules.
Python 3 Tutorial for Beginners - Modules & Packages
Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
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Python is one of the most popular programming languages currently. It looks like this trend is about to continue in 2021 and beyond. So, if you are a Python beginner, the best thing you can do is work on some real-time Python project ideas.
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Python project ideas which beginners can work on to put their Python knowledge to test. In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python
Moreover, project-based learning helps improve student knowledge. That’s why all of the upGrad courses cover case studies and assignments based on real-life problems. This technique is ideally for, but not limited to, beginners in programming skills.
But first, let’s address the more pertinent question that must be lurking in your mind:
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Module: It is a simple Python file that contains collections of functions and global variables and has a “.py” extension file. It’s an executable file and we have something called a “Package” in Python to organize all these modules.
Package: It is a simple directory which has collections of modules, i.e., a package is a directory of Python modules containing an additional init.py file. It is the init.py which maintains the distinction between a package and a directory that contains a bunch of Python scripts. A Package simply is a namespace. A package can also contain sub-packages.
When we import a module or a package, Python creates a corresponding object which is always of type module . This means that the dissimilarity is just at the file system level between module and package.
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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:
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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.
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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