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Python’s extensive library of modules and packages is one of its biggest strengths. These libraries provide pre-written code that can be easily integrated into a project, saving developers time and effort. Python has a vast collection of libraries that are constantly growing and evolving to meet the needs of developers.

Some popular Python libraries include NumPy, which provides support for numerical computations and scientific computing, Pandas, which is used for data analysis and manipulation, Matplotlib for data visualization, and Scikit-Learn for machine learning tasks.

Python also has libraries for web development, such as Django and Flask, which make it easy to build web applications. Other libraries like BeautifulSoup and Scrapy are used for web scraping and data extraction.

Python’s libraries also make it easy to work with databases, such as SQLite, MySQL, and PostgreSQL. Libraries like Pygame and PyOpenGL are used for game development and computer graphics.

Python is a high-level language, which means that it is easy to read and write, with a focus on abstracting away low-level details and providing a higher level of abstraction. However, it is also a powerful language that can be used for complex projects.

One of the main reasons why Python is so flexible is its extensive library of modules and packages. These libraries provide pre-written code that can be easily integrated into a project, saving developers time and effort. Additionally, Python can be used for web development, data analysis, machine learning, and scientific computing, among other applications.

Python’s flexibility also comes from its ability to work with other languages. It can be easily integrated with languages like C++ and Java, allowing developers to use Python for specific tasks alongside other languages for other parts of a project.

Python has a vast and active community of developers, and is known for being friendly, welcoming, and supportive. You can find support in social media groups and online forums, regardless of the language that you speak.

The Python community is also very active in contributing to open-source projects. There are many open-source libraries and frameworks available in Python that are maintained by the community.

When you learn a new language, it’s vital to have a community where you can ask questions to more experienced professionals and get their feedback.

Python is a programming language that is widely known for being beginner friendly. One of the main reasons why Python is considered easy to learn is its simple syntax. Python code is easy to read and understand, which makes it easier for beginners to write and debug code.

Another reason why this language is beginner-friendly is its versatility. Python can be used for a wide range of applications, from Python web development to data analysis and machine learning. This means that beginners can choose a domain that interests them and start learning Python in a context that is relevant to their interests.

Python also has many resources, such as online tutorials, video courses, and interactive coding platforms. These resources provide step-by-step guidance and help you build your skills in a structured way.

• Unit test is a unit testing framework of Python.
• Unit testing means testing different components of software separately. Can you think about why unit testing is important? Imagine a scenario, you are building software that uses three components namely A, B, and C. Now, suppose your software breaks at a point time. How will you find which component was responsible for breaking the software? Maybe it was component A that failed, which in turn failed component B, and this actually failed the software. There can be many such combinations.
• This is why it is necessary to test each and every component properly so that we know which component might be highly responsible for the failure of the software.

• As the name suggests, ‘slicing’ is taking parts of.
• Syntax for slicing is [start : stop : step]
• start is the starting index from where to slice a list or tuple
• stop is the ending index or where to sop.
• step is the number of steps to jump.
• Default value for start is 0, stop is number of items, step is 1.
• Slicing can be done on strings, arrays, lists, and tuples.

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(numbers[1 : : 2])  #output : [2, 4, 6, 8, 10]

Python packages and Python modules are two mechanisms that allow for modular programming in Python. Modularizing has several advantages –

• Simplicity: Working on a single module helps you focus on a relatively small portion of the problem at hand. This makes development easier and less error-prone.
• Maintainability: Modules are designed to enforce logical boundaries between different problem domains. If they are written in a manner that reduces interdependency, it is less likely that modifications in a module might impact other parts of the program.
• Reusability: Functions defined in a module can be easily reused by other parts of the application.
• Scoping: Modules typically define a separate namespace, which helps avoid confusion between identifiers from other parts of the program.

Modules, in general, are simply Python files with a .py extension and can have a set of functions, classes, or variables defined and implemented. They can be imported and initialized once using the import statement. If partial functionality is needed, import the requisite classes or functions using from foo import bar.

Packages allow for hierarchial structuring of the module namespace using dot notation. As, modules help avoid clashes between global variable names, in a similar manner, packages help avoid clashes between module names.
Creating a package is easy since it makes use of the system’s inherent file structure. So just stuff the modules into a folder and there you have it, the folder name as the package name. Importing a module or its contents from this package requires the package name as prefix to the module name joined by a dot.

Self is used to represent the instance of the class. With this keyword, you can access the attributes and methods of the class in python. It binds the attributes with the given arguments. self is used in different places and often thought to be a keyword. But unlike in C++, self is not a keyword in Python.

• Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
• Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.

import array
a = array.array('i', [1, 2, 3])
for i in a:
print(i, end=' ')    #OUTPUT: 1 2 3
a = array.array('i', [1, 2, 'string'])    #OUTPUT: TypeError: an integer is required (got type str)
a = [1, 2, 'string']
for i in a:
   print(i, end=' ')    #OUTPUT: 1 2 string

To floor a number in Python, you can use the math.floor() function, which returns the largest integer less than or equal to the given number.

• floor()method in Python returns the floor of x i.e., the largest integer not greater than x. 
• Also, The method ceil(x) in Pythonreturns a ceiling value of x i.e., the smallest integer greater than or equal to x.

import math

​

n = 3.7

F_num = math.floor(n)

​

print(F_num)

Output

3