Q1: Differentiate between range & x range?

Ans: In most applications, due to their functionality, x range and range are identical. Whenever you require, they allow you to form a list of integers. However, there is one main difference between them. That is, the range gives a Python list object. On the other hand, the x range object is returned by an x range.

In other words, the x range can’t help you make a static list at run-time. For that, you need to work with the range. An exclusive method known as yielding helps create the values the way you need them. To use this technique, you need some kind of object called generators. For instance, if you want to make a list for a massive range value, you can use the x range function.

This is helpful for extremely memory-sensitive systems, like mobile phones, because it can lead to memory Errors and, ultimately, crashing your program.

Q2: When do you use the help() and dir() function in Python?

Ans: Like several other objects in Python, functions have equal importance. They help in saving the code, enhance clarity, and protect crucial information. Both of these functions can be accessed using a Python interpreter. When you need to observe the integrated dump of built-in functions, Help() and dir() are used.

• Help(): This function helps you exhibit the documentation string. And, it makes it easier for you to view the help that is linked to keywords, modules, attributes, etc.
• Dir(): For the defined symbols, this function is powerful. It focuses on assembling the most applicable information.

Q3: Define *args, **kwargs? Also, explain their usage.

Ans: First of all, you need to know that it is possible to parameter and argument for one thing .i.e, a piece of information passed into a function. A variable listed inside the parentheses in a function is the parameter. A value that is redirected to a function when asked is called an argument. When the number of the arguments that are called are unidentified or we need to send a stored list or tuple of arguments, *args helps us.

When you are uncertain about the number of keyword arguments that need to be sent to the function, **kwargs is used. Moreover, it allows the delivery of the values of a dictionary as keyword arguments.

Q4: What are the two types of indexes? how negative indexes can be used.

Ans: In Python, all the sequences are indexed. Of course, they contain positive and negative numbers. The positive numbers have zero as the first index in the sequence. Then comes ‘1’ as the second index and the list of indexes continues further.

On the flip side, the index for the negative number contains ‘-1’ as the last index. in the sequence and ‘-2’ as the second last index and the sequence goes on like that.

From any string, you can use the negative index to eliminate any new-line spaces. Because of this, the string takes up the last character that is given as S[:-1]. It displays the index to The negative index is also used to show the index to show the string in proper order.

Q5: Why NumPy arrays are better than the (nested) Python lists?

Ans: We all know the role of Python’s lists as essential general-purpose containers. They provide appending, insertion, deletion, and concatenation. It is simple to make and alter with the Python’s list comprehensions.

Nevertheless, they are not applicable to every operation. They can’t be used for “vectorized” operations, such as elementwise addition and multiplication. It creates data that is sometimes not really efficient for mathematical operation.

In terms of efficiency and conveniences, NumPy is great. You can take benefits from many vector and matrix operations without paying. As a result, you get saved from doing work that is not required. The implementation is not a problem.

NumPy array works rapidly. Besides that, it offers FFTs, histograms, convolutions, quick searching, basic statistics, linear algebra, and much more.