Element wise operation on LIST vs ARRAY

The use of arrays over lists:

1. You can write vectorised code on numpy arrays, not on lists, which is convenient to read and write, and concise.
2. Numpy is much faster than the standard python ways to do computations.

Vectorised code typically does not contain explicit looping and indexing etc. 

(all of this happens behind the scenes, in precompiled C-code), and thus it is much more concise.

Example:2

Say you have two lists of numbers, and want to calculate the element-wise product.

The standard python list way would need you to map a lambda function (or worse - write a for loop), 

whereas with NumPy, you simply multiply the arrays.

Example:2

Tuples are immutable, But still we can change items

 Tuple is a immutable, but still we can change or append the elements to the list present in the tuples.

Example: 

Regular Expressions | Grouping

Regex: Grouping::

Suppose we have textual data with dates in it and we want to extract only the year from the dates. This can be done using regular expression pattern with grouping to match dates and then we can extract the component elements such as the day, month or the year from the date.

Example:

url = "http://www.telegraph.co.uk/formula-1/2017/10/28/mexican-grand-prix-2017-time-does-start-tv-channel-odds-lewisl/2017/05/12"

date_regex = '/(\d{4})/(\d{1,2})/(\d{1,2})/'

print(re.findall(date_regex, url))

Time | Memory Taken to execute a piece of code in Python

 Time Taken to run the code:

Generally, it is very hard to find which part of code is taking more time when you run the whole application. The below code snippet tells us time taken to run the function.

Memory used by Objects:

In Python we use the sys.getsizeof function to check the memory used by an object.

Wildcard | Character Sets | Meta Sequences

 

Wildcard: It matches any characters from characters to numbers and alphanumeric variables.

Character Sets:

For example, say we want to match phone numbers in a large document. we know that the numbers may contain hyphens, plus symbol etc. (e.g. +91-9925417854) , but it will not have any alphabet. we need to specify that we need only for numerics and some other symbols, but avoid alphabets.

 To handle such situations, we can use character sets in regular expressions

Meta Sequences:

We commonly use sets to match only digits, only alphabets, only alphanumeric characters, only whitespaces, etc. Therefore, there is a shorthand way to write commonly used character sets in regular expressions. These are called as meta-sequences

Greedy vs Non Greedy search

 When we  use a regular expression to match a string, the regex greedily tries to look for the longest pattern possible in the string.
 Examaple:
when you specify the pattern 'ab{2,7}' to match the string 'abbbbbbb', it will look for the maximum number of occurrences of 'b' (in this case 7).This is called a 'greedy approach'. By default, a regular expression is greedy in nature.
There is another approach called the non-greedy approach, also called the lazy approach, where the regex stops looking for the pattern once a particular condition is satisfied.

Code Example: 

 What is the use of __Str()__ method in Python class?

when declaring a python class, implementing a __str()__ is good practice , because it is used to read the object instance of the class in a human readable format. Also we can say when the object has to be represented as a string, the Python uses the special method called __str__.

Ex:

Here we declare a Person class and then initiate an object of Person class using __init__ method. Now if we print its object, we will get a output of object memory location.

Now if we implement an __str__() method in the Person class which help us to get a human-readable string of Person object.