Sofia Kelly

Sofia Kelly

1627031673

Mastering String Methods in Python

In solving for real world Data Science challenges, there is no escaping dealing with strings. Many features in datasets have values in the form of texts content or strings as they are referred to. It becomes very important to be comfortable dealing with strings — creating, manipulating, modifying — even if you are not dealing with stuff like Natural Language Processing.

In this post, the objective is to take you through the basics of string objects in python and what you can do with it using the plethora of functions available so that you can start dealing with strings like a pro !.

So lets get started !

This post is divided broadly into the following topics:

  1. Creating a String object
  2. Analyzing a String
  3. String Operations
  4. Formatting String Outputs

#python #data-science #programming #developer

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Mastering String Methods in Python
Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

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:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

String methods in Python

String methods:

  • str.capitalize(): Returns copy of the string with its first character capitalized and rest of the letters in lowercase.
#capitalize-Only first character of string is capitalized
	s1="example of string methods"
	print (s1.capitalize()) #Output:Example of string methods
	s2="EXAMPLE OF STRING METHODS"
	print (s2.capitalize())#Output:Example of string methods
  • str.title()- Returns copy of string where first character in every word is upper case.
s1="example of strings"
	print (s1.title()) #Output:Example Of Strings
  • str.casefold(): Returns casefolded copy of the string. Converts string to lower case. Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string.
#casefold- converts all character to lower case
	s1="Example Of String Methods"
	print (s1.casefold()) #Output:example of string methods

	s2="ß-Beta"
	#ß-lowercase is equivalent to ss. casefold converts it to ss. But lower doesn't do that.
	print (s2.casefold()) #Output: ss-beta
	print (s2.lower())#Output: ß-beta
  • str.swapcase():Returns copy of string with uppercase characters converted to lowercase and vice versa.
s1="example of strings"
	print (s1.swapcase()) #Output:EXAMPLE OF STRINGS

	s2="EXAMPLE OF STRINGS"
	print (s2.swapcase()) #Output:example of strings

	s3="Example Of Strings"
	print (s3.swapcase()) #Output:eXAMPLE oF sTRINGS
  • str.lower()-Returns copy of string in lowercase.Symbols and numbers are ignored.
s1="Example Of Strings"
	print (s1.lower()) #Output:example of strings

	s2="EXAMPLE OF STRINGS??"
	print (s2.lower())#Output:example of strings??

	s3="1.example of strings?"
	print (s3.lower()) #Output:1.example of strings?
  • str.upper()-Returns copy of string in uppercase.Symbols and numbers are ignored.
s1="Example Of Strings"
	print (s1.upper())#Output:EXAMPLE OF STRINGS

	s2="EXAMPLE OF STRINGS??"
	print (s2.upper())#Output:EXAMPLE OF STRINGS??

	s3="1.example of strings?"
	print (s3.upper()) #Output:1.EXAMPLE OF STRINGS?
  • str.encode():Returns an encoded version of the string in byte format.
str.encode(encoding=”encoding”,errors=”errors”)

encoding(Optional):Default encoding is “utf-8”

errors(Optional):Default errors is “strict”.Raise unicode error.

s1= "example öf strings"
	print (s1) #Output:example öf strings

	#Use backslash for the character that can't be encoded
	print(s1.encode(encoding="ascii",errors="backslashreplace")) #Output:b'example \\xf6f strings'

	#ignores the character that can't be encoded
	print(s1.encode(encoding="ascii",errors="ignore"))#Output:b'example f strings'

	#replace the character that can't be encoded with the text explanining the character. 
	print(s1.encode(encoding="ascii",errors="namereplace"))#Output:b'example \\N{LATIN SMALL LETTER O WITH DIAERESIS}f strings'

	#Replace the character that can't be encoded with the question mark
	print(s1.encode(encoding="ascii",errors="replace"))#Output:b'example ?f strings'

	#Replace the character that can't be encoded with xml character.
	print(s1.encode(encoding="ascii",errors="xmlcharrefreplace"))#Output:b'example öf strings'

	#strict-Raise Unicode Error
	print(s1.encode(encoding="ascii",errors="strict"))
	#Output:UnicodeEncodeError: 'ascii' codec can't encode character '\xf6' in position 8: ordinal not in range(128)

	#errors are not mentioned.Default is strict-Raise Unicode Error.
	print(s1.encode(encoding="ascii"))
	#Output:UnicodeEncodeError: 'ascii' codec can't encode character '\xf6' in position 8: ordinal not in range(128)
  • str.startswith()- Returns True, if the string starts with specified value, otherwise returns False.

#python3 #python #string-methods #python-strings #python-programming

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python

August  Larson

August Larson

1624335780

How to Master Python for Data Science

Here’s the Essential Python you Need for Data Science

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.

#python #data-science #programming #how to master python for data science #master python #master python for data science