Trending November 2023 # Top 3 Examples To Implement Of Python Curl # Suggested December 2023 # Top 18 Popular

You are reading the article Top 3 Examples To Implement Of Python Curl updated in November 2023 on the website Cancandonuts.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Top 3 Examples To Implement Of Python Curl

Introduction to Python Curl

A curl is a request tool that is used to create network requests so that it allows it to transfer the data across the network, which is done using the command line. In this article, we are going to discussing curl in Python. In python, a curl is a tool for transferring data requests to and from a server using PycURL. This tool is used for testing REST APIs, downloading files, etc. this PycURL is an interface to the libcURL library in Python, and hence the PycURL is capable of inheriting all the capabilities of libcURL.

Working of Python Curl

In this article, Python curl is used for REST API for transferring the data to and from a server. In this, we will see PycURL is a python interface used to fetch the objects from a python program identified by a URL.

Curl is a UNIX command which can send GET, POST, PUT and DELETE request to a URL. There is an HTTP library called “Requests” in Python, but this library needs to be pulled as it’s not any standard module. When this library is used, we can create a simple request, and this request returns a response object which allows access to the various status codes or headers, etc. Let us see an example below with output for each line:

Examples to Implement Python Curl

Below are the examples of Python Curl:

Example #1

Code:

import requests print url

Output:

headers={'x-api-key':'09ba90f6-dcd0-42c0-8c13-5baa6f2377d0'} print headers

Output:

resp = requests.get(url,headers=headers) print resp.status_code

Here you will get the output code as status code as 200.

print resp.content print resp

The above will print the content.

From the above code snippets, we need to first import the request library, and then we create a URL, and we will print the URL, and headers will also be defined and printed. Then we saw that request.get() method is called by passing the URL and headers obtained above to this method. This method returns a response object (resp). In the above code snippets, we can see that we will be printing the content of the request using this response object.get() method which will allow us to access and print the status_code and entire content is printed, and we can also see the list of attributes of this response object that are available. Similarly, we also can use different request methods like requests.put(), request.post(), request.delete(), etc.

We can see the syntax of each of these request methods, and we can see below:

Call.request.get(URL) this is used to send a GET request to the URL.

Call.request.post(URL, data= dict) in this dict contains a dictionary of keys and also has values to send to a POST request.

Call.request.put(URL, data =dict) this also works similarly to POST request; this will also send URL and values to a PUT request.

Call.requset.delete(URL, data =dict); this also has the same parameters as the above two request methods, and this request also sends the URL and values to the DELETE request method.

In Python, we use PycURL as a CURL tool and are used for testing REST APIs. As this PycURL supports a different variety of protocols like FILE, FTPS, HTTPS, IMAP, SMB, SCP, etc. The installation of PycURL is very simple for any of the operating systems. So below is the sampling process for installing the PycURL.

$ pip install pycurl $ easy_install pycurl

The above two can be used for installing pycurl in mac or Linux OS. Now we will see how can this be installed in Windows OS, but before this, we need to install a few dependencies. So you can run the below command in the Python terminal as below:

Command:

$ pip install pycurl

If pip is not used, we can use EXE and MSI installers available at PycURL windows.

Example #2

Let us below the sample example for sending an HTTP GET request.

Code:

import pycurl from io import BytesIO b_obj = BytesIO() crl = pycurl.Curl() crl.setopt(crl.WRITEDATA, b_obj) crl.perform() crl.close() get_body = b_obj.getvalue() print('Output of GET request:n%s' % get_body.decode('utf8'))

Output:

Similarly, there are different ways and codes in Python using PycURL for using POST, PUT, DELETE, etc., methods. Let us what code sample can be written for sending an HTTP DELETE request. This method where it deletes the server resource that is identified by the URL. This can be implemented using CUSTOMREQUEST.

Example #3

Below is a sample example:

Code:

import pycurl crl = pycurl.Curl() crl.setopt(crl.CUSTOMREQUEST, "DELETE") crl.perform() crl.close()

The above code snippet is to send the HTTP DELETE request. So we can see how to use HTTP DELETE request for sending this request using a curl tool in Python like PycURL.

Conclusion

In this article, we discussed the curl, which is a tool for transferring data from and to the server. In Python, we have the PycURL library, which uses libcurl, a standard library, and PycURL uses its values. We also saw the various methods that are called syntax. In this article, we also saw the usage of PycURL, which we first saw how to import it and how to use this and use various curl methods such as perform(), close(), etc.

Top Examples To Implement Of Datareader C#

Introduction to DataReader C#

A Data reader is an object that is used to read data from the data sources. This can only perform read operation and not update operation on the data source. The data is retrieved as a data stream from the data source. Though the data reader is restricted in terms of only reading operation, it is highly effective and optimized as it is read only and forward only. There are two types of providers in the .Net Framework, they are SQLDataReader and OleDbDataReader. The data reader increases the application performance by reducing system overhead as it stores the only row in memory at a given point of time. This article will cover in detail the data reader in c# along with appropriate examples.

Start Your Free Software Development Course

Syntax:

The sql data reader is available in the namespace System.Data.SqlClient and the corresponding assembly is chúng tôi A SQL data reader is initialized as follows.

SqlDataReadersqlReader = sqlCmd.ExecuteReader();

The execute reader is used to pass the SQL statements or procedure to the sqlconnection object and the corresponding result is stored in the sqlreader object of SqlDataReader. Before reading from any data reader, it should always be open and should point to the first record. The read() method of data reader is used to read and it moves forward to the next row.

The oledb data reader also behaves in the same way. It is available in the name space System.Data.OleDb and the corresponding assembly is chúng tôi An oledbDataReader object is used to connect to OLEDB data sources and fetch data from them. Like SQLDataReader, oledbdata reader should also be open before performing read operation. An oledb data reader is initialized as follows.

OleDbDataReaderoledbReader = oledbCmd.ExecuteReader();

Where the executereader is used to carry out the SQL statements or procedures.

Accessing Data Reader Results Working with Multiple Sets

If data reader returns multiple result sets, the NextResult method of data reader can be used to access them. During implementation, it should be noted and taken care that all the result sets are being iterated and each column inside the result set is accessible.

Examples to Implement of DataReader C#

Below are the examples of DataReader C#:

Example #1

Typically, data is read from the result set returned by the data reader is to iterate each row using a while loop. The read method return value is of bool type, if the next row is present then true is returned and for the last record, false is returned. The while loop will be executed until the condition becomes false.

Syntax:

while(rdr.Read()) { }

Like closing a SQL connection, it is always a best practice to close the Data reader. All the while part can be in enclosed in a try block and the data reader connection can be closed in the finally block.

Syntax:

try { } Catch { } finally { if (reader != null) { reader.Close(); } }

Code:

using System; using System.Windows.Forms; using System.Data.SqlClient; namespace test { public partial class test1 : Form { public test1() { InitializeComponent(); } Public static void main() { string constr = null; SqlConnectionscon ; SqlCommandscmd ; string sstat = null; constr = "Data Source=testserver;Initial Catalog=testdb;User ID=test;Password=test"; sstat = "Select * from test"; scon = new SqlConnection(constr); try { scon.Open(); scmd = new SqlCommand(sstat, scon); SqlDataReadersstatReader = scmd.ExecuteReader(); while (sstatReader.Read()) { Console.WriteLine("Name:" sstatReader.GetValue(0) + "age:" sstatReader.GetValue(1) ); } sstatReader.Close(); scmd.Dispose(); scon.Close(); } catch (Exception ex) { } } } }

Example #2

Code:

using System; using System.Windows.Forms; using System.Data.OleDb; namespace test { public partial class test : Form { public test() { InitializeComponent(); } Public static void main() { string constr = null; OleDbConnectionocon ; OleDbCommandocmd ; string sql = null; constr = "Provider=Microsoft.Jet.OLEDB.4.0;Data Source=test.mdb"; sql = "select * from emp"; ocon = new OleDbConnection(constr); try { ocon.Open(); ocmd = new OleDbCommand(sql, ocon); OleDbDataReaderordr = ocmd.ExecuteReader(); while (ordr.Read ()) { Console.WriteLine("EmpName:" ordr.GetValue(0) + "Empage:" ordr.GetValue(1) + "Esalary" ordr.GetValue(2) ); } ordr.Close(); ocmd.Dispose(); ocon.Close(); } catch (Exception ex) { Console.WriteLine("Connection Failed"); } } } }

Output:

Conclusion Recommended Article

This is a guide to DataReader C#. Here we discuss the Introduction to DataReader in C# and its examples along with Code Implementation and Output. You can also go through our other suggested articles to learn more –

Top Examples Of Scala Collections

Introduction to Scala Collections

Collections in Scala are nothing but a container where the list of data items can be placed and processed together within the memory. It consists of both mutable (scala.collection.mutable) and immutable (scala.collection.immutable) collections which in-turn varies functionally with Val and Var and also, Scala collections provide a wide range of flexible built-in methods, which can be used to perform various operations like transformations & actions, directly on the data items.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

Scala Collections Framework

At a very high-level collection contains Seq, Set & Map. All of them are the child of Traversable traits. Do keep it in mind, All of the Childs of Traversable (Parent) are Traits and not classes which makes it easier to implement in the code snippet, without having the need to create the object before calling it.

Let us discuss it in detail as follows:

Reference – Book: A Beginner’s Guide to Scala, Object Orientation and Functional Programming

Traversable: helps us to traverse through the entire collection and it implements the behavior that are common to all the collections irrespective of the data types. It simply means Traversable lets us traverse the collections repeatedly in terms of for each method.

Iterable: gives us an iterator, which lets you loop through a collection’s elements one at a time.

Note: By using an iterator, the collection can be traversed only once, because each element is consumed during the iteration process.

Mutable & Immutable Collections

Scala Provides 2 varieties of collections. They are:

Mutable

Immutable

Mutable Collections

Example:

AnyRefMap

ArrayBuffer

ArrayBuilder

ArraySeq

ArrayStack

BitSet

DoubleLinkedList

HashMap

HashSet

LinkedHashMap

LinkedHashSet

LinkedList

Stack

StringBuilder

TreeSet

WeekHashMap, etc.

Immutable Collections

This type of collection can never be changed. We can still see the methods that look like adding/ updating/ removing elements from the collection. But it actually creates a new collection internally and leaves the old one unchanged.

Example:

BitSet

HashMap

HashSet

List

ListMap

ListSet

LongMap

NumericRange

Stack

Stream

StreamIterator

TreeMap

TreeSet

Vector

IndexedSeq, etc..

String & Lazy Collection

Whenever we perform any data transformations using a filter, map, min, max, reduce, fold, etc.., on collections, it basically transforms it into another collection. There could be some collections that allow Strict Transformations means, the memory of the elements are allocated immediately when the elements are evaluated and results in the new collection. In a Lazy Collection, the transformations will not create another collection upfront. It means, the memory will not be allocated immediately and it creates the new collection when there is a demand. Collection classes can be converted into Lazy Collection by creating a view on the collection.

Let us look into the Scala REPL example for a better understanding.

Example:

Code:

object Demo { def main(args: Array[String]) { val ls = (0 to 5).toList println("List is: " + ls); } }

Output:

While creating the collection – List, the memory is allocated immediately and when we try to call the list, we are able to find the list of elements as below.

ls res0: List[Int] = List(0, 1, 2, 3, 4, 5) //collection items of ls is allocated to res0. Hence, it created the memory immediately.

To Create a Lazy Collection:

object Demo { def main(args: Array[String]) { val ls = (0 to 5).toList val lsLazy = ls.view println("View: " + lsLazy); } }

lsLazy: scala.collection.SeqView[Int] = View(?)

Note: Here when we try to create a view on the existing list, we can clearly say it doesn’t allocate the memory as it only builds a view of the list until unless any action is being called like foreach, min, max, etc.

Code:

object Demo { def main(args: Array[String]) { val ls = (0 to 5).toList val lsLazy = ls.view println("max "+lsLazy.max); } }

object Demo { def main(args: Array[String]) { val ls = (0 to 5).toList val lsLazy = ls.view lsLazy.foreach(println) } }

Let us investigate some of the basic methods that come with the Collections:

Mutable Collections: ArrayBuffer

Example:

object Demo { def main(args: Array[String]) { val arrBuff = (0 to 5).toBuffer println(" "+arrBuff); } }

Output:

Now, let’s try adding an element to the existing ArrayBuffer Collection.

object Demo { def main(args: Array[String]) { val arrBuff = (0 to 5).toBuffer arrBuff += 5 println(" "+arrBuff); } }

Look at the below result, if you look closer, you could find that data item “5” has been appended to the existing collection.

Note: += is a method that is used to append the element to the original collection. i.e., adds the new element to the collection and reassigns the result to the original collection.

Example:

Code:

object Demo { def main(args: Array[String]) { val arrBuff1 = (0 to 5).toBuffer println(" "+arrBuff1); } }

Output:

Code:

object Demo { def main(args: Array[String]) { val arrBuff1 = (0 to 5).toBuffer arrBuff1 +=66 println(" "+arrBuff1); } }

Output:

res24: scala.collection.mutable.Buffer[Int] = ArrayBuffer(0, 1, 2, 3, 4, 5, 66) // added in the copy, not the original

res25: scala.collection.mutable.Buffer[Int] = ArrayBuffer(0, 1, 2, 3, 4, 5) // check the original collection and note that the collection items remain unchanged.

Immutable Collection: List

Code:

object Demo { def main(args: Array[String]) { val ls = (1 to 5).toList println(" "+ls); } }

Note: Mutable collections doesn’t have += method to append and reassign.

object Demo { def main(args: Array[String]) { val ls = (1 to 5).toList ls :+5 println(" "+ls); } }

Note: Use ” :+= ” as the reassignment operator, while dealing with immutable collections to update the existing immutable collection. This method can be applied only on “var” and not on “val”.

Example:

Code:

object Demo { def main(args: Array[String]) { val ls = (1 to 5).toList } }

Output:

Note: map will always return the output of the same incoming data type. In the above screenshot, the input was List[Int], hence the output is also same. It output stored in “res13”.

Example:

Code:

object Demo { def main(args: Array[String]) { val ls = (1 to 5).toList ls.foreach(println) }

Output:

Note: If you look carefully in the screenshot, you can notice that the output of foreach hasn’t stored on any result variables here. This is why foreach varies with map.

Conclusion

As we have seen so far, collections are way useful to store and retrieve formatted items of the respective data types. Also, it comes with various methods to add/ change or delete items from the collection. It is even adaptable to use in most critical scenarios, as it provides mutable and immutable collections.

3 Options For Ecommerce Sites To Implement Triggered Email Messaging

A review comparing different technical solutions for Ecommerce integration with behavioural email marketing

Triggered messaging is a rapidly growing part of marketing with extremely good ROI. It’s about delivering business messages that are personalized and near-real time, resulting in very good engagement. All email marketers should be doing this if they’re not already!

My previous post in this series explained when triggered messages were effective, for example for cart abandonment and onboarding sequences. This follow-up is a brief summary of the three main implementation strategies for triggered emails, using:

1. Just your E-commerce system.

2. Just your ESP.

3. A real-time marketing system (RTMS).

Using just your Ecommerce system

Many Ecommerce systems can send a few types of triggered messages, for example ‘purchase complete’ and ‘cart abandonment, either natively or using third-party plug-ins. (There are lots of these, for example Abandon Carts Alert Pro for Magento and EDD Abandon Cart for WordPress/EDD).

This is the most basic of the three alternatives. You typically:

Install a plug-in, if required.

Create new email templates in the integrated email sending system.

Configure your remarketing parameters – for example how long to wait before sending a ‘purchase complete’ or welcome email.

The upsides are:

Simple. If you want something straightforward, you can get it working very quickly.

Cheap. It may even be free.

There are three main downsides:

Ecommerce systems were designed to handle purchasing – not cart or browse abandonment . This means they collect less data, for example they usually don’t record products browsed. They also identify less shoppers and only recognize people when they logon so you may only email 25% of the people who ought to be emailed. Hence you’ll get much worse returns.

Ecommerce systems normally use dedicated email sending systems, silo’d from your main ESP, making it difficult to keep your email designs and mailing lists consistent.

Lack of personalization/branding, so emails are less effective, because the ESPs lack functionality and also can’t mail-merge data from your main ESP.

Using ESP and a Web Analytics System

Your ESP is able to send triggered emails, once you provide real-time data, and a Web Analytics system supplements the activity data from your Ecommerce system.

This is a traditional approach to sending triggered messages and keeps everything in one place.

You:

Install a one-line script in your Ecommerce system and configure the Web Analytics system.

Create the email templates in your ESP, based on your existing content.

Configure the remarketing campaigns – for example how long to wait before sending a ‘purchase complete’ or welcome email. Some ESPs provide the capabilities for this, if not then you’ll need someone to write a custom scheduler.

The upsides are:

All your content is in one place, so managing your email templates is easy.

All your data is in one place, so managing your lists is easy.

There are several downsides:

One more system to use.

If your ESP provides a sophisticated plug-in to integrate with your Ecommerce system, then setup can be straightforward but you’ll be locked in to that exact combination. If not, as is usually the case, set up requires expensive custom programming.

Triggered emails need complex personalisation and ESPs vary greatly in what’s possible. For example you may have difficulties merging data from a shopping cart containing a variable number of products, each with several fields.

Risk of sending after purchase, because of stale data. Big-name ESP-based solutions may run hourly or even daily, leading to support calls when slow buyers are incorrectly sent cart abandonment emails

Using a Real-time Marketing System (RTMS)

There are several Real-Time Marketing/Triggered messaging systems available. These connect with your ESP and Ecommerce system, sending any type of triggered email.

Setup is straightforward. You:

1. Install a one-line script in your Ecommerce system and let the staff at the Real-Time Marketing/Triggered messaging company configure it.

2. Create the email templates in your ESP, based on your existing content.

3. Configure the remarketing campaigns – for example how long to wait before sending a ‘purchase complete’ or welcome email.

The upsides include:

These systems are highly optimized to identify and collect maximum data from the maximum number of visitors. Strategies include not waiting for logon, tight integration with the ESP, integration with third-party subscribeware such as PadiAct, and combining sessions across multiple devices. They have been shown to identify 4x as many visitors as Ecommerce plug-ins, which results in much greater ROI. Read our Triggered Messaging report on real-time email marketing.

Setup is cheap, because there is no custom programming or connectors to write. And unlike systems based on specific plug-ins or custom connectors, if you switch to another Ecommerce system your real-time marketing will still work.

Should use your own ESP, so managing your email templates and lists are easy.

There are two main downsides:

One more system to use.

Not free.

Summary on ESP

Which method you choose depends on various factors, including the size of your website, your ESP and what you’re trying to achieve with your marketing.

In general, very small online stores should look towards the built-in capabilities of their Ecommerce platform. If your website is a bit larger, you should be looking to get more marketing capabilities in order to maximize your revenue. To do this, you’ll either need to build some custom integration with an ESP with strong functionality or look towards a specialist RTMS.

You may also need a combination of methods – for example, order confirmations, password resets and sales receipts may be sent out by default by your Ecommerce system. They’ll typically be basic emails with little in the way of branding or marketing capabilities.

It may be possible to route them through your ESP so as to make the branding match and to start including other offers. Then you may be able to include upsell/recommendations using an RTMS which dynamically formats appropriate upsell offers based on the products ordered.

Finally, here’s a quick reference to the pros and cons of the different ways of implementing triggered messages for your website.

Polymorphism In Python With Examples

What is Polymorphism?

Polymorphism can be defined as a condition that occurs in many different forms. It is a concept in Python programming wherein an object defined in Python can be used in different ways. It allows the programmer to define multiple methods in a derived class, and it has the same name as present in the parent class. Such scenarios support method overloading in Python.

In this Python Polymorphism tutorial, you will learn:

Polymorphism in Operators

An operator in Python helps perform mathematical and several other programming tasks. For example, the ‘+’ operator helps in performing addition between two integer types in Python, and in the same way, the same operator helps in concatenating strings in Python programming.

Let us take an example of + (plus) operator in Python to display an application of Polymorphism in Python as shown below:

Python Code:

p = 55 q = 77 r = 9.5 g1 = "Guru" g2 = "99!" print("the sum of two numbers",p + q) print("the data type of result is",type(p + q)) print("The sum of two numbers",q + r) print("the data type of result is", type (q + r)) print("The concatenated string is", g1 + g2) print("The data type of two strings",type(g1 + g2))

Output:

the sum of two numbers 132 The sum of the two numbers 86.5 The concatenated string is Guru99!

The above example can also be regarded as the example of operator overloading.

Polymorphism in user-defined methods

A user-defined method in the Python programming language are methods that the user creates, and it is declared using the keyword def with the function name.

Polymorphism in the Python programming language is achieved through method overloading and overriding. Python defines methods with def keyword and with the same name in both child and parent class.

Let us take the following example as shown below: –

Python Code:

from math import pi class square: def __init__(self, length): self.l = length def perimeter(self): return 4 * (self.l) def area(self): return self.l * self.l class Circle: def __init__(self, radius): self.r = radius def perimeter(self): return 2 * pi * self.r def area(self): return pi * self.r * * 2 # Initialize the classes sqr = square(10) c1 = Circle(4) print("Perimeter computed for square: ", sqr.perimeter()) print("Area computed for square: ", sqr.area()) print("Perimeter computed for Circle: ", c1.perimeter()) print("Area computed for Circle: ", c1.area())

Output:

Perimeter computed for square: 40 Area computed for square: 100 Perimeter computed for Circle: 25.132741228718345 Area computed for Circle: 50.26548245743669

In the above code, there are two user-defined methods, perimeter and area, defined in circle and square classes.

As shown above, both circle class and square class invoke the same method name displaying the characteristic of Polymorphism to deliver the required output.

Polymorphism in Functions

The built-in functions in Python are designed and made compatible to execute several data types. In Python, Len() is one of the key built-in functions.

It works on several data types: list, tuple, string, and dictionary. The Len () function returns definite information aligned with these many data types.

The following figure shows how Polymorphism can be applied in Python with relation to in-built functions: –

Following program helps in illustrating the application of Polymorphism in Python: –

Python Code:

print ("The length of string Guru99 is ",len("Guru99")) print("The length of list is ",len(["Guru99","Example","Reader"])) print("The length of dictionary is ",len({"Website name":"Guru99","Type":"Education"}))

Output:

The length of string Guru99 is 6 The length of the list is 3 The length of the dictionary is 2

In the above example, Len () function of Python performs Polymorphism for string, list, and dictionary data types, respectively.

Polymorphism and Inheritance

Inheritance in Python can be defined as the programming concept wherein a child class defined inherit properties from another base class present in Python.

There are two key Python concepts termed method overriding and method overloading.

In method overloading, Python provides the feature of creating methods that have the same name to perform or execute different functionalities in a given piece of code. It allows to overload methods and uses them to perform different tasks in simpler terms.

In Method overriding, Python overrides the value that shares a similar name in parent and child classes.

Let us take the following example of Polymorphism and inheritance as shown below: –

Python Code:

class baseclass: def __init__(self, name): chúng tôi = name def area1(self): pass def __str__(self): return self.name class rectangle(baseclass): def __init__(self, length, breadth): super().__init__("rectangle") self.length = length self.breadth = breadth def area1(self): return self.length * self.breadth class triangle(baseclass): def __init__(self, height, base): super().__init__("triangle") self.height = height self.base = base def area1(self): return (self.base * self.height) / 2 a = rectangle(90, 80) b = triangle(77, 64) print("The shape is: ", b) print("The area of shape is", b.area1()) print("The shape is:", a) print("The area of shape is", a.area1())

Output:

The shape is: a triangle The area of a shape is 2464.0 The shape is: a rectangle The area of a shape is 7200

In above code, the methods have the same name defined as init method and area1 method. The object of class square and rectangle are then used to invoke the two methods to perform different tasks and provide the output of the area of square and rectangle.

Polymorphism with the Class Methods

The Python programming enables programmers to achieve Polymorphism and method overloading with class methods. The different classes in Python can have methods that are declared in the same name across the Python code.

In Python, two different classes can be defined. One would be child class, and it derives attributes from another defined class termed as parent class.

The following example illustrates the concept of Polymorphism with class methods: –

Python Code:

class amazon: def __init__(self, name, price): chúng tôi = name self.price = price def info(self): print("This is product and am class is invoked. The name is {self.name}. This costs {self.price} rupees.") class flipkart: def __init__(self, name, price): chúng tôi = name self.price = price def info(self): print(f "This is product and fli class is invoked. The name is {self.name}. This costs {self.price} rupees.") FLP = flipkart("Iphone", 2.5) AMZ = amazon("Iphone", 4) for product1 in (FLP, AMZ): product1.info()

Output:

This is a product, and fli class is invoked. The name is iPhone, and this costs 2.5 rupees. This is a product, and am class is invoked. The name is iPhone, and this costs 4 rupees.

In the above code, two different classes named as flipkart and amazon use the same method names info and init to provide respective price quotations of the product and further illustrate the concept of Polymorphism in Python.

Difference between Method overloading and compile-time Polymorphism

In compile-time Polymorphism, the compiler of the Python program resolves the call. Compile-time Polymorphism is accomplished through method overloading.

The Python compiler does not resolve the calls during run time for polymorphism. It is also classified as method overriding wherein the same methods carry similar signatures or properties, but they form a part of different classes.

Summary:

Polymorphism can be defined as a condition that occurs in many different forms.

An operator in Python helps perform mathematical and several other programming tasks.

A user-defined method in the Python programming language are methods that the user creates, and it is declared using the keyword def with the function name.

Polymorphism in Python offers several desirable qualities, such as it promotes the reusability of codes written for different classes and methods.

A child class is a derived class, and it gets its attributes from the parent class.

The Polymorphism is also achieved through run-time method overriding and compile-time method overloading.

Polymorphism in Python is also attained through operator overloading and class methods.

Steps To Avoid Eoferror In Python With Examples

Introduction to Python EOFError

EOFError in python is one of the exceptions handling errors, and it is raised in scenarios such as interruption of the input() function in both python version 2.7 and python version 3.6 and other versions after version 3.6 or when the input() function reaches the unexpected end of the file in python version 2.7, that is the functions do not read any date before the end of input is encountered. And the methods such as the read() method must return a string that is empty when the end of the file is encountered, and this EOFError in python is inherited from the Exception class, which in turn is inherited from BaseException class.

Syntax:

EOFError: EOF when reading a line

Working of EOFError in Python

Below is the working of EOFError:

1. BaseException class is the base class of the Exception class which in turn inherits the EOFError class.

2. EOFError is not an error technically, but it is an exception. When the in-built functions such as the input() function or read() function return a string that is empty without reading any data, then the EOFError exception is raised.

3. This exception is raised when our program is trying to obtain something and do modifications to it, but when it fails to read any data and returns a string that is empty, the EOFError exception is raised.

Examples

Below is the example of Python EOFError:

Example #1

Python program to demonstrate EOFError with an error message in the program.

Code:

#EOFError program #try and except blocks are used to catch the exception try: while True: #input is assigned to a string variable check check = raw_input('The input provided by the user is being read which is:') #the data assigned to the string variable is read print 'READ:', check #EOFError exception is caught and the appropriate message is displayed except EOFError as x: print x

Output:

Explanation: In the above program, try and except blocks are used to catch the exception. A while block is used within a try block, which is evaluated to true, and as long as the condition is true, the data provided by the user is read, and it is displayed using a print statement, and if the data cannot be read with an empty string being returned, then the except block raises an exception with the message which is shown in the output.

Example #2

Python program to demonstrate EOFError with an error message in the program.

Code:

#EOFError program #try and except blocks are used to catch the exception try: while True: #input is assigned to a string variable check check = raw_input('The input provided by the user is being read which is:') #the data assigned to the string variable is read print 'Hello', check #EOFError exception is caught and the appropriate message is displayed except EOFError as x: print x

Output:

Explanation: In the above program, try and except blocks are used to catch the exception. A while block is used within a try block, which is evaluated to true, and as long as the condition is true, the data provided by the user is read and it is displayed using a print statement, and if the data cannot be read with an empty string being returned, then the except block raises an exception with the message which is shown in the output.

Steps to Avoid EOFError in Python

If End of file Error or EOFError happens without reading any data using the input() function, an EOFError exception will be raised. In order to avoid this exception being raised, we can try the following options which are:

Before sending the End of File exception, try to input something like CTRL + Z or CTRL + D or an empty string which the below example can demonstrate:

Code:

#try and except blocks are used to catch the exception try: data = raw_input ("Do you want to continue?: ") except EOFError: print ("Error: No input or End Of File is reached!") data = "" print data

Output:

Explanation: In the above program, try and except blocks are used to avoid the EOFError exception by using an empty string that will not print the End Of File error message and rather print the custom message provided by is which is shown in the program and the same is printed in the output as well. The output of the program is shown in the snapshot above.

If the EOFError exception must be processed, try and catch block can be used.

Conclusion

In this tutorial, we understand the concept of EOFError in Python through definition, the syntax of EOFError in Python, working of EOFError in Python through programming examples and their outputs, and the steps to avoid EOFError in Python.