How To List NFTs With Our Comprehensive Guide

Non-fungible tokens are unique digital assets that represent products that have the ability to be digitized, which includes both, tangible and intangible products. The most commonly traded NFTs are trading cards, digital artworks, image files, video clips, domain names, etc. These NFTs are traded on a platform called the NFT marketplace. This platform is built on blockchain technology. This technology provides absolute transparency and security to the NFTs that are being traded on the marketplace. Blockchain is being the prime reason for digital audiences to be completely submerged in this domain and enjoy extensive rewards. These rewards are gained by listing the non-fungible tokens effectively in the marketplace.

Features Of NFT

• The primary virtue of NFTs is the ability to be unique. This is possible because of the authenticity of the ownership that is stored in the blockchain.
• Non-fungible tokens are completely secured and transparent due to their integration with the blockchain network.
• NFTs are easily tradeable among various marketplaces because of their uniqueness and scalability.

How To List NFTs?

• The minted non-fungible token is stored in the crypto wallet.
• The stored NFT is then initiated through the selling protocol.
• The selling protocol lists the NFTs where they are completely visible to the buyers.
• In the listing section, the NFT can be subjected to the fixed-rate mode or timed-auction mode.
• In a fixed-rate mode, the price of the non-fungible token is fixed and cannot be changed.
• In a timed auction mode, the NFT will be under an auction bid that lasts for days or even weeks.
• Once the mode is selected, the monetization process for the NFT proceeds.

Conclusion

Non-fungible tokens have become the epitome of instant success in the digital world. Therefore, trading, buying, selling, listing NFT in this domain are highly beneficial for individuals and organizations. It is proven to generate high rewards and revenue.

 

What is GEEK

Buddha Community

List Comprehension

List comprehension is nothing but a shorter and crisper version of the code and also memory efficient. By using this we can either create a new list or perform some operation in an existing list.

The normal code for creating a list of 0–9 will be like

x=[]
for i in range (10):
x.append(i)
print(x)
[0,1,2,3,4,5,6,7,8,9]

By using list comprehension

x=[i for i in range(10)]
print(x)

[0,1,2,3,4,5,6,7,8,9]

As you can see the normal code is long but the code that we did using list comprehension does the job just in one line so list comprehension is preferred over the traditional method.

#list-comprehension #lists #python #python-list-comprehension

How to Convert Loops to List Comprehension in Python

Do the same but faster

List comprehension is used for creating lists based on iterables. It can also be described as representing for and if loops with a simpler and more appealing syntax. List comprehensions are relatively faster than for loops.

The syntax of a list comprehension is actually easy to understand. However, when it comes to complex and nested operations, it might get a little tricky to figure out how to structure a list comprehension.

In such cases, writing the loop version first makes it easier to write the code for the list comprehension. We will go over several examples that demonstrate how to convert a loop-wise syntax to a list comprehension.

Basic structure of list comprehension (image by author)

Let’s start with a simple example. We have a list of 5 integers and want to create a list that contains the squares of each item. Following is the for loop that performs this operation.

lst_a = [1, 2, 3, 4, 5]

lst_b = []
for i in lst_a:
   lst_b.append(i**2)
print(lst_b)
[1, 4, 9, 16, 25]

#python #programming #how to convert loops to list comprehension in python #convert loops #list comprehension #how to convert loops to list comprehension

Beyonic API Python Example Using Flask, Django, FastAPI

Beyonic API Python Examples.

The beyonic APIs Docs Reference: https://apidocs.beyonic.com/

Discuss Beyonic API on slack

The Beyonic API is a representational state transfer, REST based application programming interface that lets you extend the Beyonic dashboard features into your application and systems, allowing you to build amazing payment experiences.

With the Beyonic API you can:

• Receive and send money and prepaid airtime.
• List currencies and networks supported by the Beyonic API.
• Check whether a bank is supported by the Beyonic API.
• View your account transactions history.
• Add, retrieve, list, and update contacts to your Beyonic account.
• Use webhooks to send notifications to URLs on your server that when specific events occur in your Beyonic account (e.g. payments).

Getting Help

For usage, general questions, and discussions the best place to go to is Beyhive Slack Community, also feel free to clone and edit this repository to meet your project, application or system requirements.

To start using the Beyonic Python API, you need to start by downloading the Beyonic API official Python client library and setting your secret key.

Install the Beyonic API Python Official client library

>>> pip install beyonic

Setting your secrete key.

To set the secrete key install the python-dotenv modeule, Python-dotenv is a Python module that allows you to specify environment variables in traditional UNIX-like “.env” (dot-env) file within your Python project directory, it helps us work with SECRETS and KEYS without exposing them to the outside world, and keep them safe during development too.

Installing python-dotenv modeule

>>> pip install python-dotenv

Creating a .env file to keep our secrete keys.

>>> touch .env

Inside your .env file specify the Beyonic API Token .

.env file

BEYONIC_ACCESS_KEY = "enter your API "

You will get your API Token by clicking your user name on the bottom left of the left sidebar menu in the Beyonic web portal and selecting ‘Manage my account’ from the dropdown menu. The API Token is shown at the very bottom of the page.

getExamples.py

import os 
import beyonic
from dotenv import load_dotenv 

load_dotenv()

myapi = os.environ['BEYONIC_ACCESS_KEY']

beyonic.api_key = myapi 

# Listing account: Working. 
accounts = beyonic.Account.list() 
print(accounts)


#Listing currencies: Not working yet.
'''
supported_currencies = beyonic.Currency.list()
print(supported_currencies)

Supported currencies are: USD, UGX, KES, BXC, GHS, TZS, RWF, ZMW, MWK, BIF, EUR, XAF, GNF, XOF, XOF
'''

#Listing networks: Not working yet.
"""
networks = beyonic.Network.list()
print(networks)
"""

#Listing transactions: Working. 
transactions = beyonic.Transaction.list()
print(transactions) 

#Listing contact: Working. 
mycontacts = beyonic.Contact.list() 
print(mycontacts) 


#Listing events: Not working yet.
'''
events = beyonic.Event.list()
print(events)

Error: AttributeError: module 'beyonic' has no attribute 'Event'
'''

Docker file

FROM python:3.8-slim-buster

COPY . .

COPY ./requirements.txt ./requirements.txt

WORKDIR .

RUN pip install -r requirements.txt

CMD [ "python3", "getExamples.py" ]

Build docker image called demo

>>> docker build -t bey .

Run docker image called demo

>>>docker run -t -i bey 

Now, I’ll create a Docker compose file to run a Docker container using the Docker image we just created.

version: "3.6"
services:
  app:
    build: .
    command: python getExamples.py
    volumes:
      - .:/pythonBeyonicExamples

Now we are going to run the following command from the same directory where the docker-compose.yml file is located. The docker compose up command will start and run the entire app.

docker compose up

Output

NB: The screenshot below might differ according to your account deatils and your transcations in deatils.

To stop the container running on daemon mode use the below command.

docker compose stop

Output

Contributing to this repository. All contributions, bug reports, bug fixes, enhancements, and ideas are welcome, You can get in touch with me on twitter @HarunMbaabu.

Download Details:
Author: HarunMbaabu
Source Code: https://github.com/HarunMbaabu/BeyonicAPI-Python-Examples
License: 

#api #python #flask #django #fastapi 

Python’s List Comprehensions

Lists are Python Data structures that are used to store multiple elements in a single variable. List comprehension is a more simple way to define and create a list in python, lists can be created in one line.

The syntax of list comprehension is easier to grasp as well.

The structure of a List comprehension program is below:

• An expression, which will be the output list
• An Input sequence
• A variable representing a member of the input sequence and
• An optional predicate part, where we can define our conditional statements

There can be multiple ways of performing a task in any programming language, similarly with python list comprehensions as well.

In my article we will straightaway not use list comprehension but will study the concept in a relational way, comparing it with loops, and then conclude the study, as I personally found this way a lot easier to understand.

Syntax of List Comprehension:

[expression for in ]

#competitive-programming #python #python3 #programming #list comprehensions #python’s list comprehensions

Beginners Guide to Web Scraping with Python

Web Scraping with Python

Imagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. 

In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics:

• Why is Web Scraping Used?
• What Is Web Scraping?
• Is Web Scraping Legal?
• Why is Python Good For Web Scraping?
• How Do You Scrape Data From A Website?
• Libraries used for Web Scraping
• Web Scraping Example : Scraping Flipkart Website

Why is Web Scraping Used?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping:

• Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products.
• Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails.
• Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending.
• Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc.) from websites, which are analyzed and used to carry out Surveys or for R&D.
• Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the user.

What is Web Scraping?

Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. 

Is Web Scraping Legal?

Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “robots.txt” file. You can find this file by appending “/robots.txt” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “robots.txt” file, the URL is www.flipkart.com/robots.txt.

Why is Python Good for Web Scraping?

Here is the list of features of Python which makes it more suitable for web scraping.

• Ease of Use: Python Programming is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use.
• Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data.
• Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster.
• Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. 
• Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code.
• Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help from.

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How Do You Scrape Data From A Website?

When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. 

To extract data using web scraping with python, you need to follow these basic steps:

1. Find the URL that you want to scrape
2. Inspecting the Page
3. Find the data you want to extract
4. Write the code
5. Run the code and extract the data
6. Store the data in the required format 

Now let us see how to extract data from the Flipkart website using Python.

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Libraries used for Web Scraping 

As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries:

• Selenium:  Selenium is a web testing library. It is used to automate browser activities.
• BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily.
• Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. 

Web Scraping Example : Scraping Flipkart Website

Pre-requisites:

• Python 2.x or Python 3.x with Selenium, BeautifulSoup, pandas libraries installed
• Google-chrome browser
• Ubuntu Operating System

Let’s get started!

Step 1: Find the URL that you want to scrape

For this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&uniqBStoreParam1=val1&wid=11.productCard.PMU_V2.

Step 2: Inspecting the Page

The data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect”.

When you click on the “Inspect” tab, you will see a “Browser Inspector Box” open.

Step 3: Find the data you want to extract

Let’s extract the Price, Name, and Rating which is in the “div” tag respectively.

Step 4: Write the code

First, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit <your file name> with .py extension.

I am going to name my file “web-s”. Here’s the command:

gedit web-s.py

Now, let’s write our code in this file. 

First, let us import all the necessary libraries:

from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pd

To configure webdriver to use Chrome browser, we have to set the path to chromedriver

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

Refer the below code to open the URL:

products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
driver.get("<a href="https://www.flipkart.com/laptops/">https://www.flipkart.com/laptops/</a>~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&amp;amp;amp;amp;amp;amp;amp;amp;amp;uniq")

Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in <div> tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:

content = driver.page_source
soup = BeautifulSoup(content)
for a in soup.findAll('a',href=True, attrs={'class':'_31qSD5'}):
name=a.find('div', attrs={'class':'_3wU53n'})
price=a.find('div', attrs={'class':'_1vC4OE _2rQ-NK'})
rating=a.find('div', attrs={'class':'hGSR34 _2beYZw'})
products.append(name.text)
prices.append(price.text)
ratings.append(rating.text) 

Step 5: Run the code and extract the data

To run the code, use the below command:

python web-s.py

Step 6: Store the data in a required format

After extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings}) 
df.to_csv('products.csv', index=False, encoding='utf-8')

Now, I’ll run the whole code again.

A file name “products.csv” is created and this file contains the extracted data.

I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. 

If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do it or you can also join our Python Master course.

Web Scraping With Python | Python Tutorial | Web Scraping Tutorial | Edureka

This Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from Flipkart.

Got a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart today..

To get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.

Original article source at: https://www.edureka.co/

#webscraping #python