How to Create a Cryptocurrency Token

This article will serve as a tutorial for creating your own cryptocurrency token. We will be using Etherum’s “ERC-20” token as a basis for our code. Once we are finished you can use it to represent currencies or anything that you want to be tradable and have a fixed supply in circulation. This should go without saying but having knowledge in coding is really beneficial here to understand completely what we are doing through the whole process.

Step 1. Create a MetaMask Account

Metamask wallet serves as a gateway to blockchain apps and this is one of the first things that you have to do to get started. It’s an extension for your browser and be sure to only use Google Chrome for this. You will have to create an account and write down a 10-word pneumatic code.

Step 2. Add some Ether to your Wallet

Like you see in the picture above I already have added 1 Ethereum and that is because when we create the token we are going to use some of Ethereum as fees. It’s called the Gas Fee and its a fee for using their blockchain to transfer your tokens. This website allows you to request at least 1 Ethereum from their faucet.

#blockchain #token #ethereum #cryptocurrency #bitcoin

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PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
2. The Tumblr API console at https://api.tumblr.com/console
3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

• state - a string, the state of the post. Supported types are published, draft, queue, private
• tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
• tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
• date - a string, the customized GMT that you want
• format - a string, the format that your post is in. Support types are html or markdown
• slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

• title - a string, the title of post that you want. Supports HTML entities.
• url - a string, the url that you want to create a link post for.
• description - a string, the desciption of the link that you have

#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

SafeMoon Clone | Create A DeFi Token Like SafeMoon | DeFi token like SafeMoon

SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.

A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.

Read More @ https://bit.ly/3oFbJoJ

#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token

Developing Chatbots project

If you want your business to prosper, you'll have to stay on top of the latest trends. The creation of a chatbot is a lengthy procedure. However, if well planned, it can be a piece of cake. The emergence of chatbots is one of the most significant recent developments in the area of customer care. On that topic, chatbots are one of the most well-known marketing tools in use today, aiding in the development of effective communication between businesses and their customers. So, read on to learn about data science projects for final year students as well as data science projects for beginners.

When it comes to chatbot creation, the most important thing to remember is to break the process down into simple steps and follow them one by one. Chatbots are quite handy if you want to improve your customer's experience by answering their questions, reducing human workload, performing remote troubleshooting, and so on. Rather than adopting a bot development framework or another platform, why not build a basic, intelligent chatbot from the ground up using deep learning? Though bots have a wide range of applications, one of the most well-known is live chat platforms, where users ask queries and a chatbot responds appropriately. There are different types of recommendation systems of the data science projects ideas.

So, in order to make your life easier, we've provided step-by-step chatbot programming guidelines. The days of waiting (not so patiently) on hold for answers to your most pressing questions are quickly fading away. In this lesson, you'll learn how to use Keras to create an end-to-end domain-specific intelligent chatbot solution.

Overview:

A chatbot is a piece of software that can communicate and conduct tasks in the same way that a human can. Because we're going to build a deep learning model, we'll need data to train it. Chatbots are marketing and automation solutions that are supposed to assist people by interacting with them and performing human-like interactions. Chatbots are widely utilised in customer service, social media marketing, and client instant messaging.

However, because this is a rudimentary chatbot, we will neither collect nor download any significant datasets. To communicate, these bots may employ Natural Language Processing (NLP) or audio analysis techniques, making them sound more natural. Based on how they're developed, there are two primary sorts of chatbot models: retrieval-based and generation-based models. These intentions may differ from one

chatbot solution to the next depending on the domain in which you are implementing a chatbot solution. AI-Chatbots are widely recommended by entrepreneurs and organizations. Let's take this data science project step by step.

Import and load the data file

import nltk

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import json

import pickle

import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation, Dropout

from keras.optimizers import SGD

import random

words=[]

classes = []

documents = []

ignore_words = ['?', '!']

data_file = open('intents.json').read()

intents = json.loads(data_file)

Preprocess data 

for intent in intents['intents']:

    for pattern in intent['patterns']:

     #tokenize each word

     w = nltk.word_tokenize(pattern)

     words.extend(w)

     #add documents in the corpus

     documents.append((w, intent['tag']))

           if intent['tag'] not in classes:

         classes.append(intent['tag'])

Create training and testing data 

training = []

 

output_empty = [0] * len(classes)

 

for doc in documents:

  

    bag = []

Build the model

model = Sequential()

model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(len(train_y[0]), activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)

model.save('chatbot_model.h5', hist)

print("model created"

 output_row = list(output_empty)

    output_row[classes.index(doc[1])] = 1

    training.append([bag, output_row])

random.shuffle(training)

training = np.array(training)

train_x = list(training[:,0])

train_y = list(training[:,1])

print("Training data created")

Predict the response (Graphical User Interface)

import nltk

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

import pickle

import numpy as np

from keras.models import load_model

model = load_model('chatbot_model.h5')

import json

import random

intents = json.loads(open('intents.json').read())

words = pickle.load(open('words.pkl','rb'))

def clean_up_sentence(sentence):

sentence_words = nltk.word_tokenize(sentence)

 return sentence_words

def bow(sentence, words, show_details=True):

 sentence_words = clean_up_sentence(sentence)

bag = [0]*len(words)

    for s in sentence_words:

     for i,w in enumerate(words):

         if w == s:

             bag[i] = 1

             if show_details:

                 print ("found in bag: %s" % w)

    return(np.array(bag))

def predict_class(sentence, model):

 p = bow(sentence, words,show_details=False)

    res = model.predict(np.array([p]))[0]

    ERROR_THRESHOLD = 0.25

     results.sort(key=lambda x: x[1], reverse=True)

    return_list = []

    for r in results:

        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})

    return return_list

.def getResponse(ints, intents_json):

    tag = ints[0]['intent']

    list_of_intents = intents_json['intents']

    for i in list_of_intents:

     if(i['tag']== tag):

         result = random.choice(i['responses'])

         break

    return result

def chatbot_response(text):

    ints = predict_class(text, model)

    res = getResponse(ints, intents)

    return res

#Creating GUI with tkinter

import tkinter

from tkinter import *

def send():

    msg = EntryBox.get("1.0",'end-1c').strip()

    EntryBox.delete("0.0",END)

    if msg != '':

     ChatLog.config(state=NORMAL)

     ChatLog.insert(END, "You: " + msg + '\n\n')

     ChatLog.config(foreground="#442265", font=("Verdana", 12 ))

     res = chatbot_response(msg)

     ChatLog.insert(END, "Bot: " + res + '\n\n')

     ChatLog.config(state=DISABLED)

     ChatLog.yview(END)

base = Tk()

base.title("Hello")

base.geometry("400x500")

base.resizable(width=FALSE, height=FALSE)

#Create Chat window

ChatLog.config(state=DISABLED)

#Bind scrollbar to Chat window

scrollbar = Scrollbar(base, command=ChatLog.yview, cursor="heart")

ChatLog['yscrollcommand'] = scrollbar.set

#Create Button to send message

SendButton = Button(base, font=("Verdana",12,'bold'), text="Send", width="12", height=5,

                 bd=0, bg="#32de97", activebackground="#3c9d9b",fg='#ffffff',

                 command= send )

#Create the box to enter message

EntryBox = Text(base, bd=0, bg="white",width="29", height="5", font="Arial")

#EntryBox.bind("", send)

#Place all components on the screen

scrollbar.place(x=376,y=6, height=386)

ChatLog.place(x=6,y=6, height=386, width=370)

EntryBox.place(x=128, y=401, height=90, width=265)

SendButton.place(x=6, y=401, height=90)

base.mainloop()

If you want to learn more about how to do data science projects step by step, visit our website Learnbay: data science course in Chennai.

 

Keywords	Search Volume	Competition
data science project	17,480	25 (Low)
data science projects ideas	7,690	30 (Low)
data science project for final year	1,180	30(Low)
Data science project for beginners	7,500	26(Low)
data science projects step by step	2,240	31 (Low)

 

 

 

How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)

How to Automate Excel with Python

In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation

What is OPENPYXL

Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.

Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.

Sheet: A sheet is a single page composed of cells for organizing data.

Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.

Row: A row is a horizontal line represented by a number (1,2, etc.).

Column: A column is a vertical line represented by a capital letter (A, B, etc.).

Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.

pip install openpyxl

CREATE A NEW WORKBOOK

We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.

from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method

ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position

#Renaming the sheet
ws.title = "Example"

#save the workbook
wb.save(filename = "example.xlsx")

READING DATA FROM WORKBOOK

We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.

#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")

 

GETTING SHEETS FROM THE LOADED WORKBOOK

 

#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']

#getting a particular sheet
sheet1 = wb["sheet2"]

#getting sheet title
sheet1.title
result = 'sheet2'

#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'

 

ACCESSING CELLS AND CELL VALUES

 

#get a cell from the sheet
sheet1["A1"] <
  Cell 'Sheet1'.A1 >

  #get the cell value
ws["A1"].value 'Segment'

#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10

 

ITERATING THROUGH ROWS AND COLUMNS

 

#looping through each row and column
for x in range(1, 5):
  for y in range(1, 5):
  print(x, y, ws.cell(row = x, column = y)
    .value)

#getting the highest row number
ws.max_row
701

#getting the highest column number
ws.max_column
19

There are two functions for iterating through rows and columns.

Iter_rows() => returns the rows
Iter_cols() => returns the columns {
  min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.

Example:

#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
  for cell in row:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C3 >

  #iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
  for cell in col:
  print(cell) <
  Cell 'Sheet1'.A2 >
  <
  Cell 'Sheet1'.A3 >
  <
  Cell 'Sheet1'.B2 >
  <
  Cell 'Sheet1'.B3 >
  <
  Cell 'Sheet1'.C2 >
  <
  Cell 'Sheet1'.C3 >

To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.

Example:

for row in ws.values:
  for value in row:
  print(value)

 

WRITING DATA TO AN EXCEL FILE

Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.

 

CREATING AND SAVING A NEW WORKBOOK

 

#creates a new workbook
wb = openpyxl.Workbook()

#saving the workbook
wb.save("new.xlsx")

 

ADDING AND REMOVING SHEETS

 

#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")

#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")

#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']

#deleting a sheet
del wb['sheet 0']

#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']

 

ADDING CELL VALUES

 

#checking the sheet value
ws['B2'].value
null

#adding value to cell
ws['B2'] = 367

#checking value
ws['B2'].value
367

 

ADDING FORMULAS

 

We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
 

For example:

import openpyxl
from openpyxl
import Workbook

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']

ws['A9'] = '=SUM(A2:A8)'

wb.save("new2.xlsx")

The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.

 

MERGE/UNMERGE CELLS

Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().

For example:
Merge cells

#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"

Adding the above code to the previous example will merge cells as below.

UNMERGE CELLS

 

#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')

The above code will unmerge cells from B2 to C9.

INSERTING AN IMAGE

To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.

Example:

import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image

wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3

ws.add_image(img, 'A3')

wb.save("new2.xlsx")

Result:

CREATING CHARTS

Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:

Example

import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series

wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active

values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")

Result

---

How to Automate Excel with Python with Video Tutorial

Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.

⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
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Build Your Own Cryptocurrency Blockchain in Python

Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.

Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.

In this article, we are going to explain how you can create a simple blockchain using the Python programming language.

Here is the basic blueprint of the Python class we’ll use for creating the blockchain:

class Block(object):
    def __init__():
        pass
    #initial structure of the block class 
    def compute_hash():
        pass
    #producing the cryptographic hash of each block 
  class BlockChain(object):
    def __init__(self):
    #building the chain
    def build_genesis(self):
        pass
    #creating the initial block
    def build_block(self, proof_number, previous_hash):
        pass
    #builds new block and adds to the chain
   @staticmethod
    def confirm_validity(block, previous_block):
        pass
    #checks whether the blockchain is valid
    def get_data(self, sender, receiver, amount):
        pass
    # declares data of transactions
    @staticmethod
    def proof_of_work(last_proof):
        pass
    #adds to the security of the blockchain
    @property
    def latest_block(self):
        pass
    #returns the last block in the chain

Now, let’s explain how the blockchain class works.

Initial Structure of the Block Class

Here is the code for our initial block class:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()

As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:

self — just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.

index — it’s used to track the position of a block within the blockchain.

previous_hash — it used to reference the hash of the previous block within the blockchain.

data—it gives details of the transactions done, for example, the amount bought.

timestamp—it inserts a timestamp for all the transactions performed.

The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.

As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.

Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.

So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.

Building the Chain

The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.

It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.

Let’s talk about the helper methods.

Adding the Constructor Method

Here is the code:

class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()

The init() constructor method is what instantiates the blockchain.

Here are the roles of its attributes:

self.chain — this variable stores all the blocks.

self.current_data — this variable stores information about the transactions in the block.

self.build_genesis() — this method is used to create the initial block in the chain.

Building the Genesis Block

The build_genesis() method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.

To create it, we’ll call the build_block() method and give it some default values. The parameters proof_number and previous_hash are both given a value of zero, though you can give them any value you desire.

Here is the code:

def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
 def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block

Confirming Validity of the Blockchain

The confirm_validity method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.

As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.

Thus, the confirm_validity method utilizes a series of if statements to assess whether the hash of each block has been compromised.

Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.

Here is the code:

def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True

Declaring Data of Transactions

The get_data method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.

Here is the code:

def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True

Effecting the Proof of Work

In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.

For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.

This way, it discourages spamming and compromising the integrity of the network.

In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.

Finalizing With the Last Block

Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.

Here is the code:

def latest_block(self):
        return self.chain[-1]

Implementing Blockchain Mining

Now, this is the most exciting section!

Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.

If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.

Here is the mining method in this simple cryptocurrency blockchain project:

def block_mining(self, details_miner):
            self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)

Summary

Here is the whole code for our crypto blockchain class in Python:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()
    def __repr__(self):
        return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()
    def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
    def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block
    @staticmethod
    def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True
    def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True        
    @staticmethod
    def proof_of_work(last_proof):
        pass
    @property
    def latest_block(self):
        return self.chain[-1]
    def chain_validity(self):
        pass        
    def block_mining(self, details_miner):       
        self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awared with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)  
    def create_node(self, address):
        self.nodes.add(address)
        return True
    @staticmethod
    def get_block_object(block_data):        
        return Block(
            block_data['index'],
            block_data['proof_number'],
            block_data['previous_hash'],
            block_data['data'],
            timestamp=block_data['timestamp']
        )
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
    sender="0", #this means that this node has constructed another block
    receiver="LiveEdu.tv", 
    amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)

Now, let’s try to run our code to see if we can generate some digital coins…

Wow, it worked!

Conclusion

That is it!

We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.

We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.

Learn More

Thanks for reading !

Do you have any comments or questions? Please share them below.

#python #cryptocurrency