The document contains a Python script for scraping and analyzing Bitcoin address data from BitInfoCharts and Blockchain.info. It retrieves details such as balances, transaction counts, and total received/sent amounts for the top 100 richest Bitcoin addresses and saves this information into an Excel file. The script utilizes libraries such as requests, BeautifulSoup, and pandas for data handling and manipulation.

1. import requests
from lxml import html
from urllib.request import urlopen
from bs4 import BeautifulSoup as soup
import re
client = requests.Session()
HOMEPAGE_URL = 'https://bitinfocharts.com/top-100-richest-bitcoin-addresses.html'
html = client.get(HOMEPAGE_URL).content
body = BeautifulSoup(html,'html.parser')
addresses = []
temp_addresses=[]
for m in re.finditer('</td><td><a href="https://bitinfocharts.com/bitcoin/address/', str(body)):
#print(m.end())
a = str(body)[m.end():m.end()+80]
a = a.split(">")
a= a[0] [:-1]
addresses.append(a)
#for m in re.finditer('">wallet:', str(body)):
#len(addresses)
#print(addresses)
# Fill in with url of page which is to be scraped
#body = body.prettify()
#text = body.get_text
#print(body.prettify)
import blockchain
import requests
client = requests.Session()
#printing our URLS to the top 100 addresses
address_data = []

2. homepage_url=[]
balance =[]
n_tx =[]
total_recieved=[]
total_sent=[]
##
#Address can be base58 or hash160
#Optional limit parameter to show n transactions e.g. &limit=50 (Default: 50, Max: 50)
#Optional offset parameter to skip the first n transactions e.g. &offset=100 (Page 2 for limit 50)
##
for i in range(0,len(addresses)):
#HOMEPAGE_URL = 'https://bitinfocharts.com/bitcoin/address/'+addresses[i]
HOMEPAGE_URL = 'https://blockchain.info/rawaddr/'+addresses[i]
homepage_url.append(HOMEPAGE_URL)
html = client.get(HOMEPAGE_URL).content
body_address = BeautifulSoup(html,'html.parser')
#body_address = str(body_address)
print(HOMEPAGE_URL)
#print(body_address.prettify)
address_data.append(body_address.prettify)
##########################################
number_transcations_performed= str(address_data[i]).split("n")[3][11:-1]
n_tx.append(number_transcations_performed)
print(number_transcations_performed)
####################|######################
recieved = str(address_data[i]).split("n")[4][21:-1-8]
sent = str(address_data[i]).split("n")[5][17:-1-8]
total_recieved.append(recieved)
total_sent.append(sent)
print(recieved)
print(sent)

3. ##########################################
Balance_URL = 'https://blockchain.info/balance?active='+addresses[i]
html = client.get(Balance_URL).content
body_balance = BeautifulSoup(html,'html.parser')
print(Balance_URL)
#print(body_balance.prettify)
a = str(body_balance)
a = a.split("n")
a = a[1][21:len(a[1])-1-8]
balance.append(a)
print(a)
#print(len(address_data))
#print(len(homepage_url))
#print(len(balance))
#Balance information extraction
#https://blockchain.info/balance?active=$address
import pandas as pd
from pandas import ExcelWriter
# Creates a datframe with columns: |Name|Twitter Handle|Path To Tweets|
#balance = [int(i) for i in balance]
df = pd.DataFrame({'Addresses': addresses,
'Balance in BTC': balance,
'URL Profile': homepage_url,
'No of Transactions performed':n_tx,
'Total Recieved': total_recieved,
'Total Sent': total_sent})
## # 'Activity Details': address_data,
# # 'Balance in BTC': str(balance)})

4. writer = ExcelWriter('Defiance.xlsx')
df.to_excel(writer,'Sheet1',index=False)
writer.save()
See below for sample output