Email Scraping From Text File

  """ A. Input Get 50% off on every purchase. contact marketing team at market@qq.com. Find all your linkedin contact...

 

"""
	A. Input
	Get 50% off on every purchase. contact marketing team at market@qq.com. Find all your linkedin
	contacts for free, jeff.peterson@b2bsearch.com. qq.com partnership program apply at
	market@qq.com
	B. Expected Output
	{ "market@qq.com" : {"Occurance":2, "EmailType": "Non-Human"} ,
	"jeff.peterson@b2bsearch.com" : {"Occurance":1, "EmailType": "Human"}
	}
	C. Explanation:
	The output must be in a nested json format.
	"Occurance" : No of times the email is repeated in the text.
	"EmailType" : Type of the email. You can have more complex logic to identify human and non-
	human emails but in this exercise, Just try the logic given below:
	Finding human emails: If the email is of format firstname.lastname@email.com then you can
	assume that the email is human.
	Finding non-human emails: If the email format is text@email.com where text is less than 8
	characters, then you can assume that the email is likely to be non-human.
	Note: Get text file from here ' '
	"""
	import json
	import re
	# Email filter using split and loop
	def filterEmail(data):
	strings = data.replace("\n"," ").split(" ")
	emailList = [string for string in strings if "@" in string and len(string)>7]
	jsonifyOutput(emailList)
	# Email filter using regex
	def filterEmailWithRegex(data):
	emailList = re.findall('\S+@\S+', data) # \S -> Matches any non-whitespace character
	jsonifyOutput(emailList)
	# create nested dictionary for the output result as per description
	def jsonifyOutput(emailList):
	mainDict = {}
	for email in set(emailList):
	subDict = {}
	occurance = emailList.count(email)
	subDict["Occurance"] = occurance
	emailSplit = email.split('@')[0]
	if '.' in emailSplit:
	subDict["EmailType"] = "Human"
	elif '.' not in emailSplit and len(emailSplit)<8:
	subDict["EmailType"] = "Non-Human"
	else:
	subDict["EmailType"] = "Null"
	mainDict[email] = subDict
	exportJsonResult(mainDict)
	# Export nested dictionary output into json
	def exportJsonResult(outputResult):
	with open("result.json","w") as resultFile:
	json.dump(outputResult,resultFile)
	# Read the test file given "websiteData.txt"
	def readFile():
	with open("websiteData.txt","r") as file:
	text_data = file.read()
	filterEmail(text_data)
	# uncomment below code for email filteration using regex
	# filterEmailWithRegex(text_data)
	if __name__ == "__main__":
	readFile()