Python program that extracts most frequently found names from .csv file - python-3.x

Question

I have created a program that generates 5000 random names, ssn, city, address, and email and stored them in fakeprofile.csv file. I am trying to extract the most common names from the file. I was able to get the program to work syntactically but fail to extract frequent names.
Here's the code:
import re
import statistics
file_open = open('fakeprofile.csv').read()
frequent_names = re.findall('[A-Z][a-z]*', file_open)
print(frequent_names)
Sample in the file:
Alicia Walters 419-52-4141 Yorkstad 66616 Schultz Extensions Suite 225
Reynoldsmouth, VA 72465 stevenserin#stein.biz
Nicole Duffy 212-38-9009 West Timothy 51077 Phillips Ports Apt. 314
Hubbardville, IN 06723 kaitlinthomas#bennett-carter.com
Stephanie Lewis 442-20-1279 Jacquelineshire 650 Gutierrez Forge Apt. 839
West Christianbury, TN 13654 ukelley#gmail.com
Michael Harris 108-81-3733 East Toddberg 14387 Douglas Mission Suite 038
Garciaview, WI 58624 kshields#yahoo.com
Aaron Moreno 171-30-7715 Port Taraburgh 56672 Wagner Path
Lake Christopher, VA 37884 lucasscott#nguyen.info
Alicia Zimmerman 286-88-9507 Barberstad 5365 Heath Extensions Apt. 731
South Randyburgh, NJ 79367 daniellewebb#yahoo.com
Brittney Mcmillan 334-44-0321 Lisahaven PSC 3856, Box 2428
APO AE 03215 kevin95#hotmail.com
Amanda Perkins 327-31-6610 Perryville 8750 Hurst Harbor Apt. 929
Sample output:
', 'Lake', 'Brianna', 'P', 'A', 'Michael', 'Smith', 'Harveymouth', 'Patricia', 'Tunnel', 'West', 'William', 'G', 'A', 'Charles', 'Perkins', 'Lake', 'Marie', 'Lisa', 'Overpass', 'Suite', 'Kennedymouth', 'C', 'A', 'Barbara', 'Perez', 'Billyshire', 'Joshua', 'Village', 'Cindymouth', 'W', 'I', 'Curtis', 'Simmons', 'North', 'Mitchellport', 'Gordon', 'Crest', 'Suite', 'Jacksonburgh', 'C', 'O', 'Cameron', 'Berg', 'South', 'Dean', 'Christina', 'Coves', 'Williamton', 'T', 'N', 'Maria', 'Williams', 'North', 'Judith', 'Carson', 'Overpass', 'Apt', 'West', 'Amandastad', 'N', 'M', 'Hannah', 'Dennis', 'Rodriguezmouth', 'P', 'S', 'C', 'Box', 'A', 'P', 'O', 'A', 'E', 'Laura', 'Richardson', 'Lake', 'Kayla', 'Johnson', 'Place', 'Suite', 'Port', 'Jennifermouth', 'N', 'H', 'John', 'Lawson', 'Hintonhaven', 'Thomas', 'Via', 'Mossport', 'N', 'J', 'Jennifer', 'Hill', 'East', 'Phillip', 'P', 'S', 'C', 'Box', 'A', 'P', 'O', 'A', 'E', 'Cody', 'Jackson', 'Lake', 'Jessicamouth', 'Snyder', 'Ways', 'Apt', 'New', 'Stacey', 'M', 'E', 'Ryan', 'Friedman', 'Shahburgh', 'Jerry', 'Pike', 'Suite', 'Toddfort', 'N', 'V', 'Kathleen', 'Fox', 'Ferrellmouth', 'P', 'S', 'C', 'Box', 'A', 'P', 'O', 'A', 'P', 'Michael', 'Thompson', 'Port', 'Jessica', 'Boone', 'Spurs', 'Suite', 'Port', 'Ashleyland', 'C', 'O', 'Christopher', 'Marsh', 'North', 'Catherine', 'Scott', 'Trail', 'Apt', 'Baileyburgh', 'F', 'L', 'Richard', 'Rangel', 'New', 'Anna', 'Ray', 'Drive', 'Apt', 'Nunezland', 'I', 'A', 'Connor', 'Stanton', 'Troyshire', 'Rodgers', 'Hill', 'West', 'Annmouth', 'N', 'H', 'James', 'Medina',
My issue here is being unable to extract most frequently found first names as well as avoiding those capital letters. Instead, I have extracted all names (including the unnecessary capital letters) and the one seen above is a small sample of all names extracted. I noticed that the first names are always on the odd rows in the output, and I am trying to capture the most frequent first names in those odd rows.
The fakeprofile.csv file was created by this program:
import csv
import faker
from faker import Faker
fake = Faker()
name = fake.name(); print(name)
ssn = fake.ssn(); print(ssn)
city = fake.city(); print(city)
address = fake.address(); print(address)
email = fake.email(); print(email)
profile = fake.simple_profile()
for i,j in profile.items():
print('{}: {}'.format(i,j))
print('Name: {}, SSN: {}, City: {}, Address: {}, Email: {}'.format(name,ssn,city,address,email))
with open('fakeprofile.csv', 'w') as f:
for i in range(0,5001):
print(f'{fake.name()} {fake.ssn()} {fake.city()} {fake.address()} {fake.email()}', file=f)

Answer 1

Does this achieve what you want?
import collections, re
# Read in all lines into a list
with open('fakeprofile.csv') as f:
lines = f.readlines()
# Throw out every other line
lines = [line for i, line in enumerate(lines) if i%2 == 0]
# Keep only first word of each line
names = [line.split()[0] for line in lines]
# Find most common names
n = 3
frequent_names = collections.Counter(names).most_common(n)
# Display most common names
for name, count in frequent_names:
print(name, count)
To do the counting it uses collections.Counter together with its most_common() method.

Answer 2

I think It would have been better if you use pandas library, for the CSV manipulation (collecting the desire information ), and then apply python collection like counter(df ['name'] ) into it, or else could you give us more information about the CSV file.
thank you

Answer 3

So the main problem you have is that you use a regexp that will capture every letter.
You are interested in the first world in the odd line.
you can do something on those lines:
# either use a dict to count or a list to transform as counter.
dico_count = {}
with open('fakeprofile.csv') as file_open: # use of context manager
line_number = 1
for line in file_open: #iterates all the lines
if line_number % 2 != 0 : # odd line
spt = line.strip().split()
dico_count[spt[0]] = dico_count.get(spt[0], 0) + 1
frequent_name_counter = [(k,v) for k,v in sorted(dico_count.items(), key=lambda x: x[1], reverse=True)]