linkedin__analyze_titles.py

# -*- coding: utf-8 -*-

import sys
import nltk
import csv
from prettytable import PrettyTable

CSV_FILE = sys.argv[1]

transforms = [
    ('Sr.', 'Senior'),
    ('Sr', 'Senior'),
    ('Jr.', 'Junior'),
    ('Jr', 'Junior'),
    ('CEO', 'Chief Executive Officer'),
    ('COO', 'Chief Operating Officer'),
    ('CTO', 'Chief Technology Officer'),
    ('CFO', 'Chief Finance Officer'),
    ('VP', 'Vice President'),
    ]

csvReader = csv.DictReader(open(CSV_FILE), delimiter=',', quotechar='"')
contacts = [row for row in csvReader]

# Read in a list of titles and split apart
# any combined titles like "President/CEO"
# Other variations could be handled as well such
# as "President & CEO", "President and CEO", etc.

titles = []
for contact in contacts:
    titles.extend([t.strip() for t in contact['Job Title'].split('/')
                  if contact['Job Title'].strip() != ''])

# Replace common/known abbreviations

for i in range(len(titles)):
    for transform in transforms:
        titles[i] = titles[i].replace(*transform)

# Print out a table of titles sorted by frequency

pt = PrettyTable(['Title', 'Freq'])
pt.align['Title'] = 'l'
titles_fdist = nltk.FreqDist(titles)
[pt.add_row([title, freq]) for (title, freq) in titles_fdist.items() if freq > 1]
print(pt)

# Print out a table of tokens sorted by frequency

tokens = []
for title in titles:
    tokens.extend([t.strip(',') for t in title.split()])
pt = PrettyTable(['Token', 'Freq'])
pt.align['Token'] =  'l'
tokens_fdist = nltk.FreqDist(tokens)
[pt.add_row([token, freq]) for (token, freq) in tokens_fdist.items() if freq > 1
 and len(token) > 2]
print(pt)