Search through specific subreddits from the command line. Returns titles and vote scores only

reddit_cmdline_search.py

import praw
import re
from tqdm import tqdm
import pandas as pd
pd.options.display.max_colwidth = 70

# pick subreddits, any delimiter but letter, number and new line is okay (will default to r/news if no valid input)
subreddits = "+".join(re.findall(r'[A-Za-z0-9]+', raw_input("Enter desired subreddits on one line\n")))
print "Your subreddits are: " + subreddits

# connect to reddit - needs your own credentials
reddit = praw.Reddit(client_id='ur_cliend_id',
                     client_secret='ur_client_secret',
                     user_agent='Hi it me')

# get submissions from subreddits according to user-selected sort
search_type = raw_input("Pick your search type. Options: 'hot' (default), 'new', 'top'\n")
def get_submissions(search_type, subreddits):	
	if search_type.lower() == 'new':
		return reddit.subreddit(subreddits).new(limit = None)
	elif search_type.lower() == 'top':
		return reddit.subreddit(subreddits).top(limit = None)
	else:
		return reddit.subreddit(subreddits).hot(limit = None)

print "This may take a bit"
submissions = []
# if you want more info than title and score, edit this bit
try:
	#tqdm inaccurate as submissions are lazily evaluated, but lets user know shit is happening
	for submission in tqdm(get_submissions(search_type, subreddits)): 
	    submissions.append([submission.title, submission.score])
except:
	print "No valid subreddit was entered, defaulting to News."
	for submission in tqdm(get_submissions(search_type, "News")):
	    submissions.append([submission.title, submission.score])
print "Fetched %d submissions" % len(submissions)
submissions = pd.DataFrame(submissions, columns = ['titles', 'scores'])

# get search terms. Can be user input or the most commonn proper nouns (excluding minor ones)
search_term_type = raw_input("Pick your search term type. Options: 'choose own' (default), 'common proper nouns' \n")

if search_term_type.lower() == 'common proper nouns':
	# find all proper nouns
	stop_words = 'In|The|Man|New|What|My|This|Woman|Best|Why|How|You|Is|Part'
	all_titles = re.sub(stop_words, "", " ". join(submissions.titles)) # removes useless words from titles. should expand.
	ProperNouns = re.findall(r'[A-Z][a-z]+', all_titles) 
	# get most common
	search_terms = pd.Series(ProperNouns).value_counts().nlargest(10).index.values
else:
	# user input search terms
	search_terms = re.findall(r'((?<=")[A-Za-z].*?(?="))|([A-Za-z]+)', raw_input("Enter all search terms (on one line):\n"))
	search_terms = [e for l in search_terms for e in l if e] 
print "your search_terms are: " + ", ".join(search_terms)

# get minimum score to show
try:
	min_score = int(raw_input("What is the minimum score for a submission to be included? Defaults to 100.\n"))
except:
	min_score = 100

# subset submissions with applicable score
relevant_submissions = submissions[submissions['scores'] >= min_score]

# how to display
output_type = raw_input("Do you want to group by search term, including duplicates? (y/n)\n defaults to all unique relevant titles.\n")

if output_type.lower() == 'y':
	# output grouped by search term
	for term in search_terms:
		print "\n Titles about " + term + ":"
		print relevant_submissions[relevant_submissions.titles.str.contains("(?i)"+term)]
else:
	# output all unique hits
	pattern =  "(?i)" + "|(?i)".join(search_terms)
	print relevant_submissions[relevant_submissions.titles.str.contains(pattern)]