def parse_answer(answer):
# Fetching answer
resp = get_page(ANSWER_URL.format(answer.question, answer.writer_uname))
doc = BeautifulSoup(resp, 'html.parser', parse_only=ANSWER_STRAIN)
# Get Credible Users who have upvoted this answer
users = doc.find('div', class_=CREDIBILITY_CLASS).find_all('a',
class_='user')
for user in users:
Profile.create_or_get(uname=user['href'].split('/')[2],
name=user.string)
# Update answer stats
answer.views = int(
doc.find('div', class_=VIEW_ROW_CLASS).strong.string.replace(',', ''))
answer.upvotes = int(
doc.find('a', class_=UPVOTE_ROW_CLASS).strong.string.replace(',', ''))
answer.last_parsed = datetime.datetime.now()
answer.save()
# Saving the HTML code of the profile
# Storing Answers in not feasible.
#filename = str(answer.id) + '.html'
#with open(os.path.join(ANSWERS_FOLDER, filename), 'w+') as fstream:
# fstream.write(resp)
sys.stdout.write('\rDone Parsing Answer id %d (%d)' %
(answer.id, len(users)))
sys.stdout.flush()
def parse_answer(answer):
# Fetching answer
resp = get_page(ANSWER_URL.format(answer.question, answer.writer_uname))
doc = BeautifulSoup(resp, 'html.parser', parse_only=ANSWER_STRAIN)
# Get Credible Users who have upvoted this answer
users = doc.find('div', class_=CREDIBILITY_CLASS).find_all('a', class_='user')
for user in users:
Profile.create_or_get(uname=user['href'].split('/')[2], name=user.string)
# Update answer stats
answer.views = int(doc.find('div', class_=VIEW_ROW_CLASS).strong.string
.replace(',', ''))
answer.upvotes = int(doc.find('a', class_=UPVOTE_ROW_CLASS).strong.string
.replace(',', ''))
answer.last_parsed = datetime.datetime.now()
answer.save()
# Saving the HTML code of the profile
filename = str(answer.id) + '.html'
with open(os.path.join(ANSWERS_FOLDER, filename), 'w+') as fstream:
fstream.write(resp)
sys.stdout.write('\rDone Parsing Answer id %d (%d)' % (answer.id, len(users)))
sys.stdout.flush()
def profile_with_max_views(limit=20):
profiles = Profile.select().order_by(Profile.views.desc()).limit(limit)
print("Top %d Writers with most answers - " % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print("{0} ({1} views)".format(u, profile.views))
def profile_with_max_followers(limit=20):
profiles = Profile.select().order_by(Profile.followers.desc()).limit(limit)
print("Top %d Most followed - " % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print("{0} ({1} followers)".format(u, profile.followers))
def profile_with_max_followers(limit=20):
profiles = Profile.select().order_by(Profile.followers.desc()).limit(limit)
print('Top %d Most followed - ' % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print('{0} ({1} followers)'.format(u, profile.followers))
def profile_with_max_views(limit=20):
profiles = Profile.select().order_by(Profile.views.desc()).limit(limit)
print('Top %d Writers with most answers - ' % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print('{0} ({1} views)'.format(u, profile.views))
def answers_with_max_upvotes(limit=20):
answers = Answer.select().order_by(Answer.upvotes.desc()).limit(limit)
print('Top %d Most upvoted answers - ' % limit)
total_views = 0
total_upvotes = 0
writer = {}
for answer in answers:
u = ANSWER_URL.format(answer.question, answer.writer_uname).encode("UTF-8")
print("{0} ({1}, {2}, {3})".format(u, answer.upvotes,
answer.views, answer.views / answer.upvotes))
total_upvotes += answer.upvotes
total_views += answer.views
writer[answer.writer_uname] = writer.get(answer.writer_uname, 0) + 1
print("Total Views = {0}".format(total_views))
print("Total Upvotes = {0}".format(total_upvotes))
print("Average Views = {0}".format(total_views / limit))
print("Average Upvotes = {0}".format(total_upvotes / limit))
avg_up = (float(total_upvotes) / float(total_views)) * 100
print("On an average %.2f viewers upvoted the answer" % avg_up)
# Writer Stat
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
notw = 0
for w in writer_list:
if w['uname'] in writer:
notw += 1
print("{0} People on this list are Top Writers(2016)".format(notw))
sorted_writer = sorted(writer.items(), key=operator.itemgetter(1),
reverse=True)
print("Total number of unique writers is {0}".format(len(sorted_writer)))
total_followers = 0
total_answers = 0
for tup in sorted_writer:
profile = Profile.get(Profile.uname == tup[0])
total_followers += int(profile.followers)
total_answers += int(profile.total_answers)
print("Average number of followers of each {0}".format(
total_followers / len(sorted_writer)))
print("Average number of answers written by each is {}".format(
total_answers / len(sorted_writer)))
# Plotting Graph
figure = mpplt.figure(figsize=(10, 10))
plt = figure.add_subplot(1, 1, 1)
plt.set_title("Views vs Upvote")
plt.plot([answer.views for answer in answers],
[answer.upvotes for answer in answers],
'.', color='green')
plt.set_xlabel('Views')
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.set_xlim([0, 1500000])
plt.set_ylim([10000, 25000])
plt.set_ylabel('Upvotes')
figure.savefig('view_upvote.png', facecolor='white', edgecolor='black')
def answers_with_max_upvotes(limit=20):
answers = Answer.select().order_by(Answer.upvotes.desc()).limit(limit)
print("Top %d Most upvoted answers - " % limit)
total_views = 0
total_upvotes = 0
writer = {}
for answer in answers:
u = ANSWER_URL.format(answer.question, answer.writer_uname).encode("UTF-8")
print("{0} ({1}, {2}, {3})".format(u, answer.upvotes, answer.views, answer.views / answer.upvotes))
total_upvotes += answer.upvotes
total_views += answer.views
writer[answer.writer_uname] = writer.get(answer.writer_uname, 0) + 1
print("Total Views = {0}".format(total_views))
print("Total Upvotes = {0}".format(total_upvotes))
print("Average Views = {0}".format(total_views / limit))
print("Average Upvotes = {0}".format(total_upvotes / limit))
avg_up = (float(total_upvotes) / float(total_views)) * 100
print("On an average %.2f viewers upvoted the answer" % avg_up)
# Writer Stat
with open("top_writers_2016.json", "r") as fstream:
writer_list = json.load(fstream)
notw = 0
for w in writer_list:
if w["uname"] in writer:
notw += 1
print("{0} People on this list are Top Writers(2016)".format(notw))
sorted_writer = sorted(writer.items(), key=operator.itemgetter(1), reverse=True)
print("Total number of unique writers is {0}".format(len(sorted_writer)))
total_followers = 0
total_answers = 0
for tup in sorted_writer:
profile = Profile.get(Profile.uname == tup[0])
total_followers += int(profile.followers)
total_answers += int(profile.total_answers)
print("Average number of followers of each {0}".format(total_followers / len(sorted_writer)))
print("Average number of answers written by each is {}".format(total_answers / len(sorted_writer)))
# Plotting Graph
figure = mpplt.figure(figsize=(10, 10))
plt = figure.add_subplot(1, 1, 1)
plt.set_title("Views vs Upvote")
plt.plot([answer.views for answer in answers], [answer.upvotes for answer in answers], ".", color="green")
plt.set_xlabel("Views")
plt.ticklabel_format(style="sci", axis="x", scilimits=(0, 0))
plt.ticklabel_format(style="sci", axis="y", scilimits=(0, 0))
plt.set_xlim([0, 1500000])
plt.set_ylim([10000, 25000])
plt.set_ylabel("Upvotes")
figure.savefig("view_upvote.png", facecolor="white", edgecolor="black")
def gateway():
if is_logged_in():
return redirect(url_for("index"))
err = ""
if request.method == "POST":
try:
email = fix(request.form["email"], "@\.")
username = fix(request.form["username"])
password = fix(request.form["password"])
confirm = fix(request.form["confirm"])
if validate_email(email):
if not Profile.already_exists(email=email):
if validate_username(username):
if not Profile.already_exists(username=username):
if password == confirm and password != "":
Profile.insert_profile(email, username,
hash_password(password))
return render_template("gateway.html",
title="Gateway")
else:
err = "Invalid Passwords Entered"
else:
err = "Username Already Exists"
else:
err = "Invalid Username Entered"
else:
err = "Email Already Exists"
else:
err = "Invalid Email Entered"
except Exception as e:
email = fix(request.form["email"], "@\.")
password = fix(request.form["password"])
if email is not None and password is not None:
if Profile.already_exists(email=email):
expected_pass = Profile.get_password_hash(email)
salt, pass_hash = str(expected_pass).split("$")
if expected_pass == str(hash_password(password, salt)):
session["email"] = email
session["username"] = Profile.get("username",
email=email)
return redirect(url_for("index"))
else:
err = "Incorrect Email or Password"
else:
err = "Incorrect Email"
else:
err = "Fill in all blanks"
reg = True if request.args.get("reg") == "true" else False
return render_template("gateway.html",
title="Gateway",
indent=False,
reg=reg,
err=err)
def profile_with_max_edits(limit=20):
profiles = Profile.select().order_by(Profile.edits.desc()).limit(limit)
# Top Writer
tw = {}
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
for writer in writer_list:
tw[writer['uname']] = 0
top_writer_count = 0
total_edits = 0
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print(u'{0} ({1} edits)'.format(u, profile.posts))
total_edits += profile.posts
if profile.uname in tw:
top_writer_count += 1
avg_edits = total_edits / len(profiles)
print("Total number of edits is %d" % total_edits)
print("Average number of edits by a single user is %d" % avg_edits)
print("Number on Top Writers 2016 on this list is %d" % top_writer_count)
# Visualisation
fig = plt.figure(figsize=(21, 14))
plt.title("Users with Most Edits on Quora")
ydata = [prof.edits for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('User')
plt.ylabel('Posts Written by User')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(avg_edits, -0.5, len(profiles) - 0.5, label='Average',
colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('most_posts.png', facecolor='white', edgecolor='black')
action='store_true',
help='Do not Crawl Profiles')
parser.add_argument('--no_answer',
action='store_true',
help='Do not Crawl Answers')
args = parser.parse_args()
# Filling Database with Top Writers 2016
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
with open('other_writers.json', 'r') as fstream:
writer_list += json.load(fstream)
create_directory(ANSWERS_FOLDER)
create_directory(PROFILE_FOLDER)
for writer in writer_list:
new = Profile.create_or_get(uname=writer['uname'],
name=writer['name'])[1]
if new: print(u'New Profile %s Created' % writer['uname'])
#print "Number of Writers Added = ", len(writer_list)
# Starting to Crawl
total_parsing = 0
max_crawl = args.max_crawl
while total_parsing < max_crawl:
if not args.no_profile:
# Parse Old Profiles
old_time = datetime.datetime.now() - datetime.timedelta(days=7)
old_profiles = Profile.select().where(
Profile.last_parsed <= old_time).limit(max_crawl -
total_parsing)
total_parsing += len(old_profiles)
print "Number of Profiles to Crawl - ", len(old_profiles)
help='Number of maximum requests to make')
parser.add_argument('--no_profile', action='store_true',
help='Do not Crawl Profiles')
parser.add_argument('--no_answer', action='store_true',
help='Do not Crawl Answers')
args = parser.parse_args()
# Filling Database with Top Writers 2016
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
with open('other_writers.json', 'r') as fstream:
writer_list += json.load(fstream)
create_directory(ANSWERS_FOLDER)
create_directory(PROFILE_FOLDER)
for writer in writer_list:
new = Profile.create_or_get(uname=writer['uname'], name=writer['name'])[1]
if new: print(u'New Profile %s Created' % writer['uname'])
#print "Number of Writers Added = ", len(writer_list)
# Starting to Crawl
total_parsing = 0
max_crawl = args.max_crawl
while total_parsing < max_crawl:
if not args.no_profile:
# Parse Old Profiles
old_time = datetime.datetime.now() - datetime.timedelta(days=7)
old_profiles = Profile.select().where(
Profile.last_parsed <= old_time).limit(max_crawl - total_parsing)
total_parsing += len(old_profiles)
print "Number of Profiles to Crawl - ", len(old_profiles)
for profile in old_profiles:
def profile_with_max_views(limit=200):
profiles = Profile.select().order_by(Profile.views.desc()).limit(limit)
total_views = 0
total_followers = 0
total_following = 0
total_answers = 0
total_views = 0
# Top Writer
tw = {}
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
for writer in writer_list:
tw[writer['uname']] = 0
top_writer_count = 0
print('Top %d Writers with most answers - ' % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print(u'{0} ({1})'.format(u, profile.views))
total_views += profile.views
total_followers += profile.followers
total_following += profile.following
total_views += profile.views
total_answers += profile.answers
if profile.uname in tw:
top_writer_count += 1
average_per_f = total_views / total_followers
# Per Follower Plot
fig = plt.figure(figsize=(21, 14))
plt.title("Views / Follower for Most Viewed Writers")
ydata = [float(prof.views) / prof.followers for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('Profile')
plt.ylabel('Views per Follower')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(average_per_f, -0.5, len(profiles) - 0.5, label='Average',
colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('views_per_follower.png', facecolor='white', edgecolor='black')
fig = plt.figure(figsize=(21, 14))
plt.title("All-time Views")
total_views -= profiles[0].views + profiles[1].views
profiles = profiles[2:]
average_views = total_views / len(profiles)
ydata = [prof.views for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('Profile')
plt.ylabel('All Time Answer Views')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(average_views, -0.5, len(profiles) - 0.5, label='Average',
colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('all_time_views.png', facecolor='white', edgecolor='black')
def profile_with_max_answers(limit=20):
profiles = Profile.select().order_by(
Profile.total_answers.desc()).limit(limit)
total_answers = 0
total_views = 0
# Top Writer
tw = {}
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
for writer in writer_list:
tw[writer['uname']] = 0
top_writer_count = 0
print('Top %d Writers with most answers - ' % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print(u'{0} ({1} answers)'.format(u, profile.total_answers))
total_answers += profile.total_answers
total_views += profile.views
if profile.uname in tw:
top_writer_count += 1
avg_views_per_answer = total_views / total_answers
avg_views_per_user = total_views / len(profiles)
avg_ans_per_user = total_answers / len(profiles)
print("Total number of answers is %d" % total_answers)
print("Average number of answers per user is %d" % avg_ans_per_user)
print("Average number of views per answer is %d" % avg_views_per_answer)
print("Average all time views for a user is %d" % avg_views_per_user)
print("Number of Top Writers 2016 in this list is %d" % top_writer_count)
fig = plt.figure(figsize=(21, 14))
plt.title("Users with most answers on Quora")
ydata = [prof.total_answers for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('Profile')
plt.ylabel('Number of answers written')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(avg_ans_per_user, -0.5, len(profiles) - 0.5,
label='Average Number of Answers', colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
plt.legend()
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('most_answers.png', facecolor='white', edgecolor='black')
fig = plt.figure(figsize=(21, 14))
plt.title("Average views on each answer for users with most answers on Quora")
ydata = [float(prof.views) / prof.total_answers for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('Profile')
plt.ylabel('All time views on answers / Number of answers')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(avg_views_per_answer, -0.5, len(profiles) - 0.5,
label='Average Number of views / answer', colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
plt.legend()
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('view_avg_most_ans.png', facecolor='white', edgecolor='black')
def answers_with_max_upvotes(limit=20):
answers = Answer.select().order_by(Answer.upvotes.desc()).limit(limit)
print('Top %d Most upvoted answers - ' % limit)
total_views = 0
total_upvotes = 0
writer = {}
max_views = 0
for answer in answers:
u = ANSWER_URL.format(answer.question, answer.writer_uname).encode("UTF-8")
print("{0} ({1}, {2}, {3})".format(u, answer.upvotes,
answer.views, answer.views / answer.upvotes))
total_upvotes += answer.upvotes
total_views += answer.views
writer[answer.writer_uname] = writer.get(answer.writer_uname, 0) + 1
if answer.views > max_views :
max_views = answer.views
print "Max - ", u, answer.views
print("Total Views = {0}".format(total_views))
print("Total Upvotes = {0}".format(total_upvotes))
print("Average Views = {0}".format(total_views / limit))
print("Average Upvotes = {0}".format(total_upvotes / limit))
avg_up = (float(total_upvotes) / float(total_views)) * 100
print("On an average %.2f viewers upvoted the answer" % avg_up)
# Writer Stat
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
notw = 0
for w in writer_list:
if w['uname'] in writer:
notw += 1
print("{0} People on this list are Top Writers(2016)".format(notw))
sorted_writer = sorted(writer.items(), key=operator.itemgetter(1),
reverse=True)
print sorted_writer[:10]
print("Total number of unique writers is {0}".format(len(sorted_writer)))
total_followers = 0
total_answers = 0
for tup in sorted_writer:
profile = Profile.get(Profile.uname == tup[0])
total_followers += int(profile.followers)
total_answers += int(profile.total_answers)
print("Average number of followers of each {0}".format(
total_followers / len(sorted_writer)))
print("Average number of answers written by each is {}".format(
total_answers / len(sorted_writer)))
# Plotting Graph
figure = plt.figure(figsize=(10, 10))
splt = figure.add_subplot(1, 1, 1)
splt.set_title("Views vs Upvote")
splt.plot([answer.views for answer in answers],
[answer.upvotes for answer in answers],
'.', color='green')
splt.set_xlabel('Views')
splt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
splt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
splt.set_xlim([0, 1500000])
splt.set_ylim([10000, 25000])
splt.set_ylabel('Upvotes')
figure.tight_layout()
figure.savefig('view_upvote.png', facecolor='white', edgecolor='black')
def profile(username):
if not Profile.already_exists(username=username):
return redirect(url_for("index"))
return render_template("profile.html",
title="%s's Profile" % username,
username=username)
"https://s3.amazonaws.com/uifaces/faces/twitter/langate/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/anoff/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/osvaldas/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/jayrobinson/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/jm_denis/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/brandclay/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/VinThomas/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/_victa/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/saschamt/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/victorerixon/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/noxdzine/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/yalozhkin/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/flame_kaizar/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/baliomega/128.jpg",
"https://s3.amazonaws.com/uifaces/faces/twitter/terryxlife/128.jpg"
]
for j in "abcdefghijklmnoprst":
for i, name in enumerate(NAMES):
email = name.lower().replace(" ", ".") + ("*****@*****.**" % (j))
face = FACES[i]
user = User(email=email,
active=True,
display_name=name,
photo_url=face)
profile = Profile(external_key=random_address_hash(), data_json="{}")
user.profiles.append(profile)
db.session.add(user)
db.session.commit()
print "Added %s" % (name)
def profile_with_max_followers(limit=20):
profiles = Profile.select().order_by(Profile.followers.desc()).limit(limit)
total_views = 0
total_followers = 0
total_following = 0
total_answers = 0
total_views = 0
# Top Writer
tw = {}
with open('top_writers_2016.json', 'r') as fstream:
writer_list = json.load(fstream)
for writer in writer_list:
tw[writer['uname']] = 0
top_writer_count = 0
print('Top %d Most followed - ' % limit)
for profile in profiles:
u = PROFILE_URL.format(profile.uname)
print(u'{0} ({1} followers)'.format(u, profile.followers))
total_views += profile.views
total_followers += profile.followers
total_following += profile.following
total_views += profile.views
total_answers += profile.total_answers
if profile.uname in tw:
top_writer_count += 1
avg_followers = total_followers / len(profiles)
avg_following = total_following / len(profiles)
avg_answers = total_answers / len(profiles)
avg_views = total_views / len(profiles)
avg_v_gain_f = total_views / total_followers
avg_f_gain_ans = total_followers / total_answers
print("Total number of all-time views is %d" % total_views)
print("Average number of all-time views is %d" % avg_views)
print("Average number of followers is %d" % avg_followers)
print("Average number of views gained per follower is %d" % avg_v_gain_f)
print("Total number of answers written by them is %d" % total_answers)
print("Average number of answers written by each is %d" % avg_answers)
print("Average number of people these writers follow is %d" % avg_following)
print("Average number of followers gained per answer is %d" % avg_f_gain_ans)
print("Number on Top Writers 2016 is %d" % top_writer_count)
fig = plt.figure(figsize=(21, 14))
plt.title("Users with most followers on Quora")
ydata = [prof.followers for prof in profiles]
xticks = [prof.name for prof in profiles]
plt.xlabel('Profile')
plt.ylabel('Number of followers')
plt.bar(range(len(profiles)), ydata, align='center')
plt.xticks(range(len(profiles)), xticks, rotation=90)
plt.hlines(avg_followers, -0.5, len(profiles) - 0.5, label='Average',
colors='red')
#plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.xlim([-0.5, len(profiles) - 0.5])
fig.tight_layout()
#plt.set_ylim([10000, 25000])
plt.savefig('most_followed_users.png', facecolor='white', edgecolor='black')
ff_ratio = []
xticks = []
fa_ratio = []
for p in profiles:
if p.total_answers > 10 and p.following > 10 and \
p.followers / p.following < 4000:
ff_ratio.append(float(p.followers) / p.following)
fa_ratio.append(float(p.followers) / p.total_answers)
xticks.append(p.name)
print(len(ff_ratio))
print(len(fa_ratio))
avg_ff = float(sum(ff_ratio)) / len(ff_ratio)
avg_fa = float(sum(fa_ratio)) / len(fa_ratio)
xpos_ff = range(len(ff_ratio))
xpos_fa = [x + 0.4 for x in range(len(fa_ratio))]
tick_pos = [x + 0.4 for x in range(len(fa_ratio))]
fig = plt.figure(figsize=(21, 14))
plt.title("Metrics of Users with most followers on Quora")
bar1 = plt.bar(xpos_ff, ff_ratio, 0.35, color='red')
bar2 = plt.bar(xpos_fa, fa_ratio, 0.35, color='blue')
l1 = plt.hlines(avg_ff, -0.5, len(ff_ratio), linestyles='dashed', color='red',
label='Average Followers / Following ratio')
l2 = plt.hlines(avg_fa, -0.5, len(ff_ratio), linestyles='dashed',
color='blue', label='Average Followers / Answers ratio')
plt.xlabel('Users')
plt.ylabel('Ratio Value')
plt.xticks(tick_pos, xticks, rotation=90)
plt.legend((bar1[0], bar2[0], l1, l2),
("Follower / Following Ratio",
"Follower / Answer Ratio",
"Average Followers / Following ratio",
"Average Followers / Answers ratio"))
plt.xlim([-0.5, len(ff_ratio)])
fig.tight_layout()
plt.savefig('user_metric.png', facecolor='white', edgecolor='black')
