The project is about analyzing the total no of posts for the title beginning with Ask HN and Show HN. And analysze which time of day Hacker News posts receives more number of comments.

In [41]:
import csv 
from csv import reader
In [42]:
 news_file = open("hacker_news.csv") # opening the csv file
 news_file = reader(news_file)
 hn = list(news_file)
 print(hn[:5])

[['id', 'title', 'url', 'num_points', 'num_comments', 'author', 'created_at'], ['12224879', 'Interactive Dynamic Video', 'http://www.interactivedynamicvideo.com/', '386', '52', 'ne0phyte', '8/4/2016 11:52'], ['10975351', 'How to Use Open Source and Shut the Fuck Up at the Same Time', 'http://hueniverse.com/2016/01/26/how-to-use-open-source-and-shut-the-fuck-up-at-the-same-time/', '39', '10', 'josep2', '1/26/2016 19:30'], ['11964716', "Florida DJs May Face Felony for April Fools' Water Joke", 'http://www.thewire.com/entertainment/2013/04/florida-djs-april-fools-water-joke/63798/', '2', '1', 'vezycash', '6/23/2016 22:20'], ['11919867', 'Technology ventures: From Idea to Enterprise', 'https://www.amazon.com/Technology-Ventures-Enterprise-Thomas-Byers/dp/0073523429', '3', '1', 'hswarna', '6/17/2016 0:01']]
In [43]:
headers = hn[0] # removing header row and saving in headers
In [44]:
hn = hn[1:] #this removes the header row
In [45]:
ask_posts = []
show_posts = []
other_posts = []
for row in hn:
    title = row[1]
    if title.lower().startswith('ask hn'):
        ask_posts.append(row)
    elif title.lower().startswith('show hn'):
        show_posts.append(row)
    else:
        other_posts.append(row)
        
print(title)
print(len(ask_posts))
print(len(show_posts))
print(len(other_posts))

            
     


        

    
    

RoboBrowser: Your friendly neighborhood web scraper
1744
1162
17194
In [46]:
ask_posts[:5]
Out[46]:
[['12296411',
  'Ask HN: How to improve my personal website?',
  '',
  '2',
  '6',
  'ahmedbaracat',
  '8/16/2016 9:55'],
 ['10610020',
  'Ask HN: Am I the only one outraged by Twitter shutting down share counts?',
  '',
  '28',
  '29',
  'tkfx',
  '11/22/2015 13:43'],
 ['11610310',
  'Ask HN: Aby recent changes to CSS that broke mobile?',
  '',
  '1',
  '1',
  'polskibus',
  '5/2/2016 10:14'],
 ['12210105',
  'Ask HN: Looking for Employee #3 How do I do it?',
  '',
  '1',
  '3',
  'sph130',
  '8/2/2016 14:20'],
 ['10394168',
  'Ask HN: Someone offered to buy my browser extension from me. What now?',
  '',
  '28',
  '17',
  'roykolak',
  '10/15/2015 16:38']]
In [47]:
def avg_comments(data):
    total_comments = 0
    for row in data:
        comments = int(row[4])
        total_comments += comments
    avg_comments = ((total_comments) / (len(data)))
    return avg_comments
In [48]:
avg_ask_comments= avg_comments(ask_posts)
avg_show_comments = avg_comments(show_posts)
print(avg_ask_comments)
print(avg_show_comments)

14.038417431192661
10.31669535283993

Based on findings , we can conclude that on an average "ask_posts" receive more number of comments when comparing to "show_posts".

In [49]:
import datetime as dt
result_list = []
for row in ask_posts:
    date_time = row[6]
    comments = int(row[4])
    result_list.append([date_time,comments])

counts_by_hour = {}
comments_by_hour = {}
for row in result_list:
    date = row[0]
    hour = dt.datetime.strptime(date,"%m/%d/%Y %H:%M").strftime("%H")
    if hour not in counts_by_hour:
        counts_by_hour[hour] = 1
        comments_by_hour[hour] = row[1]
    else:
        counts_by_hour[hour] += 1
        comments_by_hour[hour] += row[1]
        
print(counts_by_hour)
#len(counts_by_hour)
print(comments_by_hour)
    
#print(ask_posts)

{'12': 73, '17': 100, '11': 58, '20': 80, '03': 54, '21': 109, '10': 59, '15': 116, '08': 48, '01': 60, '02': 58, '14': 107, '18': 109, '09': 45, '23': 68, '04': 47, '19': 110, '00': 55, '07': 34, '16': 108, '05': 46, '06': 44, '22': 71, '13': 85}
{'12': 687, '17': 1146, '11': 641, '20': 1722, '03': 421, '21': 1745, '10': 793, '15': 4477, '08': 492, '01': 683, '02': 1381, '14': 1416, '18': 1439, '09': 251, '23': 543, '04': 337, '19': 1188, '00': 447, '07': 267, '16': 1814, '05': 464, '06': 397, '22': 479, '13': 1253}
In [50]:
avg_by_hour = []
for hour in comments_by_hour and counts_by_hour:
    avg_by_hour.append([hour, comments_by_hour[hour]/counts_by_hour[hour]])

print(avg_by_hour)

[['12', 9.41095890410959], ['17', 11.46], ['11', 11.051724137931034], ['20', 21.525], ['03', 7.796296296296297], ['21', 16.009174311926607], ['10', 13.440677966101696], ['15', 38.5948275862069], ['08', 10.25], ['01', 11.383333333333333], ['02', 23.810344827586206], ['14', 13.233644859813085], ['18', 13.20183486238532], ['09', 5.5777777777777775], ['23', 7.985294117647059], ['04', 7.170212765957447], ['19', 10.8], ['00', 8.127272727272727], ['07', 7.852941176470588], ['16', 16.796296296296298], ['05', 10.08695652173913], ['06', 9.022727272727273], ['22', 6.746478873239437], ['13', 14.741176470588234]]
In [51]:
swap_avg_by_hour = []
for row in avg_by_hour:
    p = row[0]
    q = row[1]
    p,q = q,p
    swap_avg_by_hour.append([p,q])
sorted_swap = sorted(swap_avg_by_hour,reverse = True)
sorted_swap[:5]
Out[51]:
[[38.5948275862069, '15'],
 [23.810344827586206, '02'],
 [21.525, '20'],
 [16.796296296296298, '16'],
 [16.009174311926607, '21']]

These are the top 5 ask_posts with avg_number of comments at respective time

In [61]:
for row in sorted_swap[:5]:
    hours = row[1]
    avg = "{:.2f}".format(row[0])
    hour1 = dt.datetime.strptime(hours,"%H").strftime("%H:%M:")
    print("{hour} {avg1} average comments per post".format(hour=hour1,avg1 = avg))
    

15:00: 38.59 average comments per post
02:00: 23.81 average comments per post
20:00: 21.52 average comments per post
16:00: 16.80 average comments per post
21:00: 16.01 average comments per post

After analyzing the data we can understand that there are more number of posts starting with ask_hn when comparing to show_hn.

  • The post that are posted at 15:00(afternoon 3pm) got highest number of average no of comments per post