Hacker News is a site started by the startup incubator Y Combinator, where user-submitted stories (known as "posts") receive votes and comments, similar to reddit
We'll specifically compare these two types of posts to determine the following:
-Do Ask HN or Show HN receive more comments on average?
-Do posts created at a certain time receive more comments on average?
import csv
file = open("hacker_news.csv")
hn = list(csv.reader(file))
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']]
headers = hn[0]
hn = hn[1:]
print(headers)
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'], ['10301696', 'Note by Note: The Making of Steinway L1037 (2007)', 'http://www.nytimes.com/2007/11/07/movies/07stein.html?_r=0', '8', '2', 'walterbell', '9/30/2015 4:12']]
ask_posts = []
show_posts =[]
other_posts = []
for post in hn:
title = post[1]
if title.lower().startswith("ask hn"):
ask_posts.append(post)
elif title.lower().startswith("show hn"):
show_posts.append(post)
else:
other_posts.append(post)
print(len(ask_posts))
print(len(show_posts))
print(len(other_posts))
1744 1162 17194
total_ask_comments = 0
for post in ask_posts:
total_ask_comments += int(post[4])
avg_ask_comments = total_ask_comments / len(ask_posts)
print(avg_ask_comments)
14.038417431192661
total_show_comments = 0
for post in show_posts:
total_show_comments += int(post[4])
avg_show_comments = total_show_comments / len(show_posts)
print(avg_show_comments)
10.31669535283993
import datetime as dt
result_list = []
for post in ask_posts:
result_list.append(
[post[6], int(post[4])]
)
comments_by_hour = {}
counts_by_hour = {}
date_format = "%m/%d/%Y %H:%M"
for each_row in result_list:
date = each_row[0]
comment = each_row[1]
time = dt.datetime.strptime(date, date_format).strftime("%H")
if time in counts_by_hour:
comments_by_hour[time] += comment
counts_by_hour[time] += 1
else:
comments_by_hour[time] = comment
counts_by_hour[time] = 1
comments_by_hour
{'09': 251, '13': 1253, '10': 793, '14': 1416, '16': 1814, '23': 543, '12': 687, '17': 1146, '15': 4477, '21': 1745, '20': 1722, '02': 1381, '18': 1439, '03': 421, '05': 464, '19': 1188, '01': 683, '22': 479, '08': 492, '04': 337, '00': 447, '06': 397, '07': 267, '11': 641}
avg_by_hour = []
for hr in comments_by_hour:
avg_by_hour.append([hr, comments_by_hour[hr] / counts_by_hour[hr]])
avg_by_hour
[['09', 5.5777777777777775], ['13', 14.741176470588234], ['10', 13.440677966101696], ['14', 13.233644859813085], ['16', 16.796296296296298], ['23', 7.985294117647059], ['12', 9.41095890410959], ['17', 11.46], ['15', 38.5948275862069], ['21', 16.009174311926607], ['20', 21.525], ['02', 23.810344827586206], ['18', 13.20183486238532], ['03', 7.796296296296297], ['05', 10.08695652173913], ['19', 10.8], ['01', 11.383333333333333], ['22', 6.746478873239437], ['08', 10.25], ['04', 7.170212765957447], ['00', 8.127272727272727], ['06', 9.022727272727273], ['07', 7.852941176470588], ['11', 11.051724137931034]]
swap_avg_by_hour = []
for row in avg_by_hour:
swap_avg_by_hour.append([row[1], row[0]])
print(swap_avg_by_hour)
sorted_swap = sorted(swap_avg_by_hour, reverse=True)
sorted_swap
[[5.5777777777777775, '09'], [14.741176470588234, '13'], [13.440677966101696, '10'], [13.233644859813085, '14'], [16.796296296296298, '16'], [7.985294117647059, '23'], [9.41095890410959, '12'], [11.46, '17'], [38.5948275862069, '15'], [16.009174311926607, '21'], [21.525, '20'], [23.810344827586206, '02'], [13.20183486238532, '18'], [7.796296296296297, '03'], [10.08695652173913, '05'], [10.8, '19'], [11.383333333333333, '01'], [6.746478873239437, '22'], [10.25, '08'], [7.170212765957447, '04'], [8.127272727272727, '00'], [9.022727272727273, '06'], [7.852941176470588, '07'], [11.051724137931034, '11']]
[[38.5948275862069, '15'], [23.810344827586206, '02'], [21.525, '20'], [16.796296296296298, '16'], [16.009174311926607, '21'], [14.741176470588234, '13'], [13.440677966101696, '10'], [13.233644859813085, '14'], [13.20183486238532, '18'], [11.46, '17'], [11.383333333333333, '01'], [11.051724137931034, '11'], [10.8, '19'], [10.25, '08'], [10.08695652173913, '05'], [9.41095890410959, '12'], [9.022727272727273, '06'], [8.127272727272727, '00'], [7.985294117647059, '23'], [7.852941176470588, '07'], [7.796296296296297, '03'], [7.170212765957447, '04'], [6.746478873239437, '22'], [5.5777777777777775, '09']]
print("Top 5 Hours for 'Ask HN' Comments")
for avg, hr in sorted_swap[:5]:
print(
"{}: {:.2f} average comments per post".format(
dt.datetime.strptime(hr, "%H").strftime("%H:%M"),avg
)
)