Analysing community submissions posts.
-Opening and reading the csv file:
from csv import reader
open_file = open('hacker_news.csv')
read_file = reader(open_file)
hn = list(read_file)
display(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']]
Here we'll separate the headers from the content.
-Displaying the Headers:
headers = hn[0]
display(headers)
['id', 'title', 'url', 'num_points', 'num_comments', 'author', 'created_at']
-Displaying the content:
hn = hn[1:]
display(hn[:5])
[['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']]
Separating the posts that start with either Ask HN or Show HN:
-Displaying the amount of posts that are in each list.
ask_posts = []
show_posts = []
other_posts = []
for h in hn:
title = h[1].lower()
if title.startswith('ask hn'):
ask_posts.append(h)
elif title.startswith('show hn'):
show_posts.append(h)
else:
other_posts.append(h)
display(len(ask_posts))
display(len(show_posts))
display(len(other_posts))
1744
1162
17194
-Finding the average comments that start with Ask HN:
total_ask_comments = 0
for post in ask_posts:
post = post[4]
post = int(post)
total_ask_comments += post
avg_ask_comments = total_ask_comments / (len(ask_posts))
print(avg_ask_comments)
14.038417431192661
-Finding the average comments that start with Show HN:
total_show_comments = 0
for post in show_posts:
post = post[4]
post = int(post)
total_show_comments += post
avg_show_comments = total_show_comments / (len(show_posts))
print(avg_show_comments)
10.31669535283993
Ask posts received more comments on average than Show posts.
-Calculating the amount of Ask posts created during each hour and the amount of comments received:
import datetime as dt
result_list = []
for post in ask_posts:
result_list.append([post[6], int(post[4])])
counts_by_hour = {}
comments_by_hour = {}
for result in result_list:
date = result[0]
comment = result[1]
hour = dt.datetime.strptime(date, "%m/%d/%Y %H:%M").strftime("%H")
if hour not in counts_by_hour:
comments_by_hour[hour] = comment
counts_by_hour[hour] = 1
else:
comments_by_hour[hour] += comment
counts_by_hour[hour] += 1
display(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}
-Now, we'll find out the average number of comments for posts created during each hour of the day:
avg_by_hour = []
for hr in comments_by_hour:
avg_by_hour.append([hr, comments_by_hour[hr]/counts_by_hour[hr]])
display(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]]
-Sorting and printing the averages by hour:
swap_avg_by_hour = []
for row in avg_by_hour:
swap_avg_by_hour.append([row[1], row[0]])
print(swap_avg_by_hour)
[[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']]
-Sorting those values
sorted_swap = sorted(swap_avg_by_hour, reverse=True)
print(sorted_swap[0:5])
[[38.5948275862069, '15'], [23.810344827586206, '02'], [21.525, '20'], [16.796296296296298, '16'], [16.009174311926607, '21']]
for i in sorted_swap[:5]:
hour = dt.datetime.strptime(i[1], "%H").strftime("%H:%M")
print(f"{hour}: {i[0]:.2f} average comments per post.")
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.
To have a higher change of receiving comments, you should post the question at 15:00 Eastern Time in the US.