How strong is your Qiita? Statistics on the number of Contributes seen in the data

Overview

--I tried to analyze the statistical data of Qiita ――I made a quick reference table so that you can understand the power of Qiita --Contribute number, less than 20 people are about 48% of users writing articles ――My Qiita power was about the top 17%

Preface

Recently, I've been less likely to program at home, and how much is my technical ability? I started to think. So, I started thinking, "I'll do my best until the Contribute reaches 1000 in Qiita by the end of this year," in order to improve my technical skills. However, now I have about 157 Contributes. Given that, how difficult is it? Is it realistic? I thought about the feasibility. Therefore, I collected statistical data on the number of Contributes in Qiita and investigated how difficult it was.

Method

You can crawl one by one, but I extracted the desired data from the site called QiitaUserRanking.

https://qiita-user-ranking.herokuapp.com/

The number of Contributes and the data of the number of people were summarized. First of all, scraping is easy.

wget -O - https://qiita-user-ranking.herokuapp.com/chart | grep Bar | grep -o "\[.*\]" | sed "s/\],\[/\n/g" | grep -o "[0-9][0-9]*,[0-9][0-9]*" > data.csv

As the format of data.csv

Number of Contributes,そのNumber of Contributesのユーザーの人数

It is. This data is converted and converted to percentile according to the number of Contributes. Then, write a Python script that outputs according to the Qiita notation.

qiita_stats.py


if __name__=="__main__":
    data = [
        tuple(map(int,l.split(",")))
        for l
        in open("data.csv")
    ]

    all_num = sum(l[1] for l in data)

    for num in [10,20,30,40,50,60,70,80,90,100,200,300,400,500,600,700,800,900,1000,2000,3000,4000,5000]:
        under = sum(l[1] for l in data if l[0] < num)
        print "| %5d | %5d | %5.2f |" % (num,under,(1-float(under)/all_num)*100)
    
    #For ranking yourself
    num = 159
    under = sum(l[1] for l in data if l[0] < num)
    
    print "num %5d : %5d / %5d : %5.2f%%"  % (num,under,all_num,(1-float(under)/all_num)*100)

Here is the output table.

Number of Contributes Number of people Percentile
10 7767 67.19
20 11156 52.88
30 13046 44.89
40 14307 39.57
50 15275 35.48
60 16083 32.06
70 16735 29.31
80 17231 27.22
90 17686 25.29
100 18065 23.69
200 20174 14.78
300 21092 10.91
400 21684 8.41
500 22063 6.80
600 22347 5.61
700 22556 4.72
800 22718 4.04
900 22840 3.52
1000 22943 3.09
2000 23367 1.30
3000 23515 0.67
4000 23573 0.43
5000 23606 0.29

There are 7767 users with less than 10 Contributes. Therefore, users with 10 Contributes are in the top 67.19% of Qiita users. I will take the view. In another example, there are 22063 users with less than 500 Contributes. Therefore, users with 500 Contributes are in the top 6.80%.

Consideration

According to QiitaUserRanking, there are 23,674 people with Contribute of 1 or more. As you can see from the table above, users with 20 Contributes seem to be in the top 52.88%. Therefore, it can be seen that the upper and lower ranks are divided when the number of Contributes is about 20. Currently, my current number of Contributes is 159. As a result of calculating with the script,

num   159 : 19496 / 23674 : 17.65%

The data came out. Therefore, there are 19,496 users with less than 159 Contributes. And ** my ranking seems to be in the top 17.65%. ** **

Impressions

I was aiming

** Users with 1000 Contributes are in the top 3% of Qiita. ** **

I thought it was pretty tough. It's a personal story, but my article has an average Contribute of about 14 per article. Therefore, 72 articles are required to set ** Contribute to 1000. ** Since 2017 is calculated for 7 months, ** Monthly production 10-11 articles. If you write an article about 3 times a week, you will be in time. ** No, this is spicy. If anything, I wanted to write an article with a slightly higher quality or a strong pull, and increase the number of Contributes per article.

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