Shulou(Shulou.com)06/01 Report--

This article mainly introduces "how Python analyzes Douyin user behavior data". In daily operation, I believe many people have doubts about how Python analyzes Douyin user behavior data. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "how Python analyzes Douyin user behavior data". Next, please follow the editor to study!

Data analysis 1 platform

Daily playback, daily user volume, daily author volume, daily work volume

The change trend of daily playback volume, daily user volume, daily author volume and daily work volume with time is basically the same: steady growth; in the period from 2019-10-20 to 2019-10-29, all indicators first show a huge increase, then tend to be stable, and then fall back to the normal level. Guess that the platform at this point in time has carried out activities to promote, so as to attract a large number of users.

Author: number of works, approval rate of works

The author contributed about 80% of the playback of the platform, obeying the 28-8 rule.

2 works

Source of works

The ID of the top 10 background music are: 22,220,25,68,110,33,468,57,43,238 (there is no extra information to check the corresponding song names)

There is little difference between the like rate and the completion rate of music works with different backgrounds, that is, there is little difference between likes and full playback results after the amount of playback is generated.

Combined with the above picture, most of the songs played on the platform are made up of a small number of popular songs.

The normal proportional relationship between product volume and playback volume for different periods of time.

The volume of products with a duration of 7-12s (playback) accounts for the majority.

The number of players over 23s is basically zero.

The duration and completion rate of the work, and the rate of approval

The product volume is basically proportional to the playback volume in different periods of time.

During the 10-17 time period, the volume of work and playback of the platform is low (work / study time)

The playback volume of 19-0-5 is relatively high throughout the time period.

Release time, completion rate and approval rate of the work

During the 0-5 time period, the completion rate and approval rate of the work are relatively high.

At this point, the study on "how Python analyzes Douyin user behavior data" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.