📈 1475 Unique Participants all Week
1475 Unique Participants all Week 📈 3066 Entries all Week
3066 Entries all WeekNumber of Posts and Participants per Tag
| Number of Posts | Number of Participants | Weekly Changes, % | |
|---|---|---|---|
| #foodphotography | |||
| #animalphotography | |||
| #landscapephotography | +11.6 % | ||
| #cityscapephotography | +13.4 % | ||
| #architecturalphotography | +3.4 % | ||
| #vehiclephotography | +7.7 % | ||
| #macrophotography | +135.7 % | ||
| #colourfulphotography | +234.3 % | ||
| #streetphotography | |||
| #portraitphotography | |||
| #sportsphotography | +2.7 % | ||
| #smartphonephotography | |||
| #goldenhourphotography | |||
| #longexposurephotography |
Number taking part in both Contests of the Day
| Participants in both contests | |
|---|---|
Number of Participants each Day
| Participants for the day | Weekly Changes, % | |
|---|---|---|
+3.3 % | ||
+8.7 % | ||
+141.5 % | ||
+1.4 % | ||
Authors with the Most Posts
Authors Participated in All PhotoContests this Week
Histogram of Number of Posts every 15 minutes
The data represents the CET (UTC), which is the official time for @photocontests!
Background Info
I have extracted the data using api.steemit.com and the following api call:
payload = '{"id":4,"jsonrpc":"2.0","method":"call","params":["database_api","get_discussions_by_created", [{"tag":"' + tag + '","limit":100}]]}'
I am using pandas library. Pandas is an open source and is designed for Python users for data analysis and manipulation.
Monday: foodphotography and animalphotography
Tuesday: landscapephotography and cityscapephotography
Wednesday: architecturalphotography and vehiclephotography
Thursday: macrophotography and colourfulphotography
Friday: streetphotography and portraitphotography
Saturday: sportsphotography and smartphonephotography
Sunday: goldenhourphotography and longexposurephotography