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📈 1760 Unique Participants all Week
1760 Unique Participants all Week 📈 4016 Entries all Week
4016 Entries all WeekNumber of Posts and Participants per Tag
| Number of Posts | Number of Participants | Weekly Changes, % | |
|---|---|---|---|
| #foodphotography | |||
| #animalphotography | |||
| #landscapephotography | +3.6 % | ||
| #cityscapephotography | +4.9 % | ||
| #architecturalphotography | +3.7 % | ||
| #vehiclephotography | +13.6 % | ||
| #macrophotography | |||
| #colourfulphotography | |||
| #streetphotography | |||
| #portraitphotography | |||
| #sportsphotography | |||
| #smartphonephotography | |||
| #goldenhourphotography | +35.7 % | ||
| #longexposurephotography | +56.8 % |
Number taking part in both Contests of the Day
| Participants in both contests | |
|---|---|
Number of Participants each Day
| Participants for the day | Weekly Changes, % | |
|---|---|---|
+5.8 % | ||
+37.3 % |
Histogram of Number of Posts every 15 minutes
The data represents the CET (UTC+1), which is the official time for 
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
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