📈 1750 Unique Participants all Week
1750 Unique Participants all Week 📈 3763 Entries all Week
3763 Entries all WeekNumber of Posts and Participants per Tag
| Number of Posts | Number of Participants | Weekly Changes, % | |
|---|---|---|---|
| #foodphotography | +108.2 % | ||
| #animalphotography | +105.7 % | ||
| #landscapephotography | |||
| #cityscapephotography | |||
| #architecturalphotography | |||
| #vehiclephotography | |||
| #macrophotography | |||
| #colourfulphotography | |||
| #streetphotography | +0.4 % | ||
| #portraitphotography | |||
| #sportsphotography | |||
| #smartphonephotography | +9.0 % | ||
| #goldenhourphotography | +2.3 % | ||
| #longexposurephotography | +8.8 % |
Number taking part in both Contests of the Day
| Participants in both contests | |
|---|---|
Number of Participants each Day
| Participants for the day | Weekly Changes, % | |
|---|---|---|
+98.1 % | ||
+7.9 % | ||
Authors with the Most Submissions qualifying for the Contests last week
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Histogram of Number of Posts every 15 minutes
The data represents the CET (UTC+1), which is the official time for 
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