2️⃣1️⃣8️⃣3️⃣ Unique Participants all Week
Number of Posts and Participants per #Tag
| Number of Posts | Number of Participants | Weekly Changes, % | |
|---|---|---|---|
| #foodphotography | +31.7 % | ||
| #animalphotography | +37.2 % | ||
| #landscapephotography | |||
| #cityscapephotography | +12.2 % | ||
| #architecturalphotography | +2.3 % | ||
| #vehiclephotography | +6.5 % | ||
| #macrophotography | |||
| #colourfulphotography | |||
| #streetphotography | +14.7 % | ||
| #portraitphotography | +40 % | ||
| #sportsphotography | +36.5 % | ||
| #smartphonephotography | +10.2 % | ||
| #goldenhourphotography | +7.6 % | ||
| #longexposurephotography | +6.5 % |
Number taking part in both Contests of the Day
| Participants in both contests | |
|---|---|
Number of Participants each Day
| Participants for the day | Weekly Changes, % | |
|---|---|---|
+42.6 % | ||
+2.8 % | ||
+23.9 % | ||
+12.7 % | ||
+9.5 % |
Histogram of Number of Posts every 15 minutes
The data represents the CET (UTC+1), which is the official time for 
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
