When I work with date formats, it’s nice to have them as actual “datetime” objects rather than objects. If you notice when you first import the csv, the “Time” feature has a dtype “object”. If we convert this object to a “datetime” type, we can use Pandas Grouper() to actually do a groupby unique time period (ie: days, weeks, months, years).

grouper.py

#Step1, convert Time after loading:
ufo = pd.read_csv('https://raw.githubusercontent.com/sinanuozdemir/SF_DAT_17/master/data/ufo.csv')   # can also read csvs directly from the web!
ufo['Time'] = ufo['Time'].apply(pd.to_datetime)


# Step 2:  Group by unique days
ufo.groupby([pd.Grouper(key='Time',freq='1D')])[['Shape Reported']].count()

# Also, you can concat Year, Month, and Day into a new feature, and group by that.  As an engineer, I much prefer to work on strict types and leverage current method.