I have a dataframe with a range of different format time stamps.
print df.head()
Time _datetime time_stamp hour day
0 00:00:00.0000000 1486339200000 2017-02-06 00:00:00 0 Monday
1 00:00:00.0000000 1486339200000 2017-02-06 00:00:00 0 Monday
2 00:00:00.0000000 1486339200000 2017-02-06 00:00:00 0 Monday
3 23:59:59.0000000 1486339199000 2017-02-05 23:59:59 23 Sunday
4 23:59:59.0000000 1486339199000 2017-02-05 23:59:59 23 Sunday
Time object
_datetime int64
time_stamp datetime64[ns]
hour int64
day object
I wish to plot a density plot:
p = ggplot(aes(x='_datetime'),data=df) + geom_density()
This works. However, when I add in a colour dissaggregation:
p = ggplot(aes(x='_datetime',color='day'),data=df) + geom_density()
I get:
numpy.linalg.linalg.LinAlgError: singular matrix
I have tried:
p = ggplot(aes(x='time_stamp',color='day'),data=df) + geom_density()
Returning TypeError: invalid type promotion
This could be related to Plotting event density in Python with ggplot and pandas
Edit
If I change day from an object to an int:
df['day'] = df['time_stamp'].dt.weekday
p = ggplot(aes(x='_datetime',color='day'),data=df) + geom_density()
It returns:
ValueError: RGBA sequence should have length 3 or 4
