Running with this series
X = number_of_logons_all.values
split = round(len(X) / 2)
X1, X2 = X[0:split], X[split:]
mean1, mean2 = X1.mean(), X2.mean()
var1, var2 = X1.var(), X2.var()
print('mean1=%f, mean2=%f' % (mean1, mean2))
print('variance1=%f, variance2=%f' % (var1, var2))
I get:
mean1=60785.792548, mean2=61291.266868
variance1=7483553053.651829, variance2=7603208729.348722
But I wanted something like this in my PyCharm console (pulled from another result):
>>> -103 days +04:37:13.802435724...
Tried to place the np.array in a pd.Dataframe() to get the expected value by adding
.apply(pd.to_timedelta, unit='s')
...this didn't work, so I tried
new = pd.DataFrame([mean1]).to_numpy(dtype='timedelta64[ns]')
...and (still) got something like this:
>>>> [[63394]]
Anyone out there who could assist me converting to an easily comprehended datetime result from my means calculation above?
Thx, in advance for your kind support.
You can use f-strings:
mean1, mean2 = 60785.792548, 61291.266868
variance1, variance2=7603208729.348722,7483553053.651829
print(f'mean1={pd.Timedelta(mean1, unit="s")}, mean2={pd.Timedelta(mean2, unit="s")}')
print(f'variance1={pd.Timedelta(variance1, unit="s")}, variance2={pd.Timedelta(variance2, unit="s")}')
mean1=0 days 16:53:05.792548, mean2=0 days 17:01:31.266868
variance1=88000 days 02:25:29.348722458, variance2=86615 days 04:44:13.651828766