Python: casting an integer into a datatime stamp

I am finding it very difficult to convert the following list l:

l

l = [0, 1]


into the time stamps corresponding to the indices of the following dataframe df:

df

dt val
2017-11-13 00:00:00 8
2017-11-13 01:00:00 17


Ideally the result has to be:

l = [2017-11-13 00:00:00, 2017-11-13 01:00:00]


so I can identify these two timesteps in the plot of a longer time series that overlaps df.

df

What's the best way of doing this? My attempt fails miserably and I can't make sense of the correct timestamp format:

index1 = pd.to_datetime(str(df.index[l[0]]), format='%Y-%m-%d %H:%M:%S')
index2 = pd.to_datetime(str(df.index[l[1]]), format='%Y-%m-%d %H:%M:%S')


This throws the error:

ValueError: time data '2017-11-13 00:00:00' does not match format '%Y-%m-%d %H:%M:%S' (match)


print(str(df.index[l[0]]))

2 Answers
2

I believe need list comprehension with fstrings:

f

d = pd.to_datetime([f'2017-11-13 x:00:00' for x in l], format='%Y-%m-%d %H')
print(d)

DatetimeIndex(['2017-11-13 00:00:00', '2017-11-13 01:00:00'],
dtype='datetime64[ns]', freq=None)


Performance (depends of real data):

np.random.seed(2018)
l = np.random.randint(12, size=1000).tolist()

In [48]: %%timeit
...: d = pd.to_datetime([f'2017-11-13 x:00:00' for x in l], format='%Y-%m-%d %H')
647 µs ± 2.45 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [49]: %%timeit
...: d = pd.to_datetime('2017-11-13' +
pd.Index(l).astype(str).str.zfill(2), format='%Y-%m-%d%H')
...:
4.43 ms ± 22 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)


There are several ways, but here's one without an explicit loop or specifying a format:

L = [0, 1]

datetime = pd.to_datetime('2017-11-13') + pd.to_timedelta(L, unit='h')


Result:

DatetimeIndex(['2017-11-13 00:00:00', '2017-11-13 01:00:00'],
type='datetime64[ns]', freq=None)


Your solution won't work because str does not work in a vectorised way.

str

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