CODEWITHSUNDEEP
pythonarraysnumpydatetimelist-comprehension
georgehere
georgehere

Reputation: 630

Turning Numpy array values to datetime values with List comprehensions Python

I am trying to write a list comprehension where it goes through the ts array and then turn it into readable timestamps. However the dates list comprehension is faulty how would I be able to fix it and get the Expected Output below?

from datetime import datetime
import numpy as np 

ts = np.array([1628997394, 1628997444, 1628997602, 1629006977, 1629007021])
# if you encounter a "year is out of range" error the timestamp
# may be in milliseconds, try `ts /= 1000` in that case
dates=[x for x in ts if ts > 0 datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S')]

Error:

    dates=[x for x in ts if ts > 0 datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S')]
                                          ^
SyntaxError: invalid syntax

Expected Output:

[2021-08-15 03:16:34 , 2021-08-15 03:17:24, 2021-08-15 03:20:02 , 2021-08-15 05:56:17 , 2021-08-15 05:57:01]

Upvotes: 1

Views: 145

Answers (3)

Yasmin
Yasmin

Reputation: 21

import numpy as np 

import pandas as pd

ts = np.array([1628997394, 1628997444, 1628997602, 1629006977, 1629007021])

ts1 = pd.to_datetime(ts, unit='s', errors= 'coerce')

ts1

solution:

DatetimeIndex(['2021-08-15 03:16:34', '2021-08-15 03:17:24',
               '2021-08-15 03:20:02', '2021-08-15 05:56:17',
               '2021-08-15 05:57:01'],
              dtype='datetime64[ns]', freq=None)

Upvotes: 1

I'mahdi
I'mahdi

Reputation: 24049

You iterate on ts as x for checking in if you should check x NOT check ts and end of for and if when checking condition you need write datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S') in the first of list that you want create.

try this:

from datetime import datetime
import numpy as np 

ts = np.array([1628997394, 1628997444, 1628997602, 1629006977, 1629007021])

dates=[datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S') for x in ts if x>0]

Output:

['2021-08-15 03:16:34',
 '2021-08-15 03:17:24',
 '2021-08-15 03:20:02',
 '2021-08-15 05:56:17',
 '2021-08-15 05:57:01']

For checking runtime two solution you can use %timeit and see when you can do your job with list don't use numpy or pandas because use an additional library is not good. (using list 10 times faster than using pandas for this short array)

Runtime:

ts = np.array([1628997394, 1628997444, 1628997602, 1629006977, 1629007021])

%timeit dates=[datetime.utcfromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S') for x in ts if x>0]
# 27.1 µs ± 899 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit pd.to_datetime(ts, unit='s', errors='coerce').dropna().astype(str).to_numpy()
# 708 µs ± 128 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Upvotes: 3

U13-Forward
U13-Forward

Reputation: 71560

It could be done more efficiently with pandas:

>>> import pandas as pd
>>> ts = np.array([1628997394, 1628997444, 1628997602, 1629006977, 1629007021])
>>> pd.to_datetime(ts, unit='s', errors='coerce').dropna().astype(str).to_numpy()
array(['2021-08-15 03:16:34', '2021-08-15 03:17:24',
       '2021-08-15 03:20:02', '2021-08-15 05:56:17',
       '2021-08-15 05:57:01'], dtype=object)
>>>

Upvotes: 2

Related Questions