Manipulating timestamp so that the new timestamp is still valid
I have a pandas dataframe in which each cell of a column contains a timestamp, saved as a string:
>>>dataset['DateTime'] '2018-03-14 00:34:46'
I would like to create a new column in which those dates are manipulated in the following way:
year += 1,
month += 2,
day += 3,
hour += 4,
minute += 5,
second += 6
(Important to this manipulation is that the initial date and the new date have a one-to-one relation, so that I can transform the date back later onwards)
In my case, the output I am looking for is as follows:
>>> dataset['newTimestamp'] '2019-05-17 04:39:52'
To do so I am using the
datetime library to create datetime objects (as a test, I have started with one variable first):
timestamp = dataset['DateTime'] p = datetime.datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S")
Currently I am doing arithmetics on the single variables:
year = p.year + 1 if p.month < 12: month = p.month + 1 else: month = 1 year += 1
However, as with the months, there are exceptions when you can and when you cannnot add values so that the new timestamp is still a real timestamp (12 + 1 = 13, which is not an actual month).
I could program every rule explicitly, but that seems too much work and I expect there are better ways. How could I do this faster?
dataset['newTimestamp'] = pd.to_datetime(dataset['DateTime']) + pd.DateOffset(years=1, months=2, days=3, hours=4, minutes=5, seconds=6)
You should try out beautiful-date library:
pip install beautiful-date
And use it like so:
from beautiful_date import * ... dataset['DateTime'].apply(lambda d: d + 1 * years + 2 * months + ... + 6 * seconds)
should do the trick.
Python Datetime Tutorial: Manipulate Times, Dates, and Time Spans, date – Allows us to manipulate dates independent of time (month, day, year). So now, let's start digging into a common task in data science: This is a datetime function that takes a timestamp (in float format) as an Let's create a new timedelta object that's two weeks long and see how that looks:. These types of timestamps are generated by a trusted third party using secure FIPS-compliant hardware, so they are not subject to manipulation by a local user. Trusted timestamping means that you can say with a high level of certainty that the date on the timestamp is accurate and hasn’t been tampered with.
strptime() and strftime() are the functions you are looking for. Just go ahead and google the two fuctions. surely enough,you will be abe to solve the stated problem. these can be used to directly manipulate date-time quantities
Solved: How to manipulate a timestamp token from a (timest , I then use the information entered to query two different (not Splunk) databases to find/present So, how can I a date/timestamp that is valid for one system convert it to a new token that is valid for the second system (11-15-17 16:22:30)?. I created a new column [Actual Sync Time] and created the workflow to set this column with a timestamp. The [Date/Timestamp] column captures device time, [Actual Sync Time] shows when workflow was run. This would have been my way forward. Make sure the server time is with like 10 minutes of the user time. If so, the user time is good is valid.
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Each block contains a Unix time timestamp. In addition to serving as a source of variation for the block hash , they also make it more difficult for an adversary to manipulate the block chain . A timestamp is accepted as valid if it is greater than the median timestamp of previous 11 blocks, and less than the network-adjusted time + 2 hours.
- I have found a way to strip the year, month, day, hour, minute and second from my string. However, what I am looking for is a method to manipulate my current date so that the new date is still an actual data and has a one-to-one correspondance with the old date
- just check the functions. they give you power to add 5 hours to the current timestamp or things like that. the combination of the two function can handle all time related queries.