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Questions - timedelta

Convert column of unix objects to datetime - python

I'm looking to convert a UNIX timestamp object to pandas date time. I'm importing the timestamps from a separate source, which displays a date time of 21-01-22 00:01 for the first timepoint and 21-01-...
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jonboy

python

pandas

datetime

timezone

timedelta

Votes: 0

Answers: 1

Latest Answer

You could use a regex to extract Unix time and UTC offset, then parse Unix time to datetime and add the UTC offset as a timedelta, e.g. import pandas as pd df = pd.DataFrame({ 'Time' : ['/D...
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FObersteiner

Is there a way to convert timedelta to pandas freq string?

You can do pd.to_timedelta('30min') to get a timedelta object of 30 minutes. Is there a similar way to go from a timedelta object to the freq string (like '30min', '1H', etc.)?
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dirkvbok

python

pandas

frequency

timedelta

Votes: 0

Answers: 1

Latest Answer

you can create a pd.DateOffset and use its freqstr attribute: import pandas as pd from pandas.tseries.frequencies import to_offset offset = to_offset(pd.Timedelta('30min')) print(offset.freqstr) # 30T...
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joe

Converting Pandas Dataframe column object in MM:SS format to Datetime type?

0 18:30 1 24:50 2 33:21 3 28:39 4 27:30 5 21:26 6 16:42 7 16:48 8 ...
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cdlabs45

python

pandas

dataframe

datetime

timedelta

Votes: 0

Answers: 2

Latest Answer

Replace the NaN values using df.fillna('00:00') Followed by: df['Minutes'] = pd.to_datetime(df['Minutes'], format='%M:%S', errors='coerce') Followed by: df.sort_values('Minutes') #Note Ascending ...
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itprorh66

Python: Function to fill in the previous row of a non-null value

I have a dataset which is mostly timedelta values which relate to the shift length worked by emergency workers. If certain conditions were met, then the shift time was combined with the prior shift le...
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Daniel

python

pandas

function

timedelta

Votes: 0

Answers: 1

Latest Answer

You can use pandas where() + bfill() to fill previous row with a "check" value, so my_func() will test it to calculate "final times". df['Combined Time'] = df['Combined Time'].wher...
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washolive

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