I want to convert this Origin column in the dataframe data_copy to UTC datetime format There is also some data entries with 00:00:00 Time (I need to convert this also) I tried this command data_copy[“Origin”] = pd.to_datetime(data_copy[“Origin”],infer_datetime_format=True) But I am getting error like this How could I convert the column into UTC datetime format? Answer Here is problem datetimes are
Tag: datetime
unboundlocalerror while using datetime to create new dataframe
I keep getting an error for this function: This isn’t the full function, but the whole problem lies here. I am trying to create a new dataframe that only pulls from the past year of the initial dataframe. I will need to create options for year count using a function variable once I get this figured out, but for right
Pandas Grouping by Hostname. Average of Sessions(on host) by Hour
The dataframe looks like this. What I am trying to show the average sessions per hour by individual hostname. So I would get something back like this. I think I’m getting my grouping wrong as when trying this what I end up with is typically the largest average value per hour for any given hostname ordered in date by hour.
Get a week startdate from week number for entire dateframe in python
I am looking for week start date for entire date frame , with format of dd-mm-yyyy, Below week number :(src_data[‘WEEK’]) code : Output : Thanks in advance Answer You can add a year and weekday as strings and parse to_datetime with the appropriate directives (see also here). If desired, convert to string with strftime:
How to use pd.data_range() with a frequency of X minutes/hours/seconds?
I need to create a frequent date range with pandas date_range(). This works well with frequency=… parameter. But sometimes my code needs these frequent ranges in longer frequencys. for example 4 Hours or 5 minutes instead of one. How can I do that with pd.date_range(first_X_datetime, last_X_datetime, freq=frequency)? If there is not a more efficient way, my idea would be to
Datetime Timezones from String
I am trying to figure out how to make a datetime object aware through using a variable. I grab the timezone of the user and pass it through a form to be used. I have tried both of the below methods and am not having much success This will then give me errors of TypeError: tzinfo argument must be None
List ‘none’ data type element replacing using another element with time info
I have a list of dates to be used in a time series. And the list looks like as follow: I want to fill up the missing dates with what they were actually supposed to be. For example, here in the list x, x[2] = ‘2019-07-01’ that represents the month of July, and the previous two elements are type None,
Getting all starting dates of year 2020 with ISO week frequency
I’d like to create a list of dates each of which represents the starting date of ISO week N of year 2020. Something like: I have obtained something similar using timedelta(weeks=1), and adding this to my START_DATE (date(2020, 1, 1)), but the dates I obtain are not correct. I know I could simply change my START_DATE to be date(2019, 12,
python datetime strptime format
I have a datetime string like: I used strptime() function in datetime module to convert the string to datetime object. My datetime format is “%Y-%m-%dT%H:%M:%S.%f” When I parse this above string, the function raises ValueError because of missing millisecond part in the string. How can I have datetime object with millisecond is 0 when I don’t specify it in the
How to find the most common day name in a month?
Is there a way to print the day of the week which appears most frequent in the specified month and year as a string. Like Monday and Tuesday and if there are any multiple days, the first one should be displayed. First I tried to check with the datetime output but unable to find the calendar module to get this