I’ve been diving into List Comprehensions, and I’m determined to put it into practice. The below code takes a month and year input to determine the number of business days in the month, minus the public holidays (available at https://www.gov.uk/bank-holidays.json). Additionally, I want to list all public holidays in that month/year, but I’m struck with a date format conflict. TypeError:
Tag: datetime
Add one day for certain time
I want to update my date column with certain times because some of the dates are not correct. For some reason, they key in the date with time between 00:00 and 7:30 with the day before. For example: Which supposes to be like this: I know I can update all of dates with this code. But I have no idea
Pivot matrix to time-series – Python
I’ve got a dataframe with date as first column and time as the name of the other columns. Date 13:00 14:00 15:00 16:00 … 2022-01-01 B R M M … 2022-01-02 B B B M … 2022-01-03 R B B M … How could I transform that matrix into a datetime time-series? My objective its something like this: Date Data
Is there a bug in Python 3.8 datetime with DST transitions?
I’m trying to convert a timezone-aware datetime in Europe/Sofia to the start of the day in Europe/Sofia, but returning the datetime in UTC. Doing this, I encountered a strange problem: Which prints: The bug happens when subtracting the timedelta or doing the replace(), because the result should be midnight in Europe/Sofia with +2 offset, however we’re seeing an incorrect +3
asfreq in pandas returns an empty dataframe
I’m tring to use infer_freq and asfreq and the return data frame is empty Original data set: Trying to convert the data with different frequency Answer Given: Doing: Output: We can see it successfully converted to a frequency index.
For each date – is it between any of the provided date bounds?
Data: df: df_cal: Expected result: Goal: I want to assign values to a new column col: to 1 if df.index is between any of df_cal date ranges, and to 0 otherwise. Reference: I refer this post. But it just works for one condition and mine is lots of date ranges. And I don’t want to use dataframe join method to
Select Rows Based on Time Difference [Before or After] In Columns
I have the following dataset of students taking 2 different exams: I want to select those students whose two exams are 10 days apart from each other in either direction. I am trying Timedelta, but I’m not sure if it’s optimal. Desired Output: Is there any better way of getting the desired output? Any suggestions would be appreciated. Thanks! Answer
Transforming an inconsistently formated Date Column into a consistently formatted Datetime column [duplicate]
This question already has answers here: How to change the datetime format in Pandas (8 answers) Closed 23 days ago. I have a Python DataFrame with a datetime column that has inconsistent format, and would like it to be all one format. The DataFarme contains 199622 rows, so this is not an exhaustive sample: Example of DataFrame Column as an
filter a df by all the values with dates before today’s date
I’d like to filter a df by date. But I would like all the values with any date before today’s date (python). For example from the table below, I’d like the rows that have a date before today’s date (i.e. row 1 to row 3). ID date 1 2022-03-25 06:00:00 2 2022-04-25 06:00:00 3 2022-05-25 06:00:00 4 2022-08-25 06:00:00 Thanks
How would I find the quarterly averages of these monthly figures?
My dataset is similar to the below: How can I add columns to this which show the quarterly figure, which is an average of the preceding three months? Eg, suppose we started at adding a column after ‘Dec-21’ called Q4 2021 which took the average of the columns called ‘Oct-21’, ‘Nov-21’ and ‘Dec-21’. Will I need to create a function