A common source of errors in my Python codebase are dates. Specifically, the different implementations of dates and datetimes, and how comparisons are handled between them. These are the date types in my codebase You can print them to see: Is there a canonical date representation in Python? I suppose x7: datetime.date is probably closest… Also, note comparisons are a
Tag: date
How to convert time stamp in Python from dd/mm/yy hh:mm:ss:msmsms to yyyy-mm-dd hh:mm:ss:msmsmsmsmsms
I have created an automated data client that pulls data from a txt file and inputs it into a csv file. Each data entry contains a timestamp, but it is not in the format I need it in, I need it to match the datetime.now() format: ORIGINAL FORMAT [03/11/22 01:06:09:190] DESIRED FORMAT 2022-11-03 01:06:09.190000 I am currently using the following
List iteration through JSON array with dates (date format conflict)
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:
How can I check the value that exists for the earliest date?
df: country year index Turkiye 1992 NaN Spain 1992 NaN US 1992 1 Turkiye 1993 1 Spain 1993 1 US 1993 0 Turkiye 1994 1 France 1994 0 Italy 1994 NaN Turkiye 1995 0 Here, for example, in 1992 Turkiye and Spain are NaNs but the index exists for the US. So I am only interested in the earliest date
Count occurrences in last 30 days with Pandas Dataframe
I have a pandas Dataframe with an ID column and a date column (YYYY-MM-DD), ID Date 001 2022-01-01 001 2022-01-04 001 2022-02-07 002 2022-01-02 002 2022-01-03 002 2022-01-28 There may be gaps in the date field, as shown. I would like to have a new column, “occurrences_last_month” where it counts the number of occurrences for each ID in the last
Take user input for number of months back and filter data based on that date
I am working on a problem where I have to take user input which is an integer indicating the number of months I have to look back at. For example if I want to look at the data 3 months back I must take input from user as 3. Based on this integer user input I have to filter my
Add missing timestamps for each different ID in dataframe
I have two dataframes (simple examples shown below): df1 shows every timestamp I am interested in. df2 shows data sorted by timestamp and ID. What I need to do is add every single timestamp from df1 that is not in df2 for each unique ID and add zero to the value column. This is the outcome I’m interested in My
Convert Date in JavaScript (from a Python Dataset)
How do I edit the date format that gets returned from a dataset from Python via a Flask App in Javascript? It currently shows up as datetime, but I want it to show up as a custom date format. The best type of answer would help me to understand how to access those date values and create a custom date,
I want to add date range where value is True in pandas
Dt 1/2/21 2/2/21 3/2/21 4/2/21 5/2/21 6/2/21 7/2/21 Attendance(Expected output in python) san TRUE TRUE TRUE TRUE TRUE TRUE TRUE 1/2/21 – 7/2/21 don TRUE TRUE FALSE TRUE TRUE TRUE TRUE 1/2/21 -2/2/21,4/2/21-7/2/21 sam FALSE TRUE TRUE FALSE TRUE TRUE TRUE 2/2/21 – 3/2/21,5/2/21-7/2/21 den FALSE FALSE TRUE FALSE TRUE TRUE FALSE 3/2/21,5/2/21 – 6/2/21 I want to add Attendance
How to group by time-interval from bottom to top using Pandas resample functionality?
I am working with historic data of some stocks. I want to group data by certain time intervals (like 1hr, 3days, etc). Pandas gives amazing functionality of doing this with very less efforts using resampling. But it happens from top-to-bottom (below image). Like – Here, I want to group from bottom-to-top, like – How can I do this with pandas